Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The manuscript focuses on the olfactory system of Pieris brassicae larvae and the importance of olfactory information in their interactions with the host plant Brassica oleracea and the major parasitic wasp Cotesia glomerata. The authors used CRISPR/Cas9 to knockout odorant receptor co-receptors (Orco), and conducted a comparative study on the behavior and olfactory system of the mutant and wild-type larvae. The study found that Orco-expressing olfactory sensory neurons in antennae and maxillary palps of Orco knockout (KO) larvae disappeared, and the number of glomeruli in the brain decreased, which impairs the olfactory detection and primary processing in the brain. Orco KO caterpillars show weight loss and loss of preference for optimal food plants; KO larvae also lost weight when attacked by parasitoids with the ovipositor removed, and mortality increased when attacked by untreated parasitoids. On this basis, the authors further studied the responses of caterpillars to volatiles from plants attacked by the larvae of the same species and volatiles from plants on which the caterpillars were themselves attacked by parasitic wasps. Lack of OR-mediated olfactory inputs prevents caterpillars from finding suitable food sources and from choosing spaces free of enemies.

Strengths:

The findings help to understand the important role of olfaction in caterpillar feeding and predator avoidance, highlighting the importance of odorant receptor genes in shaping ecological interactions.

Weaknesses:

There are the following major concerns:

(1) Possible non-targeted effects of Orco knockout using CRISPR/Cas9 should be analyzed and evaluated in Materials and Methods and Results.

Thank you for your suggestion. In the Materials and Methods, we mention how we selected the target region and evaluated potential off-target sites by Exonerate and CHOPCHOP. Neither of these methods found potential off-target sites with a more-than-17-nt alignment identity. Therefore, we assumed no off-target effect in our Orco KO. Furthermore, we did not find any developmental differences between WT and KO caterpillars when these were reared on leaf discs in Petri dishes (Fig S4). We will further highlight this information on the off-target evaluation in the Results section of our revised manuscript.

(2) Figure 1E: Only one olfactory receptor neuron was marked in WT. There are at least three olfactory sensilla at the top of the maxillary palp. Therefore, to explain the loss of Orco-expressing neurons in the mutant (Figure 1F), a more rigorous explanation of the photo is required.

Thank you for pointing this out. The figure shows only a qualitative comparison between WT and KO and we did not aim to determine the total number of Orco positive neurons in the maxillary palps or antennae of WT and KO caterpillars, but please see our previous work for the neuron numbers in the caterpillar antennae (Wang et al., 2023). We did indeed find more than one neuron in the maxillary palps, but as these were in very different image planes it was not possible to visualize them together. However, we will add a few sentences in the Results and Discussion section to explain the results of the maxillary palp Orco staining.

(3) In Figure 1G, H, the four glomeruli are circled by dotted lines: their corresponding relationship between the two figures needs to be further clarified.

Thank you for pointing this out. The four glomeruli in Figure 1G and 1H are not strictly corresponding. We circled these glomeruli to highlight them, as they are the best visualized and clearly shown in this view. In this study, we only counted the number of glomeruli in both WT and KO, however, we did not clarify which glomeruli are missing in the KO caterpillar brain. We will further explain this in the figure legend.

(4) Line 130: Since the main topic in this study is the olfactory system of larvae, the experimental results of this part are all about antennal electrophysiological responses, mating frequency, and egg production of female and male adults of wild type and Orco KO mutant, it may be considered to include this part in the supplementary files. It is better to include some data about the olfactory responses of larvae.

Thank you for your suggestion. We do agree with your suggestion, and we will consider moving this part to the supplementary information. Regarding larval olfactory response, we unfortunately failed to record any spikes using single sensillum recordings due to the difficult nature of the preparation; however, we do believe that this would be an interesting avenue for further research.

(5) Line 166: The sentences in the text are about the choice test between " healthy plant vs. infested plant", while in Fig 3C, it is "infested plant vs. no plant". The content in the text does not match the figure.

Thank you for pointing this out. The sentence is “We compared the behaviors of both WT and Orco KO caterpillars in response to clean air, a healthy plant and a caterpillar-infested plant”. We tested these three stimuli in two comparisons: healthy plant vs no plant, infested plant vs no plant. The two comparisons are shown in Figure 3C separately. We will aim to describe this more clearly in the revised version of the manuscript.

(6) Lines 174-178: Figure 3A showed that the body weight of Orco KO larvae in the absence of parasitic wasps also decreased compared with that of WT. Therefore, in the experiments of Figure 3A and E, the difference in the body weight of Orco KO larvae in the presence or absence of parasitic wasps without ovipositors should also be compared. The current data cannot determine the reduced weight of KO mutant is due to the Orco knockout or the presence of parasitic wasps.

Thank you for pointing this out. We did not make a comparison between the data of Figures 3A and 3E since the two experiments were not conducted at the same time due to the limited space in our BioSafety Ⅲ greenhouse. We do agree that the weight decrease in Figure 3E is partly due to the reduced caterpillar growth shown in Figure 3A. However, we are confident that the additional decrease in caterpillar weight shown in Figure 3E is mainly driven by the presence of disarmed parasitoids. To be specific, the average weight in Figure 3A is 0.4544 g for WT and 0.4230 g for KO, KO weight is 93.1% of WT caterpillars. While in Figure 3E, the average weight is 0.4273 g for WT and 0.3637 g for KO, KO weight is 85.1% of WT caterpillars. We will discuss this interaction between caterpillar growth and the effect of the parasitoid attacks more extensively in the revised version of the manuscript.

(7) Lines 179-181: Figure 3F shows that the survival rate of larvae of Orco KO mutant decreased in the presence of parasitic wasps, and the difference in survival rate of larvae of WT and Orco KO mutant in the absence of parasitic wasps should also be compared. The current data cannot determine whether the reduced survival of the KO mutant is due to the Orco knockout or the presence of parasitic wasps.

We are happy that you highlight this point. When conducting these experiments, we selected groups of caterpillars and carefully placed them on a leaf with minimal disturbance of the caterpillars, which minimized hurting and mortality. We did test the survival of caterpillars in the absence of parasitoid wasps from the experiment presented in Figure 3A, although this was missing from the manuscript. There is no significant difference in the survival rate of caterpillars between the two genotypes in the absence of wasps (average mortality WT = 8.8 %, average mortality KO = 2.9 %; P = 0.088, Wilcoxon test), so the decreased survival rate is most likely due to the attack of the wasps. We will add this information to the revised version of the manuscript.

(8) In Figure 4B, why do the compounds tested have no volatiles derived from plants? Cruciferous plants have the well-known mustard bomb. In the behavioral experiments, the larvae responses to ITC compounds were not included, which is suggested to be explained in the discussion section.

Thank you for the suggestion. We assume you mean Figure 4D/4E instead of Figure 4B. In Figure 4B, many of the identified chemical compounds are essentially plant volatiles, especially those from caterpillar frass and caterpillar spit. In Figure 4D/4E, most of the tested chemicals are derived from plants. We did include several ITCs in the butterfly EAG tests shown in figure 2A/B, however because the butterfly antennae did not respond strongly to ITCs, we did not include ITCs in the subsequent larval behavioural tests. Instead, the tested chemicals in Figure 4D/4E either elicit high EAG responses of butterflies or have been identified as significant by VIP scores in the chemical analyses. We will add this explanation to the revised version of our manuscript.

(9) The custom-made setup and the relevant behavioral experiments in Figure 4C need to be described in detail (Line 545).

We will add more detailed descriptions for the setup and method in the Materials and Methods.

(10) Materials and Methods Line 448: 10 μL paraffin oil should be used for negative control.

Thank you for pointing this out. We used both clean filter paper and clean filter paper with 10 μL paraffin oil as negative controls, but we did not find a significant difference between the two controls. Therefore, in the EAG results of Figure 2A/2B, we presented paraffin oil as one of the tested chemicals. We will re-run our statistical tests with paraffin oil as negative control, although we do not expect any major differences to the previous tests.

Reviewer #2 (Public review):

Summary:

This manuscript investigated the effect of olfactory cues on caterpillar performance and parasitoid avoidance in Pieris brassicae. The authors knocked out Orco to produce caterpillars with significantly reduced olfactory perception. These caterpillars showed reduced performance and increased susceptibility to a parasitoid wasp.

Strengths:

This is an impressive piece of work and a well-written manuscript. The authors have used multiple techniques to investigate not only the effect of the loss of olfactory cues on host-parasitoid interactions, but also the mechanisms underlying this.

Weaknesses:

(1) I do have one major query regarding this manuscript - I agree that the results of the caterpillar choice tests in a y-maze give weight to the idea that olfactory cues may help them avoid areas with higher numbers of parasitoids. However, the experiments with parasitoids were carried out on a single plant. Given that caterpillars in these experiments were very limited in their potential movement and source of food - how likely is it that avoidance played a role in the results seen from these experiments, as opposed to simply the slower growth of the KO caterpillars extending their period of susceptibility? While the two mechanisms may well both take place in nature - only one suggests a direct role of olfaction in enemy avoidance at this life stage, while the other is an indirect effect, hence the distinction is important.

We do agree with your comment that both mechanisms may be at work in nature, and we do address this in the Discussion section. In our study, we did find that wildtype caterpillars were more efficient in locating their food source and did grow faster on full plants than knockout caterpillars. This faster growth will enable wildtype caterpillars to more quickly outgrow the life-stages most vulnerable to the parasitoids (L1 and L2). The olfactory system therefore supports the escape from parasitoids indirectly by enhancing feeding efficiency directly.

In addition, we show in our Y-tube experiments that WT caterpillars were able to avoid plant where conspecifics are under the attack by parasitiods (Figure 3D). Therefore, we speculate that WT caterpillars make use of volatiles from the plant or from conspecifics via their spit or faeces to avoid plants or leaves potentially attracting natural enemies. Knockout caterpillars are unable to use these volatile danger cues and therefore do not avoid plants or leaves that are most attractive to their natural enemies, making KO caterpillars more susceptible and leading to more natural enemy harassment. Through this, olfaction also directly impacts the ability of a caterpillar to find an enemy-free feeding site.

We think that olfaction supports the enemy avoidance of caterpillars via both these mechanisms, although at different time scales. Unfortunately, our analysis was not detailed enough to discern the relative importance of the two mechanisms we found. However, we feel that this would be an interesting avenue for further research. Moreover, we will sharpen our discussion on the potential importance of the two different mechanisms in the revised version of this manuscript.

(2) My other issue was determining sample sizes used from the text was sometimes a bit confusing. (This was much clearer from the figures).

We will revise the sample size in the text to make it clearer.

(3) I also couldn't find the test statistics for any of the statistical methods in the main text, or in the supplementary materials.

Thank you for pointing this out. We will provide more detailed test statistics in the main text and in the supplementary materials of the revised version of the manuscript.

Author response:

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

We thank the editor and reviewers for their supportive comments about our modeling approach and conclusions, and for raising several valid concerns; we address them briefly below. In addition, a detailed, point-by-point response to the reviewers’ comments are below, along with additions and edits we have made to the revised manuscript. 

Concerns about model’s biological realism and impact on interpretations

The goal of this paper was to use an interpretable and modular model to investigate the impact of varying sensorimotor delays. Aspects of the model (e.g. layered architecture, modularity) are inspired by biology; at the same time, necessary abstractions and simplifications (e.g. using an optimal controller) are made for interpretability and generalizability, and they reflect common approaches from past work. The hypothesized effects of certain simplifying assumptions are discussed in detail in Section 3.5. Furthermore, the modularity of our model allows us to readily incorporate additional biological realism (e.g. biomechanics, connectomics, and neural dynamics) in future work. In the revision, we have added citations and edits to the text to clarify these points.

Concerns that the model is overly complex

To investigate the impact of sensorimotor delays on locomotion, we built a closed-loop model that recapitulates the complex joint trajectories of fly walking. We agree that locomotion models face a tradeoff between simplicity/interpretability and realism — therefore, we developed a model that was as simple and interpretable as possible, while still reasonably recapitulating joint trajectories and generalizing to novel simulation scenarios. Along these lines, we also did not select a model that primarily recreates empirical data, as this would hinder generalizability and add unnecessary complexity to the model. We do not think these design choices are significant weaknesses of this model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data. We have add citations and edits to the text to clarify these points in the revision. 

Concerns about the validity of the Kinematic Similarity (KS) metric to evaluate walking

We chose to incorporate only the first two PCA modes dimensions in the KS metric because the kernel density estimator performs poorly for high dimensional data. Our primary use of this metric was to indicate whether the simulated fly continues walking in the presence of perturbations. For technical reasons, it is not feasible to perform equivalent experiments on real walking flies, which is one of the reasons we explore this phenomenon with the model. We note the dramatic shift from walking to nonwalking as delay increases (Figure 5). To be thorough, in the revision, we have investigated the effect of incorporating additional PCA modes, and whether this affects the interpretation of our results. We have additionally added to the discussion and presentation of the KS metric to clarify its purpose in this study. We agree with the reviewers that the KS metric is too coarse to reflect fine details of joint kinematics; indeed, in the unperturbed case, we evaluate our model’s performance using other metrics based on comparisons with empirical data (Figures 2, 7, 8). 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors present a novel, multi-layer computational model of motor control to produce realistic walking behaviour of a Drosophila model in the presence of external perturbations and under sensory and motor delays. The novelty of their model of motor control is that it is modular, with divisions inspired by the fly nervous system, with one component based on deep learning while the rest are based on control theory. They show that their model can produce realistic walking trajectories. Given the mostly reasonable assumptions of their model, they convincingly show that the sensory and motor delays present in the fly nervous system are the maximum allowable for robustness to unexpected perturbations.

Their fly model outputs torque at each joint in the leg, and their dynamics model translates these into movements, resulting in time-series trajectories of joint angles. Inspired by the anatomy of the fly nervous system, their fly model is a modular architecture that separates motor control at three levels of abstraction:

(1) oscillator-based model of coupling of phase angles between legs,

(2) generation of future joint-angle trajectories based on the current state and inputs for each leg (the trajectory generator), and

(3) closed-loop control of the joint-angles using torques applied at every joint in the model (control and dynamics).

These three levels of abstraction ensure coordination between the legs, future predictions of desired joint angles, and corrections to deviations from desired joint-angle trajectories. The parameters of the model are tuned in the absence of external perturbations using experimental data of joint angles of a tethered fly. A notable disconnect from reality is that the dynamics model used does not model the movement of the body and ground contacts as is the case in natural walking, nor the movement of a ball for a tethered fly, but instead something like legs moving in the air for a tethered fly.

n order to validate the realism of the generated simulated walking trajectories, the authors compare various attributes of simulated to real tethered fly trajectories and show qualitative and quantitative similarities, including using a novel metric coined as Kinematic Similarity (KS). The KS score of a trajectory is a measure of the likelihood that the trajectory belongs to the distribution of real trajectories estimated from the experimental data. While such a metric is a useful tool to validate the quality of simulated data, there is some room for improvement in the actual computation of this score. For instance, the KS score is computed for any given time-window of walking simulation using a fraction of information from the joint-angle trajectories. It is unclear if the remaining information in joint-angle trajectories that are not used in the computation of the KS score can be ignored in the context of validating the realism of simulated walking trajectories.

The authors validate simulated walking trajectories generated by the trained model under a range of sensorimotor delays and external perturbations. The trained model is shown to generate realistic jointangle trajectories in the presence of external perturbations as long as the sensorimotor delays are constrained within a certain range. This range of sensorimotor delays is shown to be comparable to experimental measurements of sensorimotor delays, leading to the conclusion that the fly nervous system is just fast enough to be robust to perturbations.

Strengths:

This work presents a novel framework to simulate Drosophila walking in the presence of external perturbations and sensorimotor delay. Although the model makes some simplifying assumptions, it has sufficient complexity to generate new, testable hypotheses regarding motor control in Drosophila. The authors provide evidence for realistic simulated walking trajectories by comparing simulated trajectories generated by their trained model with experimental data using a novel metric proposed by the authors. The model proposes a crucial role in future predictions to ensure robust walking trajectories against external perturbations and motor delay. Realistic simulations under a range of prediction intervals, perturbations, and motor delays generating realistic walking trajectories support this claim. The modular architecture of the framework provides opportunities to make testable predictions regarding motor control in Drosophila. The work can be of interest to the Drosophila community interested in digitally simulating realistic models of Drosophila locomotion behaviors, as well as to experimentalists in generating testable hypotheses for novel discoveries regarding neural control of locomotion in Drosophila. Moreover, the work can be of broad interest to neuroethologists, serving as a benchmark in modelling animal locomotion in general.

We thank the reviewer for their positive comments.

Weaknesses:

As the authors acknowledge in their work, the control and dynamics model makes some simplifying assumptions about Drosophila physics/physiology in the context of walking. For instance, the model does not incorporate ground contact forces and inertial effects of the fly's body. It is not clear how these simplifying assumptions would affect some of the quantitative results derived by the authors. The range of tolerable values of sensorimotor delays that generate realistic walking trajectories is shown to be comparable with sensorimotor delays inferred from physiological measurements. It is unclear if this comparison is meaningful in the context of the model's simplifying assumptions.

We now discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We reproduce the relevant sentences below:

“The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

“We anticipate that the increased sensory resolution from more detailed proprioceptor models and the stability from mechanical compliance of limbs in a more detailed biomechanical model would make the system easier to control and increase the allowable range of delay parameters. Conversely, we expect that modeling the nonlinearity and noise inherent to biological sensors and actuators may decrease the allowable range of delay parameters.”

The authors propose a novel metric coined as Kinematic Similarity (KS) to distinguish realistic walking trajectories from unrealistic walking trajectories. Defining such an objective metric to evaluate the model's predictions is a useful exercise, and could potentially be applied to benchmark other computational animal models that are proposed in the future. However, the KS score proposed in this work is calculated using only the first two PCA modes that cumulatively account for less than 50% of the variance in the joint angles. It is not obvious that the information in the remaining PCA modes may not change the log-likelihood that occurs in the real walking data.

The primary reason we designed the KS metric was to determine whether the simulated fly continues walking in the presence of perturbations. We initially limited the analysis of the KS to the first 2 principal components. For completeness, we now investigate the additional principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

Overall, the results look similar when including additional components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9, which shows that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

We added the following text in the methods:

“We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

(Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

We also reference Appendix 10 in the results:

“We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

Reviewer #2 (Public Review):

Summary:

In this study, Karashchuk et al. develop a hierarchical control system to control the legs of a dynamic model of the fly. They intend to demonstrate that temporal delays in sensorimotor processing can destabilize walking and that the fly's nervous system may be operating with as long of delays as could possibly be corrected for.

Strengths:

Overall, the approach the authors take is impressive. Their model is trained using a huge dataset of animal data, which is a strength. Their model was not trained to reproduce animal responses to perturbations, but it successfully rejects small perturbations and continues to operate stably. Their results are consistent with the literature, that sensorimotor delays destabilize movements.

Weaknesses:

The model is sophisticated and interesting, but the reviewer has great concerns regarding this manuscript's contributions, as laid out in the abstract:

(1) Much simpler models can be used to show that delays in sensorimotor systems destabilize behavior (e.g., Bingham, Choi, and Ting 2011; Ashtiani, Sarvestani, and Badri-Sproewitz 2021), so why create this extremely complex system to test this idea? The complexity of the system obscures the results and leaves the reviewer wondering if the instability is due to the many, many moving parts within the model. The reviewer understands (and appreciates) that the authors tested the impact of the delay in a controlled way, which supports their conclusion. However, the reviewer thinks the authors did not use the most parsimonious model possible, and as such, leave many possible sources for other causes of instability.

We thank the reviewer for this observation — we agree that we did not make the goal of the work quite clear. The goal of this paper was to build an interpretable and generalizable model of fly walking, which was then used to investigate varying sensorimotor delays in the context of locomotion. To this end, we used a modular model to recreate walking kinematics, and then investigated the effect of delays on locomotion. Locomotion in itself is a complex phenomenon — thus, we have chosen a model that is complex enough to reasonably recapitulate joint trajectories, while remaining interpretable.

We have clarified this in the text near the end of the introduction:

“Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

We also emphasize the investigation of sensorimotor delays in the context of locomotion in the beginning of the “Effect of sensory and motor delays on walking” section:

“... we used our model to investigate how changing sensory and motor delays affects locomotor robustness.”

We also remark that while they are very relevant papers for our work, neither of the prior papers focus on locomotion: the first involves a 2D balance model of a biped, and the second involves drop landings of quadrupeds.

Lastly, we note that the investigation of delay is not the only use for this model —  in the future, this model can also be used to study other aspects of locomotion such as the role of proprioceptive feedback (see “Role of proprioceptive feedback in fly walking” section). The layered framework of the model can also be extended to other animals and locomotor strategies (see “Layered model produces robust walking and facilitates local control” section”).

(2) In a related way, the reviewer is not sure that the elements the authors introduced reflect the structure or function of the fly's nervous system. For example, optimal control is an active field of research and is behind the success of many-legged robots, but the reviewer is not sure what evidence exists that suggests the fly ventral nerve cord functions as an optimal controller. If this were bolstered with additional references, the reviewer would be less concerned.

We thank the reviewer for the comment — we have now further clarified how our model elements reflect the fly’s nervous system. The elements we introduce are plausible but only loosely analogous to the fly’s nervous system. While we draw parallels from these elements to anatomy (e.g. in Fig 1A-B, and in the first paragraph of the Results section), we do not mean to suggest that these functional elements directly correspond to specific structures in the fly’s nervous system. A substantial portion of the suggested future work (see “Towards biomechanical and neural realism”) aims to bridge the gap between these functional elements and fly physiology, which is beyond the scope of this work. 

We have added clarifying text to the Results section:

“While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

In the specific case of optimal control — optimal control is a theoretical model that predicts various aspects of motor control in humans, there is evidence that optimal control is implemented by the human nervous system (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011). Based on this, we make the assumption that optimal control is a reasonable model for motor control in flies implemented by the fly nervous system as well. Fly movement makes use of proprioceptive feedback signals (Mendes et al., 2013; Pratt et al., 2024; Berendes et al., 2016), and optimal control is a plausible mechanism that incorporates feedback signals into movement.

We have added the following clarifying text in the Results section: 

“The optimal controller layer maintains walking kinematics in the presence of sensori motor delays and helps compensate for external perturbations. This design was inspired by optimal control-based models of movements in humans (Todorov and Jordan, 2002; Scott, 2004; Berret et al., 2011)”

(3) "The model generates realistic simulated walking that matches real fly walking kinematics...". The reviewer appreciates the difficulty in conducting this type of work, but the reviewer cannot conclude that the kinematics "match real fly walking kinematics". The range of motion of several joints is 30% too small compared to the animal (Figure 2B) and the reviewer finds the video comparisons unpersuasive. The reviewer would understand if there were additional constraints, e.g., the authors had designed a robot that physically could not complete the prescribed motions. However the reviewer cannot think of a reason why this simulation could not replicate the animal kinematics with arbitrary precision, if that is the goal.

We agree with the reviewer that the model-generated kinematics are not perfectly indistinguishable from real walking kinematics, and now clarify this in the text. We also agree with the reviewer that one could build a model that precisely replicates real kinematics, but as they intuit, that was not our goal. Our goal was to build a model that both replicates animal kinematics, and is interpretable and generalizable (which allows us to investigate what happens when perturbations and varying sensorimotor delays are introduced). There is a trade-off between realism and generalizability — a simulation that fully recreates empirical data would require a model that is completely fit to data, which is likely to be more complex (in terms of parameters required) and less generalizable to novel scenarios. We have made design choices that result in a model that balances these trade-offs. We do not consider this to be a weakness of the model; in fact, few comparable models account for all joints involved in locomotion, and fewer explicitly compare model kinematics with kinematics from data.

We have tempered the language in the abstract:

“The model generates realistic simulated walking that resembles real fly walking kinematics”

The tempered statement, we believe, is a fair characterization of the walking — it resembles but does not perfectly match real kinematics.

We have also introduced clarifying text in the introduction:

“Overall, existing walking models focus on either kinematic or physiological accuracy, but few achieve both, and none consider the effect of varying sensorimotor delays. Here, we develop a new, interpretable, and generalizable model of fly walking, which we use to investigate the impact of varying sensorimotor delays in Drosophila locomotion.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Potential typo on page 5:

2.1.2 Joint kinematics trajectory generator

Paragraph 4, last line: Original text - ".....it also estimates the current phase". Suggested correction - "...it also estimates the current phase velocity"

Done

Potential typo on page 8:

2.3 Model maintains walking under unpredictable external perturbations.

Paragraph 3, line 2: Original text - "...brief, unexpected force (e.g. legs slipping on an unstable surface)".

Consider replacing force with motion, or providing an example of a force as opposed to displacement (slipping).

Done

Potential typo on page 8:

2.3 Model maintains walking under unpredictable external perturbations.

Paragraph 3, line 4: Original text - "The magnitude of this velocity is drawn from a normal distribution...".

Is this really magnitude? If so, please discuss how the sign (+/-) is assigned to velocity, and how the normal distribution is centred so as to sample only positive values representing magnitude.

Indeed the magnitude of the velocity is drawn from a normal distribution. A positive or negative sign is then assigned with equal odds. We have added text to clarify this:

“The sign of the velocity was drawn separately so that there is equal likelihood for negative or positive perturbation velocities.”

Page 8:

2.3 Model maintains walking under unpredictable external perturbations.

In Paragraph 5: Why is the data reduced to only 2 dimensions? Could higher order PCA modes (cumulatively accounting for more than 50% variance in the data) not have distinguishing information between realistic and unrealistic walking trajectories?

We provide a longer response for this in the public review above.

Page 11:

Why wouldn't a system trained in the presence of external perturbations perform better? What is the motivation to remove external perturbations during training?

We agree that a system trained in the presence of external perturbations would probably perform better — however, we do not have data that contains walking with external perturbations. Nothing was removed — all the data used in this study involve a fly walking without perturbations.

We have added a clarification:

“our model maintains realistic walking in the presence of external dynamic perturbations, despite being trained only on data of walking without perturbations (no perturbation data was available).”

Page 16:

4.1 Tracking joint angles of D. melanogaster walking in 3D.

Paragraph 1: Readers who wish to collect similar data might benefit from specifying the exposure time, animal size in pixels (or camera sensor format and field of view), in addition to the frame rate. Alternatively, consider mentioning the camera and lens part numbers provided by the manufacturer.

This is a good point. We have updated the text to include these specifications:

“We obtained fruit fly D. melanogaster walking kinematics data following the procedure previously described in (Karashchuk et al, 2021). Briefly, a fly was tethered to a tungsten wire and positioned on a frictionless spherical treadmill ball suspended on compressed air. Six cameras (Basler acA800-510um with Computar zoom lens MLM3X-MP) captured the movement of all of the fly's legs at 300 Hz. The fly size in pixels ranges from about 300x300 up to 700x500 pixels across the 6 cameras. Using Anipose, we tracked 30 keypoints on the fly, which are the following 5 points on each of the 6 legs: body-coxa, coxa-femur, femur-tibia, and tibia-tarsus joints, as well as the tip of the tarsus.”

Potential typos on page 18:

4.3.3 Training procedure

Paragraph 2, line 1: Original text - "..(, p)"

Do the authors mean "...(, )"

Paragraph 2, line 2: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

Paragraph 3, line 3: Original text - "... (,, v, p)" Do the authors mean "... (,, v, )"?

Thank you for pointing out this issue. We have now fixed the phase p to be \phi to be consistent with the rest of the text.

Paragraph 3, line 3: Original text - "...()"

Do the authors mean "(d)"? If not, please discuss the difference between and d.

Thank you for pointing this out. \hat \theta and \theta_d were used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

Page 19:

Typo after eqn. (6):

Original text: "where x := q - q, ... A and B are Jacobians with respect to...."

Correction: "where x := q - q, ... Ac and Bc are Jacobians with respect to...."

Similar corrections in eqn. 7 and eqn. 8: A and B should be replaced with Ac and Bc. Done

Page 19, eqn. (10b):

Should the last term be qd(t+T) as opposed to qd(t+1)?

No: in fact (10a) contains the typo: it should be y(t+1) as opposed to y(t+T). This has been fixed.

Page 19

The authors' detailed description of the initial steps leading up to the dynamics model, involving the construction of the ODE, linearizing the system about the fixed point makes the text broadly accessible to the general reader. Similarly, adding some more description of the predictive model (eqn. 11 - 15) could improve the text's accessibility and the reader's appreciation for the model. This is especially relevant since the effects of sensorimotor delay and external perturbations, which are incorporated in the control and dynamics model, form a major contribution to this work. What do the matrices F, G, L, H, and K look like for the Drosophila model? Are there any differences between the model in Stenberg et al. (referenced in the paper) and the authors' model for predictive control? Are there any differences in the assumptions made in Stenberg et al. compared to the model presented in this work? The readers would likely also benefit from a figure showing the information flow in the model, and describing all the variables used in the predictive control model in eqn. 11 through eqn. 15 (analogous to Figure 1 in Stenberg et al. (2022)). Such a detailed description of the control and dynamics model would help the reader easily appreciate the assumptions made in modelling the effects of sensorimotor delay and external perturbations.

Done

Page 20:

Eqn. 12: Should z(t+1) be z(t+T) instead?

Similar comment for eqn. 14

No: we made a mistake in (10a); there should be no (t+T) terms; all terms should be (t+1) terms to reflect a standard discrete-time difference equation.

Eqn. 13: r(t) can be defined explicitly

Done

4.5 Generate joint trajectories of the complete model with perturbations Paragraph 2, line 2: Please read the previous comment

\hat \theta and \theta_d were previously used interchangeably which is confusing. We have standardized our reference to the desired trajectory as \theta_d throughout the text.

Original text - "Every 8 timesteps, we set :=...."

Does this mean dis set to? If so, the motivation for this is not clear.

We mean that \theta_d is set to be equal to \theta. We have replaced “:=” with “=” for clarity.

General comments for the authors:

Could the authors discuss the assumptions regarding Drosophila physiology implied in the control model?

The control model is primarily included as a plausible functional element of the fly’s nervous system, and as such implies minimal assumptions on physiology itself. The main assumption, which is evident from the description of the model components, is that the fly uses proprioceptive feedback information to inform future movements.

We have added clarifying text to the Results section:

“While the model is inspired by neuroanatomy, its components do not strictly correspond to components of the nervous system --- the construction of a neuroanatomically accurate model is deferred to future work (see Discussion).”

The authors acknowledge the absence of ground contact forces in the model. It is probably worth discussing how this simplification may affect inferences regarding the acceptable range of sensorimotor delay in generating realistic walking trajectories.

We agree, and discuss how some of these assumptions affect the quantitative results in the section “Towards biomechanical and neural realism”. We replicate the relevant sentences below:

“The inclusion of explicit leg-ground contact interactions would also make it harder for the model to recover when perturbed, because perturbations during walking often occur upon contact with the ground (e.g. the ground is slippery or bumpy).”

The effects of other simplifications are also mentioned in the same section.

Can the authors provide an insight into why the use of a second derivative of joint angles as the output of the trajectory generator () leads to more realistic trajectories (4.3.1 Model formulation, paragraph 1)?

Does the use of a second-order derivative of joint angles lead to drift error because of integration?

Could the distribution of θd produced be out of the domain due to drift errors? Could this affect the performance of the neural network model approximating the trajectory generator?

We are not sure why the second derivative works better than the first derivative. It is possible that modeling the system as a second order differential equation gives the network more ability to produce complex dynamics. 

As can be seen in the example time series in Figures 2 and 3 and supplemental videos, there is no drift error from integration, so it is unlikely to affect the performance of the neural network.

What does the model's failure (quantified by a low KS score) look like in the context of fly dynamics? What do the joint angles look like for low values of KS score? Does the fly fall down, for example?

Since the model primarily considers kinematics, a low KS score means that kinematics are unrealistic, e.g. the legs attain unnatural angles or configurations. Examples of this can be seen in videos 4-7 (linked from Appendix 1 of the paper), as well as in the bottom row of Fig. 5, panel A. Here, at 40ms of motor delay, L2 femur rotation is seen to attain values that far exceed the normal ranges. 

We have added a small clarification in the caption of Fig.5 panel A:

“low KS indicates that the perturbed walking deviates from data and results in unnatural angles

(as seen at 40ms motor delay)” 

We remark that since our simulations do not incorporate contact forces (as the reviewer remarks above, we simulate something like legs moving in the air for a tethered fly), the fly cannot “fall down” per se. However, if forces were incorporated then yes, these unrealistic kinematics would correspond to a fly that falls down or is no longer walking.

Reviewer #2 (Recommendations For The Authors):

L49: "Computational models of locomotion do not typically include delay as a tunable parameter, and most existing models of walking cannot sustain locomotion in the presence of delays and external perturbations". This remark confuses the reviewer.

(1) If models do not "typically" include delay as a tunable parameter, this suggests that atypical models do. Which models do? Please provide references.

Our initial phrasing was confusing. We meant to say that most models do not include delay, and some models do include delay as a fixed value (rather than a tunable value). We clarify in the updated text, which is replicated below:

“Computational models of locomotion typically have not included delays as a tunable parameter, although some models have included them as fixed values (Geyer and Herr, 2010; Geijtenbeek et al., 2013).”

(2) Has the statement that most existing models cannot sustain locomotion with delays been tested? If so, provide references. If not, please remove this statement or temper the language.

Since most models don’t include delays, they cannot be run in scenarios with delays. We clarify in the updated text, which is replicated below:

“Computational models of locomotion have not typically included delays. Some have included delay as a fixed value rather than a tunable parameter (Geyer and Herr, 2010; Geijtenbeek et al., 2013). However, in general, the impact of sensorimotor delays on locomotor control and robustness remains an underexplored topic in computational neuroscience.”

L57: "two of six legs lift off the ground at a time" - Two legs are off the ground at any time, but they do not "lift off" simultaneously in the fruit fly. To lift off simultaneously, contralateral leg pairs would need to be 33% out of phase with one another, but they are almost always 50% out of phase.

Thank you for pointing out this oversight. We have updated the text accordingly:

“Flies walk rhythmically with a continuum of stepping patterns that range from tetrapod (where two of six legs are off the ground at a time) to tripod (where three of six legs are off the ground at a time)"

L88: "a new model of fly walking" - The intention of the authors is to produce a model from which to learn about walking in the fly, is that correct? The reviewer has read the paper several times now and wants to be sure that this is the authors' goal, not to engineer a control system for an animation or a robot.

Indeed, this is our goal. We were previously unclear about this, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

L126: "These desired phases are synchronized across pairs of legs to maintain a tripod coordination pattern, even when subject to unpredictable perturbations." - Does the animal maintain tripod coordination even when perturbed? In the reviewer's experience, flies vary their interleg coordination all the time. The reviewer would also expect that if perturbed strongly (as the supplemental videos show), the animal would adapt its interleg coordination in response. The author finds this assumption to be a weak point in the paper for the use of this disturbance exploring animal locomotion.

We do not know exactly how flies may react to our mechanical perturbations. However, we may hypothesize based on past papers. 

Couzin-Fuchs et al (2015) apply a mechanical perturbation to walking cockroaches. They find that that tripod is temporarily broken immediately after the perturbation but the cockroach recovers to a full tripod within one step cycle. 

DeAngelis et al (2019) apply optogenetic perturbations to fly moonwalker neurons that drive backward walking. Flies slow down following perturbation, but then recover after 200ms (about 2-3 steps) to their original speed (on average). 

Thus, we think it is reasonable to model a fly’s internal phase coupling to maintain tripod and for its intended speed to remain the same even after a perturbation. 

We do agree with the reviewer that it is plausible a fly might also slow down or even stop after a perturbation and we do not model such cases. We have added some text to the discussion on future work:

“Future work may also model how higher-level planning of fly behavior interacts with the lowerlevel coordination of joint angles and legs. Walking flies continuously change their direction and speed as they navigate the environment (Katsov et al, 2017; Iwasaki et al 2024). Past work shows that flies tend to recover and walk at similar speeds following perturbations (DeAngelis et al, 2019), but individual flies might still change walking speed, phase coupling, or even transition to other behaviors, such as grooming. Modeling these higher-level changes in behavior would involve combining our sensorimotor model with models for navigation (Fisher 2022) or behavioral transitions (Berman et al, 2016).”

L136: "...to output joint torques to the physical model of each leg" - Is this the ultimate output of the nervous system? Muscles are certainly not idealized torque generators. There are dynamics related to activation and mechanics. The reviewer is skeptical that this is a model of neural control in the animal, because the computation of the nervous system would be tuned to account for all these additional dynamics.

We agree with the reviewer that joint torques are not the ultimate output of the nervous system. We use a torque controller because it is parsimonious, and serves our purpose of creating an interpretable and modular locomotion model.

We also agree that muscles are an important consideration — we make mention of them later on in the paper under the section “Toward biomechanical and neural realism”, where we state “Another step toward biological realism is the incorporation of explicit dynamical models of proprioceptors, muscles, tendons, and other biomechanical aspects of the exoskeleton.”

Our goal is not to directly model neural control of the animal. We have introduced text clarifications to emphasize this — we provide a longer response for this in the public review above (see (2)).

L143: "To train the network from data, we used joint kinematics of flies walking on a spherical treadmill..." This is an impressive approach, but then the reviewer is confused about why the kinematics of the model are so different from those of the animal. The animal takes longer strides at a lower frequency than the model. If the model were trained with data, why aren't they identical? This kind of mismatch makes the reviewer think the approach in this paper is too complicated to address the main problem.

The design of our trajectory generator model is one of the simplest for reproducing the output of a dynamical system. It consists of a multilayer perceptron model that models the phase velocity and joint angle accelerations at each timestep. All of its inputs are observable and interpretable: the current joint angles, joint angle derivatives, desired walking speed, and phase angle. 

We chose this model for ease of interpretability, integration with the optimal controller, and to allow for generalization across perturbations. Given all of these constraints, this is the best model of desired kinematics we could obtain. We note that the simulated kinematics do match real fly kinematics qualitatively (Figure 2A and supplemental videos) and are close quantitatively (Figure 2B and C). We speculate that matching the animals’ strides at all walking frequencies may require explicitly modeling differences across individual flies. We leave the design and training of more accurate (but more complex) walking models for future work.

We add some further discussion about fitting kinematics in the discussion:

“Although we believe our model matches the fly walking sufficiently for this investigation, we do note that our model still underfits the joint angle oscillations in the walking cycle of the fly (see Figure 2 and Appendix 3). More precise fitting of the joint angle kinematics may come from increasing the complexity of the neural network architecture, improving the training procedure based on advances in imitation learning (Hussein et al., 2018), or explicitly accounting for individual differences in kinematics across flies (Deangelis et al., 2019; Pratt et al., 2024).”

Figure 2: The reviewer thinks the violin plots in Figure 2C are misleading. Joint angles could be greater or less than 0, correct? If so, why not keep the sign (pos/neg) in the data? Taking the absolute value of the errors and "folding over" the distribution results in some strange statistics. Furthermore, the absolute value would shroud any systematic bias in the model, e.g., joint angles are always too small. The reviewer suggests the authors plot the un-rectified data and simply include 2 dashed lines, one at 5.56 degrees and one at -5.56 degrees.

These violin plots are averages of errors over all phases within each speed. We chose to do this to summarize the errors across all phase angle plots, which are shown in detail in Appendix 3 and 4.

For the reviewer, we have added a plot of the raw errors across all phase angle plots in Appendix 5, E.

L156: Should "\phi\dot" be "\phi"?

We originally had a typo: we said “phase” when we meant “phase velocity”. This has been fixed. \phi\dot is correct.

L160: "This control is possible because the controller operates at a higher temporal frequency than the trajectory generator...". This statement concerns the reviewer. To the reviewer, this sounds like the higher-level control system communicates with the "muscles" at a higher frequency than the low-level control system, which conflicts with the hierarchical timescales at which the nervous system operates. Or do the authors mean that the optimal controller can perform many iterations in between updates from the trajectory generator level? If so, please clarify.

We mean that the optimal controller can perform many iterations in between updates from the trajectory generator level. The text has been clarified:

“This control is possible because the controller operates at a higher temporal frequency than the trajectory generator in the model. The controller can perform many iterations (and reject disturbances) in between updates to and from the trajectory generator.”

L225: "We considered two types of perturbations: impulse and persistent stochastic". Are these realistic perturbations? Realistic perturbations such as a single leg slipping, or the body movement being altered would produce highly correlated joint velocities.

These perturbations are not quite realistic — nonetheless, we illustrate their analogousness to real perturbations in the subsequent text in the paper, and restrict our simulations to ranges that would be biologically plausible (see Appendix 7). We agree that realistic perturbations would produce highly correlated joint accelerations and velocities, whereas our perturbations produce random joint accelerations. 

L265: "...but they are difficult to manipulate experimentally..." This is true, but it can and has been done. The authors should cite:

Bässler, U. (1993). The femur-tibia control system of stick insects-A model system for the study of the neural basis of joint control. Brain Research Reviews, 18(2), 207-226. 

Thank you for the suggestion, we have incorporated it into the text at the end of the referenced sentence.

L274: "...since the controller can effectively compensate for large delays by using predictions of joint angles in the future". But can the nervous system do this? Or, is there a reason to think that the nervous system can? The reviewer thinks the authors need stronger justification from the literature for their optimal control layer.

To clarify, this sentence describes a feature of the model’s behavior when no external perturbations are present. This is not directly relevant to the nervous system, since organisms do not typically exist in an environment free of perturbations — we are not suggesting that the nervous system does this.

In response to the question of whether the nervous system can compensate for delays using predictions: we know that delays are present in the nervous system, perturbations exist in the environment, and that flies manage to walk in spite of them. Thus, some type of compensation must exist to offset the effects of delays (the reviewer themself has provided some excellent citations that study the effects of delays). In our model, we use prediction as the compensation mechanism — this is one of our central hypotheses. We further discuss this in the section “Predictive control is critical for responding to perturbations due to motor delay”.

L319: "The formulation of a modular, multi-layered model for locomotor control makes new experimentally-testable hypotheses about fly motor control...". What testable hypotheses are these? The authors should explicitly state them. They are not clear to the reviewer, especially given the nonphysiological nature of the control system and the mechanics.

A number of testable hypotheses are mentioned throughout the Discussion section:

“Our model predicts that at the same perturbation magnitude, walking robustness decreases as delays increase. This could be experimentally tested by altering conduction velocities in the fly, for example by increasing or decreasing the ambient temperature (Banerjee et al, 2021).  If a warmer ambient temperature decreases delays in the fly, but fly walking robustness remains the same in response to a fixed perturbation, this would indicate a stronger role for central control in walking than our modeling results suggest.”

“In our model, robust locomotion was constrained by the cumulative sensorimotor delay. This result could be experimentally validated by comparing how animals with different ratios of sensory to motor delays respond to perturbations. Alternatively, it may be possible to manipulate sensory vs. motor delays in a single animal, perhaps by altering the development of specific neurons or ensheathing glia (Kottmeier et al., 2020). If sensory and motor delays have significantly different effects on walking quality, then additional compensatory mechanisms for delays could play a larger role than we expect, such as prediction through sensory integration, mechanical feedback, or compensation through central control.”

“we hypothesize that removing proprioceptive feedback would impair an insect's ability to sustain locomotion following external perturbations.”

“We propose that fly motor circuits may encode predictions of future joint positions, so the fly may generate motor commands that account for motor neuron and muscle delays.”

L323: "...and biomechanical interactions between the limb and the environment". In the reviewer's experience, the primary determinant of delay tolerance is the mechanical parameters of the limb: inertia, damping, and parallel elasticity. For example, in Ashtiani et al. 2021, equation 5 shows exactly how this comes about: the delay changes the roots and poles of the control system. This is why the reviewer is confused by the complexity of the model in this submission; a simpler model would explain why delays cannot be tolerated in certain circumstances.

We were previously unclear about the goal of the model, and have made text edits to clarify this — we provide a longer response for this in the public review above (see (1)).

L362: Another highly relevant reference here would be Sutton et al. 2023.

Done

L366: Szczecinski et al. 2018 is hardly a "model"; it is mostly a description of experimental data. How about Goldsmith, Szczecinski, and Quinn 2020 in B&B? Their model of fly walking has patterngenerating elements that are coordinated through sensory feedback. In their model, motor activation is also altered by sensory feedback. The reviewer thinks the statement "Models of fly walking have ignored the role of feedback" is inaccurate and their description of these references should be refined.

Thank you for the suggestion; we have tempered the language and revised this section to include more references, including the suggested one — text is replicated below. 

“Many models of fly walking ignore the role of feedback, relying instead on central pattern generators (Lobato-Rios et al., 2022; Szczecinski et al., 2018; Aminzare et al., 2018) or metachondral waves (Deangelis et al., 2019) to model kinematics. Some models incorporate proprioceptive feedback, primarily as a mechanism that alters timing of movements in inter-leg coordination (Goldsmith et al., 2020; Wang-Chen et al., 2023).”

We remark that Szczecinski et al does include a model that replicates data without using sensory feedback, so we think it is fair to include.  

L371: "...highly dependent on proprioceptive feedback for leg coordination during walking." What about Berendes et al. 2016, which showed that eliminating CS feedback from one leg greatly diminished its ability to coordinate with the other legs? This suggests that even flies depend on sensory feedback for proper coordination, at least in some sense.

Interesting suggestion – we have integrated it into the text a little further down, where it better fits:

“Silencing mechanosensory chordotonal neurons alters step kinematics in walking Drosophila (Mendes et al., 2013; Pratt et al., 2024). Additionally, removing proprioceptive signals via amputation interferes with inter-leg coordination in flies at low walking speeds (Berendes et al., 2016)”

L426: "The layered model approach also has potential applications for bio-mimetic robotic locomotion.". How fast can this model be computed? Can it run faster than real-time? This would be an important prerequisite for use as a robot control system.

The model should be able to be run quite fast, as it involves only

(1) Addition, subtraction, matrix multiplication, and sinusoidal computation on scalars (for the phase coordinator and optimal controller)

(2) Neural network inference with a relatively small network (for the trajectory generator) Whether this can run in real-time depends on the hardware capabilities of the specific robot and the frequency requirements — it is possible to run this on a desktop or smaller embedded device.

We do note that the model needs to first be set up and trained before it can be run, which takes some time (see panel D of Figure 1).

L432: "...which is a popular technique in robotics.". Please cite references supporting this statement.

We have added citations: the text and relevant citations are reproduced below:

“... which is a popular technique in robotics (Hua et al., 2021; Johns, 2021)

Hua J, Zeng L, Li G, Ju Z. Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning. Sensors. 2021; 21(4):1278

Johns E. Coarse-to-fine imitation learning: Robot manipulation from a single demonstration. In:

2021 IEEE international conference on robotics and automation (ICRA) IEEE; 2021. p. 4613–4619

L509: "We find that the phase offset across legs is not modulated across walking speeds in our dataset". This is a surprising result to the reviewer. Looking at Figure 6C, the reviewer understands that there are no drastic changes in coordinate with speed, but there are certainly some changes, e.g., L1-R3, L3-R1. In the reviewer's experience, even very small changes in interleg phasing can change the visual classification of walking from "tripod" to "tetrapod" or "metachronal". Furthermore, several leg pairs do not reside exactly at 0 or \pi radians apart, e.g., L1-L3, L2-L3, R1-R3, R2-R3. In conclusion, the reviewer thinks that setting the interleg coordination to tripod in all cases is a large assumption that requires stronger justification (or, should be eliminated altogether).

We made a simplifying assumption of a tripod coordination across all speeds. The change in relative phase coordination across speeds is indeed relatively small and additionally we see little change in our results across forward speeds (see Figures 4B, 5C and 5D). 

We have added text to clarify this assumption and what could be changed for future studies in the methods:

“We estimate $\bar \phi_{ij}$ from the walking data by taking the circular mean over phase differences of pairs the legs during walking bouts. We find that the phase offset across legs is not strongly modulated across walking speeds in our dataset (see Appendix 2) so we model $\bar \phi_{ij}$ as a single constant independent of speed. In future studies, this could be a function of forward and rotation speeds to account for fine phase modulation differences.”

L581: "of dimension...". Should the asterisk be replaced by \times? The asterisk makes the reviewer think of convolution. This change should be made throughout this paragraph.

Good point, done.

Figure 6: Rotational velocities in all 3 sections are reported in mm/s, but these units do not make sense. Rotational velocities must be reported in rad/s or deg/s.

The rotation velocity of mm/s corresponded to the tangential velocity of the ball the fly walked on. We agree that this does not easily generalize across setups, so we have updated the figure rotation velocities in rad/s. 

L619: The reviewer is unconvinced by using only 2 principal components of the data to compare the model and animal kinematics. The authors state on line 626 that the 2 principal components do not capture 56.9% of the variation in the data, which seems like a lot to the reviewer. This is even more extreme considering that the model has 20 joints, and the authors are reducing this to 2 variables; the reviewer can't see how any of the original waveforms, aside from the most fundamental frequencies, could possibly be represented in the PCA dataset. If the walking fly models looked similar to each other, the reviewer could accept that this method works. But the fact that this method says the kinematics are similar, but the motion is clearly different, leads the reviewer to suspect this method was used so the authors could state that the data was a good match.

Our primary use of the KS metric was to indicate whether the simulated fly continues walking in the presence of perturbations, hence we limited the analysis of the KS to the first 2 principal components. 

For completeness, we investigate the principal components in Appendix 9 and the effect of evaluating KS with different numbers of components in Appendix 10. 

The results look similar across components for impulse perturbations. For stochastic perturbations, the range of similar walking decreases as we increase the number of components used to evaluate walking kinematics. Comparing this with Appendix 9 showing that higher components represent higher frequencies of the walking cycle, we conclude that at the edge of stability for delays (where sum of sensory and actuation delays are about 40ms), flies can continue walking but with impaired higher frequencies (relative to no perturbations) during and after perturbation. 

We add text in the methods:

“We chose 2 dimensions for PCA for two key reasons. First, these 2 dimensions alone accounted for a large portion of the variance in the data (52.7% total, with 42.1% for first component and 10.6% for second component)). There was a big drop in variance explained from the first to the second component, but no sudden drop in the next 10 components (see Appendix 9). Second, the KDE procedure only works effectively in low-dimensional spaces, and the minimal number of dimensions needed to obtain circular dynamics for walking is 2. We investigate the effect of varying the number of dimensions of PCA in Appendix 10.”

(Note that we have corrected the percentage of variance accounted for by the principal components, as these numbers were from an older analysis prior to the first draft.)

We also reference Appendix 10 in the results:

“We observed that robust walking was not contingent on the specific values of motor and sensory delay, but rather the sum of these two values (Fig. 5E). Furthermore, as delay increases, higher frequencies of walking are impacted first before walking collapses entirely (Appendix 10).”

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, the authors study the effects of synaptic activity on the process of eye-specific segregation, focusing on the role of caspase 3, classically associated with apoptosis. The method for synaptic silencing is elegant and requires intrauterine injection of a tetanus toxin light chain into the eye. The authors report that this silencing leads to increased caspase 3 in the contralateral eye (Figure 1) and demonstrate evidence of punctate caspase 3 that does not overlap neuronal markers like map2. However, the quantifications showing increased caspase 3 in the silenced eye (done at P5) are complicated by overlap with the signal from entire dying cells in the thalamus. The authors also show that global caspase 3 deficiency impairs the process of eye-specific segregation and circuit refinement (Figures 3-4).

The reviewer states: “this silencing leads to increased caspase 3 in the contralateral eye”. We observed increased caspase-3 activity, not protein levels, in the contralateral dLGN, not eye.

The reviewer states: “and demonstrate evidence of punctate caspase 3 that does not overlap neuronal markers like map2”. We do not believe that this statement is accurate, as we show that the punctate active caspase-3 signals overlap with the dendritic marker MAP2 (Figure S4A).

The reviewer also states: “, the quantifications showing increased caspase 3 [activity] in the silenced [dLGN] (done at P5) are complicated by overlap with the signal from entire dying cells in the thalamus”. We do not believe that this statement is accurate. The apoptotic neurons we observed are relay neurons (confirmed by their morphology and positive staining of NeuN – Figure S4B-C) located in the dLGN (the dLGN is clearly labeled by expression of fluorescent proteins in RGCs, and only caspase-3 activity in the dLGN area is analyzed), not “cells” of unknown lineage (as suggested by the reviewer) in the general “thalamus” area (as suggested by the reviewer). If the dying cells were non-neuronal cells, that would indeed confound our quantification and conclusions, but that is not the case.

We argue that whole-cell caspase-3 activation in dLGN relay neurons is a bona fide response to synaptic silencing by TeTxLC and therefore should be included in the quantification. We have two sets of controls: one is between the strongly inactivated dLGN and the weakly inactivated dLGN in the same TeTxLC-injected animal; and the second is between the dLGN of TeTxLC-injected animals and mock-injected animals. In both controls, only the dLGNs receiving strong synapse inactivation have more apoptotic dLGN relay neurons, demonstrating that these cells occur because of synapse inactivation. It is also unlikely that our perturbation is causing cell death through a non-synaptic mechanism. Since mock injections do not cause apoptosis in dLGN neurons, this phenomenon is not related to surgical damage. TeTxLC is injected into the eyes and only expressed in presynaptic RGCs, not in postsynaptic relay neurons, so this phenomenon is also unlikely to be caused by TeTxLC-related toxicity. Furthermore, if apoptosis of dLGN relay neurons is not related to synapse inactivation, then when TeTxLC is injected into both eyes, one would expect to see either the same amount or more apoptotic relay neurons, but we instead observed a reduction in dLGN neuron apoptosis, suggesting that synapse-related mechanisms are responsible. Considering the above, occasional whole-cell caspase-3 activation in relay neurons in TeTxLC-inactivated dLGN is causally linked to synapse inactivation and should be included in the quantification.

We also revised the manuscript to better explain the possible mechanistic connection between localized caspase-3 activity and whole-cell caspase-3 activity. We propose that whole-cell caspase-3 activation occurs because of uncontrolled accumulation of localized caspase-3 activation. Please see line 127-140 and line 403-413 for details.

Additionally, we would like to clarify that we are not claiming that synapse inactivation leads to only localized caspase-3 activation or only whole-cell caspase-3 activation, as is suggested by the editors and reviewers in the eLife assessment. We have clearly stated in the manuscript that both types of signals were observed. However, we reasoned that, because whole-cell caspase-3 activation in unperturbed dLGNs – which undergo normal synapse elimination – is infrequently observed, whole-cell caspase-3 activation may not be a significant driver of synapse elimination during normal development. In this revision, we included a new experiment to corroborate this hypothesis. If whole-cell caspase-3 activation in dLGN relay neurons is a prevalent phenomenon during normal development, such caspase-3 activity would lead to significant death of dLGN relay neurons during normal development. Consequently, if we block caspase-3 activation by deleting caspase-3, the number of relay neurons in the dLGN should increase. However, in support of our hypothesis, we observed comparable numbers of relay neurons in Casp3^+/+ and Casp3^-/- mice. Please see Figure S7 for details.

The authors also report that "synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes" (abstract). They report that microglia engulf fewer RGC axon terminals in caspase 3 deficient animals (Figure 5), and that this preferentially occurs in silenced terminals, but this preferential effect is lost in caspase 3 knockouts. Based on this, the authors conclude that caspase 3 directs microglia to eliminate weaker synapses. However, a much simpler and critical experiment that the authors did not perform is to eliminate microglia and show that the caspase 3 dependent effects go away. Without this experiment, there is no reason to assume that microglia are directing synaptic elimination.

The reviewer states: “microglia engulf fewer RGC axon terminals in caspase 3 deficient animals (Figure 5), and that this preferentially occurs in silenced terminals, but this preferential effect is lost in caspase 3 knockouts”. We are not sure what the reviewer means by “this preferentially occurs in silenced terminals”. Our results show that microglia preferentially engulf silenced terminals, and such preference is lost in caspase-3 deficient mice (Figure 6).

We do not understand the experiment where the reviewer suggested to: “eliminate microglia and show that the caspase 3 dependent effects go away”. To quantify caspase-3 dependent engulfment of synaptic material by microglia or preferential engulfment of silenced terminals by microglia, microglia must be present in the tissue sample. If we eliminate microglia, neither of these measurements can be made. What could be measured if microglia are eliminated is the refinement of retinogeniculate pathway. This experiment would test whether microglia are required for caspase-3 dependent phenotypes. This is not a claim made in the manuscript. Instead, we claimed caspase-3 is required for microglia to engulf weak synapses, as supported by the evidence presented in Figure 6.

We did not claim that “microglia are directing synaptic elimination”. Our claim is that synapse inactivation induces caspase-3 activity, and caspase-3 activation in turn leads to engulfment of weak synapses by microglia. Based on this model, it is the neuronal activity that fundamentally directs synapse elimination. Synapse engulfment by microglia is only a readout we used to measure the outcome of activity-dependent synapse elimination. We have revised all sections in the manuscript that are related to synapse engulfment by microglia to emphasize the logic of this model.

We have also revised the abstract and title of the paper to better align it with our main claims, removed the reference to astrocytes, and clarified that microglia engulfment measurements are used as readouts of synapse elimination.

Finally, the authors also report that caspase 3 deficiency alters synapse loss in 6-month-old female APP/PS1 mice, but this is not really related to the rest of the paper.

We respectfully disagree that Figure 7 is not related to the rest of the paper. Many genes involved in postnatal synapse elimination, such as C1q and C3, have been implicated in neurodegeneration. It is therefore natural and important to ask whether the function of caspase-3 in regulating synaptic homeostasis extends to neurodegenerative diseases in adult animals. The answer to this question may have broad therapeutic impacts.

Reviewer #2 (Public Review):

Summary:

This manuscript by Yu et al. demonstrates that activation of caspase-3 is essential for synapse elimination by microglia, but not by astrocytes. This study also reveals that caspase 3 activation-mediated synapse elimination is required for retinogeniculate circuit refinement and eye-specific territories segregation in dLGN in an activity-dependent manner. Inhibition of synaptic activity increases caspase-3 activation and microglial phagocytosis, while caspase-3 deficiency blocks microglia-mediated synapse elimination and circuit refinement in the dLGN. The authors further demonstrate that caspase-3 activation mediates synapse loss in AD, loss of caspase-3 prevented synapse loss in AD mice. Overall, this study reveals that caspase-3 activation is an important mechanism underlying the selectivity of microglia-mediated synapse elimination during brain development and in neurodegenerative diseases.

Strengths:

A previous study (Gyorffy B. et al., PNSA 2018) has shown that caspase-3 signal correlates with C1q tagging of synapses (mostly using in vitro approaches), which suggests that caspase-3 would be an underlying mechanism of microglial selection of synapses for removal. The current study provides direct in vivo evidence demonstrating that caspase-3 activation is essential for microglial elimination of synapses in both brain development and neurodegeneration.

The paper is well-organized and easy to read. The schematic drawings are helpful for understanding the experimental designs and purposes.

Weaknesses:

It seems that astrocytes contain large amounts of engulfed materials from ipsilateral and contralateral axon terminals (Figure S11B) and that caspase-3 deficiency also decreased the volume of engulfed materials by astrocytes (Figures S11C, D). So the possibility that astrocyte-mediated synapse elimination contributes to circuit refinement in dLGN cannot be excluded.

We would like to clarify that we do not claim that astrocytes are unimportant for synapse elimination or circuit refinement. We acknowledge that the claim made in the original submitted manuscript that caspase-3 does not regulate synapse elimination by astrocytes lacks strong supporting evidence. We have removed this claim and revised the section related to synapse engulfment by astrocytes to provide a more rigorous interpretation of our data. We also removed the section in discussion regarding distinct substrate preferences of microglia and astrocytes.

Does blocking single or dual inactivation of synapse activity (using TeTxLC) increase microglial or astrocytic engulfment of synaptic materials (of one or both sides) in dLGN?

We assume that by “blocking single or dual inactivation of synapse activity”, the reviewer refers to inactivating retinogeniculate synapses from one or both eyes.

We showed that inactivating retinogeniculate synapses from one eye (single inactivation) increases engulfment of inactive synapses by microglia (Figure 6). We did not measure synapse engulfment by microglia while inactivating retinogeniculate synapses from both eyes (dual inactivation). However, based on the total active caspase-3 signal (Figure 2) in the dual inactivation scenario, we do not expect to see an increase in engulfment of synaptic material by microglia.

We did not measure astrocyte-mediated engulfment with single or dual inactivation, as we did not see a robust caspase-3 dependent phenotype in synapse engulfment by astrocytes.

Recommendations for the authors:

Reviewer #1 (Recommendations for the Authors):

(1) Figure 1 - It is not clear from this figure whether the authors are measuring caspase 3 in dendritic compartments or in dying relay neurons in the thalamus. The authors state that "either" whole cell death (1B) or smaller punctate signals (1F) were observed. When quantifying "photons" in Figure 1E, it appears most of the signal captured will be of dying relay neurons. What determined which signal was observed, and what is being quantified in Figure 1E? This also applies to the quantifications being reported in Figure 2.

The quantification includes both types of signals – it is sum of all active caspase-3 signal within the dLGN boundary. We note that there is a significant amount of punctate signal in the TeTxLC-inactivated dLGN. Unfortunately, due to file compression, these signals are not clearly visible in the submitted manuscript file. We have provided high resolution figures in this revision.

As argued above in the response to the public review, apoptotic relay neurons in TeTxLC-inactivated dLGN (not the general thalamus area) occur as a direct consequence of synapse inactivation. Therefore, active caspase-3 signals in these relay neurons should be included in the quantification.

We believe it is the extent of synapse inactivation (i.e., the number of synapses that are inactivated) that determines whether dLGN relay neuron apoptosis occurs or not. Such apoptosis is expected considering the nature of the apoptosis signaling cascade. In the intrinsic apoptosis pathway, release of cytochrome-c from mitochondria induces cleavage of the initiator caspase, caspase-9, and caspase-9 in turn cleaves the executioner caspases, caspase-3/7, which causes apoptosis. Caspase-3 can cleave upstream factors in the apoptosis pathway, leading to explosive amplification of caspase-3 activity (McComb et al., DOI: 10.1126/sciadv.aau9433). When a relay neuron receives a few inactivated synapses, caspase-3 activation in the postsynaptic dendrite can remain local (as we observed in Figure 1), constrained by mechanisms such as proteasomal degradation of cleaved caspase-3 (Erturk et al., DOI: 10.1523/JNEUROSCI.3121-13.2014). However, when a relay neuron receives many inactivated synapses, the cumulative caspase-3 activity induced in the dendrite can overwhelm negative regulation and lead to significantly higher levels of caspase-3 activity in entire dendrites (Figure S4B) through positive feedback amplification, eventually leading to caspase-3 activation in entire relay neurons. Please see line 127-140 and line 403-413 for our discussion in the main text.

(2) Figure 5 - Figures 5c-d and Fig 6 are confounded by pseudoreplication, whereby performing statistics on 50-60 microglia inflates statistical significance. Could the authors show all these data per mouse?

If we understand the reviewer correctly, the reviewer is suggesting that reporting measurements from multiple microglia in one animal constitutes pseudo-replication. This is correct in a strict sense, as microglia in the same animal are more likely to be similar than microglia from different animals. In the revised version, we have plotted the data by animal in Figure S11 and S13. The observations remain valid. However, we would like to point out that averaging measurements from all microglia in each animal and report by mouse is very conservative, as measurements from microglia in the same animal still vary greatly due to cell-to-cell differences.

(3) Although the authors are not the only ones to use this strategy, it is worth noting that performing all microglial experiments in Cx3cr1 heterozygotes could lead to alterations in microglial function that may not be reflective of their homeostatic roles.

We acknowledge that Cx3cr1 heterozygosity could cause alterations in microglial physiology.

While Cx3cr1 heterozygosity may impact microglia physiology, we note that the engulfment assay in Figure 5 is comparing microglia in Cx3cr1^+/-; Casp3^+/- and Cx3cr1^+/-; Casp3^-/- animals. Therefore, the impact of Cx3cr1 heterozygosity is controlled for in our experiment, and the observed difference in engulfed synaptic material in microglia is an effect specific to caspase-3 deficiency. However, we acknowledge that this difference could be quantitatively affected by Cx3cr1 heterozygosity.

It is important to note that we did not perform all microglia engulfment analyses using Cx3cr1^+/- mice. We have edited the manuscript to make this more clear. In the activity-dependent microglia engulfment analysis performed in Figure 6, we used Casp3^+/+ and Casp3^-/- animals and detected microglia with anti-Iba1 immunostaining. Therefore, the impact of Cx3cr1 heterozygosity is not a problem for this experiment.

Minor:

(1) Figures are presented out of order, which makes the manuscript difficult to follow.

We have revised text regarding the segregation analysis to align with the order of figures.

(2) Figure S3 is very confusing- the terms "left" and "right" are used in three or four partly overlapping contexts (which eye, which injection, which panel or subpanel of the figure is being referred to). Would this not be more appropriately analyzed with a repeated measures ANOVA (multiple comparisons not necessary) rather than multiple separate T-tests?

We have revised Figure S3 and S5 with better annotation and legends.

Yes, it is possible to use repeated measure two-way ANOVA. The analysis reports significant effect from genotypes, with a dF of 1, SoS and MoS of 0.0001081, F(1,13) = 7.595, and p = 0.0164. We used multiple separate t-tests because we wanted to show how genotype effects change with increasing thresholds, whereas two-way ANOVA only provides one overall p-value.

(3) Could the authors clarify why the percentage overlap (in the controls) is so different between Figure 3C and Figure S3C, and why different thresholds are applied?

This difference is primary due to difference in age. Figure 3 and Figure S5 are acquired at age of P10, while Figure S3 is acquired at P8. While the segregation process is largely complete by P8, the segregation continues from P8 to P10. Therefore, overlap measured at P10 will be lower than that measured at P8. If we compare overlap at the same threshold (e.g., 10%) and at the same age in Figure 3 and S5, the overlap is very similar.

The choice of threshold is related to the methods of labeling. In Figure 3, RGC terminals are labeled with AlexaFlour conjugated cholera toxin subunit-beta (CTB). In Figure S3 and S5, RGC axons are labeled by expression of fluorescent proteins. Labeling with CTB only labels membrane surfaces but yields stronger and slightly different signals at fine scales than labeling with fluorescent protein which are cell fillers. For Figure S3 and S5 (which use fluorescent protein labeling), higher thresholds such as those used in Figure 3 (which use CTB labeling) can be applied and the same trend still holds, but the data will be noisier. Regardless of the small difference in thresholds used, the important observation is that the defects in TeTxLC-injected or caspase-3 deficient animals are clear across multiple thresholds.

(4) Many describe the eye-specific segregation process as being complete "between P8-10". Other studies have quantified ESS at P10 (Stevens 2007). The authors state they did all quantifications at P8 (l. 82) and refer to Figure 3, but Figure 3 shows images from P10, whereas Figure S3 shows data from P8.

We did not say we performed all quantification at P8. In line 85, we said “To validate the efficacy of our synapse inactivation method, we injected AAV-hSyn-TeTxLC into the right eye of wildtype E15 embryos and analyzed the segregation of eye-specific territories at postnatal day 8 (P8), when the segregation process is largely complete”. The age of postnatal day 8 in this context is specifically referring to the experiment shown in Figure S3. For the segregation analysis in Figure 3, we specifically stated that the experiment was conducted at P10 (line 277).

Although the experiment in Figure S3 is conducted at P8, and Figure S5 and Figure 3 show results at P10, each dataset always included appropriate age-matched controls.  P8 is generally considered an age where segregation is mostly complete and sufficient for us to assess the potency of TeTxLC-delivered AAV on eye segregation.  We don’t think performing the experiment shown in Figure S3 at P8 impacts the interpretation of the data.

(5) Is Figure 6 also using Cx3cr1 GFP to label microglia? This is not clarified.

We apologize for this oversight. In Figure 6 microglia are labeled by anti-Iba1 immunostaining. We have clarified this in figure legends and text.

Reviewer #2 (Recommendations for the Authors):

(1) The authors quantified the caspase-3 activity using immunostaining and confocal microscopy (Figures 1B-E). They may need to verify the result (increased level of activated caspase-3 upon synapse inactivation) using alternative methods, such as western blotting.

Both western blot and immunostaining are based on antibody-antigen interaction. These two methods are not likely sufficiently independent. Additionally, to perform a western blot, we would need to surgically collect the TeTxLC-inactivated dLGN to avoid sample contamination from other brain regions. Such collection at the age we are interested in (P5) is very challenging. We have tested the anti-cleaved caspase-3 antibody using caspase-3 deficient mice and we can confirm it is a highly specific antibody that doesn’t generate signal in the caspase-3 deficient tissue samples.

(2) Does caspase-3 deficiency alter the density of microglia or astrocytes in dLGN?

No. Neither the density of microglia nor astrocytes changed with caspase-3 deficiency. In the case of microglia, we find that the mean density of microglia per unit area of dLGN is virtually the same in wild type and caspase-3 deficient mice (two-tailed t test P = 0.8556, 6 wild type and 5 Casp3^-/- mice). Some overviews showing microglia in dLGNs of wildtype and caspase-3 deficient mice can be found in Figure S10.  Similarly for astrocytes, we did not observe overt changes in astrocytes dLGN density linked to caspase-3 deficiency.

(3) During dLGN eye-specific segregation in normal developing animals, did the authors observe different levels of activated caspase-3 in different regions (territories)?

For normal developing animals, the activated caspase-3 signal is generally sparse, and it is difficult to distinguish whether the signal is related to synapse elimination. For animals receiving TeTxLC-injection, we did notice that in the dLGN contralateral to the injection, where most inactivated synapses are located, the punctate caspase-3 signal tends to concentrate on the ventral-medial side of the dLGN (Figure 1B), which is the region preferentially innervated by the contralateral eye.

(4) Recording of NMDAR-mediated synaptic currents may not be necessary for demonstrating that caspase 3 is essential for dLGN circuit refinement. In addition, the PPR may not necessarily reflect the number of innervations that a dLGN neuron receives. Instead, showing the changes in the frequency of mEPSCs (or synapse/spine density) may be more supportive.

Thank you for the comment. We have performed the suggested mEPSC measurements and reported the results in revised Figure 4D-F.

(5) Why is caspase 3 activation enhanced (compared to control) only at 4 months of age, when A-beta deposition has not formed yet, but not at later time points in AD mice (Figure S17)?

A prevailing hypothesis in the field is that the form of A-beta that is most neurotoxic is the soluble oligomeric form, not the fibril form that leads to plaque deposition. As the oligomeric form appears before plaque deposition, the enhanced caspase-3 activation we observed at 4-month may reflect an increase in oligomeric A-beta, which occurs before any visible A-beta plaque formation.

(6) The manuscript can be made more concise, and the figures more organized.

We removed superfluous details and corrected text-figure mismatches in the revised manuscript to improve readability.

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-0284z

Corresponding author(s): Bérénice, Benayoun A

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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

This paper by McGill and colleagues explores sex differences in murine macrophages from different niches. They use a combination of publicly available, and newly developed datasets, and combine these using meta-analysis approaches. They explore DEGs between sexes - both common across niches, and specific to certain niches - and use enrichment analyses to identify pathways linked to these genes. Their overall conclusions are that gene expression changes in females are more consistent across niches, than for males, and are enriched in extracellular matrix-related genes. The paper is easy to follow and very well written.

Major Comments:

1. I would suggest Figure 1 be moved to a supplemental figure. We agree that the Xist and Ddx3y is QC and can be removed. However, we believe that the separation of macrophage transcriptomes based on sex in the Multidimensional Scaling plot is an important result. Thus, we have revised Figure 1 to only include the MDS plots and have moved the Xist/Ddx3y plots to the supplement (new Supplemental Figure S1) in line with the reviewer’s suggestion.

Line 106 - It should be clarified why 50 DEGs was selected as the cut off for exclusion.

We apologize that our cut off criteria was not explained clearly enough. Because these are publicly available datasets, every lab used different numbers of biological replicates, methods, and sequencing depths, impacting the power of the assay to detect differences in gene expression robustly. Since we were interested in functions that were sex-dimorphic, and that requires running functional enrichment analysis, we needed to have a minimum gene set size to be able to run these analyses, which, in the field, is usually accepted to be 50 genes for robustness. Thus, we used 50 DEGs and have updated the methods to explain our reasoning: “Applying a cutoff for the number of differentially expressed genes (DEGs) helps ensure data consistency and comparability across datasets with varying methodologies and sequencing depths. This prevents datasets with excessively low DEG counts from disproportionately influencing downstream analyses. A cutoff also reduces noise from spurious findings, prioritizing datasets with robust transcriptional changes that are more likely to be biologically meaningful. The excluded microglia dataset contained only 11 DEGs (whereas all other microglia datasets had hundreds of DEGs), the pleural macrophage dataset had 37 (whereas all other lung-related macrophage datasets had above 50), and the spleen macrophage dataset had only 30.” (page 12, lines 381-388).

Optional - would suggest sex chromosome-linked genes are excluded and the analysis redone to see if there are other autosomal genes that are statistically shadowed by the X and Y linked genes.

We thank the reviewer for this great suggestion, and we now added this point to the discussion (page 9, lines 260-268). However, we think that genes on the X and Y chromosomes will impact overall function of the macrophages and that they are necessary to understand how macrophages from males and females may support differences in immune function throughout life. We now add this in the discussion as a potential future direction: “We find that a majority of genes similarly differential across sexes among the macrophage niches are sex chromosome linked. X-linked genes like Tlr7, Cxcr3, and Kdm6a enhance immune responses in female macrophages, potentially increasing inflammation with age (Feng et al., 2024). Meanwhile, Y-linked genes such as Uty and Sry influence transcriptional regulation and inflammatory signaling in male macrophages, which may contribute to chronic low-grade inflammation (Lusis, 2019). These genetic differences affect macrophage activity, tissue-specific immune responses, and susceptibility to age-related diseases, highlighting the importance of sex-specific factors in immune research. Future research should also explore how non-sex chromosome-linked genes interact with these sex-specific mechanisms to further shape macrophage and immune function.” (page 9, lines 260-268).

More metadata about the included studies should be included eg mouse ages, strains, experimental manipulations etc. I can't seem to access all of the Supplemental tables so this may already be included in Table S1.

We agree that this information is important to take into consideration and have now included this information in Supplemental Table S1A, along with the accession numbers to each dataset. All mice were aged between 2 to 24 weeks and all on variations of the C57BL/6 background.

How relevant the findings in mice are for humans should be explained further in the discussion.

We agree that our discussion needs to better explain broader implications. Our findings are relevant for human health because macrophages play key roles in immunity, inflammation, and tissue homeostasis, and their functions are known to differ between sexes. Understanding these sex-specific transcriptional differences in mice can provide insights into how male and female immune systems respond differently to infections, autoimmune diseases, and aging in humans. Since macrophage phenotypes are influenced by both systemic factors (e.g., hormones) and tissue-specific environments, studying multiple macrophage subtypes from different organs helps identify conserved and context-dependent sex differences. Indeed, our findings suggest the ECM may be a potential mechanism underlying sex-biased diseases, such as higher autoimmune prevalence in females or increased susceptibility to certain infections in males. We have added this detail to the discussion (page 10, lines 269-275).

Minor Comments:

1. Lines 63-66 - need references here. This mirrors Reviewer 2’s major point #2. We agree with the reviewer that references are needed and now cite PMID: 31541153, PMID: 29533975, PMID: 37863894, PMID: 33415105, and PMID: 37491279 (page 4 line 68-69).

Line 61 and 69 - repeated.

We thank the reviewer for catching this oversight and have deleted the first instance of the sentence.

Reviewer #1 (Significance (Required)):

Although this study is primarily descriptive, it adds to the current knowledge about sex differences in macrophages, an important and relatively understudied area. Those interested in sex differences and in the innate immune system generally, plus those who study macrophages in any context, should be interested in this work.

We thank the reviewer for their interest in our work and their helpful suggestions.

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

Summary: The study investigates sex-specific differences in macrophage gene expression across various tissue niches by analyzing both newly generated and publicly available datasets of varying quality. The key finding is the identification of three consistently differentially expressed genes (DEGs) across all macrophage niches: the Y-chromosome-encoded genes Ddx3y and Eif2s3y, and the X-chromosome-specific gene Xist. However, the number of sex-dimorphic DEGs varied significantly between macrophage niches, with female-biased genes showing more consistency across datasets. To further explore these sex-specific differences, the authors performed an overrepresentation analysis of the DEGs across datasets. They found enriched gene sets associated with specific biological terms in female-biased macrophages from peritoneal macrophages, bone marrow-derived macrophages (BMDMs), and osteoclast progenitors (OCPs), while male-biased enrichment was observed in microglia, exudate macrophages, OCPs, and BMDMs. Notably, extracellular matrix (ECM)-related genes were specifically enriched in female peritoneal macrophages and OCPs, whereas the term "nucleic acid binding" was more prominent in male samples from microglia, BMDMs, and OCPs, driven by the Y-chromosome genes Uty and Kdm5d. A gene set enrichment analysis (GSEA) using Gene Ontology (GO) and Reactome databases further confirmed the enrichment of sex-biased pathways. Based on these findings, the authors conclude that three sex chromosome-associated genes are consistently differentially expressed across all datasets and that female-associated gene expression appears to be more stable, particularly in relation to ECM-associated processes.

Major Comments:

Are the key conclusions convincing?

1. The study provides valuable insights into sex-dimorphic gene expression in macrophages across different niches. However, some conclusions appear overinterpreted due to the limited number of differentially expressed genes (DEGs) driving specific terms in the overrepresentation analysis. The reliance on only a few recurring genes (e.g., Kdm5d, Eif2s3y, Uty, and Ddx3y) raises concerns about the biological significance of some enriched terms. A clearer discussion on the limitations of such findings is necessary. We apologize for the confusion. Although the Venn Diagram may give the impression that our comparisons are limited to those few genes, we only highlight them with bold text because they are a good quality control mechanism for our analyses.

Importantly, methods like gene set enrichment analysis [GSEA] use whole-transcriptome ranking, which means the results we obtain are driven by the entire transcriptome and not just a few genes (GSEA results are reported in Figure 5). We agree that further explanation of these methodologies would improve interpretation of our findings for readers unfamiliar with these analytical techniques. To address this, we have now added the following to the methods: “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417).

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?Some claims, particularly those regarding the role of macrophages in diseases such as AD, histiocytosis, and osteoporosis, lack relevant references.

This mirrors minor point #1 from Reviewer #1. We apologize for not originally including references for this statement and have now updated the introduction and discussion with appropriate references: “Excessive macrophage activation is associated with numerous conditions, including neurodegeneration, atherosclerosis, osteoporosis, and cancer, many of which exhibit sex-biased tendencies (Chen et al., 2020; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 4, lines 67-69) and “Thus, investigating female and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis(Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-288).

Would additional experiments be essential to support the claims of the paper? While additional wet-lab experiments are not strictly necessary, a deconvolution analysis of the datasets could be highly beneficial. This would allow the identification of enriched macrophage subtypes and help assess whether differences between datasets are driven by specific macrophage populations rather than global sex differences. Since peritoneal macrophage origin is influenced by age and inflammation status, deconvolution could also clarify dataset comparability.

The reviewer makes an interesting point. We apologize for the confusion regarding the purity and origin of these datasets. All the datasets we curated from public repositories for our analysis are from purified populations of macrophages. To clarify this, we now include a column with the purification method used for each of the datasets based on the original manuscript in revised Supplemental Table S1A.

Since all the used datasets were derived from pure macrophage populations, deconvolution (which is used to identify cellular proportions in heterogeneous contexts) would not accomplish much, predicting that all the cells in the data are macrophages. While some people have argued that deconvolution may be used to identify different cell states, this is very controversial, especially since the “pure” reference and the heterogeneous query are subject to batch effects (i.e. either from differences in bench processing, sex of provenance for target/query datasets, transcriptional impact of sorting methods, differences in transcriptomic quantification methods, etc.) which overshadow most differences beyond cell types. Thus, due to the known batch sensitivity of deconvolution methods and the fact that we only selected pure macrophage transcriptomic profiling datasets, using deconvolution to identify macrophage subtypes would not be informative/feasible. Importantly, we focused our analyses on datasets derived only from young, healthy, naïve animals (2 to 24 weeks), without any interference from age-related inflammation.

To make this caveat clearer, we have added sentences to the results section indicating the age range of the animals (page 6, lines 100-101), as well as in the discussion to discuss how inflammation states and age may change some of our findings (page 10, lines 295-299).

Are the suggested experiments realistic in terms of time and resources? Performing cell-type deconvolution using established computational tools (e.g., CIBERSORT, BisqueRNA, or single-cell deconvolution methods) would be a realistic approach within a few weeks and would significantly strengthen the study. This analysis would not require additional experimental work but could refine the interpretation of the dataset. Additionally, a PCA of all datasets could help identify potential similarities among macrophages from different niches and between sexes.

As explained in our response to point #4, the use of only datasets from purified macrophages from young animals (before any influence of age or disease) makes deconvolution analysis meaningless, especially due to batching concerns. Specifically, it would require us to generate paired single-cell and bulk datasets on all macrophage subtypes in house to remove batch-inducing experimental biases, which we believe is outside of the scope of this small bioinformatics study.

To the second point, doing a PCA of all the datasets together would not provide much new information beyond cell type of origin due to batching concerns that could not be corrected, which are a known problem in transcriptomics analyses (PMID:20838408, PMID:28351613). Since datasets come from different labs, using different isolation methods, RNA capture choices, library construction kits and sequencing platforms, the main separating effects overall will be batch/dataset, not biology (PMID:20838408, PMID:28351613). Indeed, this is what we observe (Reviewer Figure 1), with broad separation of datasets by tissue of origin, then dataset of origin. Additionally, the top 10 loadings for PC1 and PC2 are primarily associated to autosomal genes (i.e. not on the sex chromosomes; Reviewer Table 1).

Reviewer Figure 1. (A) PCA of all samples across datasets. Read counts were processed together through R package sva v.3.46.0 for surrogate variable estimation, and surrogate variables were removed using the removeBatchEffect function from ‘limma’ v.3.54.2. DESeq2 normalized counts were used to make the PCA. (B) Zoomed in PCA excluding three outlier sample to enable easier visual discrimination of samples.

Principal Component – Gene

Loading

Chromosome

PC1- Srcin1

0.013601

11

PC1- Cacna1c

0.013593

6

PC1- Pclo

0.01357

5

PC1- Tro

0.013547

X

PC1- Ppp4r4

0.013541

12

PC1- Ppp1r1a

0.01354

15

PC1- Homer2

0.013538

7

PC1- Caskin1

0.013535

17

PC1- Arhgef9

0.013527

X

PC1- Slc4a3

0.013499

1

PC2- Gm15446

0.017978

5

PC2- 1810034E14Rik

0.017897

13

PC2- Gm19557

0.017871

19

PC2- Pkd1l2

0.017792

8

PC2- H60b

0.017274

10

PC2- Appbp2os

0.01723

11

PC2- Mir7050

0.017221

7

PC2- Nkapl

0.017166

13

PC2- Tmem51os1

0.017083

4

PC2- Dpep3

0.016962

8

Reviewer Table 1. Top 10 loadings for principal component 1 and principal component 2 with their respective chromosomal location.

Thus, since batch effects can only be accounted for rigorously when they are not confounded by biology (and in our case since each dataset only looks at one type of macrophage), this cannot be corrected in a rigorous manner to yield the desired results.

We have added a sentence to the discussion to highlight how future work where macrophages from diverse niches would be profiled in parallel may give greater insights into niche-specific sex-dimorphic effects (page 10, line 295-296).

Are the data and the methods presented in such a way that they can be reproduced? Some methodological details are missing, particularly regarding:

The isolation of mouse peritoneal macrophages (details on injection and harvesting procedure needed). Quality control of isolated macrophages (How were contaminating cells excluded? Was additional validation performed beyond using the kit?)

The age of mice used for bone marrow-derived macrophages (BMDMs) is not provided, which is important given that immune responses can be age-dependent.

We appreciate the reviewer’s request for additional methodological details. We apologize for not being clear with our details and have updated the methods to be clearer (page 11, lines 320-346), as well as added this information in revised Supplemental Table S1A (e.g. age of animals and purification method as described in the original papers). For all our in house datasets, mice were 4-months old, and the text is now updated to reflect this: “Long bones (tibia and femur) of young (4-months-old) from both sexes were collected and bone marrow was flushed into 1.5mL Eppendorf tubes via centrifugation (30 seconds, 10,000g) (Amend et al., 2016)” (page 11, lines 334-336).

While we couldn’t check the purity post hoc for published datasets we identified for meta-analysis, we performed a purity check on our isolated peritoneal macrophages using Cd11b-F4/80 staining by flow cytometry and have now included this data (including gating strategy) in Supplemental Figure S4. For BMDMs, no purity check was performed, as there is extensive literature on the efficiency of this differentiation protocol which consistently yields > 90% of macrophages. This has been added to the methods: “We used a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (Mendoza et al., 2022; Toda et al., 2021)” (page 11, lines 336-337).

Are the experiments adequately replicated and statistical analysis adequate? The statistical analysis appears generally appropriate, but there are concerns about dataset inconsistencies that should be addressed. Some datasets were not used across all analyses, which is not clearly indicated in figures or text. This should be explicitly mentioned to avoid misleading interpretations.

We appreciate the reviewer’s careful evaluation of our statistical analysis and the concern regarding dataset inconsistencies.

We believe that the reviewer is referring to the omission of the exudate dataset from the Venn Diagram analysis (Figure 2C), as this is the only time that we did not report the results from all datasets. We originally chose not to include the exudate dataset in the shared differentially expressed gene (DEG) analysis, because it contained over 1,300 DEGs, whereas all other datasets had between 4–30 DEGs, resulting in an unreadable figure.

However, we agree that it is important to include for the readers, and while we have decided to still exclude the exudate dataset from Figure 1C for readability purposes, we now include the overlap analyses for all datasets in Supplemental Figure S2 using an upset plot (an alternative visualization method) showing all 6 niches, as well as a table panel that lists the shared genes across niches “Three genes were found to be differentially expressed across all six niches: Xist, Ddx3y, and Eif2s3y (Figure 2C, Supplemental Figure 2A,B)” (page 6, lines 124-126). We thank the reviewer for drawing our attention to this and making our analysis clearer for future readers.

Minor Comments

1. Figures are included twice in the manuscript. We apologize for this, and figures are now only included once.

The use of stereotypic colors in figures (e.g., blue for male, pink for female) could be reconsidered for better readability and to avoid reinforcing gender stereotypes.

While we understand that this color choice might feel gender normative, we respectfully disagree with the reviewer, as we believe that for the expediency of scientific communication it is important to choose a color palette that is easily understandable without confusion without even needing to consult a legend.

Importantly, we have been using the same color palette in all publications from the lab on sex-differences for consistency (Lu et al, Nat aging 2021 PMID: 34514433; McGill et al, PLoS ONE, 2023 PMID: 38032907; Kang et al, J Neuroinflammation, 2024 PMID: 38840206; McGill et al, STAR Protocols, 2021 PMID: 34820637), which is crucial for scientific rigor and communication consistency.

Results - Section 1

Line 92: The word 'identified' may not be the most appropriate choice here, as it implies discovery rather than selection. Consider rephrasing to 'compiled' or 'gathered' to more accurately reflect the process of assembling the datasets. Additionally, the sentence structure could be refined for clarity, such as specifying that the datasets include both newly generated and publicly available data.

We have changed two instances of using the word identified to “collected” and “gathered” (page 4, line 83 and page 6, line 98). We also adjusted the sentence to say, “Although we initially collected 21 datasets, both newly generated and publicly available, for our study, only 18 datasets were retained after various quality filtering steps for downstream analysis” (page 4, lines 83-85).

Line 95: Specify the source of exudate-derived macrophage data.

We have updated Supplemental Table S1A to make sure it was comprehensively describing the datasets we used in our analysis and double checked that it was complete (including for the exudate data). We have updated the text to reflect this: “All accession numbers and corresponding manuscripts are found in Supplemental Table S1A” (page 6, lines 103-104).

Figure 1/2A: The scheme overview lacks clarity-its purpose is unclear. The two identical boxes are redundant and do not provide additional insight. Consider illustrating the origins of different macrophage subtypes instead. The cutoff of >50 DEGs should be included in the schematic to improve clarity. Overrepresentation and GSEA analysis should not be illustrated multiple times across different figures-it is redundant.

In Figure 1A, we included the identical boxes to indicate that no datasets were excluded for incorrect labeling of males/females. However, we agree that this is unnecessary and have removed the second box as suggested.

In Figure 2A, we agree the identical boxes are unneeded as the Xist/Ddx3y quality control step was listed in Figure 1A, and we have modified the figure accordingly.

We also agree that including the DEG cutoff and removing the GSEA mention will streamline the figures and have updated them accordingly as well.

Line 100: The mention of R software should be moved to the Methods section instead of appearing in the Results section.

We have now updated the text to say, “Expression levels of male-specific Ddx3y and female-specific Xist genes across all samples were examined to ensure proper sex labeling of samples (Supplemental Figure 1A-U)” (page 6, lines 111-112).

Figure 1B-V: The current figure layout is visually cluttered. Consider plotting male and female datasets together in a single graph with different point shapes instead of separate panels for each specific niche.

This seems to echo the above request for a global PCA in Reviewer 2’s Major Point #4, which unfortunately cannot be included due to the disproportionate impact of batch effects that has been well documented in the literature (Reviewer Figure 1; PMID:20838408, PMID:28351613). However, to make the figure clearer and less cluttered, and to address related Reviewer 1’s Major Point #1, we have moved the Xist/Ddx3y plots to Supplemental Figure S1 and only include the Multidimensional Scaling plots in Figure 1 to showcase the sex separation in each dataset.

Text-Figure alignment: The text describes male/female-specific gene expression levels first, while the figure starts with MDS analysis. The order should be consistent.

We agree and have adjusted the text accordingly (lines 109-112).

Figure 2C: Exudate data is missing-explain why.

This point echoes major point #6. As explained above, we have clarified this and included new data panels for clarity (New Supplemental Figure S2).

Results - Section 2

Line 151: Use consistent terminology-either "DEGs" or "DE genes", not both.

We replaced all instances of “DE genes” with DEGs (lines 132, 137, 141, 147, 149, 163, and 397).

Figure 3A: The text suggests not all datasets were included in this analysis-this should be explicitly indicated in the figure.

We apologize for the confusion. All datasets were included in this analysis; however, some niches did not have any GO terms passing the FDR

Show the number of DEGs used for analysis.

We apologize for the confusion. For the ORA analyses (Figures 3 and 4), we indicate the number of DEGs used for analysis in the panel header. For the GSEA analysis (Figure 5, Supplemental Figure S3), all expressed genes are ranked based on effect size without any prior filter (see response to major point #1), so DEGs are irrelevant for these analyses.

Figure 3B: Smaller pale dots in the bubble plot are difficult to distinguish-consider using a darker outline.

We have now added outlines to all the bubbles in the plots to help improve visibility.

Line 158: The term "phagocytosis" appears inconsistent with the figure, where it is labeled "phagocytosis, recognition".

We have updated the text accordingly (page 7, line 170).

Figure 4B, D, E: The overrepresentation analysis is based on very few genes (often only 1-2 genes per term), which may lead to overinterpretation.

We apologize for the lack of clarity of our previous manuscript. The number of genes used for DEG analysis is in the panel titles of Figure 3 and 4. While the overlap is small, this is unlikely to be spurious since all of the pathways we discuss show significant enrichment with FDR

Consider explicitly naming these genes and discussing their biological role instead of assigning terms based on minimal evidence.

We now discuss these genes in the results: “Male-biased GO terms for microglia, OCPs, and BMDMs derived from four genes: Kdm5d, Uty, Ddx3y, and Eif2s3y. All of these are Y-linked genes and play crucial roles in regulating innate and adaptive immune responses (Meester et al., 2020). Kdm5d and Uty influence adaptive immunity through chromatin remodeling and histone modification, while Ddx3y and Eif2s3y shape innate immune responses by modulating macrophage activation and cytokine production via translation initiation and RNA processing (Bloomer et al., 2013; Hamlin et al., 2024; Meester et al., 2020) “(page 8, lines 195-200).

Figures S3G and S3H seem to be switched.

We are puzzled by this comment, as our original manuscript did not include a Supplemental Figure S3. Out of an abundance of caution, however, we checked that Supplemental Table S3G and H were correctly labelled, and independently confirmed that they are not switched.

Results - Section 3

Figure 5A does not add significant new insights. Consider refining its content to highlight key findings more effectively.

We respectfully disagree and believe that schematic overviews help readers understand what is accomplished in any specific figure and have thus decided to keep it.

Number of genes included in the analysis is not provided-this is important to assess significance and should be stated in methods and figure legends.

We apologize for the lack of clarity. As explained above, GSEA uses all the genes in rank order (PMID: 16199517), we now explain GSEA more explicitly in the text “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes” (page 13, lines 415-417).

Discussion 20. Line 201-203: Missing reference.

We have now updated the text with the proper reference: “Tissue-resident macrophages are crucial to proper immune system function (Guilliams et al., 2020). While all macrophages share the responsibility of clearing cellular debris and foreign bodies, tissue-resident macrophages also have unique responsibilities that facilitate homeostasis throughout the body (Guilliams et al., 2020; Varol et al., 2015)” (page 9, lines 227-230).

Reference 23 (1999) is outdated. Newer literature should be cited to reflect modern insights into sex differences in macrophages.

We have now updated the text with an updated reference for two outdated references: (i) “Sex differences have previously been reported in macrophages, with female macrophages having higher phagocytic activity than males (Scotland et al., 2011)” (page 9, lines 232-233) and (ii) “Dysfunctional OCPs are associated with development of osteoporosis, a disease that is four times more prevalent in women (Alswat, 2017)” (page 10, lines 284-285).

Peritoneal macrophages and OCPs originate from monocytes. Would deconvolution help identify enriched subtypes and assess dataset comparability?

As noted in Reviewer 2’s Major Points #3 and #4, deconvolution analysis is not meaningful for subtype analysis without paired isolated/bulk datasets, which are outside of the scope of this study to generate.

The 'more consistent' pathways found for female datasets are not discussed.

We now discuss pathways found among the female datasets: “In addition, GSEA analysis of REACTOME gene sets showed male-biased expression for cell cycle related pathways (average set size 499), and female-biased expression for G protein-coupled receptor (GPCR) signaling (average set size 122) and extracellular matrix organization (average set size 127) (Figure 5C, Supplemental Table S4S-AJ; consistent with our ECM observation, Supplemental Figure S3A). Macrophages express a wide variety of GPCRs that allow them to respond to different stimuli. The expression of specific GPCRs influences macrophage polarization toward either a pro-inflammatory or anti-inflammatory state (Wang et al., 2019). A manual review of the genes contributing to this GPCR enrichment reveals the presence of several chemokine-related genes (such as Ccl4, Ccr4, Cxcl1, and others) (Supplemental Table S4). This suggests that females may have an increased abundance of chemokine GPCRs, potentially contributing to heightened autoimmune activity, among other factors.” (page 8, lines 212-222).

Methods - Peritoneal macrophage isolation:

Details on injection and harvesting are missing.

We apologize for not being clear with our details and have modified the methods to be clearer (page 11, lines 320-331).

How was contamination from other cell types assessed? F4/80 selection may not be fully macrophage-specific, and contamination could occur due to insufficient washing or the presence of non-macrophage F4/80+ cells.

For the peritoneal macrophage datasets we generated, the macrophages were checked for purity through flow cytometry using Cd11b and F4/80 antibodies. We considered double positive Cd11b+ F4/80+ cells to be macrophages, which represents >95% of cells using our methodology (Supplemental Figure S4), without a difference between sexes.

For the BMDMs, we utilize a protocol that is expected to yield ~90% Cd11b+ F4/80+ cells (PMID: 35212988 and PMID: 33458708).

Finally, we now include the purification method for all publicly available datasets according to their original manuscript in Supplemental Table S1A and explicitly discuss the information for our in-house datasets in the methods (page 11, lines 321-346).

• Bone marrow macrophages:

Mouse age is not provided in the results part.

We now provide this information in the methods (page 11, line 334). All ages for all datasets are now included in Supplemental Table S1A.

Figure Legends

Figure 2: Peritoneal macrophages are abbreviated as PeriMac-consider using this abbreviation consistently in the text.

We respectfully disagree with the reviewer and choose to keep Peritoneal Macrophages spelled out in the text for clarity. We use the shorthand “PeriMac” in Figure 2 and Figure 5 solely for spacing purposes, but these are explained in the figure legend.

Reviewer #2 (Significance (Required)):

The study's strengths include the integration of multiple datasets, the use of both overrepresentation and GSEA, and the exploration of tissue-specific macrophage niches. These findings have relevance for diverse communities, including immunologists, sex-difference researchers, and those studying macrophage-driven diseases such as osteoporosis, neurodegeneration, and chronic inflammation. The work provides a foundation for further studies on sex-specific macrophage biology and may have implications for sex-specific therapeutic strategies. However, the study has limitations. The conclusions regarding enriched pathways rely heavily on a small number of DEGs, raising concerns about overinterpretation. Additionally, dataset variability and missing data for some analyses (e.g., exudate macrophages) could affect the robustness of the results.

Despite these limitations, the study makes a meaningful but incremental advance by highlighting stable sex-dimorphic patterns in macrophage biology. It provides insights for both fundamental and translational research, particularly for audiences focused on immune regulation, sex-specific gene expression, and tissue-specific macrophage function.

We thank the reviewer for understanding the importance of our work.

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

Summary: McGill et al. explore sex-based differences in macrophage gene expression across various tissues. Using a meta-analysis of publicly available and newly generated datasets, they identify conserved and divergent sex-dimorphic genes and pathways between tissues. Overall, the report is easy to follow and guides the reader through the analysis. The authors highlight the relevance of the report by noting sex differences in immune responses to infection, autoimmunity, and chronic diseases. The inclusion of 17 independent transcriptomic datasets provides a robust and extensive analysis of sex-based transcriptional differences. The authors explore potential biological implications of sex-based transcriptional differences using pathway analysis. Despite the overall strengths, there are some points for which further clarification and analysis would improve the manuscript. Detailed comments are listed below.

Major comments:

1. A comparison of the overall transcriptomic profiles of macrophages regardless of sex would be additive. Knowing the degree of similarities and differences among macrophages from different niches would help the reader determine what genetic programs vary by compartment. If macrophages are very different by niche, it is not surprising that they share few sex-dimorphic patterns. This mirrors Reviewer 2’s Major Point #4. While this approach may seem valuable, it would only be feasible if all datasets were generated simultaneously by the same lab using identical sequencing and library preparation protocols to avoid batch effects. In this case, biology and batch effects are confounded, making any global analysis misleading. Although the reviewer may find the limited overlap unsurprising, given that macrophages are generally considered to be the same cell type, our goal was to explore the extent of shared versus distinct features across datasets, which we believe to be an invaluable question for the field.

Although it would not be possible to do this rigorously with the data we curated, the question of niche specific gene regulation of macrophages has been studied, showing extensive niche-specific regulation: “While the question of niche-specific gene regulation has been studied, showing extensive niche-specific regulation (Gosselin et al., 2014; Lavin et al., 2014), a comprehensive and systematic study of sex-differences across macrophage subtypes has not yet been performed” (page 4, lines 78-81).

It is unclear what age and strain the mice were and the number of samples that were included (n) for each dataset. This information should be included in S1A. If different ages or strains were used, how might this impact findings?

This mirrors Reviewer 1’s Major Point #4. We agree that this information is important to take into consideration and have now included this information in Supplemental Table 1A, along with the accession numbers to each dataset. Because there is no aging effect (all mice are aged between 2 to 24 weeks) and all mice are on a variation of the C57BL/6 background, we don’t expect this to be a major problem impacting our findings.

The authors used a Jaccard index to examine similarities in sex-based differences across tissue compartments. They claim that there are more similarities in females. However, the male are female graphs (Fig. 1E,D) do not look that different. Is there a better way to display this?

We apologize for the lack of clarity. We clustered the Jaccard matrices using hierarchical clustering to determine patterns of sharing. Thus, in these figures, the samples cluster based on the degree of similarity in sex-biased genes. In the females, there is clear separation by macrophage origin (yolk sac or circulating monocytes); whereas males have some separation but also have some mixing (e.g. Peritoneal Macrophage 2 clustering with the yolk-sac derived macrophage datasets). Additionally, four microglia datasets are together in the females with only one separate, whereas in the males they are split into three. We included colored bars by the dataset names to help highlight clear separation by niche of origin.

We have added this detail to the text to better explain the similarities: “Our results indicate that female-biased genes were more consistent among the cell types compared to male-biased genes (Figures 2D,E). In females, there is clear separation by macrophage origin (yolk sac or circulating monocytes), with all the peritoneal macrophages clustering together, followed by bone-related macrophages, then microglia and lung macrophages. In the males, the five microglia datasets are split into three groups, and Peritoneal Macrophage 2 clusters with the yolk-sac derived macrophage datasets” (page 7, lines 155-160).

In the Gene Ontology analysis, it is unclear what type of GO pathways were included (biological process, cellular component, molecular function). Also, some of the GO analyses were done with very few genes (as little as 4).

This echoes Reviewer #2’s Major Comment #1. For the Overrepresentation analysis (ORA) using Gene Ontology, we use the “ALL” option to include biological process, cellular component, and molecular function terms. We used ORA to look at shared DEGs across datasets of the same niche which is why some have very low input. For this reason, we also performed Gene Set Enrichment Analysis that uses all genes, not just those differentially expressed at FDR 5%, to examine gene changes at a broader level. In the methods we have added this information: “The differentially expressed genes shared within each niche were divided into up and down-regulated based on the sign of the DEseq2 log2 fold change. These gene lists were used as the shared genes and all expressed genes across datasets in that specific niche were used as the universe for the clusterProfiler function ‘enrichGO’, using the “ALL” option to include biological process, cellular component, and molecular function terms” (page 13, lines 405-410) and “GSEA relies on whole-transcriptome ranking, ensuring that the results reflect global transcriptomic patterns rather than being influenced by only a few genes.” (page 13, lines 415-417)”.

Is it possible to combine datasets by tissue to remove potential batch effects before downstream analyses? At the very least, PCA on combined data may help determine if some biological (e.g., age, strain) or technical (batch) differences are contributing to identifying few common sex differences.

This mirrors Reviewer #2’s Major Point #4. Unfortunately, since every dataset only examined a single niche, biology and batches are confounded, and thus performing a PCA on all datasets together will be driven by technical rather than biological drivers. Batch effects are a well-documented issue in genomics (PMID:20838408, PMID:28351613) Indeed, this is largely observed when we attempt this analysis, with datasets clustering by batch (Reviewer Figure 1). Due to the issue of uncorrectable batch effects, we do not believe this analysis meets the rigor required to be included in the revised manuscript and have chosen to not include it.

Validation of key results would further strengthen the manuscript.

We agree that future validation is important but is beyond the scope of this purely bioinformatic analysis. We have included text in the revision to highlight the importance of future validation studies: “Thus, investigating female- and male-biased processes in macrophages, including the contribution of the ECM, will be an important step in developing treatments for diseases including, but not limited to, AD, histiocytosis, and osteoporosis, and future research will be essential to validate these findings and further refine therapeutic strategies (Chen et al., 2020; Cox et al., 2021; Hou et al., 2023; Li et al., 2023; Mammana et al., 2018)” (page 10, lines 285-289).

Further contextualization of key results would enhance the discussion. For example, ECM-related differences in female macrophages could have broader roles in wound healing, fibrosis, and migration.

We agree with the reviewers and have added this detail to the discussion: “ECM components are emerging as key regulators of innate immune responses (García-García & Martin, 2019). Macrophages contribute to ECM remodeling by producing and degrading collagens (Sutherland et al., 2023), and ECM-related differences in female macrophages may impact wound healing, fibrosis, and migration. In lung and kidney tissues, macrophages recruit and activate fibroblasts, influencing fibrosis through direct interactions and ECM-degrading enzymes (Nikolic-Paterson et al., 2014). The balance between ECM deposition and degradation is crucial for tissue homeostasis, as excessive fibrosis leads to pathology (Nikolic-Paterson et al., 2014; Ran et al., 2025). Mechanical properties of the ECM, such as stiffness and collagen crosslinking, enhance macrophage adhesion, migration, and inflammatory activation (Hsieh et al., 2019). These ECM cues direct macrophage behavior during injury response, influencing their ability to reach inflammation sites and promote repair. Thus, female-biased expression of ECM-related genes may contribute to phenotypes such as enhanced wound healing or even fibrosis(Balakrishnan et al., 2021; Harness-Brumley et al., 2014; Rønø et al., 2013) “ (page 9, lines 248-259).

Minor comments:

1. Line 51: In the introduction, the authors state that macrophages produce chemokines. There are other signaling molecules produced by macrophages (e.g., cytokines) that also contribute to immune responses. We apologize for this and have updated the text to say: “Macrophages are a key component of the mammalian immune system and are responsible for producing a diverse array of signaling molecules including (but not limited to) cytokines, chemokines, and interferons that activate the rest of the immune system to combat infection (Shapouri-Moghaddam et al., 2018)” (page 4, lines 49-52).

Line 53: The authors state that after birth the primary source of new macrophages come from differentiation of monocytes. However, some tissue resident macrophages are self-renewing.

We apologize for this oversight and have adjusted the text to say: “After birth, the primary source of new macrophages comes from the differentiation of monocytes, which can be recruited to tissues throughout life. However, some tissue resident macrophages can self-renew, including those from the pleural and peritoneal cavities (Röszer, 2018)” (page 4, lines 53-56).

Line 123: "spermatogenial" should be "spermatogonial"

We have updated the text accordingly (page 6, line 130).

Reviewer #3 (Significance (Required)):

Significance: • General assessment: The study provides a novel and comprehensive analysis of sex-dimorphic gene expression in macrophages, with key findings that emphasize the importance of ECM remodeling in female macrophages. The strengths include the broad dataset inclusion, rigorous quality control, and methodological rigor. However, consideration of potential confounding variables (e.g., age, strain) should be included and validation of key results would strengthen the manuscript. • Advance: This study advances knowledge by analyzing sex differences across multiple macrophage niches rather than focusing on a single tissue type. It extends findings from previous immune studies. • Audience: This report would be of interest to immunologists and researchers studying sex differences. Expertise: Immunology, sex differences in disease, macrophage biology, transcriptomics, and inflammation research.

We thank the reviewer for their positive comments on the impact of our work and for their useful feedback.

__ __

References

Alswat, K. A. (2017). Gender Disparities in Osteoporosis. J Clin Med Res, 9(5), 382-387. https://doi.org/10.14740/jocmr2970w

Amend, S. R., Valkenburg, K. C., & Pienta, K. J. (2016). Murine Hind Limb Long Bone Dissection and Bone Marrow Isolation. J Vis Exp(110). https://doi.org/10.3791/53936

Balakrishnan, M., Patel, P., Dunn-Valadez, S., Dao, C., Khan, V., Ali, H., El-Serag, L., Hernaez, R., Sisson, A., Thrift, A. P., Liu, Y., El-Serag, H. B., & Kanwal, F. (2021). Women Have a Lower Risk of Nonalcoholic Fatty Liver Disease but a Higher Risk of Progression vs Men: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol, 19(1), 61-71.e15. https://doi.org/10.1016/j.cgh.2020.04.067

Bloomer, L. D., Nelson, C. P., Eales, J., Denniff, M., Christofidou, P., Debiec, R., Moore, J., Zukowska-Szczechowska, E., Goodall, A. H., Thompson, J., Samani, N. J., Charchar, F. J., & Tomaszewski, M. (2013). Male-specific region of the Y chromosome and cardiovascular risk: phylogenetic analysis and gene expression studies. Arterioscler Thromb Vasc Biol, 33(7), 1722-1727. https://doi.org/10.1161/atvbaha.113.301608

Chen, K., Jiao, Y., Liu, L., Huang, M., He, C., He, W., Hou, J., Yang, M., Luo, X., & Li, C. (2020). Communications Between Bone Marrow Macrophages and Bone Cells in Bone Remodeling. Front Cell Dev Biol, 8, 598263. https://doi.org/10.3389/fcell.2020.598263

Cox, N., Pokrovskii, M., Vicario, R., & Geissmann, F. (2021). Origins, Biology, and Diseases of Tissue Macrophages. Annu Rev Immunol, 39, 313-344. https://doi.org/10.1146/annurev-immunol-093019-111748

Gosselin, D., Link, V. M., Romanoski, C. E., Fonseca, G. J., Eichenfield, D. Z., Spann, N. J., Stender, J. D., Chun, H. B., Garner, H., Geissmann, F., & Glass, C. K. (2014). Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell, 159(6), 1327-1340. https://doi.org/10.1016/j.cell.2014.11.023

Hamlin, R. E., Pienkos, S. M., Chan, L., Stabile, M. A., Pinedo, K., Rao, M., Grant, P., Bonilla, H., Holubar, M., Singh, U., Jacobson, K. B., Jagannathan, P., Maldonado, Y., Holmes, S. P., Subramanian, A., & Blish, C. A. (2024). Sex differences and immune correlates of Long Covid development, symptom persistence, and resolution. Sci Transl Med, 16(773), eadr1032. https://doi.org/10.1126/scitranslmed.adr1032

Harness-Brumley, C. L., Elliott, A. C., Rosenbluth, D. B., Raghavan, D., & Jain, R. (2014). Gender differences in outcomes of patients with cystic fibrosis. J Womens Health (Larchmt), 23(12), 1012-1020. https://doi.org/10.1089/jwh.2014.4985

Hou, P., Fang, J., Liu, Z., Shi, Y., Agostini, M., Bernassola, F., Bove, P., Candi, E., Rovella, V., Sica, G., Sun, Q., Wang, Y., Scimeca, M., Federici, M., Mauriello, A., & Melino, G. (2023). Macrophage polarization and metabolism in atherosclerosis. Cell Death Dis, 14(10), 691. https://doi.org/10.1038/s41419-023-06206-z

Lavin, Y., Winter, D., Blecher-Gonen, R., David, E., Keren-Shaul, H., Merad, M., Jung, S., & Amit, I. (2014). Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell, 159(6), 1312-1326. https://doi.org/10.1016/j.cell.2014.11.018

Li, M., Yang, Y., Xiong, L., Jiang, P., Wang, J., & Li, C. (2023). Metabolism, metabolites, and macrophages in cancer. J Hematol Oncol, 16(1), 80. https://doi.org/10.1186/s13045-023-01478-6

Mammana, S., Fagone, P., Cavalli, E., Basile, M. S., Petralia, M. C., Nicoletti, F., Bramanti, P., & Mazzon, E. (2018). The Role of Macrophages in Neuroinflammatory and Neurodegenerative Pathways of Alzheimer's Disease, Amyotrophic Lateral Sclerosis, and Multiple Sclerosis: Pathogenetic Cellular Effectors and Potential Therapeutic Targets. Int J Mol Sci, 19(3). https://doi.org/10.3390/ijms19030831

Meester, I., Manilla-Muñoz, E., León-Cachón, R. B. R., Paniagua-Frausto, G. A., Carrión-Alvarez, D., Ruiz-Rodríguez, C. O., Rodríguez-Rangel, X., & García-Martínez, J. M. (2020). SeXY chromosomes and the immune system: reflections after a comparative study. Biol Sex Differ, 11(1), 3. https://doi.org/10.1186/s13293-019-0278-y

Rønø, B., Engelholm, L. H., Lund, L. R., & Hald, A. (2013). Gender affects skin wound healing in plasminogen deficient mice. PLoS One, 8(3), e59942. https://doi.org/10.1371/journal.pone.0059942

Röszer, T. (2018). Understanding the Biology of Self-Renewing Macrophages. Cells, 7(8). https://doi.org/10.3390/cells7080103

Scotland, R. S., Stables, M. J., Madalli, S., Watson, P., & Gilroy, D. W. (2011). Sex differences in resident immune cell phenotype underlie more efficient acute inflammatory responses in female mice. Blood, 118(22), 5918-5927. https://doi.org/10.1182/blood-2011-03-340281

Shapouri-Moghaddam, A., Mohammadian, S., Vazini, H., Taghadosi, M., Esmaeili, S. A., Mardani, F., Seifi, B., Mohammadi, A., Afshari, J. T., & Sahebkar, A. (2018). Macrophage plasticity, polarization, and function in health and disease. J Cell Physiol, 233(9), 6425-6440. https://doi.org/10.1002/jcp.26429

Wang, X., Iyer, A., Lyons, A. B., Körner, H., & Wei, W. (2019). Emerging Roles for G-protein Coupled Receptors in Development and Activation of Macrophages. Front Immunol, 10, 2031. https://doi.org/10.3389/fimmu.2019.02031

Reviewer #1 (Public review):

Summary:

This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T) it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.<br /> The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aims to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).<br /> It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins and the bipolar crushing is not expected to help with this.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.<br /> VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

(3) The comparison with VASO is misleading.<br /> The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.<br /> Koiso et al. has performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation) and 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).<br /> Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that want to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?<br /> Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion it looks like random noise, with most of the activation outside the ROI (in white matter).

(4) The repeatability of the results is questionable.<br /> The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. Location of peaks turn into locations of dips and vice versa.<br /> The methods are not described in enough detail to reproduce these results.<br /> The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.<br /> No data are shared for reproduction of the analysis.

(5) The application of NODRIC is not validated.<br /> Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

Comments on revisions:

Among all the concerns mentioned above, I think there is only one of the specific issues that was sufficiently addressed.<br /> The authors implemented a combination of three consecutive-dimensional flow crushers. Other concerns were not sufficiently addressed to change my confidence level of the study.<br /> - While the abstract is still focusing on the utility of using 3T, they do not give credit to early 3T layer-fMRI papers leading the way to larger coverage and connectivity applications.<br /> - While the author's choice of using custom SMS 2D readout is justified for them. I do not think that this very method will utilize widespread 3T whole brain connectivity experiments across the global 3T community. This lowers the impact of the paper.<br /> - The images in Fig. 5 are still suspiciously similar. To the level that the noise pattern outside the brain is identical across large parts of the maps with and without PR.<br /> - Maybe it's my ignorance, but I still do not agree why flow crushing focuses the local BOLD responses to small vessels.<br /> - While my feel of a misleading representation of the literature had been accompanied by explicit references, the authors claim that they cannot find them?!? Or claim that they are about something else (which they are not, in my viewpoint).<br /> Data and software are still not shared (not even example data, or nii data).

Reviewer #2 (Public review):

This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper laminar specificity of gradient-echo BOLD.

Strengths:

The setup includes 0.9 mm isotropic acquisitions with large coverage at a reasonable TR. These parameters are hard to optimize simultaneously, and I applaud the ambitious attempt to get "the best from all worlds" (large coverage, high spatio/temporal resolution, spatial specificity, sensitivity), which is sought after in the field. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*). Furthermore, I like that the authors took steps to improve the original manuscript by e.g., collecting more data, adjusting the VN implementation to include flow-suppression along three rather than a single dimension, and adjusting the ROI-definition procedure to avoid circularity issues.

That being said, I still find the evidence weak in terms of this sequence achieving high spatial specificity and sensitivity. The results feel oversold and further validation is needed to make a case for the authors' conclusion that "[...] the potential impact of this development is expected to be extensive across various domains of neuroscience research". This is elaborated in the comments below:

The authors acknowledge that the VN setup in its current form probably does not suppress the impact of most ascending veins (these are also not targeted by phase regression, as most are probably too small to produce sufficiently large phase responses). This seems to limit the theoretical support for the author's claim of reduced inter-layer blurring (e.g. the claim that deep and superficial signals are less coupled with VN gradients than without based on Fig 6-7). This limitation withstanding, the method may still be helpful for limiting laminar dependencies by suppressing pial vein responses (which may carry signal from distant regions and layers that blur into superficial layers if left unsuppressed). Unfortunately, the empirical support of VN gradients suppressing superficial bias seems quite weak and is hard to evaluate. For example, the profiles in Figure 4 does not consistently show clearly less superficial bias when VN gradients are on - this might partly be due to the fact that clear bias was not always present in the profiles even without VN. I suspect this is largely explained by the selection of very small and quite unrepresentative ROIs. The corresponding activation maps appear strongly weighted towards CSF which is not always captured in the profile. I recommend sampling a much larger patch of cortex to more accurately capture the actual underlying bias. In this way, all non-VN profiles should have clear bias which should be clearly suppressed for VN if the method is effective. The authors do evaluate the effect of VN/phase regression based on a large activated region in visual cortex (Fig 5) - why not show laminar profiles from here, which is an obvious way to show the effect on superficial bias? I think such evaluations would be a more direct way of evaluating the methods impact on specificity, and are necessary for subsequent FC evaluations to be convincing.

The phase regression results are described inconsistently. In the results section, the authors, in my opinion, "correctly" acknowledge that phase regression seemed to have a very minor impact. However, in the discussion section it is described as if phase regression was effective in suppressing macrovascular responses (L 553-558), which the results do not support (especially based on profiles in Fig 4). There is barely any difference with/without phase regression, which may be due to the fact that ordinary least squares regression was chosen over a deming model which accounts for noise on the phase regressor. Although the authors correctly mentioned in their "answers to reviewers" that the required noise-ratio between magnitude and phase data can be hard to estimate, attempts of that has been described in previous phase regression studies which showed much larger effects (see e.g. Stanley et al. 2020, Knudsen et al. 2023).

I like that the authors put in additional efforts to provide analyses to validate their NORDIC implementation. However, this needs to be done on the VN setup directly, not the "regular BOLD setup" with b=0, since the ability of NORDIC to distinguish signal and noise components depends on CNR which is expected to deviate for these setups. Also, it seems z-scores and confidence intervals were computed based on GLM residuals which may lead to inflated z-values and overly narrow CI's due to reduced degrees of freedom following denoising. The denoised z-maps from Fig 3 indeed look somewhat strange, i.e. seemingly increased false positives (more salt/pepper and a bunch of white matter activation) with very weak hand knob activation. Also, something must be wrong with the CIs on the laminar profiles - they seem extremely narrow despite noise levels obviously being high for highly accelerated 3T submillimeter results extracted from a very small ROI. The authors may consider computing these statistics from variance across trials instead.

Given that the idea of the setup is to take advantage in terms of sensitivity by using GE-BOLD contrast relative to e.g. SE-EPI or CBV-weighted setups, they need to carefully demonstrate the sensitivity of their setup, which could be limited by high acceleration factors, the VN gradients, low field strength, etc. I like that they now put more emphasis on non-masked activation maps, but further comparison could be made through tSNR maps, raw single-volume images, raw timeseries, CNR based on across-trial variance, etc.

The major rationale for the setup is to achieve functional connectivity (FC) with brain-wide coverage at laminar resolutions, but it is framed as if this is something that has not been possible in the past with existing setups (statements such as: "Despite advancements in acquisition speed, current CBV/CBF-based fMRI techniques remain inadequate for layer-dependent resting-state fMRI" (L138-140). To me, the functional connectivity results presented here with the VN setup are clearly less convincing than what has been shown with e.g. CBV-weighted acquisitions (e.g. Huber et al. 2021, Chai et al. 2024). The VN setup might also have advantages such as larger coverage as mentioned by the authors, but they fail to balance the comparison by highlighting where previous studies had clear edges. Thus, the impact of the results needs to be down-stated and a more balanced comparison with existing laminar FC studies is warranted. For example, acknowledging that the CBV-weighted studies demonstrate much higher spatial specificity.

Overall I would recommend a stronger emphasis on validating the claims about the sequence on task-based data for which there is a large body of literature to benchmark against (e.g. laminar fMRI studies in V1 and M1), before going to FC where the base for comparison and reference is much more limited in humans at laminar scales.

Author response:

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

General responses:

The authors sincerely thank all the reviewers for their valuable and constructive comments. We also apologize for the long delay in providing this rebuttal due to logistical and funding challenges. In this revision, we modified the bipolar gradients from one single direction to all three directions. Additionally, in response to the concerns regarding data reliability, we conducted a thorough examination of each step in our data processing pipeline. In the original processing workflow, the projection-onto-convex-set (POCS) method was used for partial Fourier reconstruction. Upon examination, we found that applying the POCS method after parallel image reconstruction significantly altered the signal and resulted in considerable loss of functional feature. Futhermore, the original scan protocol employed a TE of 46 ms, which is notably longer than the typical TE of 33 ms. A prolonged TE can increase the ratio of extravascular to intravascular contributions. Importantly, the impact of TE on the efficacy of phase regression remains unclear, introducing potential confounding effects. To address these issues, we revised the protocol by shortening the TE from 46 ms to 39 ms. This adjustment was achieved by modifying the SMS factor to 3 and the in-plane acceleration rate to 3, thereby minimizing the confounding effects associated with an extended TE.

Following these changes, we recollected task-based fMRI data (N=4) and resting-state fMRI data (N=14) under the updated protocol. Using the revised dataset, we validated layer-specific functional connectivity (FC) through seed-based analyses. These analyses revealed distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with statistically significant inter-layer differences. Furthermore, additional analyses with a seed in the primary sensory cortex (S1) corroborated the robustness and reliability of the revised methodology. We also changed the ‘directed’ functional connectivity in the title to ‘layer-specific’ functional connectivity, as drawing conclusions about directionality requires auxiliary evidence beyond the scope of this study.

We provide detailed responses to the reviewers’ comments below.

Reviewer #1 (Public Review):

Summary:

(1)   This study aims to provide imaging methods for users of the field of human layer-fMRI. This is an emerging field with 240 papers published so far. Different than implied in the manuscript, 3T is well represented among those papers. E.g. see the papers below that are not cited in the manuscript. Thus, the claim on the impact of developing 3T methodology for wider dissemination is not justified. Specifically, because some of the previous papers perform whole brain layer-fMRI (also at 3T) in more efficient, and more established procedures.

3T layer-fMRI papers that are not cited:

Taso, M., Munsch, F., Zhao, L., Alsop, D.C., 2021. Regional and depth-dependence of cortical blood-flow assessed with high-resolution Arterial Spin Labeling (ASL). Journal of Cerebral Blood Flow and Metabolism. https://doi.org/10.1177/0271678X20982382

Wu, P.Y., Chu, Y.H., Lin, J.F.L., Kuo, W.J., Lin, F.H., 2018. Feature-dependent intrinsic functional connectivity across cortical depths in the human auditory cortex. Scientific Reports 8, 1-14. https://doi.org/10.1038/s41598-018-31292-x

Lifshits, S., Tomer, O., Shamir, I., Barazany, D., Tsarfaty, G., Rosset, S., Assaf, Y., 2018. Resolution considerations in imaging of the cortical layers. NeuroImage 164, 112-120. https://doi.org/10.1016/j.neuroimage.2017.02.086

Puckett, A.M., Aquino, K.M., Robinson, P.A., Breakspear, M., Schira, M.M., 2016. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage 139, 240-248. https://doi.org/10.1016/j.neuroimage.2016.06.019

Olman, C.A., Inati, S., Heeger, D.J., 2007. The effect of large veins on spatial localization with GE BOLD at 3 T: Displacement, not blurring. NeuroImage 34, 1126-1135. https://doi.org/10.1016/j.neuroimage.2006.08.045

Ress, D., Glover, G.H., Liu, J., Wandell, B., 2007. Laminar profiles of functional activity in the human brain. NeuroImage 34, 74-84. https://doi.org/10.1016/j.neuroimage.2006.08.020

Huber, L., Kronbichler, L., Stirnberg, R., Ehses, P., Stocker, T., Fernández-Cabello, S., Poser, B.A., Kronbichler, M., 2023. Evaluating the capabilities and challenges of layer-fMRI VASO at 3T. Aperture Neuro 3. https://doi.org/10.52294/001c.85117

Scheeringa, R., Bonnefond, M., van Mourik, T., Jensen, O., Norris, D.G., Koopmans, P.J., 2022. Relating neural oscillations to laminar fMRI connectivity in visual cortex. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac154

We thank the reviewer for listing out 8 papers related to 3T layer-fMRI papers. The primary goal of our work is to develop a methodology for brain-wide, layer-dependent resting-state functional connectivity at 3T. Upon review of the cited papers, we found that:

(1) One study (Lifshits et al.) was not an fMRI study.

(2) One study (Olman et al.) was conducted at 7T, not 3T.

(3) Two studies (Taso et al. and Wu et al.) employed relatively large voxel sizes (1.6 × 2.3 × 5 mm³ and 1.5 mm isotropic, respectively), which limits layer specificity.

(4) Only one of the listed studies (Huber et al., Aperture Neuro 2023) provides coverage of more than half of the brain.

While each of these studies offers valuable insights, the VASO study by Huber et al. is the most relevant to our work, given its brain-wide coverage. However, the VASO method employs a relatively long TR (14.137 s), which may not be optimal for resting-state functional connectivity analyses.

To address these limitations, our proposed method achieves submillimeter resolution, layer specificity, brain-wide coverage, and a significantly shorter TR (<5 s) altogether. We believe this advancement provides a meaningful contribution to the field, enabling broader applicability of layer-fMRI at 3T.

(2) The authors implemented a sequence with lots of nice features. Including their own SMS EPI, diffusion bipolar pulses, eye-saturation bands, and they built their own reconstruction around it. This is not trivial. Only a few labs around the world have this level of engineering expertise. I applaud this technical achievement. However, I doubt that any of this is the right tool for layer-fMRI, nor does it represent an advancement for the field. In the thermal noise dominated regime of sub-millimeter fMRI (especially at 3T), it is established to use 3D readouts over 2D (SMS) readouts. While it is not trivial to implement SMS, the vendor implementations (as well as the CMRR and MGH implementations) are most widely applied across the majority of current fMRI studies already. The author's work on this does not serve any previous shortcomings in the field.

We would like to thank the reviewer for their comments and the recognition of the technical efforts in implementing our sequence. We would like to address the points raised:

(1) We completely agree that in-house implementation of existing techniques does not constitute an advancement for the field. We did not claim otherwise in the manuscript. Our focus was on the development of a method for brain-wide, layer-dependent resting-state functional connectivity at 3T, as mentioned in the response above.

(2) The reviewer stated that "it is established to use 3D readouts over 2D (SMS) readouts". This is a strong claim, and we believe it requires robust evidence to support it. While it is true that 3D readouts can achieve higher tSNR in certain regions, such as the central brain, as shown in the study by Vizioli et al. (ISMRM 2020 abstract; https://cds.ismrm.org/protected/20MProceedings/PDFfiles/3825.html?utm_source=chatgpt.com ), higher tSNR does not necessarily equate to improved detection power in fMRI studies. For instance, Le Ster et al. (PLOS ONE, 2019; https://doi.org/10.1371/journal.pone.0225286 ). demonstrated that while 3D EPI had higher tSNR in the central brain, SMS EPI produced higher t-scores in activation maps.

(3) When choosing between SMS EPI and 3D EPI, multiple factors should be taken into account, not just tSNR. For example, SMS EPI and 3D EPI differ in their sensitivity to motion and the complexity of motion correction. The choice between them depends on the specific research goals and practical constraints.

(4) We are open to different readout strategies, provided they can be demonstrated suitable to the research goals. In this study, we opted for 2D SMS primarily due to logistical considerations. This choice does not preclude the potential use of 3D readouts in the future if they are deemed more appropriate for the project objectives.

The mechanism to use bi-polar gradients to increase the localization specificity is doubtful to me. In my understanding, killing the intra-vascular BOLD should make it less specific. Also, the empirical data do not suggest a higher localization specificity to me.

We will elaborate the mechanism and reasoning in the later responses.

Embedding this work in the literature of previous methods is incomplete. Recent trends of vessel signal manipulation with ABC or VAPER are not mentioned. Comparisons with VASO are outdated and incorrect.

The reproducibility of the methods and the result is doubtful (see below).

In this revision, we updated the scan protocol and recollected the imaging data. Detailed explanations and revised results are provided in the later responses.

I don't think that this manuscript is in the top 50% of the 240 layer-fmri papers out there.

We respect the reviewer’s personal opinion. However, we can only address scientific comments or critiques.

Strengths:

See above. The authors developed their own SMS sequence with many features. This is important to the field. And does not leave sequence development work to view isolated monopoly labs. This work democratises SMS.

The questions addressed here are of high relevance to the field: getting tools with good sensitivity, user-friendly applicability, and locally specific brain activity mapping is an important topic in the field of layer-fMRI.

Weaknesses:

(1) I feel the authors need to justify why flow-crushing helps localization specificity. There is an entire family of recent papers that aim to achieve higher localization specificity by doing the exact opposite. Namely, MT or ABC fRMRI aims to increase the localization specificity by highlighting the intravascular BOLD by means of suppressing non-flowing tissue. To name a few:

Priovoulos, N., de Oliveira, I.A.F., Poser, B.A., Norris, D.G., van der Zwaag, W., 2023. Combining arterial blood contrast with BOLD increases fMRI intracortical contrast. Human Brain Mapping hbm.26227. https://doi.org/10.1002/hbm.26227.

Pfaffenrot, V., Koopmans, P.J., 2022. Magnetization Transfer weighted laminar fMRI with multi-echo FLASH. NeuroImage 119725. https://doi.org/10.1016/j.neuroimage.2022.119725

Schulz, J., Fazal, Z., Metere, R., Marques, J.P., Norris, D.G., 2020. Arterial blood contrast ( ABC ) enabled by magnetization transfer ( MT ): a novel MRI technique for enhancing the measurement of brain activation changes. bioRxiv. https://doi.org/10.1101/2020.05.20.106666

Based on this literature, it seems that the proposed method will make the vein problem worse, not better. The authors could make it clearer how they reason that making GE-BOLD signals more extra-vascular weighted should help to reduce large vein effects.

The proposed VN fMRI method employs VN gradients to selectively suppress signals from fast-flowing blood in large vessels. Although this approach may initially appear to diverge from the principles of CBV-based techniques (Chai et al., 2020; Huber et al., 2017a; Pfaffenrot and Koopmans, 2022; Priovoulos et al., 2023), which enhance sensitivity to vascular changes in arterioles, capillaries, and venules while attenuating signals from static tissue and large veins, it aligns with the fundamental objective of all layer-specific fMRI methods. Specifically, these approaches aim to maximize spatial specificity by preserving signals proximal to neural activation sites and minimizing contributions from distal sources, irrespective of whether the signals are intra- or extra-vascular in origin. In the context of intravascular signals, CBV-based methods preferentially enhance sensitivity to functional changes in small vessels (proximal components) while demonstrating reduced sensitivity to functional changes in large vessels (distal components). For extravascular signals, functional changes are a mixture of proximal and distal influences. While tissue oxygenation near neural activation sites represents a proximal contribution, extravascular signal contamination from large pial veins reflects distal effects that are spatially remote from the site of neuronal activity. CBV-based techniques mitigate this challenge by unselectively suppressing signals from static tissues, thereby highlighting contributions from small vessels. In contrast, the VN fMRI method employs a targeted suppression strategy, selectively attenuating signals from large vessels (distal components) while preserving those from small vessels (proximal components). Furthermore, the use of a 3T scanner and the inclusion of phase regression in the VN approach mitigates contamination from large pial veins (distal components) while preserving signals reflecting local tissue oxygenation (proximal components). By integrating these mechanisms, VN fMRI improves spatial specificity, minimizing both intravascular and extravascular contributions that are distal to neuronal activation sites. We have incorporated the responses into Discussion section.

The empirical evidence for the claim that flow crushing helps with the localization specificity should be made clearer. The response magnitude with and without flow crushing looks pretty much identical to me (see Fig, 6d).

In the new results in Figure 4, the application of VN gradients attenuated the bias towards pial surface. Consistent with the results in Figure 4, Figure 5 also demonstrated the suppression of macrovascular signal by VN gradients.

It's unclear to me what to look for in Fig. 5. I cannot discern any layer patterns in these maps. It's too noisy. The two maps of TE=43ms look like identical copies from each other. Maybe an editorial error?

In this revision, the original Figure 5 has been removed. However, we would like to clarify that the two maps with TE = 43 ms in the original Figure 5 were not identical. This can be observed in the difference map provided in the right panel of the figure.

The authors discuss bipolar crushing with respect to SE-BOLD where it has been previously applied. For SE-BOLD at UHF, a substantial portion of the vein signal comes from the intravascular compartment. So I agree that for SE-BOLD, it makes sense to crush the intravascular signal. For GE-BOLD however, this reasoning does not hold. For GE-BOLD (even at 3T), most of the vein signal comes from extravascular dephasing around large unspecific veins, and the bipolar crushing is not expected to help with this.

The reviewer’s statement that "most of the vein signal comes from extravascular dephasing around large unspecific veins" may hold true for 7T. However, at 3T, the susceptibility-induced Larmor frequency shift is reduced by 57%, and the extravascular contribution decreases by more than 35%, as shown by Uludağ et al. 2009 ( DOI: 10.1016/j.neuroimage.2009.05.051 ).

Additionally, according to the biophysical models (Ogawa et al., 1993; doi: 10.1016/S0006-3495(93)81441-3 ), the extravascular contamination from the pial surface is inversely proportional to the square of the distance from vessel. For a vessel diameter of 0.3 mm and an isotropic voxel size of 0.9 mm, the induced frequency shift is reduced by at least 36-fold at the next voxel. Notably, a vessel diameter of 0.3 mm is larger than most pial vessels. Theoretically, the extravascular effect contributes minimally to inter-layer dependency, particularly at 3T compared to 7T due to weaker susceptibility-related effects at lower field strengths. Empirically, as shown in Figure 7c, the results at M1 demonstrated that layer specificity can be achieved statistically with the application of VN gradients. We have incorporated this explanation into the Introduction and Discussion sections of the manuscript.

(2) The bipolar crushing is limited to one single direction of flow. This introduces a lot of artificial variance across the cortical folding pattern. This is not mentioned in the manuscript. There is an entire family of papers that perform layer-fmri with black-blood imaging that solves this with a 3D contrast preparation (VAPER) that is applied across a longer time period, thus killing the blood signal while it flows across all directions of the vascular tree. Here, the signal cruising is happening with a 2D readout as a "snap-shot" crushing. This does not allow the blood to flow in multiple directions.

VAPER also accounts for BOLD contaminations of larger draining veins by means of a tag-control sampling. The proposed approach here does not account for this contamination.

Chai, Y., Li, L., Huber, L., Poser, B.A., Bandettini, P.A., 2020. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. NeuroImage 207, 116358. https://doi.org/10.1016/j.neuroimage.2019.116358

Chai, Y., Liu, T.T., Marrett, S., Li, L., Khojandi, A., Handwerker, D.A., Alink, A., Muckli, L., Bandettini, P.A., 2021. Topographical and laminar distribution of audiovisual processing within human planum temporale. Progress in Neurobiology 102121. https://doi.org/10.1016/j.pneurobio.2021.102121

If I would recommend anyone to perform layer-fMRI with blood crushing, it seems that VAPER is the superior approach. The authors could make it clearer why users might want to use the unidirectional crushing instead.

We understand the reviewer’s concern regarding the directional limitation of bipolar crushing. As noted in the responses above, we have updated the bipolar gradient to include three orthogonal directions instead of a single direction. Furthermore, flow-related signal suppression does not necessarily require a longer time period. Bipolar diffusion gradients have been effectively used to nullify signals from fast-flowing blood, as demonstrated by Boxerman et al. (1995; DOI: 10.1002/mrm.1910340103). Their study showed that vessels with flow velocities producing phase changes greater than p radians due to bipolar gradients experience significant signal attenuation. The critical velocity for such attenuation can be calculated using the formula: 1/(2gGDd) where g is the gyromagnetic ratio, G is the gradient strength, d is the gradient pulse width and D is the time between the two bipolar gradient pulses. In the framework of Boxerman et al. at 1.5T, the critical velocity for b value of 10 s/mm² is ~8 mm/s, resulting in a ~30% reduction in functional signal. In our 3T study, b values of 6, 7, and 8 s/mm² correspond to critical velocities of 16.8, 15.2, and 13.9 mm/s, respectively. The flow velocities in capillaries and most venules remain well below these thresholds. Notably, in our VN fMRI sequences, bipolar gradients were applied in all three orthogonal directions, whereas in Boxerman et al.'s study, the gradients were applied only in the z-direction. Given the voxel dimensions of 3 × 3 × 7 mm³ in the 1.5T study, vessels within a large voxel are likely oriented in multiple directions, meaning that only a subset of fast-flowing signals would be attenuated. Therefore, our approach is expected to induce greater signal reduction, even at the same b values as those used in Boxerman et al.'s study. We have incorporated this text into the Discussion section of the manuscript.

(3) The comparison with VASO is misleading.

The authors claim that previous VASO approaches were limited by TRs of 8.2s. The authors might be advised to check the latest literature of the last years.

Koiso et al. performed whole brain layer-fMRI VASO at 0.8mm at 3.9 seconds (with reliable activation), 2.7 seconds (with unconvincing activation pattern, though), and 2.3 (without activation).

Also, whole brain layer-fMRI BOLD at 0.5mm and 0.7mm has been previously performed by the Juelich group at TRs of 3.5s (their TR definition is 'fishy' though).

Koiso, K., Müller, A.K., Akamatsu, K., Dresbach, S., Gulban, O.F., Goebel, R., Miyawaki, Y., Poser, B.A., Huber, L., 2023. Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset. Aperture Neuro 34. https://doi.org/10.1101/2022.08.19.504502

Yun, S.D., Pais‐Roldán, P., Palomero‐Gallagher, N., Shah, N.J., 2022. Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols. Human Brain Mapping. https://doi.org/10.1002/hbm.25855

Pais-Roldan, P., Yun, S.D., Palomero-Gallagher, N., Shah, N.J., 2023. Cortical depth-dependent human fMRI of resting-state networks using EPIK. Front. Neurosci. 17, 1151544. https://doi.org/10.3389/fnins.2023.1151544

We thank the reviewer for providing these references. While the protocol with a TR of 3.9 seconds in Koiso’s work demonstrated reasonable activation patterns, it was not tested for layer specificity. Given that higher acceleration factors (AF) can cause spatial blurring, a protocol should only be eligible for comparison if layer specificity is demonstrated.

Secondly, the TRs reported in Koiso’s study pertain only to either the VASO or BOLD acquisition, not the combined CBV-based contrast. To generate CBV-based images, both VASO and BOLD data are required, effectively doubling the TR. For instance, if the protocol with a TR of 3.9 seconds is used, the effective TR becomes approximately 8 seconds. The stable protocol used by Koiso et al. to acquire whole-brain data (94.08 mm along the z-axis) required 5.2 seconds for VASO and 5.1 seconds for BOLD, resulting in an effective TR of 10.3 seconds. The spatial resolution achieved was 0.84 mm isotropic.

Unfortunately, we could not find the Juelich paper mentioned by the reviewer.

To have a more comprehensive comparison, we collated relevant literature on brain-wide layer-specific fMRI. We defined brain-wide acquisition as imaging protocols that cover more than half of the human brain, specifically exceeding 55 mm along the superior-inferior axis. We identified five studies and summarized their scan parameters, including effective TR, coverage, and spatial resolution, in Table 1.

The authors are correct that VASO is not advised as a turn-key method for lower brain areas, incl. Hippocampus and subcortex. However, the authors use this word of caution that is intended for inexperienced "users" as a statement that this cannot be performed. This statement is taken out of context. This statement is not from the academic literature. It's advice for the 40+ user base that wants to perform layer-fMRI as a plug-and-play routine tool in neuroscience usage. In fact, sub-millimeter VASO is routinely being performed by MRI-physicists across all brain areas (including deep brain structures, hippocampus etc). E.g. see Koiso et al. and an overview lecture from a layer-fMRI workshop that I had recently attended: https://youtu.be/kzh-nWXd54s?si=hoIJjLLIxFUJ4g20&t=2401

In this revision, we decided to focus on cortico-cortical functional connectivity and have removed the LGN-related content. Consequently, the text mentioned by the reviewer was also removed. Nevertheless, we apologize if our original description gave the impression that functional mapping of deep brain regions using VASO is not feasible. The word of caution we used is based on the layer-fMRI blog ( https://layerfmri.com/2021/02/22/vaso_ve/ ) and reflects the challenges associated with this technique, as outlined by experts like Dr. Huber and Dr. Strinberg.

According to the information provided, including the video, functional mapping of the hippocampus and amygdala using VASO is indeed possible but remains technically challenging. The short arterial arrival times in these deep brain regions can complicate the acquisition, requiring RF inversion pulses to cover a wider area at the base of the brain. For example, as of 2023, four or more research groups were attempting to implement layer-fMRI VASO in the hippocampus. One such study at 3T required multiple inversion times to account for inflow effects, highlighting the technical complexity of these applications. This is the context in which we used the word of caution. We are not sure whether recent advancements like MAGEC VASO have improved its applicability. As of 2024, we have not identified any published VASO studies specifically targeting deep brain structures such as the hippocampus or amygdala. Therefore, it is difficult to conclude that “sub-millimeter VASO is routinely being performed by MRI physicists on deep brain structures such as the hippocampus.”

Thus, the authors could embed this phrasing into the context of their own method that they are proposing in the manuscript. E.g. the authors could state whether they think that their sequence has the potential to be disseminated across sites, considering that it requires slow offline reconstruction in Matlab?

We are enthusiastic about sharing our imaging sequence, provided its usefulness is conclusively established. However, it's important to note that without an online reconstruction capability, such as the ICE, the practical utility of the sequence may be limited. Unfortunately, we currently don’t have the manpower to implement the online reconstruction. Nevertheless, we are more than willing to share the offline reconstruction codes upon request.

Do the authors think that the results shown in Fig. 6c are suggesting turn-key acquisition of a routine mapping tool? In my humble opinion, it looks like random noise, with most of the activation outside the ROI (in white matter).

As we mentioned in the ‘general response’ in the beginning of the rebuttal, the POCS method for partial Fourier reconstruction caused the loss of functional feature, potentially accounting for the activation in white matter. In this revision, we have modified the pulse sequence, scan protocol and processing pipelines.

According to the results in Figure 4, stable activation in M1 was observed at the single-subject level across most scan protocols. Yet, the layer-dependent activation profiles in M1 were spatially unstable, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to various factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Furthermore, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons between protocols, leaving residual artifacts unaddressed. Inconsistency in performing the button-pressing task across sessions may also have contributed to the observed variability. These results suggest that submillimeter-resolution fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping, unless group-level statistics are incorporated to enhance robustness. We have incorporated this text into the Limitation section of the manuscript.

(4) The repeatability of the results is questionable.

The authors perform experiments about the robustness of the method (line 620). The corresponding results are not suggesting any robustness to me. In fact, the layer profiles in Fig. 4c vs. Fig 4d are completely opposite. The location of peaks turns into locations of dips and vice versa.

The methods are not described in enough detail to reproduce these results.

The authors mention that their image reconstruction is done "using in-house MATLAB code" (line 634). They do not post a link to github, nor do they say if they share this code.

We thank the reviewer for the comments regarding reproducibility and data sharing. In response, we have revised the Methods section and elaborated on the technical details to improve clarity and reproducibility.

Regarding code sharing, we acknowledge that the current in-house MATLAB reconstruction code requires further refinement to improve its readability and usability. Due to limited manpower, we have not yet been able to complete this task. However, we are committed to making the code publicly available and will upload it to GitHub as soon as the necessary resources are available.

For data sharing, we face logistical challenges due to the large size of the dataset, which spans tens of terabytes. Platforms like OpenNeuro, for example, typically support datasets up to 10TB, making it difficult to share the data in its entirety. Despite this limitation, we are more than willing to share offline reconstruction codes and raw data upon request to facilitate reproducibility.

Regarding data robustness, we kindly refer the reviewer to our response to the previous comment, where we addressed these concerns in greater detail.

It is not trivial to get good phase data for fMRI. The authors do not mention how they perform the respective coil-combination.

No data are shared for reproduction of the analysis.

Obtaining phase data is relatively straightforward when the images are retrieved directly from raw data. For coil combination, we employed the adaptive coil combination approach described by (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g ) The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab .

(5) The application of NODRIC is not validated.

Previous applications of NORDIC at 3T layer-fMRI have resulted in mixed success. When not adjusted for the right SNR regime it can result in artifactual reductions of beta scores, depending on the SNR across layers. The authors could validate their application of NORDIC and confirm that the average layer-profiles are unaffected by the application of NORDIC. Also, the NORDIC version should be explicitly mentioned in the manuscript.

Akbari, A., Gati, J.S., Zeman, P., Liem, B., Menon, R.S., 2023. Layer Dependence of Monocular and Binocular Responses in Human Ocular Dominance Columns at 7T using VASO and BOLD (preprint). Neuroscience. https://doi.org/10.1101/2023.04.06.535924

Knudsen, L., Guo, F., Huang, J., Blicher, J.U., Lund, T.E., Zhou, Y., Zhang, P., Yang, Y., 2023. The laminar pattern of proprioceptive activation in human primary motor cortex. bioRxiv. https://doi.org/10.1101/2023.10.29.564658

We appreciate the reviewer’s suggestion. To validate the application of NORDIC denoising in our study, we compared the BOLD activation maps before and after denoising in the visual and motor cortices, as well as the depth-dependent activation profiles in M1. These results are presented in Figure 3. The activation patterns in the denoised maps were consistent with those in the non-denoised maps but exhibited higher statistical significance. Notably, BOLD activation within M1 was only observed after NORDIC denoising, underscoring the necessity of this approach. Figure 3c shows the depth-dependent activation profiles in M1, highlighted by the green contours in Figure 3b. Both denoised and non-denoised profiles followed similar trends; however, as expected, the non-denoised profile exhibited larger confidence intervals compared to the NORDIC-denoised profile. These results confirm that NORDIC denoising enhances sensitivity without introducing distortions in the functional signal. The corresponding text has been incorporated into the Results section.

Regarding the implementation details of NORDIC denoising, the reconstructed images were denoised using a g-factor map (function name: NIFTI_NORDIC). The g-factor map was estimated from the image time series, and the input images were complex-valued. The width of the smoothing filter for the phase was set to 10, while all other hyperparameters were retained at their default values. This information has been integrated into the Methods section for clarity and reproducibility.

Reviewer #2 (Public Review):

This study developed a setup for laminar fMRI at 3T that aimed to get the best from all worlds in terms of brain coverage, temporal resolution, sensitivity to detect functional responses, and spatial specificity. They used a gradient-echo EPI readout to facilitate sensitivity, brain coverage and temporal resolution. The former was additionally boosted by NORDIC denoising and the latter two were further supported by parallel-imaging acceleration both in-plane and across slices. The authors evaluated whether the implementation of velocity-nulling (VN) gradients could mitigate macrovascular bias, known to hamper the laminar specificity of gradient-echo BOLD.

The setup allows for 0.9 mm isotropic acquisitions with large coverage at a reasonable TR (at least for block designs) and the fMRI results presented here were acquired within practical scan-times of 12-18 minutes. Also, in terms of the availability of the method, it is favorable that it benefits from lower field strength (additional time for VN-gradient implementation, afforded by longer gray matter T2*).

The well-known double peak feature in M1 during finger tapping was used as a test-bed to evaluate the spatial specificity. They were indeed able to demonstrate two distinct peaks in group-level laminar profiles extracted from M1 during finger tapping, which was largely free from superficial bias. This is rather intriguing as, even at 7T, clear peaks are usually only seen with spatially specific non-BOLD sequences. This is in line with their simple simulations, which nicely illustrated that, in theory, intravascular macrovascular signals should be suppressible with only minimal suppression of microvasculature when small b-values of the VN gradients are employed. However, the authors do not state how ROIs were defined making the validity of this finding unclear; were they defined from independent criteria or were they selected based on the region mostly expressing the double peak, which would clearly be circular? In any case, results are based on a very small sub-region of M1 in a single slice - it would be useful to see the generalizability of superficial-bias-free BOLD responses across a larger portion of M1.

We appreciate and understand the reviewer’s concerns. Given the small size of the hand knob region within M1 and its intersubject variability in location, defining this region automatically remains challenging. However, we applied specific criteria to minimize bias during the delineation of M1: 1) the hand knob region was required to be anatomically located in the precentral sulcus or gyrus; 2) it needed to exhibit consistent BOLD activation across the majority of testing conditions; and 3) the region was expected to show BOLD activation in the deep cortical layers under the condition of b = 0 and TE = 30 ms. Once the boundaries across cortical depth were defined, the gray matter boundaries of hand knob region were delineated based on the T1-weighted anatomical image and the cortical ribbon mask but excluded the BOLD activation map to minimize potential bias in manual delineation. Based on the new criteria, the resulting depth-dependent profiles, as shown in Figure 4, are no longer superficial-bias-free.

As repeatedly mentioned by the authors, a laminar fMRI setup must demonstrate adequate functional sensitivity to detect (in this case) BOLD responses. The sensitivity evaluation is unfortunately quite weak. It is mainly based on the argument that significant activation was found in a challenging sub-cortical region (LGN). However, it was a single participant, the activation map was not very convincing, and the demonstration of significant activation after considerable voxel-averaging is inadequate evidence to claim sufficient BOLD sensitivity. How well sensitivity is retained in the presence of VN gradients, high acceleration factors, etc., is therefore unclear. The ability of the setup to obtain meaningful functional connectivity results is reassuring, yet, more elaborate comparison with e.g., the conventional BOLD setup (no VN gradients) is warranted, for example by comparison of tSNR, quantification and comparison of CNR, illustration of unmasked-full-slice activation maps to compare noise-levels, comparison of the across-trial variance in each subject, etc. Furthermore, as NORDIC appears to be a cornerstone to enable submillimeter resolution in this setup at 3T, it is critical to evaluate its impact on the data through comparison with non-denoised data, which is currently lacking.

We appreciate the reviewer’s comments and acknowledge that the LGN results from a single participant were not sufficiently convincing. In this revision, we have removed the LGN-related results and focused on cortico-cortical FC. To evaluate data quality, we opted to present BOLD activation maps rather than tSNR, as high tSNR does not necessarily translate to high functional significance. In Figure 3, we illustrate the effect of NORDIC denoising, including activation maps and depth-dependent profiles. Figure 4 presents activation maps acquired under different TE and b values, demonstrating that VN gradients effectively reduce the bias toward the pial surface without altering the overall activation patterns. The results in Figure 4 and Figure 5 provide evidence that VN gradients retain sensitivity while reducing superficial bias. The ability of the setup to obtain meaningful FC results was validated through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences (see Figure 7). Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method (see Figure 8). For further details on the results, including the impact of VN gradients and NORDIC denoising, please refer to Figures 3 to 8 in the Results section.

Additionally, we acknowledge the limitations of our current protocol for submillimeter-resolution fMRI at the individual level. We found that robust layer-dependent functional mapping often requires group-level statistics to enhance reliability. This issue has been discussed in detail in the Limitations section.

The proposed setup might potentially be valuable to the field, which is continuously searching for techniques to achieve laminar specificity in gradient echo EPI acquisitions. Nonetheless, the above considerations need to be tackled to make a convincing case.

Reviewer #3 (Public Review):

Summary:

The authors are looking for a spatially specific functional brain response to visualise non-invasively with 3T (clinical field strength) MRI. They propose a velocity-nulled weighting to remove the signal from draining veins in a submillimeter multiband acquisition.

Strengths:

- This manuscript addresses a real need in the cognitive neuroscience community interested in imaging responses in cortical layers in-vivo in humans.

- An additional benefit is the proposed implementation at 3T, a widely available field strength.

Weaknesses:

- Although the VASO acquisition is discussed in the introduction section, the VN-sequence seems closer to diffusion-weighted functional MRI. The authors should make it more clear to the reader what the differences are, and how results are expected to differ. Generally, it is not so clear why the introduction is so focused on the VASO acquisition (which, curiously, lacks a reference to Lu et al 2013). There are many more alternatives to BOLD-weighted imaging for fMRI. CBF-weighted ASL and GRASE have been around for a while, ABC and double-SE have been proposed more recently.

The major distinction between diffusion-weighted fMRI (DW-fMRI) and our methodology lies in the b-value employed. DW-fMRI typically measures cellular swelling using b-values greater than 1000 s/mm² (e.g., 1800 s/mm(sup>2</sup>). In contrast, our VN-fMRI approach measures hemodynamic responses by employing smaller b-values specifically designed to suppress signals from fast-flowing draining veins rather than detecting microstructural changes.

Regarding other functional contrasts, we agree that more layer-dependent fMRI approaches should be mentioned. In this revision, we have expanded the Introduction section to include discussions of the double spin-echo approach and CBV-based methods, such as MT-weighted fMRI, VAPER, ABC, and CBF-based method ASL. Additionally, the reference to Lu et al. (2013) has been cited in the revised manuscript. The corresponding text has been incorporated into the Introduction section to provide a more comprehensive overview of alternative functional imaging techniques.

- The comparison in Figure 2 for different b-values shows % signal changes. However, as the baseline signal changes dramatically with added diffusion weighting, this is rather uninformative. A plot of t-values against cortical depth would be much more insightful.

- Surprisingly, the %-signal change for a b-value of 0 is not significantly different from 0 in the gray matter. This raises some doubts about the task or ROI definition. A finger-tapping task should reliably engage the primary motor cortex, even at 3T, and even in a single participant.

- The BOLD weighted images in Figure 3 show a very clear double-peak pattern. This contradicts the results in Figure 2 and is unexpected given the existing literature on BOLD responses as a function of cortical depth.

- Given that data from Figures 2, 3, and 4 are derived from a single participant each, order and attention affects might have dramatically affected the observed patterns. Especially for Figure 4, neither BOLD nor VN profiles are really different from 0, and without statistical values or inter-subject averaging, these cannot be used to draw conclusions from.

We appreciate the reviewer’s suggestions. In this revision, we have made significant updates to the participant recruitment, scan protocol, data processing, and M1 delineation. Please refer to the "General Responses" at the beginning of the rebuttal and the first response to Reviewer #2 for more details.

Previously, the variation in depth-dependent profiles was calculated across upscaled voxels within a specific layer. However, due to the small size of the hand knob region, the number of within-layer voxels was limited, resulting in inaccurate estimations of signal variation. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section. Furthermore, while the initial submission used percentage signal change for the profiles of M1, the dramatic baseline fluctuations observed previously are no longer an issue after the modifications. For this reason, we retained the use of percentage signal change to present the depth-dependent profiles. After these adjustments, the profiles exhibited a bias toward the pial surface, particularly in the absence of VN gradients.

- In Figure 5, a phase regression is added to the data presented in Figure 4. However, for a phase regression to work, there has to be a (macrovascular) response to start with. As none of the responses in Figure 4 are significant for the single participant dataset, phase regression should probably not have been undertaken. In this case, the functional 'responses' appear to increase with phase regression, which is contra-intuitive and deserves an explanation.

We agreed with reviewer’s argument. In the revised results, the issues mentioned by the reviewer are largely diminished. The updated analyses demonstrate that phase regression effectively reduces superficial bias, as shown in Figures 4 and 5.

- Consistency of responses is indeed expected to increase by a removal of the more variable vascular component. However, the microvascular component is always expected to be smaller than the combination of microvascular + macrovascular responses. Note that the use of %signal changes may obscure this effect somewhat because of the modified baseline. Another expected feature of BOLD profiles containing both micro- and microvasculature is the draining towards the cortical surface. In the profiles shown in Figure 7, this is completely absent. In the group data, no significant responses to the task are shown anywhere in the cortical ribbon.

We agreed with reviewer’s comments. In the revised manuscript, the results have been substantially updated to addressing the concerns raised. The original Figure 7 is no longer relevant and has been removed.

- Although I'd like to applaud the authors for their ambition with the connectivity analysis, I feel that acquisitions that are so SNR starved as to fail to show a significant response to a motor task should not be used for brain wide directed connectivity analysis.

We appreciate the reviewer’s comments and share the concern about SNR limitations. In the updated results presented in Figure 5, the activation patterns in the visual cortex were consistent across TEs and b values. At the motor cortex, stable activation in M1 was observed at the single-subject level across most scan protocols. However, the layer-dependent activation profiles in M1 exhibited spatial instability, irrespective of the application of VN gradients. This spatial instability is not entirely unexpected, as T2*-based contrast is inherently sensitive to factors that perturb the magnetic field, such as eye movements, respiration, and macrovascular signal fluctuations. Additionally, ICA-based artifact removal was intentionally omitted in Figure 4 to ensure fair comparisons across protocols, leaving some residual artifacts unaddressed. Variability in task performance during button-pressing sessions may have further contributed to the observed inconsistencies.

Although these findings suggest that submillimeter-resolution fMRI may not yet be reliable for individual-level layer-dependent functional mapping, the group-level FC analyses can still yield robust results. In Figure 7, group-level statistics revealed distinct functional connectivity (FC) patterns associated with superficial and deep layers in M1. These FC maps exhibited significant differences between layers, demonstrating that VN fMRI enhances inter-layer independence. Additional FC analyses with a seed placed in S1 further validated these findings (see Figure 8).

The claim of specificity is supported by the observation of the double-peak pattern in the motor cortex, previously shown in multiple non-BOLD studies. However, this same pattern is shown in some of the BOLD weighted data, which seems to suggest that the double-peak pattern is not solely due to the added velocity nulling gradients. In addition, the well-known draining towards the cortical surface is not replicated for the BOLD-weighted data in Figures 3, 4, or 7. This puts some doubt about the data actually having the SNR to draw conclusions about the observed patterns.

We appreciate the reviewer’s comments. In the updated results, the efficacy of the VN gradients is evident near the pial surface, as shown in Figures 4 and 5. In Figure 4, comparing the second and third columns (b = 0 and b = 6 s/mm², respectively, at TE = 38 ms), the percentage signal change in the superficial layers is generally lower with b = 6 s/mm² than with b = 0. This indicates that VN gradient-induced signal suppression is more pronounced in the superficial layers. Additionally, in Figure 5, the VN gradients effectively suppressed macrovascular signals as highlighted by the blue circles. These observations support the role of VN gradients in enhancing specificity by reducing superficial bias and macrovascular contamination. Furthermore, bias towards cortical surface was observed in the updated results in Figure 4.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

(1) L141: "depth dependent" is slightly misleading here. It could be misunderstood to suggest that the authors are assessing how spatial specificity varies as a function of depth. Rather, they are assessing spatial specificity based on depth-dependent responses (double peak feature). Perhaps "layer-dependent spatial specificity" could be substituted with laminar specificity?

We thank the reviewer for the suggestion. The term “depth dependent” has been replaced by “layer dependent” in the revised manuscript.

(2) L146-149: these do not validate spatial specificity.

The original text is removed.

(3) L180: Maybe helpful to describe what the b-value is to assist unfamiliar readers.

We have clarified the b-value as “the strength of the bipolar diffusion gradients” where it is first mentioned in the manuscript.

(4) Figure 1B: I think it would be appropriate with a sentence of how the authors define micro/macrovasculature. Figure 1B seems to suggest that large ascending veins are considered microvascular which I believe is a bit unconventional. Nevertheless, as long as it is clearly stated, it should be fine.

In our context, macrovasculature refers to vessels that are distal to neural activation sites and contribute to extravascular contamination. These vessels are typically larger in size (e.g., > 0.1 mm in diameter) and exhibit faster flow rates (e.g., > 10 mm/s).

(5) I think the authors could be more upfront with the point about non-suppressed extravascular effects from macrovasculature, which was briefly mentioned in the discussion. It could already be highlighted in the introduction or theory section.

We thank the reviewer’s suggestions. We have expanded the discussion of extravascular effects from macrovasculature in both the Introduction (5th paragraph) and Discussion (3rd paragraph) sections.

(6) The phase regression figure feels a bit misplaced to me. If the authors agree: rather than showing the TE-dependency of the effect of phase regression, it may be more relevant for the present study to compare the conventional setup with phase regression, with the VN setup without phase regression. I.e., to show how the proposed setup compares to existing 3T laminar fMRI studies.

In this revision, both the TE-dependent and VN-dependent effects of phase regression were investigated. The results in Figure 4 and Figure 5 demonstrated that phase regression effectively suppresses macrovascular contributions primarily near the gray matter/CSF boundary, irrespective of TE or the presence of VN gradients.

(7) L520: It might be beneficial to also cite the large body of other laminar studies showing the double peak feature to underscore that it is highly robust, which increases its relevance as a test-bed to assess spatial specificity.

We agreed. More literatures have been cited (Chai et al., 2020; Huber et al., 2017a; Knudsen et al., 2023; Priovoulos et al., 2023).

(8) L557: The argument that only one participant was assessed to reduce inter-subject variability is hard to buy. If significant variability exists across subjects, this would be highly relevant to the authors and something they would want to capture.

We thank the reviewer for the suggestions. In this revision, we have increased the number of participants to 4 for protocol development and 14 for resting-state functional connectivity analysis, allowing us to better assess and account for inter-subject variability.

(9) L637: add download link and version number.

The download link has been added as requested. The version number is not applicable.

(10) L638: How was the phase data coil-combined?

The reconstructed multi-channel data, which were of complex values, were combined using the adaptive combination method (Walsh et al.; DOI: 10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g). The MATLAB code for this implementation was developed by Dr. Diego Hernando and is publicly available at https://github.com/welton0411/matlab . The phase data were then extracted using the MATLAB function ‘angle’.

(11) L639: Why was the smoothing filter parameter changed (other parameters were default)?

The smoothing filter parameter was set based on the suggestion provided in the help comments of the NIFTI_NORDIC function:

function  NIFTI_NORDIC(fn_magn_in,fn_phase_in,fn_out,ARG)

% fMRI

%

%  ARG.phase_filter_width=10;

In other words, we simply followed the recommendation outlined in the NIFTI_NORDIC function’s documentation.

(12) I assume the phase data was motion corrected after transforming to real and imaginary components and using parameters estimated from magnitude data? Maybe add a few sentences about this.

Prior to phase regression, the time series of real and imaginary components were subjected to motion correction, followed by phase unwrapping. The phase regression was incorporated early in the data processing pipeline to minimize the discrepancy in data processing between magnitude and phase images (Stanley et al., 2021).

(13) Was phase regression applied with e.g., a deming model, which accounts for noise on both the x and y variable? In my experience, this makes a huge difference compared with regular OLS.

We appreciate the reviewer’s insightful comment. We are aware that the noise present in both magnitude and phase data therefore linear Deming regression would be a good fit to phase regression (Stanley et al., 2021). To perform Deming regression, however, the ratio of magnitude error variance to phase error variance must be predefined. In our initial tests, we found that the regression results were sensitive to this ratio. To avoid potential confounding, we opted to use OLS regression for the current analysis. However, we agreed Deming model could enhance the efficacy of phase regression if the ratio could be determined objectively and properly.

(14) Figure 2: What is error bar reflecting? I don't think the across-voxel error, as also used in Figure 4, is super meaningful as it assumes the same response of all voxels within a layer (might be alright for such a small ROI). Would it be better to e.g. estimate single-trial response magnitude (percent signal change) and assess variability across? Also, it is not obvious to me why b=30 was chosen. The authors argue that larger values may kill signal, but based on this Figure in isolation, b=48 did not have smaller response magnitudes (larger if anything).

We agreed with the reviewer’s opinion on the across-voxel error. In the revised manuscript, the signal was averaged within each layer before performing the GLM analysis, and signal variation was calculated using the temporal residuals. The technical details of these changes are described in the "Materials and Methods" section.

Additionally, the bipolar diffusion gradients were modified from a single direction to three orthogonal directions. As a result, the questions and results related to b=30 or b=48 are no longer applicable.

(15) Figure 5: would be informative to quantify the effect of phase regression over a large ROI and evaluate reduction in macrovascular influence from superficial bias in laminar profiles.

We appreciate the reviewer’s suggestion. In the revised manuscript, the reduction in macrovascular influence from superficial bias across a large ROI is displayed in Figure 5. Additionally, the impact on laminar profiles is demonstrated in Figure 4.

(16) L406-408: What kind of robustness?

We acknowledge that describing the protocol as “robust” was an overstatement. The updated results indicate that the current protocol for submillimeter fMRI may not yet be suitable for reliable individual-level layer-dependent functional mapping. However, group-level functional connectivity (FC) analyses demonstrated clear layer-specific distinctions with VN fMRI, which were not evident in conventional fMRI. These findings highlight the enhanced layer specificity achievable with VN fMRI.

(17) Figure 8: I think C) needs pointers to superficial, middle, and deep layers? Why is it not in the same format as in Figure 9C? The discussion of the FC results could benefit from more references supporting that these observations are in line with the literature.

In the revised results, the layer pooling shown in Figure 9c has been removed, making the question regarding format alignment no longer applicable. Additionally, references supporting the FC results have been added to the revised Discussion section (7th paragraph).

(18) L456-457: But correlation coefficients may also be biased by different CNR across layers.

That is correct. In the updated FC results in Figure 7 to 9, we used group-level statistics rather than correlation coefficients.

Reviewer #3 (Recommendations For The Authors):

The results in Figure 2-6 should be repeated over, or averaged over, a (small) group of participants. N=6 is usual in this field. I would seriously reconsider the multiband acceleration - the acquisition seemingly cannot support the SNR hit.

A few more specific points are given below:

(1) Abstract: The sentence about LGN in the abstract came for me out of the blue - why would LGN be important here, it's not even a motor network node? Perhaps the aims of the study should be made more clear - if it's about networks as suggested earlier then a network analysis result would be expected too. Expanding the directed FC findings would improve the logical flow of the abstract. Given the many concerns, removing the connectivity analysis altogether would also be an option.

We thank the reviewer for the suggestions. The LGN-related results indeed diluted the focus of this study and have been completely removed in this revision.

(2) Line 105: in addition to the VASO method, ..

The corresponding text has been revised, and as a result, the reviewer’s suggestion is no longer applicable.

(3) If out of the set MB 4 / 5 / 6 MB4 was best, why did the authors not continue with a comparison including MB3 and MB2? It seems to me unlikely that the MB4 acquisition is actually optimal.

Results: We appreciate the reviewer’s suggestions. In this revision, we decreased the MB factor to 3, as it allowed us to increase the in-plane acceleration rate to 3, thereby shortening the TE. The resulting sensitivity for both individual and group-level results is detailed in earlier responses, such as the response to Q16 for Reviewer #2.

(4) The formatting of the references is occasionally flawed, including first names and/or initials. Please consider using a reliable reference manager.

We used Zotero as our reference manager in this revision to ensure consistency and accuracy. The references have been formatted according to the APA style.

(5) In the caption of Figure 5, corrected and uncorrected p values are identical. What multiple comparisons correction was made here? A multiple comparisions over voxels (as is standard) would usually lead to a cut-off ~z=3.2. That would remove most of the 'responses' shown in figure 5.

We appreciate the reviewer’s comment. The original results presented in Figure 5 have been removed in the revised manuscript, making this comment no longer applicable.

many different subgroups and headers. helps organize information and is easier for the reader to read through.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors address an important issue in Babesia research by repurposing cipargamin (CIP) as a potential therapeutic against selective Babesia spp. In this study, CIP demonstrated potent in vitro inhibition of B. bovis and B. gibsoni with IC₅₀ values of 20.2 ± 1.4 nM and 69.4 ± 2.2 nM, respectively, and the in vivo efficacy against Babesia spp. using mouse model. The authors identified two key resistance mutations in the BgATP4 gene (BgATP4^L921I and BgATP4^L921V) and explored their implications through phenotypic characterization of the parasite using cell biological experiments, complemented by in silico analysis. Overall, the findings are promising and could significantly advance Babesia treatment strategies.

Strengths:

In this manuscript, the authors effectively repurpose cipargamin (CIP) as a potential treatment for Babesia spp. They provide compelling in vitro and in vivo data showing strong efficacy. Key resistance mutations in the BgATP4 gene are identified and analyzed through both phenotypic and in silico methods, offering valuable insights for advancing treatment strategies.

Thank you for your insightful comments and for taking the time to review our manuscript.

Weaknesses:

The manuscript explores important aspects of drug repurposing and rational drug design using cipargamin (CIP) against Babesia. However, several weaknesses should be addressed. The study lacks novelty as similar research on cipargamin has been conducted, and the experimental design could be improved. The rationale for choosing CIP over other ATP4-targeting compounds is not well-explained. Validation of mutations relies heavily on in silico predictions without sufficient experimental support. The Ion Transport Assay has limitations and would benefit from additional assays like Radiolabeled Ion Flux and Electrophysiological Assays. Also, the study lacks appropriate control drugs and detailed functional characterization. Further clarity on mutation percentages, additional safety testing, and exploration of cross-resistance would strengthen the findings.

We appreciate your feedback and for giving us the chance to improve our paper. We have specified how we revised the below comments one by one. I hope these address your concerns.

Comment 1: It is commendable to explore drug repurposing, drug deprescribing, drug repositioning, and rational drug design, especially using established ATP4 inhibitors that are well-studied in Plasmodium and other protozoan parasites. While the study provides some interesting findings, it appears to lack novelty, as similar investigations of cipargamin on other protozoan parasites have been conducted. The study does not introduce new concepts, and the experimental design could benefit from refinement to strengthen the results. Additionally, the rationale for choosing CIP over other MMV compounds targeting ATP4 is not clearly articulated. Clarifying the specific advantages CIP may offer against Babesia would be beneficial. Finally, the validation of the identified mutations might be strengthened by additional experimental support, as reliance on in silico predictions alone may not fully address the functional impact, particularly given the potential ambiguity of the mutations (BgATP4 L to V and I).

Thank you for your thoughtful feedback. We have addressed the concerns as follows: (1) Introduction of new concepts and experimental design: While our study primarily builds on existing frameworks, it provides novel insights into the interaction of CIP with Babesia parasites, which we believe contribute to the field. Regarding the experimental design, we acknowledge its limitations and have revised the manuscript to include additional experiments to strengthen the robustness of our findings. Specifically, we have added experiments on the detection of BgATP4-associated ATPase activity (Figure 3H), the evaluation of cross-resistance to antibabesial agents (Figures 5A and 5B), and the efficacy of CIP plus TQ combination in eliminating B. microti infection with no recrudescence in SCID mice (Figure 5C).

(2) Rationale for choosing CIP over other MMV compounds targeting ATP4: We appreciate this point and have expanded the introduction section to articulate our rationale for selecting CIP (Lines 94-97). Specifically, CIP was chosen due to its previously demonstrated efficacy against Plasmodium and other protozoan parasites.

(3) Validation of identified mutations: We agree that additional experimental data would strengthen the validation of the identified mutations. In response, we have indicated the ratio of wild-type to mutant parasites by Illumina NovaSeq6000 to validate the impact of the BgATP4 C-to-G and A mutations (Figure 2D).

Comment 2: Conducting an Ion Transport Assay is useful but has limitations. Non-specific binding or transport by other cellular components can lead to inaccurate results, causing false positives or negatives and making data interpretation difficult. Indirect measurements, like changes in fluorescence or electrical potential, can introduce artifacts. To improve accuracy, consider additional assays such as

a. Radiolabeled Ion Flux Assay: tracks the movement of Na⁺ using radiolabeled ions, providing direct evidence of ion transport.

b. Electrophysiological Assay: measures ionic currents in real-time with patch-clamp techniques, offering detailed information about ATP4 activity.

Thank you for highlighting the limitations of the ion transport assay and suggesting alternative approaches to improve accuracy. However, they require specialized equipment and expertise not currently available in our laboratory. We have acknowledged these limitations and included these alternative methods as part of the study's future directions. Thank you for your suggestions which will undoubtedly enhance the rigor and depth of our research.

Comment 3: In-silico predictions can provide plausible outcomes, but it is essential to evaluate how the recombinant purified protein and ligand interact and function at physiological levels. This aspect is currently missing and should be included. For example, incorporating immunoprecipitation and ATPase activity assays with both wild-type and mutant proteins, as well as detailed kinetic studies with Cipargamin, would be recommended to validate the findings of the study.

Thank you for your insightful suggestions regarding the validation of in-silico predictions. We recognize the importance of evaluating the interaction and function of recombinant purified proteins and ligands at physiological levels to strengthen the study's findings. (1) Incorporating experimental validation:

a. Immunoprecipitation assays: We agree that immunoprecipitation could provide valuable evidence of protein-ligand interactions. While this was not included in the current study due to limitations in sample availability, we plan to incorporate this assay in follow-up experiments.

b. ATPase activity assays: Assessing ATPase activity in both wild-type and mutant proteins is a crucial step in validating the functional impact of the identified mutations. We included the results in the revised manuscript (Figure 3H).

(2) Detailed kinetic studies with cipargamin: We appreciate the recommendation to conduct detailed kinetic analyses. These studies would provide deeper insights into the binding affinity and inhibition dynamics of cipargamin. We have included the results of these experiments in the current study (Figure 3I).

Comment 4: The study lacks specific suitable control drugs tested both in vitro and in vivo. For accurate drug assessment, especially when evaluating drugs based on a specific phenotype, such as enlarged parasites, it is important to use ATP4 gene-specific inhibitors. Including similar classes of drugs, such as Aminopyrazoles, Dihydroisoquinolines, Pyrazoleamides, Pantothenamides, Imidazolopiperazines (e.g., GNF179), and Bicyclic Azetidine Compounds, would provide more comprehensive validation.

Thank you for emphasizing the importance of including suitable control drugs. We acknowledge the absence of specific control drugs in the previous version of the manuscript. To date, no drug targeting ATP4 proteins in Babesia has been definitively identified. The suggested drugs could potentially disrupt the parasite's ability to regulate sodium levels by inhibiting PfATP4, a protein essential for its survival. This highlights PfATP4 as an attractive target for antimalarial drug development. However, further studies are required to evaluate whether these drugs exhibit similar activity against ATP4 homologs in Babesia.

Comment 5: Functional characterization of CIP through microscopic examination and quantification for assessing parasite size enlargement is not entirely reliable. A Flow Cytometry-Based Assay is recommended instead 9 along with suitable control antiparasitic drugs). To effectively monitor Cipargamin's action, conducting time-course experiments with 6-hour intervals is advisable rather than relying solely on endpoint measurements. Additionally, for accurate assessment of parasite morphology, obtaining representative qualitative images using Scanning Electron Microscopy (SEM) or Transmission Electron Microscopy (TEM) for treated versus untreated samples is recommended for precise measurements.

Thank you for your constructive feedback regarding the methods for functional characterization of CIP and the evaluation of parasite morphology.

(1) Flow Cytometry-Based Assay: We agree that a flow cytometry-based assay would enhance the accuracy of detecting changes in parasite size and morphology. We will implement this method in future studies as our laboratory currently does not have the capability to conduct such experiments.

(2) Microscopy for Morphology Assessment: We acknowledge the importance of obtaining high-resolution, representative images of treated and untreated samples. Utilizing Scanning Electron Microscopy (SEM) or Transmission Electron Microscopy (TEM) for qualitative analysis will significantly improve the precision of our morphological assessments. However, both methods have limitations.

a. SEM: This technique can only scan the erythrocytes' surface; it cannot scan the parasite itself because it is inside the erythrocytes.

b. TEM: Since the parasite is fixed, observations from various angles may reveal longitudinal or cross-sectional portions, making it impossible to precisely view the parasite's dimensions. As a result, we employed TEM to precisely observe the parasite's internal structure alterations both before and after treatment, as seen in Figure 3C.

Comment 6: A notable contradiction observed is that mutant cells displayed reduced efficacy and affinity but more pronounced phenotypic effects. The BgATP4^L921I mutation shows a 2x lower susceptibility (IC₅₀ of 887.9 ± 61.97 nM) and a predicted binding affinity of -6.26 kcal/mol with CIP. However, the phenotype exhibits significantly lower Na⁺ concentration in BgATP4^L921I (P = 0.0087) (Figure 3E).

The seemingly contradicting observation of reduced CIP binding and efficacy in the BgATP4^L921I mutant with a significant decrease in intracellular Na⁺ concentration may be explained by factors other than the direct CIP interaction. Logically, we consider that CIP binds less effectively to its target in the BgATP4^L921I mutant, but the observed phenotype may be attributed to the functional consequences of the mutation. The BgATP4^L921I mutation probably directly impacts the function of BgATP4's ion transport mechanism, which likely disrupts Na⁺ homeostasis independently. Thus, we hypothesize that the dysregulated Na⁺ homeostasis is driven by the mutation itself rather than the already weakened inhibitory effect of CIP.

Comment 7: The manuscript does not clarify the percentage of mutations, and the number of sequence iterations performed on the ATP4 gene. It is also unclear whether clonal selection was carried out on the resistant population. If mutations are not present in 100% of the resistant parasites, please indicate the ratio of wild-type to mutant parasites and represent this information in the figure, along with the chromatograms.

Thank you for your valuable comments. We appreciate your detailed observations and giving us the opportunity to clarify these points. During the long-term culture process, subculturing was performed every three days. Although clonal selection was not conducted, mutant strains were effectively selected during this process. Using the Illumina NovaSeq6000 sequencing platform, high-throughput next-generation sequencing was performed to detect ratio of wild-type to mutant parasites. Results showed that for BgATP4^L921V, 99.97% of 7,960 reads were G, and for BgATP4^L921I, 99.92% of 7,862 reads were A. To enhance clarity, we have included a new figure (Figure 2D) illustrating the sequencing results. We believe this addition will help provide a clearer understanding for the readers.

Comment 8: While the compound's toxicity data is well-established, it is advisable to include additional testing in epithelial cells and liver-specific cell lines (e.g., HeLa, HCT, HepG2) if feasible for the authors. This would provide a more comprehensive assessment of the compound's safety profile.

Thank you for your thoughtful suggestion. We included toxicity testing in human foreskin fibroblasts (HFF) as supplemental toxicity data to provide a more comprehensive evaluation of the compound's safety profile (Figure supplement 1B).

Comment 9: In the in vivo efficacy study, recrudescent parasites emerged after 8 days of treatment. Did these parasites harbor the same mutation in the ATP4 gene? The authors did not investigate this aspect, which is crucial for understanding the basis of recrudescence.

Thank you for raising this important point. We acknowledge that understanding the genetic basis of recrudescence is critical for elucidating mechanisms of resistance and treatment failure. Although our current study did not include an analysis of the BrATP4 gene in relapse parasites due to limitations in sample availability, we evaluated CIP efficacy in SCID mice and performed sequencing analysis of the BmATP4 gene in recrudescent samples. However, no mutation points were identified (Lines 211-212). We believe that if a relapse occurs after the 7-day treatment, it is unlikely that the parasites would easily acquire mutations.  

Comment 10: The authors should explain their choice of BABL/c mice for evaluating CIP efficacy, as these mice clear the infection and may not fully represent the compound's effectiveness. Investigating CIP efficacy in SCID mice would be valuable, as they provide a more reliable model and eliminate the influence of the immune system. The rationale for not using SCID mice should be clarified.

We appreciate the reviewer's suggestion regarding the use of SCID mice to evaluate the efficacy of CIP. In response to your suggestion, we have now included an experiment using SCID mice to evaluate the efficacy of CIP and to eliminate the confounding influence of the immune system. We further investigated the potential of combined administration of CIP plus TQ to eliminate parasites, as we are concerned that the long-term use of CIP as a monotherapy may be limited due to its potential for developing resistance. The results are shown in Figure 5C.

Comment 11: Do the in vitro-resistant parasites show any potential for cross-resistance with commonly used antiparasitic drugs? Have the authors considered this possibility, and what are their expectations regarding cross-resistance?

Thank you for your insightful question regarding the potential for cross-resistance between in vitro-resistant parasites and commonly used antiparasitic drugs. In response to your suggestion, we have now included experiments to assess whether B. gibsoni parasites that are resistant to CIP exhibit any cross-resistance to other commonly used antiparasitic drugs, such as atovaquone (ATO) and tafenoquine (TQ). The IC₅₀ values for both ATO and TQ in the resistant strains showed only slight changes compared to the wild-type strain, with less than a onefold difference (Figure 5A, 5B). This minimal variation suggests that the resistant strain has a mild alteration in susceptibility to ATO and TQ, but not enough to indicate strong resistance or significant cross-resistance. This suggests that CIP could be used in combination with TQ to treat babesiosis.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors have tried to repurpose cipargamin (CIP), a known drug against plasmodium and toxoplasma against babesia. They proved the efficacy of CIP on babesia in the nanomolar range. In silico analyses revealed the drug resistance mechanism through a single amino acid mutation at amino acid position 921 on the ATP4 gene of Babesia. Overall, the conclusions drawn by the authors are well justified by their data. I believe this study opens up a novel therapeutic strategy against babesiosis.

Strengths:

The authors have carried out a comprehensive study. All the experiments performed were carried out methodically and logically.

Thank you for the comments and your time to review our manuscript.

Weaknesses:

The introduction section needs to be more informative. The authors are investigating the binding of CIP to the ATP4 gene, but they did not give any information about the gene or how the ATP4 inhibitors work in general. The resolution of the figures is not good and the font size is too small to read properly. I also have several minor concerns which have been addressed in the "Recommendations for the authors" section.

We thank the reviewer for their valuable comments. In response, we have revised the introduction to include a more detailed explanation of the ATP4 gene, its biological significance, and the mechanism of ATP4 inhibitors to provide a better context of the study (Lines 86-93). Additionally, we have reformatted the figures to enhance resolution and increased the font size to ensure improved readability. We also appreciate the reviewer's careful assessment of the manuscript and have addressed all minor concerns outlined in the "Recommendations for the Authors" section. A detailed, point-by-point response to each concern is provided in the response letter, and the corresponding revisions have been incorporated into the manuscript.

Reviewer #3 (Public review):

Summary:

The authors aim to establish that cipargamin can be used for the treatment of infection caused by Babesia organisms.

Strengths:

The study provides strong evidence that cipargamin is effective against various Babesia species. In vitro, growth assays were used to establish that cipargamin is effective against Babesia bovis and Babesia gibsoni. Infection of mice with Babesia microti demonstrated that cipargamin is as effective as the combination of atovaquone plus azithromycin. Cipargamin protected mice from lethal infection with Babesia rodhaini. Mutations that confer resistance to cipargamin were identified in the gene encoding ATP4, a P-type Na⁺ ATPase that was found in other apicomplexan parasites, thereby validating ATP4 as the target of cipargamin.

We appreciate the reviewer for taking the time to review our manuscript.

Weaknesses:

Cipargamin was tested in vivo at a single dose administered daily for 7 days. Despite the prospect of using cipargamin for the treatment of human babesiosis, there was no attempt to identify the lowest dose of cipagarmin that protects mice from Babesia microti infection. Exposure to cipargamin can induce resistance, indicating that cipargamin should not be used alone but in combination with other drugs. There was no attempt at testing cipargamin in combination with other drugs, particularly atovaquone, in the mouse model of Babesia microti infection. Given the difficulty in treating immunocompromised patients infected with Babesia microti, it would have been informative to test cipargamin in a mouse model of severe immunosuppression (SCID or rag-deficient mice).

We thank the reviewer for raising these important comments. We address each concern as follows:

(1) Identifying the lowest protective dose of CIP:

Although our current study was designed to assess the efficacy of CIP at a single therapeutic dose over a 7-day period, we acknowledge that identifying the lowest effective dose would provide valuable information for optimizing treatment regimens. We plan to address this in future studies by conducting a dose-response experiment to identify the minimal protective dose of CIP.

(2) Testing CIP in combination with other drugs:

In the current study, we have tested the efficacy of tafenoquine (TQ) combined with CIP, as well as CIP or TQ administered individually, in a mouse model of B. microti infection. Our results demonstrated that, compared with monotherapy, the combination of CIP and TQ completely eliminated the parasites within 90 days of observation (Figure 5C).

(3) Testing in an immunocompromised mouse model:

We agree with the reviewer that evaluating CIP in immunocompromised models is critical for understanding its potential in treating immunocompromised patients. To address this, we have conducted experiments using SCID mice infected with B. microti. Our results indicated that the combination therapy of CIP plus TQ was effective in eliminating parasites in the severely immunocompromised model (Figure 5D).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Comment 1: Table: Include the in-silico binding energies for each mutation and ligand.

We have added binding energies for each mutation and ligand in Table supplement 3.

Comment 2: Did the authors investigate the potential of combination therapies involving CIP?

We have tested the efficacy of TQ combined with CIP in a mouse model of B. microti infection.

Comment 3: Does this mutation affect the transmission of the parasite?

Based on our observations, the growth and generation rates of the mutant strain are comparable to those of the wild-type strain. These findings suggest that the mutation does not significantly affect the spread or transmission of the parasite. We have included this observation in the revised manuscript (Lines 243-244).

Comment 4: 60: Use abbreviations CLN for clindamycin and QUI for quinine.

We have revised them accordingly (Lines 59-60).

Comment 5: 86: The hypothesis is not strong or convincing; it needs to be modified to be more specific and convincing.

We have revised the hypothesis to reflect the rationale behind the study better and to support our claim more strongly (Lines 94-97).

Comment 6: 93: Change to: "In vitro efficacy of CIP against B. bovis and B. gibsoni.".

We have changed the suggested content in the manuscript (Line 104).

Comment 7: 96: Define CC₅₀.

We have added the definition of CC₅₀ (Line 106).

Comment 8: 102: Change to: "...Balb/c mice increased dramatically in the...".

We have changed the word following your recommendation (Line 114).

Comment 9: 108: "...significant decrease at 12 DPI...".

We have revised it according to your suggestion (Line 120).

Comment 10: 110: "This indicates that the administration...".

We have revised it according to your suggestion (Line 122).

Comment 11: Figure 1:

(1) Panels A and B should clearly indicate parasite species within the graph for better self-explanation.

We have indicated parasite species within the graph.

(2) For panels C, D, and E, if mice were eliminated or euthanized in the study, include a symbol in the graph to indicate this.

For panels C and D, no mice were eliminated during the study; therefore, no symbol was added to these graphs. Panel F already provides information about the number of eliminated mice, which corresponds to the data in Panel E.

(3) In panels C, D, and E, use a continuation arrow for drug treatment rather than a straight line, to cover the duration of the treatment.

We have updated the figures to use continuation arrows instead of straight lines to represent the duration of drug treatment.

Comment 12: Figure 2: The color combination for the WT and mutant curves is hard to read; consider using regular, less fluorescent, and more distinguishable colors.

We have adjusted the color scheme to use more distinguishable and less fluorescent colors, ensuring better readability and clarity. The revised figure with the updated color scheme has been included in the updated manuscript, and we hope this resolves the readability concern.

Comment 13: Figure 3:

(1) Panel A: Represent a single infected iRBC rather than a field for better visualization.

We have updated Panel A to display a single infected iRBC instead of a field.

(2) Panels E and F: Change the color patterns, as the current colors, especially the green variants (WT and mutant L921V), are difficult to read.

To improve readability, we have updated the color patterns for these panels by selecting more distinguishable colors with higher contrast (Figure 3F, 3G).

Comment 14: Figure 4: Panels B, C, and D: The text is too small to read; increase the font size or change the resolution.

We have increased the font size and replaced the panels with high-resolution versions (Figure 4B, 4C, 4D).

Reviewer #2 (Recommendations for the authors):

Comment 1: In the last paragraph of the introduction, the authors mentioned determining the activity of CIP in vitro in B. bovis and B. gibsoni while in vivo in B. microti and B. rodhaini. It is not explained why they are testing the in vitro and in vivo effects on different Babesia species. Could you please add some logic there? Also, why did they mention measuring the inhibitory activity of CIP by monitoring the Na⁺ and H⁺ balance? This part needs to be rewritten with more information. The ATP4 gene is not properly introduced in the manuscript.

We thank the reviewer for raising these important points. Below, we address each aspect of the comment in detail:

(1) Rationale for testing different Babesia spp. in vitro and in vivo:

B. bovis and B. gibsoni are well-established Babesia models for in vitro culture systems, allowing evaluation of CIP's inhibitory activity under controlled laboratory conditions. B. microti and B. rodhaini, on the other hand, are commonly used rodent models for the in vivo studies of babesiosis, enabling the assessment of drug efficacy in a mammalian host system. This multi-species approach provides a comprehensive evaluation of CIP's efficacy across Babesia spp. with different biological characteristics.

(2) Measuring CIP's inhibitory activity via Na⁺ and H⁺ balance:

We acknowledge that this section of the introduction requires more context. The revised manuscript now includes additional information explaining that the ATP4 gene, which encodes a Na⁺/H⁺ transporter, is the proposed target of CIP (Lines 86-93). CIP disrupts the ion homeostasis maintained by ATP4, leading to an imbalance in Na⁺ and H⁺ concentrations. Monitoring these ionic changes provides a mechanistic understanding of CIP's mode of action and its impact on parasite viability. This rationale has been expanded in the introduction to clarify its significance.

Comment 2: The figure fonts are too small. The resolution for the images is also poor.

We have increased the font size in all figures to improve readability. Additionally, we have replaced the figures with high-resolution versions to ensure clarity and visual quality.

Comment 3: Figures 1A and 1B: one of the error bars merged to the X-axis legend. Please modify these panels. Which curve was used to determine the IC₅₀ values (although it's mentioned in the methods section, would it be better to have the information in the figure legends as well)?

We thank the reviewer for their comments regarding Figures 1A and 1B.

(1) Error bars overlapping the X-axis legend:

The error bars in the figures were automatically generated using GraphPad Prism9 based on the data and are determined by the values themselves. Unfortunately, this overlap cannot be avoided without altering the data representation.

(2) IC₅₀ curve information:

To clarify the determination of IC₅₀ values, we have already included gray dashed lines in the graphs to indicate where the IC₅₀ values were derived from the curves. This visual representation provides clear information about the IC₅₀ points.

Comment 4: Supplementary Figure 1: what are MDCK cells? What is CC₅₀? Please mention their full forms in the text and figure legends (they should be described here because the methods section comes later). What is meant by a predicted selectivity index? There should be an explanation of why and how they did it. Which curve was used to determine the IC₅₀ values?

We thank the reviewer for pointing out the need to clarify terms and provide additional context in the supplementary figure and text. We have updated the figure legend and text to include the full forms of MDCK (Madin-Darby canine kidney) cells and CC₅₀ (50% cytotoxic concentration), ensuring clarity for readers encountering these terms for the first time. In text, now we have included a brief explanation of the selectivity index as a measure of a drug's safety and specificity (Lines 108-110). The selectivity index is calculated as the ratio between the half maximal inhibitory concentration (IC₅₀) and the 50% cytotoxic concentration (CC₅₀) values (Lines 333-335). We also have already included gray dashed lines in the graphs to indicate where the IC₅₀ values were derived from the curves (Figure supplement 1).

Comment 5: Figures 1C-F: It feels unnecessary to write down n=6 for each panel and each group. Since "n" is equal for all, it would be nice to just mention it in the figure legend only.

We appreciate the reviewer's suggestion regarding the notation of "n=6" in Figures 1C-F. To improve clarity and reduce redundancy, we have removed the "n=6" notation from the individual panels and included it in the figure legend instead.

Comment 6: Figure 2A: was never mentioned in the text.

We have described the sequencing results for the wild-type B. gibsoni ATP4 gene with a reference to Figure 2A in the revised manuscript (Lines 134-135).

Comment 7: Figure 2D: some of the error bars merged to the X-axis legend. Please modify. Again, which curve was used to determine the IC₅₀ values? Can the authors explain why the pH declined after 4 minutes?

We thank the reviewer for this insightful question.

(1) Error bars overlapping the X-axis legend:

The error bars in Figure 2E were automatically generated using GraphPad Prism9 and are determined by the underlying data values. Unfortunately, this overlap cannot be avoided without altering the data representation.

(2) IC₅₀ curve information:

Since Figure 2E contains three separate curves, adding dashed lines to indicate the IC₅₀ for each curve would make the figure overly cluttered and reduce readability. To address this, we have clearly indicated the IC₅₀ values in Figures 1A and 1B and described the methodology for determining IC₅₀ values in the Methods section. We believe this approach provides sufficient clarity without compromising the visual experience of Figure 2E.

(3) The pH decline observed after 4 minutes (Figure 3E) may be attributed to the following factors:

a. Ion transport dynamics:

The initial rise in pH likely reflects the rapid inhibition of Na⁺/H⁺ exchange mediated by CIP, which temporarily alkalinizes the intracellular environment. However, after this initial phase, compensatory mechanisms, such as proton influx or metabolic acid production, may lead to a subsequent decline in pH.

b. Drug kinetics and target interaction:

The decline could also result from the time-dependent effects of CIP on ATP4-mediated ion transport. As the drug action stabilizes, the parasite may partially restore ionic balance, leading to a decrease in intracellular pH.

Comment 8: Supplementary Figure 2: It's difficult to distinguish between red and pink colors, so it would be wise to use two contrasting colors to distinguish between Pf and Tg CIP resistant cites.

We have updated the figure to enhance clarity. Purple squares and arrows now represent sites linked to P. falciparum CIP resistance, replacing the previous red squares. Similarly, gray squares and arrows have replaced the green squares to denote sites associated with T. gondii (Figure supplement 2).

Comment 9: Line 65: Is it possible to add a reference here?

We have added a reference in line 65.

Comment 10: Line 69: Please spell the full form of G6PD as it was never mentioned before.

We have added the full form of G6PD in lines 69-70.

Comment 11: Line 103: mention what DPI is (irrespective of the methods section which comes later).

We have spelled out DPI (days postinfection) in line 115.

Comment 12: Line 120: It's not explained why B. gibsoni ATP4 gene was investigated? There should be more explanation and references to previous work.

We thank the reviewer for pointing out the need to provide more context for investigating the B. gibsoni ATP4 gene. To address this, we have added more information to the introduction, explaining that the ATP4 gene, which encodes a Na⁺/H⁺ transporter, is the proposed target of CIP (Lines 86-93).

Comment 13: Line 203-219: line spacing seems different from the rest of the manuscript.

We have corrected the incorrect format (Lines 262-278).

Reviewer #3 (Recommendations for the authors):

Comment 1: Lines 66-68: The report by Marcos et al. 2022 did not demonstrate that tafenoquine was effective in curing relapsing babesiosis. In the discussion of that article, the authors state that "it is impossible to conclude that the drug tafenoquine provided any clinical benefit." The first demonstration of tafenoquine efficacy against relapsing babesiosis was reported by Rogers et al. 2023 and confirmed by Krause et al. 2024. Please rephrase the statement and use relevant citations.

We thank the reviewer for pointing out this issue and we have rephrased the statement and used relevant citations (Lines 66-68).

Comment 2: Line 103: mean parasitemia at 10 DPI is reported to be 35.88% but Figure 1C appears to indicate otherwise.

We are sorry for the carelessness, the correct mean parasitemia at 10 DPI is 38.55%, and this has been updated in line 115 of the revised manuscript to reflect the data shown in Figure 1C.

Comment 3: Line 116: parasitemia is said to recur on day 14 post-infection but Figure 1E indicates that recurrence was already noted on day 12 post-infection.

We thank the reviewer for pointing out this inconsistency. We have corrected the relapse day to reflect that recurrence was noted on day 12 post-infection, as shown in Figure 1E. This correction has been made in the revised manuscript (Line 128).

Comment 4: Line 120: Replace "wells" with "strains". Also, start the paragraph with one brief sentence to state how resistant parasites were generated.

We have replaced "wells" with "strains" and added one brief sentence to explain how resistant parasites were generated (Lines 132-134).

Comment 5: Line 169: is Ji et al, 2022b truly the appropriate reference to support a statement on tafenoquine?

We thank the reviewer for highlighting this point. We have added one other reference to support a statement on tafenoquine. The IC₅₀ value of TQ was 20.0 ± 2.4 μM against B. gibsoni (Ji et al., 2022b), and 31 μM against B. bovis (Carvalho et al., 2020) (Lines 223-225).

Comment 6: Lines 184-185: given that exposure to CIP induces mutations in the ATP4 gene and therefore resistance to CIP, what is the prospect of using CIP for the treatment of babesiosis? Can the authors speculate on whether CIP should not be used alone but rather in combination with other drugs currently used for the treatment of human babesiosis?

We thank the reviewer for raising this important question. Given that exposure to CIP induces mutations in the ATP4 gene, leading to resistance, we acknowledge that the long-term use of CIP as a monotherapy may be limited due to the potential for resistance development. To address this concern, we investigated the combination therapy of TQ and CIP to achieve the complete elimination of B. microti in infected mice (a model for human babesiosis). The results of this study are presented in Figure 5C.

Comment 7: Lines 258-259: it is stated that drug treatment was initiated on day 4 post-infection when mean parasitemia was 1% and that drug treatment was continued for 7 days. This is not the case for B. rodhaini infection. As reported in Figure 1E, treatment was initiated on day 2 post-infection.

We apologize for the oversight and any confusion caused. We have corrected the statement to reflect that drug treatment for B. rodhaini-infected mice was initiated at 2 DPI, as reported in Figure 1E (Lines 347-349).

Comment 8: Lines 282-285: RBCs are said to be exposed to CIP for 3 days but parasite size is said to be measured on day 4. Which is correct?

We thank the reviewer for pointing out this discrepancy. To clarify, the infected erythrocytes were exposed to CIP for three consecutive days (72 hours). Blood smears were then prepared at the 73^rd hour, corresponding to the fourth day.

Comment 9: Lines 35-37: this sentence can be omitted from the abstract as it does not summarize additional insight or additional data.

We have omitted this sentence from the abstract.

Comment 10: Line 55: replace Drews et al. 2023 with Gray and Ogden 2021 (doi: 10.3390/pathogens10111430). This excellent article directly supports the statement made by the authors.

We appreciate the reviewer's suggestion and have replaced the reference with Gray and Ogden, 2021 (doi: 10.3390/pathogens10111430) (Line 54).

Comment 11: Line 55: modify the start of sentence to read "The disease is known as babesiosis ...".

We have modified the sentence (Line 54).

Comment 12: Line 56: rephrase to read ".... but chronic infections can be asymptomatic".

We have modified the sentence (Line 55).

Comment 13: Line 57: rephrase to read "The fatality rate ranges from 1% among all cases to 3% among hospitalized cases but has been as high as 20% in immunocompromised patients."

We have rephrased the sentence (Lines 55-57).

Comment 14: Line 61: replace Holbrook et al. 2023 with Krause et al. 2021 (doi: 10.1093/cid/ciaa1216).

We have replaced Holbrook et al. 2023 with Krause et al. 2021 (doi: 10.1093/cid/ciaa1216) (Line 60).

Comment 15: Line 62: rephrase to read "... cytochrome b, which is targeted by atovaquone, were identified in patients with relapsing babesiosis." Here, also cite Lemieux et al., 2016; Simon et al., 2017; Rosenblatt et al, 2021, Marcos et al., 2022; Rogers et al., 2023; Krause et al., 2024.

We have rephrased the sentence and cited the suggested references (Lines 61-64).

Comment 16: Line 65: rephrase "Despite its efficacy, this combination can elicit adverse drug reactions (Vannier and Krause, 2012)."

We have rephrased the sentence (Lines 65-66).

Comment 17: Lines 75-77: rephrase to read "... of the drug indicated that CIP taken orally had good absorption, a long half-life, and ...".

We have rephrased the sentence (Lines 76-77).

Comment 18: Line 79: remove "the".

We have removed "the" (Lines 79-80).

Comment 19: Lines 83-85: rephrase to read "Mice infected with T. gondii that were treated with CIP on the day of infection and the following day had 90% fewer parasites 5 days post-infection (Zhou et al., 2014).".

We have rephrased the sentence (Lines 83-85).

Comment 20: Line 90: shorten the sentence to end as follows "... of CIP on Babesia parasites.".

We have shortened the sentence in line 100 with your suggestion.

Comment 21: Line 96: spell out CC₅₀.

We have spelled out the full form of CC₅₀ (Line 106).

Comment 22: Line 104: remove "of body weight".

We have removed "of body weight" (Line 116).

Comment 23: Line 108: delete "from 8 DPI to 24 DPI, with statistically significant decreases".

We have deleted "from 8 DPI to 24 DPI, with statistically significant decreases" (Line 120).

Comment 24: Line 111: start a new paragraph with the sentence "BALB/c mice infected ...".

We have started a new paragraph with the sentence "BALB/c mice infected ..." (Line 124).

Comment 25: Line 123: replace "showed" with "occurred".

We have replaced "showed" with "occurred" (Line 138).

Comment 26: Line 127: rephrase to read "... sensitivity of the resistant parasite lines ...".

We have rephrased the sentence (Line 144).

Comment 27: Lines 137-140: rephrase to read ".... lines were lower when compared with ..." .

We have rephrased the sentence (Line 158).

Comment 28: Line 149: replace "BgATP4" with "B. gibsoni ATP4".

We have replaced "BgATP4" with "B. gibsoni ATP4" (Line 183).

Comment 29: Line 154: spell out "pLDDT" prior to pLDDT.

We have provided the full form of pLDDT in the revised manuscript (Line 188).

Comment 30: Lines 165-166: rephrase to read "CIP is a novel compound that inhibits Plasmodium development by targeting ATP4 and has been ...".

We have rephrased the sentence (Lines 219-220).

Comment 31: Lines 171-172: rephrase to read "...AZI, the combination recommended by the CDC in the United States.

We have rephrased the sentence (Lines 226-227).

Comment 32: Line 173: rephrase to read "... B. rodhaini infection, with survival up to 67%.".

We have rephrased the sentence (Line 228).

Comment 33: Lines 175-178: rephrase to read "In a previous study, a P. falciparum Dd2 strain that acquired resistance to CIP carried the G358S mutation in the ...".

We have rephrased the sentence (Lines 230-231).

Comment 34: Lines 179-180: rephrase to read "ATP4 is found in the parasite plasma membrane and is specific to the subclass of apicomplexan parasites.".

We have rephrased the sentence (Lines 232-233).

Comment 35: Lines 182-184: rephrase to read "In another study of Toxoplasma gondii, a cell line that carried the mutation G419S in the TgATP4 gene was 34 times ...".

We have rephrased the sentence (Lines 235-237).

Comment 36: Lines 201-202: deleted the last sentence of this paragraph.

We have deleted the last sentence of the paragraph (Line 261).

Comment 37: Line 228: rephrase to read "... that CIP had a weaker binding to BgATP4^L921I than to BgATP4^L921V.".

We have rephrased the sentence (Lines 294-295).

Comment 38: Lines 261-262: please state that drugs were prepared in sesame oil. Add "20 mg/kg" in front of AZI.

We have stated that drugs were prepared in sesame oil and added "20 mg/kg" in front of AZI (Lines 350-352).

Comment 39: Line 265: replace "care" with "treatments".

We have replaced "care" with "treatments" (Line 355).

Comment 40: Line 267: replace "observe" with "assess".

We have replaced "observe" with "assess" (Line 357).

Comment 41: Lines 269-271: please provide the absolute numbers of B. gibsoni infected RBCs and the absolute numbers of uninfected RBCs that were added to the culture medium.

We thank the reviewer for this suggestion. In the revised manuscript, we have included the absolute numbers of B. gibsoni-infected RBCs and uninfected RBCs added to the culture medium. Specifically, the culture medium contained 10 μL (5×10 ⁶) B. gibsoni iRBCs mixed with 40 μL (4×10 ⁸) uninfected RBCs (Lines 360-361).

Comment 42: Line 279: replace "confirmed" with "identified".

We have replaced "confirmed" with "identified" (Line 370).

Comment 43: Figure Supplement 2: the squares are not readily visible. Could the entire column corresponding to the mutation position be highlighted?

We thank the reviewer for this suggestion. To improve visibility, we have changed the color of the squares and added arrows to make the mutation sites as prominent as possible. Unfortunately, due to software limitations, we were unable to highlight the entire column corresponding to the mutation position.

Comment 44: Figure Supplement 4: for the parasite that carries a mutation in BgATP4, please delete the arrows that are next to BgATP4. These arrows send the message that the mutation ATP4 has an active role in pumping back Na⁺ and H⁺ back in their compartment, which is not the case.

We thank the reviewer for their observation. The dotted arrows next to BgATP4 are intended to indicate the recovery of H⁺ and Na⁺ balance facilitated by the mutated ATP4, which reduces susceptibility to ATP4 inhibitors. To avoid potential confusion, we have revised the figure legend to clearly explain the role of the arrows, ensuring the intended message is accurately conveyed.

Author response:

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

Reviewer 1:

Comment 1- I would like the authors to discuss and justify their use of high-dose (1.3%) isolfurane. A recent consensus paper on rat fMRI (Grandjean et al., "A Consensus Protocol for Functional Connectivity Analysis in the Rat Brain.") found that medetomidine combined with low dose isoflurane provided optimal control of physiology and fMRI signal. To overcome any doubts about the effects of the high-dose anaesthetic I'd encourage the authors to show the results of their functional connectivity specificity using the same or similar image processing protocol as described in that consensus paper. This is especially true since the fMRI ICs in Figure 2A appear fairly restricted.

We thank the reviewer for their insightful comments. We agree that the combination of medetomidine and isoflurane, as recommended by Grandjean et al. in their consensus paper, provides superior physiological stability and fMRI signal quality, and should indeed be considered the preferred protocol for future studies. In fact, we have adopted this combination in our subsequent research [1]. However, the data acquired in the present study were acquired prior to the publication of the consensus recommendations and have been previously published [2, 3]. While isoflurane is not the ideal anesthetic for functional connectivity studies, we have demonstrated in earlier work [4], that using isoflurane at 1.3% maintains stable physiological parameters and avoids burst suppression, a key issue with higher isoflurane doses.

Regarding preprocessing, we acknowledge the importance of standardized approaches as outlined in the consensus paper. However, to maintain methodological consistency with our prior work, we retained the original preprocessing pipeline for this study. This decision ensures comparability with our previous analyses. To address the reviewer’s concerns and encourage further verification, we have uploaded the full dataset to a public repository (as suggested in Comment 4). This will enable other researchers to reanalyze the data using updated preprocessing pipelines or explore additional analyses.

We have updated the manuscript discussion (page 19) to clearly acknowledge these points:

“One limitation of our study is that our experimental protocols predate the recently published consensus recommendations for rat fMRI [42], particularly concerning anesthesia and preprocessing pipelines. The use of isoflurane anesthesia, although common at the time of data acquisition, introduces a potential confound due to its known effects on neuronal activity. However, we previously demonstrated that isoflurane at 1.3% maintains stable physiological parameters and avoids burst suppression [43], a concern at higher doses. Furthermore, other studies have reported that low-dose isoflurane remains feasible for resting-state functional connectivity studies [44]. While isoflurane, as a GABA-A agonist, could theoretically interact with the mechanisms of MDMA in the brain, we found no evidence in the literature suggesting significant cross-talk between these substances. Future studies employing medetomidine-based protocols may help minimize this potential confound.

Regarding data preprocessing, we chose to retain the same pipeline used in our prior publications [13, 14] to maintain methodological consistency. While we recognize the advantages of adopting standardized preprocessing as outlined in the consensus guidelines, this approach ensures comparability with our previous analyses. To facilitate further investigation, we have made the full dataset publicly available (see Data Availability Statement), enabling reanalysis with updated pipelines or additional explorations of this dataset.”

Comment 2 - I'd also be interested to read more about why the cerebellum was chosen as a reference region, given that serotonin is highly expressed in the cerebellum, and what effects the choice of reference region has on their quantification.

This is something we ourselves have examined in a paper, dedicated to determine the most suitable reference region for [11C]DASB, and while the reviewer is correct in saying there is also serotonin in the cerebellum, we found the lowest binding for this tracer in the cerebellar gray matter, recommending this region as a valid reference area. (“Displaceable binding of (11)C-DASB was found in all brain regions of both rats and mice, with the highest binding being in the thalamus and the lowest in the cerebellum. In rats, displaceable binding was largely reduced in the cerebellar cortex”, please refer to [5]).

We amended our materials and methods part to specify that we had shown in this previous publication that the cerebellar gray matter is appropriate as a reference region (page 6):

“Binding potentials were calculated frame-wise for all dynamic PET scans using the DVR-1 (equation 1) to generate regional BPND values with the cerebellar gray matter as a reference region, which our earlier studies have demonstrated to be the most appropriate for this tracer in rats [5, 6]:”

Comment 3 - The PET ICs appear less bilateral than the fMRI ICs. Is that simply a thresholding artefact or is it a real signal?

We thank the reviewer for this observation. The reduced bilaterality of PET ICs compared to fMRI ICs is likely due to the inherent limitation in the temporal resolution of PET, which provides significantly fewer frames (100 frames compared to 3000 frames for fMRI). This lower temporal resolution leads to reduced signal-to-noise ratio when computing the ICA, which can affect the stability and symmetry of the ICs during ICA computation, particularly at higher IC numbers. While thresholding may also a minor role, we believe the primary factor is poorer SNR associated with the PET data. We have clarified this point in the discussion section (page 17) as follows:

“In our analysis, PET ICs appeared less bilateral than fMRI ICs. This is likely due to the lower temporal resolution of PET (100 frames) compared to fMRI (3000 frames), resulting in reduced signal-to-noise ratio (SNR) and potentially affecting the stability and symmetry of the independent components.”

Comment 4 - "The data will be made available upon reasonable request" is not sufficient - please deposit the data in an open repository and link to its location.

We agree with the request of the reviewer and uploaded the data to a Dryad repository. We amended our Data Availability Statement accordingly.

Comment 5 (recommendation) - Please add the age and sex of the rats in lines 92-97.

Amended.

Comment 6 (recommendation) - There are multiple typos throughout the manuscript - for example, "z-vlaue" on line 164, "negligable" on line 194, etc.. Sometimes the 11 in 11C is superscripted, sometimes it isn't. This paper would benefit from a careful proofread.

Thank you for pointing this out. We sent the manuscript for language and grammar editing to AJE (see certificate).

Reviewer 2:

Comment 1 - While the study protocol is referenced in the paper, it would be useful to at least report whether the study uses bolus, constant infusion, or a combination of the two and the duration of the frames chosen for reconstruction. Minimal details on anesthesia should also be reported, clarifying whether an interaction between the pharmacological agent for anesthesia and MDMA can be expected (whole-brain or in specific regions).

We fully agree that this would improve the readability of our manuscript and added the information to the materials and methods and discussion accordingly. Please refer to page 4/5.

Comment 2 - Some terminology is used in a bit unclear way. E.g. "seed-based" usually refers to seed-to-voxel and not ROI-to-ROI analysis, or e.g. it is a bit confusing to have IC1 called SERT network when in fact all ICs derived from DASB data are SERT networks. Perhaps a different wording could be used (IC1 = SERT xxxxx network; IC2= SERT salience network).

Based on the reviewer´s suggestion, we suggest to rename IC1 and IC2 according to their anatomical and functional characteristics (page 13):

“IC1 = SERT Salience Network: This name highlights the involvement of the regions typically associated with the salience network (e.g., CPu, Cg, NAc, Amyg, Ins, mPFC), which play key roles in emotional and cognitive processing.”

“IC2 = SERT Subcortical Network: This name reflects the involvement of subcortical regions which play a role in arousal, stress response, and autonomic regulation, which are heavily modulated by serotonin in areas like the hypothalamus, PAG, and thalamus.”

Comment 3 - The limited sample size for the rats undergoing pharmacological stimulation which might make the study (potentially) not particularly powerful. This could not be a problem if the MDMA effect observed is particularly consistent across rats. Information on inter-individual variability of FC, MC, and BPND could be provided in this regard.

We thank the reviewer for raising this point. To address the concern about limited sample size and inter-individual variability, we have added this information to Figures 5 B and D. Regarding the BPND variability, the dotted lines in Figure 3 indicate the standard deviation in the regional BPNDs, however, this was not clearly stated in the original figure description. We have now amended the figure legend to explicitly clarify this point.

Comment 4 (recommendation) - "Our research employs a novel approach named "molecular connectivity" (MC), which merges the strengths of various imaging methods to offer a comprehensive view of how molecules interact within the brain and affect its function." I'd recommend rephrasing to "..how molecular interact across different areas within the brain..". Molecular connectivity is a potentially ambiguous term (used to study interactions across different molecules (in the same compartment/environment) vs. to study interactions across the same molecules in different areas). I'd add a couple of references to help the reader disambiguate too (e.g. https://pubmed.ncbi.nlm.nih.gov/30544240/ , https://pubmed.ncbi.nlm.nih.gov/36621368/)

We appreciate the reviewer’s suggestion and agree that the term "Molecular Connectivity" could be ambiguous. To clarify, we rephrased the description to emphasize that our approach specifically examines interactions of the same molecule (i.e., serotonin transporter) across different brain regions, rather than interactions between different molecules within the same environment. We propose the following revised text (page 2):

“Our research employs a novel approach termed molecular connectivity (MC), which combines the strengths of various imaging methods to provide a comprehensive view of how specific molecules, such as the serotonin transporter, interact across different brain regions and influence brain function.”

Additionally, we will incorporate the suggested references to help the reader further contextualize the use of this term.

Comment 5 - In the methods, it is not clear if for MC the authors also compute ROI-to-ROI correlations or only ICA.

Thank you for highlighting this point. To clarify, our MC analysis, includes both ROI-to-ROI correlations and ICA. Specifically, as described at the end of the “Molecular Connectivity Analysis” subchapter, we compute ROI-to-ROI correlations using the following steps: 1. The first 20 minutes of each scan are discarded to account for perfusion effects. 2. A detrending approach is applied to the remaining 60 minutes of BP_ND time courses. 3. ROI-to-ROI calculations are then calculated and organized into subject-level correlation matrices, which are subsequently z-transformed to generate mean correlation matrices across subjects.

We revised the methods section to explicitly state that both ROI-to-ROI correlations and ICA are integral components of the MC analysis to ensure this point is clear to readers (page 6).

“The BP_ND time courses were then used to calculate MC as described above for fMRI: ROI-to-ROI subject-level correlation matrices between all regional time courses were generated and z-transformed correlation coefficients were used to calculate mean correlation matrices.”

Comment 7 - In the discussion, it could be useful to relate IC1 and IC2 to well-established neuroanatomical/molecular knowledge of the serotoninergic system. Did the authors expect the IC1 and IC2 anatomical distributions? is there a plausible biological reason as to why the time courses of BPnd variations would be somehow different between IC1 and IC2?

We appreciate the reviewer’s insightful comment and agree on the importance of relating IC1 and IC2 to well-established neuroanatomical and molecular knowledge of the serotonergic system.

In our discussion, we noted that IC1 primarily encompasses subcortical structures such as the brainstem, midbrain, and thalamus. These regions are consistent with areas housing dense serotonergic projections originating from the raphe nuclei, the primary source of serotonin release. In contrast, IC2 involves limbic and cortical regions - including the striatum, amygdala, cingulate, insular, and prefrontal cortices - which are key targets of the serotonergic pathways. This anatomical distinction aligns with the hierarchical organization of the serotonergic system, where the brainstem nuclei exert both local and distal serotonergic modulation.

The observed differences in the temporal dynamics of the binding potential (BP_ND) variations between IC1 and IC2 likely reflect the distinct functional roles of these regions within the serotonergic network. The more immediate changes in IC1 could be attributed to the direct effect of MDMA on the raphe nuclei, leading to rapid serotonin release in subcortical structures. In contrast, the delayed changes in IC2 may reflect downstream modulation in cortical and limbic regions involved in processing more complex emotional and cognitive functions.

That said, while these interpretations are plausible based on current neuroanatomical and functional knowledge, the exact biological mechanisms underlying the differential time courses remain unclear. As discussed in the manuscript, future studies incorporating direct, simultaneous measurements of serotonin levels and imaging data will be essential to fully elucidate the temporal and spatial dynamics of serotonin transmission in these regions. We have revised to better highlight this limitation in the discussion section (page 17) as an important area for further investigation:

“Our results demonstrate that compared with FC, MDMA induces more pronounced changes in MCs, particularly in regions associated with the SERT subcortical network. The distinct temporal dynamics of BPnd variations between these components may reflect the hierarchical organization of the serotonergic system. Specifically, the raphe nuclei, as the primary source of serotonin, are likely to exert more immediate modulation on posterior subcortical structures (IC2), whereas downstream effects on limbic and cortical regions (IC1) may occur more gradually. While these findings align with current neuroanatomical and molecular knowledge, the precise biological mechanisms driving these temporal differences remain unclear. Future investigations are warranted to elucidate these mechanisms. Future studies combining direct measurements of serotonin levels with neuroimaging data will be critical to fully understanding these components’ distinct roles and temporal profiles in regulating serotonergic function.”

Comment 8 - In the discussion (physiological basis), could the authors detail the expected "time scale" in changes in SERT expression? How quickly can SERT expression change, especially under resting-state conditions? Is it reasonable to consider tracer fluctuations under rest conditions as biologically meaningful?

SERT regulation can occur over different time scales depending on the mechanism involved [7].

Acute, rapid changes (milliseconds to seconds): Protein-protein interactions with key regulatory proteins (e.g., syntaxin1A, neuronal nitric oxide synthase) can lead to rapid modulation of SERT surface expression [8-11]. These interactions often involve changes in transporter trafficking or conformational states and can occur within milliseconds to seconds. For example, syntaxin1A directly interacts with the N-terminus of SERT, influencing its availability on the plasma membrane within short timescales.

Intermediate time scales (seconds to minutes): Posttranslational modifications, such as phosphorylation by kinases (e.g., protein kinase C) or dephosphorylation by phosphatases, are known to influence SERT function and surface expression [12-14]. These processes are typically initiated in response to cellular signaling and occur over seconds to minutes, affecting the SERT trafficking dynamics and serotonin uptake capacity [15, 16].

Longer-term changes (minutes to hours): Longer-term regulation involves processes like endocytosis, recycling, or degradation of SERT. These pathways typically take minutes to hours and are often part of more sustained cellular responses to changes in neuronal activity or serotonin levels. Such changes are slower but contribute to the overall cellular homeostasis of SERT under prolonged stimulation.

Under resting-state conditions, where neurons are not subjected to rapid or dramatic fluctuations in neurotransmitter release or signaling, SERT expression and activity are generally stable but still subject to subtle fluctuations due to ongoing basal regulatory processes. Basal phosphorylation or low-level protein-protein interactions can still dynamically modulate SERT trafficking and function, albeit at a lower intensity than under stimulated conditions. These fluctuations, although smaller in magnitude, may reflect fine-tuning of serotonin homeostasis and can occur on shorter timescales (seconds to minutes).

Biological Relevance of Tracer Fluctuations at Rest:

It is reasonable to consider that tracer fluctuations under resting conditions could reflect biologically meaningful variations in SERT expression and function. Even subtle shifts in SERT surface availability or activity can impact serotonin clearance and signaling, given the fine balance required to maintain serotonergic tone. These fluctuations may reflect intrinsic neuronal variability or ongoing homeostatic adjustments to maintain optimal neurotransmitter levels or serve as early indicators of adaptive responses to environmental or physiological changes before more overt modifications in transporter expression or activity become apparent.

In summary, while SERT expression can change rapidly in response to signaling events (milliseconds to minutes), even under resting-state conditions, subtle regulatory fluctuations can be biologically meaningful. These fluctuations likely reflect ongoing regulatory adjustments essential for maintaining serotonergic balance and should not be disregarded as noise, particularly in experimental measurements using tracers.

We added the following paragraph to the discussion (page 16):

“In addition, SERT regulation occurs over multiple time scales, ranging from milliseconds to hours, depending on the mechanism involved [31]. Rapid changes in SERT surface expression can be mediated by protein-protein interactions or posttranslational modifications [32, 33], such as phosphorylation, which occur on a timescale of milliseconds to minutes. These processes dynamically modulate surface availability and function, allowing fine-tuned regulation of serotonin uptake even under resting-state conditions. Additionally, while slower processes involving endocytosis, recycling, and degradation typically occur over minutes to hours, subtle fluctuations in SERT trafficking and activity can still occur under basal conditions. These minor yet biologically relevant changes likely reflect ongoing homeostatic regulation essential for maintaining serotonergic balance. Therefore, tracer fluctuations observed during resting-state measurements should not be dismissed, as they may represent meaningful variations in SERT regulation that contribute to the fine control of serotonin clearance.”

Comment 9 - In the discussion, the SERT network results should be commented on more extensively, as there is now only a generic reference to MC changes being stronger than FC ones, without spatial reference to the SERT network (while only negative salience network results are referenced explicitly instead, making the paragraph a bit confusing).

We expanded the discussion to accommodate a more thorough contemplation of this network. This revised paragraph (page 17) directly addresses the spatial aspects of the SERT network, highlighting the specific regions involved in serotonergic connectivity and contrasting molecular and functional connectivity changes induced by MDMA.

Comment 10 - Figure 3; I'd switch left and right charts in the bottom panel (last row only), to keep the SERT network always on the left of the Figure.

We agree with the suggestion and changed the figure accordingly.

Comment 11 - Figure 4: I'd add FC decreases to the figure, to allow the reader to compare BPnd, MC, and FC changes more easily and I'd add a horizontal line at the equivalent of e.g. Z-1.96 (or similar) so that it is clear which measures/regions display significant changes.

We prefer to keep the figure focusing on the two analyses of PET alterations, since we want to emphasize their complementarity in the context of PET specifically. However, we added lines indicating significances, in line with the reviewer’s suggestion.

Comment 12 - In Figure 5D, the y-axis mentioned FC but I suppose it should mention MC.

We amended the figure accordingly, together with the changes to the names of the networks implemented across the manuscript.

(1) Marciano, S., et al., Combining CRISPR-Cas9 and brain imaging to study the link from genes to molecules to networks. Proc Natl Acad Sci U S A, 2022. 119(40): p. e2122552119.

(2) Ionescu, T.M., et al., Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity. Neuroimage, 2021. 243: p. 118501.

(3) Ionescu, T.M., et al., Neurovascular Uncoupling: Multimodal Imaging Delineates the Acute Effects of 3,4-Methylenedioxymethamphetamine. J Nucl Med, 2023. 64(3): p. 466-471.

(4) Ionescu, T.M., et al., Elucidating the complementarity of resting-state networks derived from dynamic [(18)F]FDG and hemodynamic fluctuations using simultaneous small-animal PET/MRI. Neuroimage, 2021. 236: p. 118045.

(5) Walker, M., et al., In Vivo Evaluation of 11C-DASB for Quantitative SERT Imaging in Rats and Mice. J Nucl Med, 2016. 57(1): p. 115-21.

(6) Walker, M., et al., Imaging SERT Availability in a Rat Model of L-DOPA-Induced Dyskinesia. Mol Imaging Biol, 2020. 22(3): p. 634-642.

(7) Lau, T. and P. Schloss, Differential regulation of serotonin transporter cell surface expression. Wiley Interdisciplinary Reviews: Membrane Transport and Signaling, 2012. 1(3): p. 259-268.

(8) Haase, J., et al., Regulation of the serotonin transporter by interacting proteins. Biochem Soc Trans, 2001. 29(Pt 6): p. 722-8.

(9) Quick, M.W., Regulating the conducting states of a mammalian serotonin transporter. Neuron, 2003. 40(3): p. 537-49.

(10) Ciccone, M.A., et al., Calcium/calmodulin-dependent kinase II regulates the interaction between the serotonin transporter and syntaxin 1A. Neuropharmacology, 2008. 55(5): p. 763-70.

(11) Chanrion, B., et al., Physical interaction between the serotonin transporter and neuronal nitric oxide synthase underlies reciprocal modulation of their activity. Proc Natl Acad Sci U S A, 2007. 104(19): p. 8119-24.

(12) Qian, Y., et al., Protein kinase C activation regulates human serotonin transporters in HEK-293 cells via altered cell surface expression. J Neurosci, 1997. 17(1): p. 45-57.

(13) Ramamoorthy, S., et al., Phosphorylation and regulation of antidepressant-sensitive serotonin transporters. J Biol Chem, 1998. 273(4): p. 2458-66.

(14) Jayanthi, L.D., et al., Evidence for biphasic effects of protein kinase C on serotonin transporter function, endocytosis, and phosphorylation. Mol Pharmacol, 2005. 67(6): p. 2077-87.

(15) Steiner, J.A., A.M. Carneiro, and R.D. Blakely, Going with the flow: trafficking-dependent and -independent regulation of serotonin transport. Traffic, 2008. 9(9): p. 1393-402.

(16) Lau, T., et al., Monitoring mouse serotonin transporter internalization in stem cell-derived serotonergic neurons by confocal laser scanning microscopy. Neurochem Int, 2009. 54(3-4): p. 271-6.

Author response:

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

Alternate explanations for major conclusions.

The major conclusions are (a) surface motility of W3110 requires pili which is not novel, (b) pili synthesis and pili-dependent surface motility require putrescine — 1 mM is optimal, and 4 mM is inhibitory, and (c) the existence of a putrescine homeostatic network that maintains intracellular putrescine that involves compensatory mechanisms for low putrescine, including diversion of energy generation toward putrescine synthesis.

Conclusion a: Reviewer 3 suggests that the mutant may have lost surface motility because of outer surface structures that actually mediate motility but are co-regulated with or depend on pili synthesis. The reviewer explicitly suggests flagella as the alternate appendage, although flagella and pili are reciprocally regulated. Most experiments were performed in a Δ_fliC_ background, which lacks the major flagella subunit, in order to prevent the generation of fast-moving flagella-dependent variants. Furthermore, no other surface structure that could mediate surface motility is apparent in the electron microscope images. This observation does not definitively rule out this possibility, especially because of the large transcriptomic changes with low putrescine. Our explanation is the simplest.

Conclusion b, first comment: Reviewer 1 states that “it is not possible to conclude that the effects of gene deletions to biosynthetic, transport or catabolic genes on pili-dependent surface motility are due to changes in putrescine levels unless one takes it on faith that there must be changes to putrescine levels.” The comment ignores both the nutritional supplementation and the transcript changes that strongly suggest compensatory mechanisms for low putrescine. Why compensate if the putrescine concentration does not change? The reviewer then implicitly acknowledges changes in putrescine content: “it is important to know how much putrescine must be depleted in order to exert a physiological effect”.

Conclusion b, second comment: Reviewer 1 proposes that agmatine accumulation can account for some of the observed properties, but which property is not specified. With respect to motility, agmatine accumulation cannot account for motility defects because motility is impaired in (a) a speA mutant which cannot make agmatine and (b) a speC speF double mutant which should not accumulate agmatine. With respect to the transcriptomic results, even if high agmatine is the reason for some transcript changes, the results still suggest a putrescine homeostasis network.

Conclusion c: the reviewers made no comments on the RNAseq analysis or the interpretation of the existence of a homeostatic network.

Additional experiments proposed.

Complementation. Reviewers 1 and 3 suggested complementation experiments, but the latter states that nutritional supplementation strengthens our arguments. The most relevant complementation is with speB.  We tried complementation and found that our control plasmid inhibited motility by increasing the lag time before movement commenced. A plasmid with speB did stimulate motility relative to the control plasmid, but movement with the speB plasmid took 4 days, while wild-type movement took 1.5 days. We think that interpretation of this result is ambiguous. We did not systematically search for plasmids that had no effect on motility.

The purpose of complementation is to determine whether a second-site mutation is the actual cause of the motility defect. In this case, the artifact is that an alteration in polyamine metabolism is not the cause of the defect. However, external putrescine reverses the effects on motility and pili synthesis in the speB mutant. This result is inconsistent with a second-site mutation. Still, we agree that complementation is important, and because of our difficulties, we tested numerous mutants with defects in polyamine metabolism. The results present an interpretable and coherent pattern. For example, if putrescine is not the regulator, then mutants in putrescine transport and catabolism should have had no effect. Every single mutant is consistent with a role in movement and pili synthesis. The simplest explanation is that putrescine affects movement and pili synthesis.

Phase variation. Reviewer 2 noted that we did not discuss phase variation. The comment came from the observation that the speB mutant had fewer fimB transcripts which could explain the loss of motility. The reviewer also suggested a simple experiment, which we performed and found that putrescine does not control phase variation. We present those results in the supplemental material. Our discussion of this topic includes a major qualification.

Testing of additional strains. Published results from another lab showed that surface motility of MG1655 requires spermidine instead of putrescine (PMID 19493013 and 21266585). MG1655 and the W3110 that we used in our study are E. coli K-12 derivatives and phylogenetic group A. Any number of changes in enzymes that affect intracellular putrescine concentration could result in different responses to putrescine. We are currently studying pili synthesis and motility in other strains. While that study is incomplete, loss of speB in a strain of phylogenetic group D eliminates no surface motility. This work was intended as our initial analysis and the focus was on a single strain.

Measuring intracellular polyamines. We felt that we had provided sufficient evidence to conclude that putrescine controls pili synthesis and putrescine concentrations are lower in the speB mutant: the nutritional supplementation, the lower levels of transcripts for putrescine catabolic enzymes which require putrescine for their expression strongly suggest lower putrescine in a mutant lacking a putrescine biosynthesis gene, and a transcriptomic analysis that found the speB mutant had transcript changes to compensate for low putrescine. We understand the importance of measuring intracellular polyamines. We are currently examining the quantitative relationship between intracellular polyamines and pili synthesis in multiple strains which respond differently to loss of speB.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

The authors should measure putrescine, agmatine, cadaverine, and spermidine levels in their gene deletion strains.

Polyamine concentration measurements will be part of a separate study on polyamine control of pili synthesis of a uropathogenic strain. A comparison is essential, and the results from W3110 will be part of that study.

Reviewer #2 (Recommendations for the authors):

(1) Line 28. Your statements about urinary tract infections are pure speculation. They are fine for the discussion, but should not be in the abstract.

The abstract from line 27 on has been reworked. The comment of the reviewer is fair.

(2) Line 65. Do we need this discussion about the various strains? If you keep it, you should point out that they were all W3110 strains. But you could just say that you confirmed that your background strain can do PDSM (since you are also not showing any data for the other isolates). Discussing the various strains implies that you are not confident in your strain and raises the question of why you didn't use a sequenced wt MG1655, or something like that.

This section has been reworked. Our strain of W3110 has an insertion in fimB which is relevant for movement but does not affect our results. The insertion limits our conclusions about phase variation. We want to point out that strains variations are large. We also sequenced our strain of W3110.

(3) Related. You occasionally use "W3110-LR" to designate the wild type. You use this or not, but be consistent throughout the text.

Fixed

(4) Line 99. Does eLife allow "data not shown"?  

(5) Line 119. As you note, the phenotype of the puuA patA double mutant is exactly the opposite of what one would expect. Although you provide additional evidence that high levels also inhibit motility, complementing the double mutant would provide confidence that the strain is correct.

We rapidly ran into issues with complementation which are discussed in public responses to reviewer comments.

(6) Figure 6C. Either you need to quantify these data or you need a better picture.

The files were corrupted. It was repeated several time, but we lost the other data.

(7) Figure 7. Label panels A and B to indicate that these strains are speB. Also, you need to switch panels C and D to match the order of discussion in the manuscript.

Done

(8) Line 134. Is there a statistically significant difference in the ELISA between 1 and 4 mM? You need to say one way or the other.

No statistical significance and this has been added to the paper

(9) Figure 10C. You need to quantify these data.

Quantification added as an extra panel.

(10) Line 164. You include H-NS in the group of "positive effectors that control fim operon expression" and you reference Ecocyc, rather than any primary reference. Nowhere in the manuscript do you mention phase variation. In the speB mutant, you see decreased fimB, increased fimE, and decreased hns expression. My interpretation of the literature suggests that this would drive the fim switch to the off-state. This could certainly explain some of the results. It is also easily measurable with PCR. This might require testing cells scraped directly from the plates.

The experiments were performed. There is no need to scrap cells from plates because the fimB result from RNAseq was from a liquid culture, and the prediction would be that the phase-locking should be evident in these cells.

(11) Figure 10. Likewise, do you know that your hns mutant is not locked in the off-state? Granted, the original hns mutants (pilG) showed increased rates of switching, but growth conditions might matter.

We also did phase variation for the hns mutant and the hns mutant was not phase locked. This result is shown. In addition to growth conditions, the strain probably matters.

(12) Line 342. You describe the total genome sequencing of W3110, yet this is not mentioned anywhere else in the manuscript.

It is now

Minor points:

(13) Line 192. "One of the most differentially expressed genes...".

(14) Line 202. "...implicates extracellular putrescine in putrescine homeostasis."

(15) Line 209. "...potential pili regulators...".

(16) You are using a variety of fonts on the figures. Pick one.

(17) Figure 9A. It took me a few minutes to figure out the labeling for this figure and I was more confused after reading the legend. It would be simpler to independently label red triangles, blue triangles, red circles, and blue circles.

(18) Figure 9B and 10. The reader can likely figure out what W3110_1.0_3 means, but more straightforward labeling would be better, or you need to define these labels.

All points were addressed and fixed.

Reviewer #3 (Recommendations for the authors):

Other comments:

(1) Please go through the figures and the reference to figures in the text, as they often do not refer to the right panel (ex: figures 2 and 7 for instance). In the text, please homogenize the reference to figures (Figure 2C vs Figure 3). To help compare motility experiments between figures, please use the same scale in all figures.

This has been fixed.

(2) Lines 65-70: I am not sure I get the reason behind choosing the W3110 strain from your lab stock. In what background were the initial mutants constructed (from l.64-65)? Were the nine strains tested, all variations of W3110? If so, is the phenotype described in the manuscript robust in all strains?

We have provided more explanation. W3110 was the most stable: insertions that allowed flagella synthesis in the presence of glucose were frequent. We deleted the major flagella subunit for most experiments. Before introduction of the fliC deletion, we needed to perform experiments 10 times so that fast-moving variants, which had mutationally altered flagella synthesis, did not complicate results.

(3) Line 82-84: As stated in the public review, I think more controls are needed before making this conclusion, especially as type I fimbriae are usually involved in sessile phenotypes.

Response provided in the public response.

(4) In Figure 3: Changing the order of the image to follow the text would make the figure easier to follow.

Fixed as requested

(5) Lines 100-101: simultaneous - the results presented here do not support this conclusion. In Figure 4b, the addition of putrescine to speB mutants is actually not different from WT. From the results, it seems like one of biosynthesis or transport is needed, but it's not clear if both are needed simultaneously. For this, a mutant with no biosynthesis and no transport is needed and/or completely non-motile mutants would be needed to compare.

We disagree. If there are two pathways of putrescine synthesis and both are needed, then our conclusion follows.

(6) Lines 104-105: '... because E. coli secretes putrescine.' - not sure why this statement is there, as most transporters tested after are importers of putrescine? It is also not clear to me if putrescine is supplemented in the media in these experiments. If not, is there putrescine in the GT media?

Good points, and this section has been reworded to clarify these issues. Some of the material was moved to the discussion.

(7) Line 109: 'We note that potE and plaP are more highly expressed than potE and puuP...' - first potE should be potF?

This has been corrected.

(8) Figure 8: What is the difference between the TEM images in Figure 1 and here? The WT in Figure 1 does show pili without the supplementation unless I'm missing something here. Please specify.

The reviewer means Figure 2 and not Figure 1. Figure 2 shows a wild-type strain which has both putrescine anabolic pathways while Figure 8 is the ΔspeB strain which lacks one pathway.

(9) Line160-162: Transcripts for the putrescine-responsive puuAP and puuDRCBE operons, which specify genes of the major putrescine catabolic pathway, were reduced from 1.6- to 14- fold (FDR {less than or equal to} 0.02) in the speB mutant (Supplemental Table 1), which implies lower intracellular putrescine. I might not get exactly the point here. If the catabolic pathways are repressed in the speB mutant, then there will be less degradation which means more putrescine!?

Expression of these genes is a function of intracellular putrescine: higher expression means more putrescine. Any discussion of steady putrescine must include the anabolic pathways: the catabolic pathways do not determine the intracellular putrescine, they are a reflection of intracellular putrescine.

(10) Lines 162-163: Deletion of speB reduced transcripts for genes of the fimA operon and fimE, but not of fimB. It seems that the results suggest the opposite a reduction of fimB but not fimE!?

The reviewer is correct, and it is our mistake, and the text now states what is in the figure..

Author response:

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

Reviewer #1 (Public review):

This manuscript presents an interesting exploration of the potential activation mechanisms of DLK following axonal injury. While the experiments are beautifully conducted and the data are solid, I feel that there is insufficient evidence to fully support the conclusions made by the authors.

In this manuscript, the authors exclusively use the puc-lacZ reporter to determine the activation of DLK. This reporter has been shown to be induced when DLK is activated.

However, there is insufficient evidence to confirm that the absence of reporter activation necessarily indicates that DLK is inactive. As with many MAP kinase pathways, the DLK pathway can be locally or globally activated in neurons, and the level of DLK activation may depend on the strength of the stimulation. This reporter might only reflect strong DLK activation and may not be turned on if DLK is weakly activated. The results presented in this manuscript support this interpretation. Strong stimulation, such as axotomy of all synaptic branches, caused robust DLK activation, as indicated by puc-lacZ expression. In contrast, weak stimulation, such as axotomy of some synaptic branches, resulted in weaker DLK activation, which did not induce the puc-lacZ reporter. This suggests that the strength of DLK activation depends on the severity of the injury rather than the presence of intact synapses. Given that this is a central conclusion of the study, it may be worthwhile to confirm this further. Alternatively, the authors may consider refining their conclusion to better align with the evidence presented.

In Figure 1E we have replotted the puc-lacZ data to show comparisons between different injuries that leave different numbers of spared (or lost) boutons and branches.  We observed no differences between injuries that remove only a small fraction of boutons (injury location (a)) and injuries that remove nearly all of them (injury locations (b) and (c)) and uninjured neurons (Figure 1E). These observations argue against the interpretation that the strength of DLK activation (at least within the cell body) depends on the severity of injury. Rather, puc-lacZ induction appears to be bimodal. It is either induced (in various injuries that remove all synaptic boutons), or not induced, including in injuries that spared only a small fraction of the total boutons. We therefore think that the presence of a remaining synaptic connection rather than the extent of the injury per se is a major determinant of whether the cell body component of Wnd signaling can be activated. 

The reviewer (and others) fairly point out that our current study focuses on puc-lacZ as a reporter of Wnd signaling in the cell body. We consider this to be a downstream integration of events in axons that are more challenging to detect. It is striking that this integration appears strongly sensitized to the presence of spared synaptic boutons. Examination of Wnd’s activation in axons and synapses is a goal for our future work.

As noted by the authors, DLK has been implicated in both axon regeneration and degeneration. Following axotomy, DLK activation can lead to the degeneration of distal axons, where synapses are located. This raises an important question: how is DLK activated in distal axons? The authors might consider discussing the significance of this "synapse connection-dependent" DLK activation in the broader context of DLK function and activation mechanisms.

While it has been noted that inhibition of DLK can mildly delay Wallerian degeneration (Miller et al., 2009), this does not appear to be the case for retinal ganglion cell axons following optic nerve crush (Fernandes et al., 2014). It is also not the case for Drosophila motoneurons and NMJ terminals following peripheral nerve injury (Xiong et al., 2012; Xiong and Collins, 2012). Instead, overexpression of Wnd or activation of Wnd by a conditioning injury leads to an opposite phenotype - an increase in resiliency to Wallerian degeneration for axons that have been previously injured (Xiong et al., 2012; Xiong and Collins, 2012). The downstream outcome of Wnd activation is highly dependent on the context; it may be an integration of the outcomes of local Wnd/DLK activation in axons with downstream consequences of nuclear/cell body signaling.  The current study suggests some rules for the cell body signaling, however, how Wnd is regulated at synapses and why it promotes degeneration in some circumstances but not others are important future questions.

For the reviewer’s suggestion, it is interesting to consider DLK’s potential contributions to the loss of NMJ synapses in a mouse model of ALS (Le Pichon et al., 2017; Wlaschin et al., 2023). Our findings suggest that the synaptic terminal is an important locus of DLK regulation, while dysfunction of NMJ terminals is an important feature of the ‘dying back’ hypothesis of disease etiology (Dadon-Nachum et al., 2011; Verma et al., 2022). We propose that the regulation of DLK at synaptic terminals is an important area for future study, and may reveal how DLK might be modulated to curtail disease progression. Of note, DLK inhibitors are in clinical trials (Katz et al., 2022; Le et al., 2023; Siu et al., 2018), but at least some have been paused due to safety concerns (Katz et al., 2022). Further understanding of the mechanisms that regulate DLK are needed to understand whether and how DLK and its downstream signaling can be tuned for therapeutic benefit.

Reviewer #2 (Public review):

Summary:

The authors study a panel of sparsely labeled neuronal lines in Drosophila that each form multiple synapses. Critically, each axonal branch can be injured without affecting the others, allowing the authors to differentiate between injuries that affect all axonal branches versus those that do not, creating spared branches. Axonal injuries are known to cause Wnd (mammalian DLK)-dependent retrograde signals to the cell body, culminating in a transcriptional response. This work identifies a fascinating new phenomenon that this injury response is not all-or-none. If even a single branch remains uninjured, the injury signal is not activated in the cell body. The authors rule out that this could be due to changes in the abundance of Wnd (perhaps if incrementally activated at each injured branch) by Wnd, Hiw's known negative regulator. Thus there is both a yet-undiscovered mechanism to regulate Wnd signaling, and more broadly a mechanism by which the neuron can integrate the degree of injury it has sustained. It will now be important to tease apart the mechanism(s) of this fascinating phenomenon. But even absent a clear mechanism, this is a new biology that will inform the interpretation of injury signaling studies across species.

Strengths:

(1) A conceptually beautiful series of experiments that reveal a fascinating new phenomenon is described, with clear implications (as the authors discuss in their Discussion) for injury signaling in mammals.

(2) Suggests a new mode of Wnd regulation, independent of Hiw.

Weaknesses:

(1) The use of a somatic transcriptional reporter for Wnd activity is powerful, however, the reporter indicates whether the transcriptional response was activated, not whether the injury signal was received. It remains possible that Wnd is still activated in the case of a spared branch, but that this activation is either local within the axons (impossible to determine in the absence of a local reporter) or that the retrograde signal was indeed generated but it was somehow insufficient to activate transcription when it entered the cell body. This is more of a mechanistic detail and should not detract from the overall importance of the study

We agree. The puc-lacZ reporter tells us about signaling in the cell body, but whether and how Wnd is regulated in axons and synaptic branches, which we think occurs upstream of the cell body response, remains to be addressed in future studies.

(2) That the protective effect of a spared branch is independent of Hiw, the known negative regulator of Wnd, is fascinating. But this leaves open a key question: what is the signal?

This is indeed an important future question, and would still be a question even if Hiw were part of the protective mechanism by the spared synaptic branch. Our current hypothesis (outlined in Figure 4) is that regulation of Wnd is tied to the retrograde trafficking of a signaling organelle in axons. The Hiw-independent regulation complements other observations in the literature that multiple pathways regulate Wnd/DLK (Collins et al., 2006; Feoktistov and Herman, 2016; Klinedinst et al., 2013; Li et al., 2017; Russo and DiAntonio, 2019; Valakh et al., 2013). It is logical for this critical stress response pathway to have multiple modes of regulation that may act in parallel to tune and restrain its activation. 

Reviewer #3 (Public review):

Summary:

This manuscript seeks to understand how nerve injury-induced signaling to the nucleus is influenced, and it establishes a new location where these principles can be studied. By identifying and mapping specific bifurcated neuronal innervations in the Drosophila larvae, and using laser axotomy to localize the injury, the authors find that sparing a branch of a complex muscular innervation is enough to impair Wallenda-puc (analogous to DLK-JNKcJun) signaling that is known to promote regeneration. It is only when all connections to the target are disconnected that cJun-transcriptional activation occurs.

Overall, this is a thorough and well-performed investigation of the mechanism of sparedbranch influence on axon injury signaling. The findings on control of wnd are important because this is a very widely used injury signaling pathway across species and injury models. The authors present detailed and carefully executed experiments to support their conclusions. Their effort to identify the control mechanism is admirable and will be of aid to the field as they continue to try to understand how to promote better regeneration of axons.

Strengths:

The paper does a very comprehensive job of investigating this phenomenon at multiple locations and through both pinpoint laser injury as well as larger crush models. They identify a non-hiw based restraint mechanism of the wnd-puc signaling axis that presumably originates from the spared terminal. They also present a large list of tests they performed to identify the actual restraint mechanism from the spared branch, which has ruled out many of the most likely explanations. This is an extremely important set of information to report, to guide future investigators in this and other model organisms on mechanisms by which regeneration signaling is controlled (or not).

Weaknesses:

The weakest data presented by this manuscript is the study of the actual amounts of Wallenda protein in the axon. The authors argue that increased Wnd protein is being anterogradely delivered from the soma, but no support for this is given. Whether this change is due to transcription/translation, protein stability, transport, or other means is not investigated in this work. However, because this point is not central to the arguments in the paper, it is only a minor critique.

We agree and are glad that the reviewer considers this a minor critique; this is an area for future study. In Supplemental Figure 1 we present differences in the levels of an ectopically expressed GFP-Wnd-kinase-dead transgene, which is strikingly increased in axons that have received a full but not partial axotomy. We suspect this accumulation occurs downstream of the cell body response because of the timing. We observed the accumulations after 24 hours (Figure S1F) but not at early (1-4 hour) time points following axotomy (data not shown). Further study of the local regulation of Wnd protein and its kinase activity in axons is an important future direction.

As far as the scope of impact: because the conclusions of the paper are focused on a single (albeit well-validated) reporter in different types of motor neurons, it is hard to determine whether the mechanism of spared branch inhibition of regeneration requires wnd-puc (DLK/cJun) signaling in all contexts (for example, sensory axons or interneurons). Is the nerve-muscle connection the rule or the exception in terms of regeneration program activation?

DLK signaling is strongly activated in DRG sensory neurons following peripheral nerve injury (Shin et al., 2012), despite the fact that sensory neurons have bifurcated axons and their projections in the dorsal spinal cord are not directly damaged by injuries to the peripheral nerve. Therefore it is unlikely that protection by a spared synapse is a universal rule for all neuron types. However the molecular mechanisms that underlie this regulation may indeed be shared across different types of neurons but utilized in different ways. For instance, nerve growth factor withdrawal can lead to activation of DLK (Ghosh et al., 2011), however neurotrophins and their receptors are regulated and implemented differently in different cell types. We suspect that the restraint of Wnd signaling by the spared synaptic branch shares a common underlying mechanism with the restraint of DLK signaling by neurotrophin signaling. Further elucidation of the molecular mechanism is an important next step towards addressing this question. 

Because changes in puc-lacZ intensity are the major readout, it would be helpful to better explain the significance of the amount of puc-lacZ in the nucleus with respect to the activation of regeneration. Is it known that scaling up the amount of puc-lacZ transcription scales functional responses (regeneration or others)? The alternative would be that only a small amount of puc-lacZ is sufficient to efficiently induce relevant pathways (threshold response).

While induction of puc-lacZ expression correlates with Wnd-mediated phenotypes, including sprouting of injured axons (Xiong et al., 2010), protection from Wallerian degeneration (Xiong et al., 2012; Xiong and Collins, 2012) and synaptic overgrowth (Collins et al., 2006), we have not observed any correlation between the degree of puc-lacZ induction (eg modest, medium or high) and the phenotypic outcomes (sprouting, overgrowth, etc). Rather, there appears to be a striking all-or-none difference in whether puc-lacZ is induced or not induced. There may indeed be a threshold that can be restrained through multiple mechanisms. We posit in figure 4 that restraint may take place in the cell body, where it can be influenced by the spared bifurcation. 

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

This is a beautiful study. Naturally, you're searching now for the underlying mechanism.

A few questions:

(1) At present you can not determine if the Wnd signal is never initiated (when a spared branch is present) or if it gets to the cell body but is incapable of activating the puckered reporter. Is there any optical reporter (JNK activation?) that could differentiate this?

The reviewer is correct that a tool to detect local activity of JNK kinase in axons would be ideal for probing the mechanisms that underlie our observations. A FRET reporter for JNK kinase activity has been developed and utilized in cultured cells (Fosbrink et al. 2010). It would be interesting to implement this reporter in Drosophila; it would need to be sensitive enough to visualize  in single Drosophila axons. We have previously noted Wnd-dependent phosphorylated JNK in the cell body of injured motoneurons following nerve crush (Xiong et al., 2010). However anti-pJNK antibodies detect what appears to be a constitutive signal in uninjured axons that does not appear to be influenced by activation or inhibition of Wnd (Xiong et al., 2010).

(2) What happens when you injure the axon in a dSarm KO? This is more of a curiosity, not a necessity, but is it the axon dying or the detection of the injury itself?

We have tested whether overexpression of Nmnat or the WldS transgene, which inhibit Wallerian degeneration of injured axons, affect the induction of puc-lacZ following nerve injury. This manipulation has no effect on puc-lacZ expression in uninjured animals, and also has no effect on the induction of puc-lacZ following peripheral nerve crush (TJ Waller, personal communication).

(3) Are Wnd rescue experiments possible in this context? Would be an interesting place to do Wnd structure-function and compare it to the synaptic work.

This is not possible with current reagents. Expression of wild type wnd cDNA under the Gal4/UAS promoter leads to strong induction of puc-lacZ in uninjured animals, even when weak Gal4 driver lines are used (Xiong et al., 2012, 2010). Similar observations of constitutively active signaling have been observed for expression studies of DLK in mammalian cells ((Hao et al., 2016; Huntwork-Rodriguez et al., 2013; Nihalani et al., 2000), and data not shown). These and other observations suggest that the levels of Wnd/DLK protein are tightly controlled by posttranscriptional mechanisms. Delineation of sequences within Wnd/DLK that are required for its regulation would be helpful for addressing this question.

This will be required reading in my lab.

That is an honor. We look forward to help from the field to understand how and why this pathway is restrained at synapses. Your students may bring new ideas to the table.

Reviewer #3 (Recommendations for the authors):

Piezo is spelled incorrectly in the supplemental table in multiple places.

Thank you for pointing this out! We have made the correction.

References cited (in rebuttal)

Collins CA, Wairkar YP, Johnson SL, DiAntonio A. 2006. Highwire restrains synaptic growth by attenuating a MAP kinase signal. Neuron 51:57–69.

Dadon-Nachum M, Melamed E, Offen D. 2011. The “dying-back” phenomenon of motor neurons in ALS. J Mol Neurosci 43:470–477.

Feoktistov AI, Herman TG. 2016. Wallenda/DLK protein levels are temporally downregulated by Tramtrack69 to allow R7 growth cones to become stationary boutons. Development 143:2983–2993.

Fernandes KA, Harder JM, John SW, Shrager P, Libby RT. 2014. DLK-dependent signaling is important for somal but not axonal degeneration of retinal ganglion cells following axonal injury. Neurobiol Dis 69:108–116.

Ghosh AS, Wang B, Pozniak CD, Chen M, Watts RJ, Lewcock JW. 2011. DLK induces developmental neuronal degeneration via selective regulation of proapoptotic JNK activity. J Cell Biol 194:751–764.

Hao Y, Frey E, Yoon C, Wong H, Nestorovski D, Holzman LB, Giger RJ, DiAntonio A, Collins C. 2016. An evolutionarily conserved mechanism for cAMP elicited axonal regeneration involves direct activation of the dual leucine zipper kinase DLK. Elife 5. doi:10.7554/eLife.14048

Huntwork-Rodriguez S, Wang B, Watkins T, Ghosh AS, Pozniak CD, Bustos D, Newton K, Kirkpatrick DS, Lewcock JW. 2013. JNK-mediated phosphorylation of DLK suppresses its ubiquitination to promote neuronal apoptosis. J Cell Biol 202:747–763.

Katz JS, Rothstein JD, Cudkowicz ME, Genge A, Oskarsson B, Hains AB, Chen C, Galanter J, Burgess BL, Cho W, Kerchner GA, Yeh FL, Ghosh AS, Cheeti S, Brooks L, Honigberg L, Couch JA, Rothenberg ME, Brunstein F, Sharma KR, van den Berg L, Berry JD, Glass JD. 2022. A Phase 1 study of GDC-0134, a dual leucine zipper kinase inhibitor, in ALS. Ann Clin Transl Neurol 9:50–66.

Klinedinst S, Wang X, Xiong X, Haenfler JM, Collins CA. 2013. Independent pathways downstream of the Wnd/DLK MAPKKK regulate synaptic structure, axonal transport, and injury signaling. J Neurosci 33:12764–12778.

Le K, Soth MJ, Cross JB, Liu G, Ray WJ, Ma J, Goodwani SG, Acton PJ, Buggia-Prevot V, Akkermans O, Barker J, Conner ML, Jiang Y, Liu Z, McEwan P, Warner-Schmidt J, Xu A, Zebisch M, Heijnen CJ, Abrahams B, Jones P. 2023. Discovery of IACS-52825, a potent and selective DLK inhibitor for treatment of chemotherapy-induced peripheral neuropathy. J Med Chem 66:9954–9971.

Le Pichon CE, Meilandt WJ, Dominguez S, Solanoy H, Lin H, Ngu H, Gogineni A, Sengupta Ghosh A, Jiang Z, Lee S-H, Maloney J, Gandham VD, Pozniak CD, Wang B, Lee S, Siu M, Patel S, Modrusan Z, Liu X, Rudhard Y, Baca M, Gustafson A, Kaminker J, Carano RAD, Huang EJ, Foreman O, Weimer R, Scearce-Levie K, Lewcock JW. 2017. Loss of dual leucine zipper kinase signaling is protective in animal models of neurodegenerative disease. Sci Transl Med 9. doi:10.1126/scitranslmed.aag0394

Li J, Zhang YV, Asghari Adib E, Stanchev DT, Xiong X, Klinedinst S, Soppina P, Jahn TR, Hume RI, Rasse TM, Collins CA. 2017. Restraint of presynaptic protein levels by Wnd/DLK signaling mediates synaptic defects associated with the kinesin-3 motor Unc-104. Elife 6. doi:10.7554/eLife.24271

Miller BR, Press C, Daniels RW, Sasaki Y, Milbrandt J, DiAntonio A. 2009. A dual leucine kinase-dependent axon self-destruction program promotes Wallerian degeneration. Nat Neurosci 12:387–389.

Nihalani D, Merritt S, Holzman LB. 2000. Identification of structural and functional domains in mixed lineage kinase dual leucine zipper-bearing kinase required for complex formation and stress-activated protein kinase activation. J Biol Chem 275:7273–7279.

Russo A, DiAntonio A. 2019. Wnd/DLK is a critical target of FMRP responsible for neurodevelopmental and behavior defects in the Drosophila model of fragile X syndrome. Cell Rep 28:2581–2593.e5.

Shin JE, Cho Y, Beirowski B, Milbrandt J, Cavalli V, DiAntonio A. 2012. Dual leucine zipper kinase is required for retrograde injury signaling and axonal regeneration. Neuron 74:1015– 1022.

Siu M, Sengupta Ghosh A, Lewcock JW. 2018. Dual Leucine Zipper Kinase Inhibitors for the Treatment of Neurodegeneration. J Med Chem 61:8078–8087.

Valakh V, Walker LJ, Skeath JB, DiAntonio A. 2013. Loss of the spectraplakin short stop activates the DLK injury response pathway in Drosophila. J Neurosci 33:17863–17873.

Verma S, Khurana S, Vats A, Sahu B, Ganguly NK, Chakraborti P, Gourie-Devi M, Taneja V. 2022. Neuromuscular junction dysfunction in amyotrophic lateral sclerosis. Mol Neurobiol 59:1502–1527.

Wlaschin JJ, Donahue C, Gluski J, Osborne JF, Ramos LM, Silberberg H, Le Pichon CE. 2023. Promoting regeneration while blocking cell death preserves motor neuron function in a model of ALS. Brain 146:2016–2028.

Xiong X, Collins CA. 2012. A conditioning lesion protects axons from degeneration via the Wallenda/DLK MAP kinase signaling cascade. J Neurosci 32:610–615.

Xiong X, Hao Y, Sun K, Li J, Li X, Mishra B, Soppina P, Wu C, Hume RI, Collins CA. 2012. The Highwire ubiquitin ligase promotes axonal degeneration by tuning levels of Nmnat protein. PLoS Biol 10:e1001440.

Xiong X, Wang X, Ewanek R, Bhat P, Diantonio A, Collins CA. 2010. Protein turnover of the Wallenda/DLK kinase regulates a retrograde response to axonal injury. J Cell Biol 191:211– 223.

There looks to be a good amount of courses used, all of which are relevant to the article. There seems to be a good mix of references and original research.

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-02713

Corresponding author(s): Igor, Kramnik

[Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

Dear Editors,

We are grateful for constructive reviewers’ comments and criticisms and have thoroughly addressed all major and minor comments in the revised manuscript.

Summary of new data.

We have performed the following additional experiments to support our concept:

1. The kinetcs of ROS production in B6 and B6.Sst1S macrophages after TNF stimulation (Fig. ____3I and J, Suppl. Fig. 3G)____;
2. __ Time course of stress kinase activation (_Fig.3K)_ that clearly demonstrated the persistent stress kinase (phospho-ASK1 and phospho-cJUN) activation exclusively in. the B6.Sst1S macrophages;__
3. New Fig.4 C – E panels include comparisons of the B6 and B6.Sst1S macrophage responses to TNF and effects of IFNAR1 blockade in both backgrounds.
4. We performed new experiments demonstrating that the synthesis of lipid peroxidation products (LPO) occurs in TNF-stimulated macrophages earlier than the IFNβ super-induction (__Suppl.Fig.____4A and B). __
5. We demonstrated that the IFNAR1 blockade 12, 24 and 32 h after TNF stimulation still reduced the accumulation of LPO product (4-HNE) in TNF-stimulated B6.Sst1S BMDMs (Suppl.Fig.4 E – G).
6. We added comparison of cMyc expression between the wild type B6 and B6.Sst1S BMDMs during TNF stimulation for 6 – 24 h (Fig.__5I–J). __
7. New data comparing 4-HNE levels in Mtb-infected B6 wild type and B6.Sst1S macrophages and quantification of replicating Mtb was added (Fig.____6B, Suppl.Fig.7C and D).
8. In vivo data described in Fig.7 was thoroughly revised and new data was included. We demonstrated increased 4-HNE loads in multibacillary lesions (Fig.7A, Suppl. Fig.9A) and the 4-HNE accumulation in CD11b+ myeloid cells (Fig.7B __and __Suppl.Fig.9B). We demonstrated that the Ifnb – expressing cells are activated iNOS+ macrophages (Fig.7D and Suppl.Fig.13A). Using new fluorescent multiplex IHC, we have shown that stress markers phopho-cJun and Chac1 in TB lesions are expressed by Ifnb- and iNOS-expressing macrophages (Fig.7E and Suppl.Fig.13D – F).
9. We performed additional experiment to demonstrate that naïve (non-BCG vaccinated) lymphocytes did not improve Mtb control by Mtb-infected macrophages in agreement with previously published data (Suppl.Fig.7H). Summary of updates

Following reviewers requests we updated figures to include isotype control antibodies, effects of inhibitors on non-stimulated cells, positive and negative controls for labile iron pool, additional images of 4-HNE and live/dead cell staining.

Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M

Suppl.Fig.4F -G, 7I.

Positive and negative controls for labile iron pool measurements were added to Fig.3E, Fig.5D, Suppl.Fig.3B

Cell death staining images were added Suppl.Fig.3H

Co-staining of 4-HNE with tubulin was added to Suppl.Fig.3A.

High magnification images for Figure 7 __were added in __Suppl.Fig.8 to demonstrate paucibacillary and multibacillary image classification.

Single-channel color images for individual markers were provided in Fig.____7E and Suppl.Fig.13B–F.

Inhibitor effects on non-stimulated cells were included in Fig.____5 D – H, Suppl.Fig.6A and B.

Titration of CSF1R inhibitors for non-toxic concentration determination are included in Suppl.Fig.6D.

In addition, we updated the figure legends in the revised manuscript to include more details about the experiments. We also clarified our conclusions in the Discussion.

Responses to every major and minor comment of the reviewers are provided below.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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

Summary

The study by Yabaji et al. examines macrophage phenotypes B6.Sst1S mice, a mouse strain with increased susceptibility to M. tuberculosis infection that develops necrotic lung lesions. Extending previous work, the authors specifically focus on delineating the molecular mechanisms driving aberrant oxidative stress in TNF-activated B6.Sst1S macrophages that has been associated with impaired control of M. tuberculosis. The authors use scRNAseq of bone marrow-derived macrophages to further characterize distinctions between B6.Sst1S and control macrophages and ascribe distinct trajectories upon TNF stimulation. Combined with results using inhibitory antibodies and small molecule inhibitors in in vitro experimentation, the authors propose that TNF-induced protracted c-Myc expression in B6.Sst1S macrophages disables the cellular defense against oxidative stress, which promotes intracellular accumulation of lipid peroxidation products, fueled at least in part by overexpression of type I IFNs by these cells. Using lung tissue sections from M. tuberculosis-infected B6.Sst1S mice, the authors suggest that the presence of a greater number of cells with lipid peroxidation products in lung lesions with high counts of stained M. tuberculosis are indicative of progressive loss of host control due to the TNF-induced dysregulation of macrophage responses to oxidative stress. In patients with active tuberculosis disease, the authors suggest that peripheral blood gene expression indicative of increased Myc activity was associated with treatment failure.

__Major comments __ The authors describe differences in protein expression, phosphorylation or binding when referring to Fig 2A-C, 2G, 3D, 5B, 5C. However, such differences are not easily apparent or very subtle and, in some cases, confounded by differences in resting cells (e.g. pASK1 Fig 3L; c-Myc Fig 5B) as well as analyses across separate gels/blots (e.g. Fig 3K, Fig 5B). Quantitative analyses across different independent experiments with adequate statistical analyses are required to strengthen the associated conclusions.

Author: We updated our Western blots as follows: 1. Densitometery of normalized bands is included above each lane (Fig.2A – C; Fig.3C – D and 3K; Fig.4A – B; Fig.5B,C,I,J). New data in Fig.3K is added to highlight differences between B6 and B6.Sst1S at individual timepoints after TNF stimulation. In Fig.5I we added new data comparing Myc levels in B6 and B6.Sst1S with and without JNK inhibitor and updated the results accordingly. New Fig.3K clearly demonstrates the persistent activation of p-cJun and p-Ask1 at 24 and 36h of TNF stimulation. In Fig.5B we clearly demonstrate that Myc levels were higher in B6.Sst1S after 12 h of TNF stimulation. At 6h, however, the basal differences in Myc levels are consistently higher in B6.Sst1S and the induction by TNF is 1.6-fold similar in both backgrounds. We noted this in the text.

A representative experiment is shown in individual panels and the corresponding figure legend contains information on number of biological repeats. Each Western blot was repeated 2 – 4 times.

The representative images of fluorescence microscopy in Fig 3H, 4H, 5H, S3C, S3I, S5A, S6A seem to suggest that under some conditions the fluorescence signal is located just around the nucleus rather than absent or diminished from the cytoplasm. It is unclear whether this reflects selective translocation of targets across the cell, morphological changes of macrophages in culture in response to the various treatments, or variations in focal point at which images were acquired. Control images (e.g. cellular actin, DIC) should be included for clarification. If cell morphology changes depending on treatments, how was this accounted for in the quantitative analyses? In addition, negative controls validating specificity of fluorescence signals would be warranted.

Author: Our conclusion of higher LPO production is based on several parameters: 4-HNE staining, measurements of MDA in cell lysates and oxidized lipids using BODIPY C11. Taken together they demonstrate significant and reproducible increase in LPO accumulation in TNF-stimulated B6.Sst1S macrophages. This excludes imaging artefact related to unequal 4-HNE distribution noted by the reviewer. In fact, we also noted that the 4-HNE was spread within cell body of B6.Sst1S macrophages and confirmed it using co-staining with tubulin, as suggested by the reviewer (new Suppl.Fig.3A). Since low molecular weight LPO products, such as MDA and 4-HNE, traverse cell membranes, it is unlikely that they will be strictly localized to a specific membrane bound compartment. However, we agree that at lower concentrations, there might be some restricted localization, explaining a visible perinuclear ring of 4-HNE staining in B6 macrophages. This phenomenon may be explained just by thicker cytoplasm surrounding nucleus in activated macrophages spread on adherent plastic surface or by proximity to specific organelles involved in generation or clearance of LPO products and definitively warrants further investigation.

We also included images of non-stimulated cells in Fig.3H, Suppl.Fig.3A and 3E. We used multiple fields for imaging and quantified fluorescence signals (Suppl. Fig.3D and 3F, Suppl.Fig.4G, Suppl.Fig.6A and B).

We used negative controls without primary antibodies for the initial staining optimization, but did not include it in every experiment.

To interpret the evaluation on the hierarchy of molecular mechanisms in B6.Sst1S macrophages, comparative analyses with B6 control cells should be included (e.g. Fig 4C-I, Fig 5, Fig 6B, E-M, S6C, S6E-F). This will provide weight to the conclusions that the dysregulated processes are specifically associated with the susceptibility of B6.Sst1S macrophages.

Author: Understanding the sst1-mediated effects on macrophage activation is the focus of our previously published studies Bhattacharya et al., JCI, 2021) and this manuscript. The data comparing B6 and B6.Sst1S macrophage are presented in Fig.1, Fig.2, Fig.3, Fig.4, Fig.5A – C, I and J, Fig.6A – C, 6J and corresponding supplemental figures 1, 2, 3, 4A and B, Suppl.Fig.5, Suppl.Fig.6C, Suppl.Fig.7A-D,7F.

Once we identified the aberrantly activated pathways in the B6.Sst1S, we used specific inhibitors to correct the aberrant response in B6.Sst1S.

All experiments using inhibitory antibodies require comparison to the effect of a matched isotype control in the same experiment (e.g. Fig 3J, 4F, G, I; 6L, 6M, S3G, S6F).

Author: Isotype control for IFNAR1 blockade were included in Fig.3M, Fig.4C -E, Fig.6L-M

Suppl.Fig.4F -G, 7I.

Experiments using inhibitors require inclusion of an inhibitor-only control to assess inhibitor effects on unstimulated cells (e.g. Fig 4I, 5D-I)

Author: Inhibitor effects on non-stimulated cells were included in Fig.5 D – H, Suppl.Fig.6A and B.

Fig 3K and Fig 5J appear to contain the same images for p-c-Jun and b-tubulin blots.

Author: Fig.3K and 5J partially overlapped but had different focus – 3K has been updated to reflect the time course of stress kinase activation. Fig.5J is updated (currently Fig.5I and J) to display B6 and B6.Sst1S macrophage data including cMyc and p-cJun levels.

Data of TNF-treated cells in Fig 3I appear to be replotted in Fig 3J.

Author: Currently these data is presented in Fig.3L and 3M and has been updated to include comparison of B6 and B6.Sst1S cells (Fig.3L) and effects of inhibitors in Fig.3M.

Rev.1: It is stated that lungs from 2 mice with paucibacillary and 2 mice with multi-bacillary lesions were analyses. There is contradicting information on whether these tissues were collected at the same time post infection (week 14?) or whether the pauci-bacillary lesions were in lungs collected at earlier time points post infection (see Fig S8A). If the former, how do the authors conclude that multi-bacillary lesions are a progression from paucibacillary lesions and indicative of loss of M. tuberculosis control, especially if only one lesion type is observed in an individual host? If the latter, comparison between lesions will likely be dominated by temporal differences in the immune response to infection. In either case, it is relevant to consider density, location, and cellular composition of lesions (see also comments on GeoMx spatial profiling). Is the macrophage number/density per tissue area comparable between pauci-bacillary and multi-bacillary lesions?

Author: We did not collect lungs at the same time point. As described in greater detail in our preprints (Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) pulmonary TB lesions in our model of slow TB progression are heterogeneous between the animals at the same timepoint, as observed in human TB patients and other chronic TB animal models. Therefore, we perform analyses of individual TB lesions that are classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8. Currently it is impossible to monitor progression of individual lesions in mice. However, in mice TB is progressive disease and no healing and recovery from the disease have been observed in our studies or reported in literature. Therefore, we assumed that paucibacillary lesions preceded the multibacillary ones, and not vice versa, thus reflecting the disease progression. In our opinion, this conclusion most likely reflects the natural course of the disease. However, we edited the text : instead of disease progression we refer to paucibacillary and multibacillary lesions.

Rev1: Does 4HNE staining align with macrophages and if so, is it elevated compared to control mice and driven by TNF in the susceptible vs more resistant mice?

Author: We performed additional staining and analyses to demonstrate the 4-HNE accumulation in CD11b+ myeloid cells of macrophage morphology. Non-necrotic lesions contain negligible proportion of neutrophils (Fig.7B, Suppl.Fig.9B). B6 mice do not develop advanced multibacillary TB lesions containing 4-HNE+ cells. Also, 4-HNE staining was localized to TB lesions and was not found in uninvolved lung areas of the infected mice, as shown in Suppl.Fig.9A (left panel).

It is well established that TNF plays a central role in the formation and maintenance of TB granulomas in humans and in all animal models. Therefore, TNF neutralization would lead to rapid TB progression, rapid Mtb growth and lesions destruction in both B6 and B6.Sst1S genetic backgrounds.

Pathway analysis of spatial transcriptomic data (Suppl.Fig.11) identified TNF signaling via NF-kB among dominant pathways upregulated in multibacillary lesions, suggesting that the 4-HNE accumulation paralleled increased TNF signaling. In addition, in vivo other cytokines, including IFN-I, could activate macrophages and stimulate production of reactive oxygen and nitrogen species and lead to the accumulation of LPO products as shown in this manuscript.

Rev.1: It would be relevant to state how many independent lesions per host were sampled in both the multiplex IHC as well as the GeoMx data. Can the authors show the selected regions of interest in the tissue overview and in the analyses to appreciate within-host and across-host heterogeneity of lesions. The nature of the spatial transcriptomics platform used is such that the data are derived from tissue areas that contain more than just Iba1+ macrophages. At later stages of infection, the cellular composition of such macrophage-rich areas will be different when compared to lesions earlier in the infection process. Hence, gene expression profiles and differences between tissue regions cannot be attributed to macrophages in this tissue region but are more likely a reflection of a mix of cellular composition and per-cell gene expression.

Author: We used Iba1 staining to identify macrophages in TB lesions and programmed GeoMx instrument to collect spatial transcriptomics probes from Iba1+ cells within ROIs. Also, we selected regions of interest (ROI) avoiding necrotic areas (depicted in Suppl.Fig.10). We agree that Iba1+ macrophage population is heterogenous – some Iba1+ cells are activated iNOS+ macrophages, other are iNOS-negative (Fig.7C and D, and Suppl.Fig.13A). Multibacillary lesions contain larger areas occupied by activated (iNOS+) macrophages (Fig.7D, Suppl.Fig.13B and 13F). Although the GeoMx spatial transcriptomic platform does not provide single cell resolution, it allowed us to compare populations of Iba1+ cells in paucibacillary and multibacillary TB lesions and to identify a shift in their overall activation pattern.

It is stated that loss of control of M. tuberculosis in multibacillary lesions was associated with "downregulation of IFNg-inducible genes". If the authors base this on the tissue expression of individual genes, this requires further investigation to support such conclusion (also see comment on GeoMx above). Furthermore, how might this conclusion be compatible with significantly elevated iNOS+ cells (Fig 7D) in multibacillary lesions?

Author: We demonstrated that Ciita gene expression is specifically induced by IFN-gamma and is suppressed by IFN-I (Fig.6M). The expression of Ciita in paucibacillary lesions suggest the presence of the IFN-gamma activated cells and its disappearance in the multibacillary lesion is consistent with massive activation of IFN-I pathway (Fig.7C).

Rev1. It is appreciated that the human blood signature analyses contain Myc-signatures but the association with treatment failure is not very strong based on the data in Fig 13B and C (Suppl.Fig.15B and C now). The authors indicate that they have no information on disease severity, but it should perhaps not be assumed that treatment failure is indicative of poor host control of the infection. Perhaps independent analyses in separate cohort/data set can add strength and provide -additional insights (e.g. PMID: 35841871; PMID: 32451443, PMID: 17205474, PMID: 22872737). In addition, the human data analyses could be strengthened by extension to additional signatures such as IFN, TNF, oxidative stress. Details of the human study design are not very clear and are lacking patient demographics, site of disease, time of blood collection relative to treatment onset, approving ethics committees.

Author: X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets (MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gsea-msigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set. The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis.

Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice. The detailed analysis of differentially regulated pathways in human TB patients is beyond the scope of this study and is presented in another manuscript entitled “ Tuberculosis risk signatures and differential gene expression predict individuals who fail treatment” by Arthur VanValkenburg et al., submitted for publication.

Blood collection for PBMC gene expression profiling of TB patients was prior to TB treatment or within a first week of treatment commencement. Boxplot of bootstrapped ssGSEA enrichment AUC scores from several oncogene signatures ranked from lowest to highest AUC score, with myc_up and myc_dn genes highlighted in red.

We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

We updated the details of the study, including study sites and the ethics committee approval statement and references describing these cohorts. __ Other comments__

It is excellent that the authors provide individual data points. Choosing a colour other than black would increase clarity when black bars are used.

Author: We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

Error bars are inconsistently depicted as either bi-directional or just unidirectional.

Author: We used bi-directional error bars in the revised manuscript.

Fig 1E, G, H- please include a scale to clarify what the heat map is representing.

Author: We have included the expression key in Fig.1E,G and H and Suppl.Fig.1C and D in the revised version.

Fig 2K, Fig S10A gene information cannot be deciphered.

Author: We increased the font in previous Fig.2K and moved to supplement to keep larger fonts (current Suppl.Fig.2G).

Fig S4A,B please add error bars.

Author: These data are presented as Suppl.Fig.5 in the revised version. We performed one experiment to test the hypothesis. Because the data indicated no clear increase in transposon small RNAs in the sst1S macrophages, we did not pursue this hypothesis further, and therefore, the error bars were not included. However, we decided to include these negative data because it rejects a very attractive and plausible hypothesis.

Please use gene names as per convention (e.g. Ifnb1) to distinguish gene expression from protein expression in figures and text.

Author: We addressed the comment in the revised manuscript.

Fig S8B. Contrary to the description of results, there seems to be minimal overlap between the signal for YFP and the Ifnb1 probe. Is the Ifnb1 reporter mouse a legacy reporter? If so, it is worth stating this and including such considerations in the data interpretation.

Author: The YFP reporter expresses YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells and while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. So YFP is not a lineage tracing reporter, but its accumulation marks the Ifnb1 promoter activity in cells, although the YFP protein half-life is longer than that of the Ifnb1 mRNA that is rapidly degraded (Witt et al., BioRxiv, 2024; doi:10.1101/2024.08.28.61018). Therefore, there is no precise spatiotemporal coincidence of these readouts.

Please clarify what is meant by "normal interstitium" ? If the tissue is from uninfected mice, please state clearly.

Author: In this context we refer to the uninvolved lung areas of the infected lungs. In every sample we compare uninvolved lung areas and TB lesions of the same animal. Also, we performed staining of lung of non-infected mice as additional controls.

Rev1: If macrophage cultures underwent media changes every 48h, how was loss of liberated Mtb taken into account especially if differences in cell density/survival were noted? The assessment of M. tuberculosis load by qPCR is not well described. In particular, the method of normalization applied within the experiments (not within the qPCR) here remains unclear, even with reference to the authors' prior publication.

Author: Our lab has many years of experience working with macrophage monolayers infected with virulent Mtb and uses optimized protocols to avoid cell losses and related artifacts. Recently we published a detailed protocol for this methodology in STAR Protocols (Yabaji et al., 2022; PMID 35310069). In brief, it includes preparation of single cell suspensions of Mtb by filtration to remove clumps, use of low multiplicity of infection, preparation of healthy confluent monolayers and use of nutrient rich culture medium and medium change every 2 days. We also rigorously control for cell loss using whole well imaging and quantification of cell numbers and live/dead staining.

Please add citation for the limma package.

Author: The references has been added (Ritchie et al, NAR 2015; PMID 25605792).

The description of methodology relating to the "oncogene signatures" is unclear.

Author: This signature was described in Bild etal, Nature, 2006 and McQuerry JA, et al, 2019 “Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes”. BMC Cancer 19: 881 and is cited in Methods section Oncogene signatures

Please clearly state time points post infection for mouse analyses.

Author: We collected lung samples from Mtb infected mice 12 – 20 weeks post infection. The lesions were heterogeneous and were individually classified using criteria described above.

Reference is made to "a list of genes unique to type I [interferon] genes [....]" (p29). Can the authors indicate the source of the information used for compiling this list?

Author: The lists were compiled from Reactome, EMBL's European Bioinformatics Institute and GSEA databases. The links for all datasets are provided in Suppl.Table 8 “Expression of IFN pathway genes in Iba1+ cells from pauci- and multi-bacillary lesions of Mtb infected B6.Sst1S mouse lungs” in the “Pool IFN I & II gene sets” worksheet.

The discussion at present is very long, contains repetition of results and meanders on occasion.

Author: Thank you for this suggestion, We critically revised the text for brevity and clarity.

Reviewer #1 (Significance (Required)):

Strengths and limitations

Strengths: multi-pronged analysis approaches for delineating molecular mechanisms of macrophage responses that might underpin susceptibility to M. tuberculosis infection; integration of mouse tissues and human blood samples

Weaknesses: not all conclusions supported by data presented; some concerns related to experimental design and controls; links between findings in human cohort and the mechanistic insights gained in mouse macrophage model uncertain

Author: The revised manuscript addresses every major and minor comment of the reviewers, including isotype controls and naïve T cells, to provide additional support for our conclusions. Our study revealed causal links between Myc hyperactivity with the deficiency of anti-oxidant defense and type I interferon pathway hyperactivity. We have shown that Myc hyperactivity in TNF-stimulated macrophages compromises antioxidant defense leading to autocatalytic lipid peroxidation and interferon-beta superinduction that in turn amplifies lipid peroxidation, thus, forming a vicious cycle of destructive chronic inflammation. This mechanism offers a plausible mechanistic explanation of for the association of Myc hyperactivity with poorer treatment outcomes in TB patients and provide a novel target for host-directed TB therapy.

Advance

The study has the potential to advance molecular understanding of the TNF-driven state of oxidative stress previously observed in B6.Sst1S macrophages and possible implications for host control of M. tuberculosis in vivo.

Audience

Experts seeking understanding of host factors mediating M. tuberculosis control, or failure thereof, with appreciation for the utility of the featured mouse model in assessing TB diseases progression and severe manifestation. Interest is likely extended to audience more broadly interested in TNF-driven macrophage (dys)function in infectious, inflammatory, and autoimmune pathologies.

Reviewer expertise

In preparing this review, I am drawing on my expertise in assessing macrophage responses and host defense mechanisms in bacterial infections (incl. virulent M. tuberculosis) through in vitro and in vivo studies. This includes but is not limited to macrophage infection and stimulation assays, microscopy, intra-macrophage replication of M. tuberculosis, analyses of lung tissues using multi-plex IHC and spatial transcriptomics (e.g. GeoMx). I am familiar with the interpretation of RNAseq analyses in human and mouse cells/tissues, but can provide only limited assessment of appropriateness of algorithms and analysis frameworks.

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

Yabaji et al. investigated the effects of BMDMs stimulated with TNF from both WT and B6.Sst1S mice, which have previously been identified to contain the sst1 locus conferring susceptibility to Mycobacterium tuberculosis. They identified that B6.Sst1S macrophages show a superinduction of IFNß, which might be caused by increased c-Myc expression, expanding on the mechanistic insights made by the same group (Bhattacharya et al. 2021). Furthermore, prolonged TNF stimulation led to oxidative stress, which WT BMDMs could compensate for by the activation of the antioxidant defense via NRF2. On the other hand, B6.Sst1S BMDMs lack the expression of SP110 and SP140, co-activators of NRF2, and were therefore subjected to maintained oxidative stress. Yabaji et al. could link those findings to in vivo studies by correlating the presence of stressed and aberrantly activated macrophages within granulomas to the failure of Mtb control, as well as the progression towards necrosis. As the knowledge regarding Mtb progression and necrosis of granulomas is not yet well understood, findings that might help provide novel therapy options for TB are crucial. Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn. In particular a) important controls are often missing, e.g. T-cells form non-immune mice in Fig. 6J, in F, effectivity of BCG in B6 mice in 6N; b) single experiments are shown throughout the manuscript, in particular western blots and histology without proper quantification and statistics, this is absolutely not acceptable; c) very few repetitions are shown in in vitro experiments, where there is no evidence for limitation in resources (usually not more than 3), it is not clear what "independent experiment means" - i.e. the robustness of the findings is questionable; d) data are often normalized multiple times, e.g. in the case of qPCR, and the methods of normalization are not clear (what house-keeping gene exactly?);

Moreover, experiments regarding IFN I signaling (e.g. short term TNF treatment of BMDMs to analyze LPO, making sure that the reporter mouse for IFNß works in vivo) and c-Myc (e.g. the increase after M-CSF addition might impact on other analysis as well and the experiments should be adjusted to control for this effect; MYC expression in the human samples) should be carefully repeated and evaluated to draw correct conclusions.

In addition, we would like to strongly encourage the authors to more precisely outline the experimental set-ups and figure legends, so that the reader can easily understand and follow them. In other words: The legends are - in part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

Author: We appreciate a very thorough evaluation of our manuscript by this reviewer. Their insightful comments helped us improve the manuscript. As outlined below in point-by-point responses 1) we added important controls including isotype control antibodies in IFNAR blocking experiments and non-vaccinated T cells in T cell – macrophage interactions experiments; updated figure legends to indicate number of repeated experiment where a representative experiment is shown, numbers of mouse lungs and individual lesions, methods of data normalization, where it was missing. We also explained our in vitro experimental design and how we analyzed and excluded effects of media change and fresh CSF1 addition, by using a rest period before TNF stimulation and Mtb infection. The data shown in Suppl. Fig. 6C (previously Suppl. Fig. 5B) demonstrate that Myc levels induced by CSF1 return to the basal level at 12 h after media change. Our detailed in vitro protocol that contains these details has been published (Yabaji et al., STAR Protocols, 2022). We added new data demonstrating the ROS and LPO production at 6h of TNF stimulation, while the Ifnb1 mRNA super-induction occurred at 16 – 18 h, and edited the text to highlight these dynamics. The upregulation of Myc pathway in human samples does not necessarily mean the upregulation of Myc itself, it could be due to the dysregulation of downstream pathways. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. The detailed analysis of this cell populations in human patients is suggested by our findings but it is beyond the scope of this study.

The reviewer’s comments also suggested that a summary of our findings was necessary. The main focus of our study was to untangle connections between oxidative stress and Ifnb1 superinduction. It revealed that Myc hyperactivity caused partial deficiency of anti-oxidant defense leading to type I interferon pathway hyperactivity that in turn amplifies lipid peroxidation, thus establishing a vicious cycle driving inflammatory tissue damage.

Our laboratory worked on mechanisms of TB granuloma necrosis over more than two decades using genetic, molecular and immunological analyses in vitro and in vivo. It provided mechanistic basis for independent studies in other laboratories using our mouse model and further expanding our findings, thus supporting the reproducibility and robustness of our results and our lab’s expertise.

Specific comments to the experiments and data:

• Fig. 1E: Evaluation of differences in up- and downregulation between B6 and B6.Sst1S cells should highlight where these cells are within the heatmap, as it is only labelled with the clusters, or it should be depicted differently (in particular for cluster 1 and 2). Furthermore, a more simple labelling of the pathways would increase the readability of the data.

Author: For our scRNAseq data presentation, we used formats accepted by computational community. To clarify Fig.1E, we added labels above B6 and B6.Sst1S-specific clusters.

• Fig. 2D, E: The staining legend is missing. For the quantification it is not clear what % total means. Is this based on the intensity or area? What do the dots represent in the bar chart? Is one data point pooled from several pictures? If not, the experiments need to be repeated, as three pictures might not be representative for evaluation.

• Fig. 2E: Statistics comparing B6/ B6,SsT1S with TNF (different) is required: Absence of induction is not a proof for a difference!

Author: We included staining with NRF2-specific antibodies and performed area quantification per field using ImageJ to calculate the NRF2 total signal intensity per field. Each dot in the graph represents the average intensity of 3 fields in a representative experiment. The experiment was repeated 3 times. We included pairwise comparison of TNF-stimulated B6 and B6.Sst1S macrophages and updated the figure legend.

• Fig. 3E: Positive and negative control need to be depicted in the figure (see legend).

Author: We have added the positive and negative controls for the determination of labile iron pool to the data in Fig. 3E and related Suppl. Fig. 3B and to Fig. 5D that also demonstrates labile iron determination.

• Fig. 3I: A quantification by flow cytometry or total cell counts are important, as 6% cell death in cell culture is a very modest observation. Otherwise, confocal images of the quantification would be a good addition to judge the specificity of the viability staining.

Author: To validate the specificity of the viability staining method, we have provided fluorescent images as Suppl.Fig.3H. The main point of this experiment was to demonstrate a modest, but reproducible, increase in cell death in the sst1-mutant macrophages that suggested an IFN-dependent oxidative damage. In our study, we did not focus on mechanisms of cell death, but on a state of chronic oxidative stress in the sst1 mutant live cells during TNF stimulation.

• Fig. 3I, J: What does one dot represent?

Author: We performed this assay in 96 well format and each dot represent the % cell death in an individual well.

• Fig. 3K,L: For the B6 BMDMs it seems that p-cJun is highly increased at 12h in (L), while it is not in (K). On the other hand, for the B6.Sst1S BMDMs it peaks at 24h in (K), while in (L) it seems to at 12h. According to the data in (L) it seems that p-cJun is rather earlier and stronger activated in B6 BMDMs and has a weakened but prolonged activation in the B6.Sst1S BMDMs, which would not fit with your statement in the text that B6.Sst1S BMDMs show an upregulation. !These experiments need repetitions and quantification and statistiscs!

Fig. 3L: ASK1 seems to be higher at 12h for the B6 BMDMs and similar for both lines at 24h, which is not fitting to the statement in the text. ("Also, the ASK1 - JNK - cJun stress kinase axis was upregulated in B6.Sst1S macrophages, as compared to B6, after 12 - 36 h of TNF stimulation")

Author: These experiments were repeated, and new data were added to highlight differences in ASK1 and c-Jun phosphorylation between B6 and B6.Sst1S at individual timepoints after TNF stimulation (presented in new Fig.3K). It demonstrated that after TNF stimulation the activation of stress kinases ASK1 and c-Jun initially increased in both genetic backgrounds. However, their upregulation was maintained exclusively in the sst1-susceptible macrophages from 24 to 36 h of TNF stimulation, while in the resistant macrophages their upregulation was transient. Thus, during prolonged TNF stimulation, B6.Sst1S macrophages experience stress that cannot be resolved, as evidenced by this kinetic analysis. The quantification of the band intensity was added to Western blot images above individual lanes.

Reviewer 2 pointed to missing isotype control antibodies in Fig.3 and Fig.4:

• Figure 3J: the isotype control for the IFNAR antibody is missing

• Figure 4E: It seems the isotype control itself has already an effect in the reduction of IFNb.

• Fig. 4H: It seems that the Isotype control antibody had an effect to increase 4-HNE (compared to TNF stimulated only).

Author: We always include isotype control antibodies in our experiments because antibodies are known to modulate macrophage activation via binding to Fc receptor. To address the reviewer’s comments, we updated all panels that present the effects of IFNAR1 blockade with isotype-matched non-specific control antibodies in the revised manuscript. Specifically, we included isotype control in Fig. 3M (previously Fig.3J), Fig.4I, Suppl.4E – G, Fig.6L-M), Suppl.Fig.7I (previously Suppl.Fig.6F).

• Fig.4A - C: "IFNAR1 blockade, however, did not increase either the NRF2 and FTL protein levels, or the Fth, Ftl and Gpx1 mRNA levels above those treated with isotype control antibodies"

Maybe not above the isotype but it is higher than the TNF alone stimulation at least for NRF2 at 8h and for Ftl at both time points. Why does the isotype already cause stimulation/induction of the cells? !These experiments need repetitions and quantification and statistics!

Author: To determine specific effects of IFNAR blockade we compared effects of non-specific isotype control and IFNAR1-specific antibodies. In our experiments, the isotype control antibody modestly increased of Nrf2 and Ftl protein levels and the Fth and Ftl mRNA levels, but their effects were similar to the effect of IFNAR-specific antibody. The non-IFN -specific effects of antibodies, although are of potential biological significance, are modest in our model and their analysis is beyond the scope of this study.

• Fig.4H Was the AB added also at 12h post stimulation? Figure legend should be adjusted.

Author: The IFNAR1 blocking antibodies and isotype control antibodies were added at 2 h after TNF stimulation in Fig.4H and 4I, as described in the corresponding figure legend. The data demonstrating effects of IFNAR blockade after 12, 24,and 33h of TNF stimulation are presented in Suppl.Fig.4 E - G.

• Figure 4I: How was the data measured here, i.e. what is depicted? The isotype control is missing. It seems a two-way ANOVA was used, yet it is stated differently. The figure legend should be revised, as Dunnett's multiple comparison would only check for significances compared to the control.

Author: The microscopy images and bar graphs were updated to include isotype control and presented in Suppl. Fig.4E - G of the revised version. We also revised the statistical analysis to include correction for multiple comparisons.

Figure 4C and subsequent: How exactly was the experiment done (house-keeping gene)?

Author: We included the details in the figure legends of revised version. We quantified the gene expression by DDCt method using b-actin (for Fig. 4C-E) and 18S (For Fig. 4F and G) as internal controls.

• Figure 4D,E: Information on cells used is missing. Why the change in stimulation time? Did it not work after 12h? Then the experiments in A-C should be repeated for 16h.

Author: The updated Fig. 4D and E present comparison of B6 and B6.Sst1S BMDMs clearly demonstrating significant difference between these macrophages in Ifnb1 mRNA expression 16 h after TNF stimulation, in agreement with our previous publication(Bhattacharya, et al., 2021). There we studied the time course of responses of B6 and B6.Sst1S macrophages to TNF at 2h intervals and demonstrated the divergence between their activation trajectories starting at 12 h of TNF stimulation Therefore, to reveal the underlying mechanisms we focus our analyses on this critical timepoint, i.e. as close to the divergence as possible. However, the difference between the strains in Ifnb1 mRNA expression achieved significance only by 16h of TNF stimulation. That is why we have used this timepoint for the Ifnb1 and Rsad2 analyses. It clearly shows that the superinduction was not driven by the positive feedback via IFNAR, as has been shown by the Ivashkiv lab for B6 wild type macrophages previously PMID 21220349.

• Figure 4E: It would be helpful to see if these transcripts are actually translated into protein levels, e.g. perform an ELISA. Authors state that IFNAR blockages does not alter the expression but you statistic says otherwise.

-The data for Ifnb expression (or better protein level) should be provided for B6 BMDMs as well.

Author: We have previously reported the differences in Ifnb protein secretion (He et al., Plos Pathogens, 2013 and Bhattacharya et al., JCI 2021). We use mRNA quantification by qRT-PCR as a more sensitive and direct measurement of the sst1-mediated phenotype. The revised Fig.4D and E include responses of B6 in addition to the B6.Sst1S to demonstrate that the IFNAR blockade does not reduce the Ifnb1 mRNA levels in TNF-stimulated B6.Sst1S mutant to the B6 wild type levels. A slight reduction can be explained by a known positive feedback loop in the IFN-I pathway (see above). In this experiment we emphasized that the effect of the sst1 locus is substantially greater, as compared to the effect of the IFNAR blockade (Fig.4D), and updated the text accordingly.

• Fig. 4F: To what does the fold induction refer to? If it is again to unstimulated cells, then why is the induction now so much higher than in (E) where it was only 50x (now to 100x).

• Figure 4G: Again to what is the fold induction referring to? It seems your Fer-1 treatment only contains 2 data points. This needs to be fixed.

Author: Yes, the fold induction was calculated by normalizing mRNA levels to untreated control incubated for the same time. Regarding the variation in Ifnb1 mRNA levels - a two-fold variation is not unusual in these experiments that may result in the Ifnb1 mRNA superinduction ranging from 50 -200-fold at this timepoint (16h). The graph in Fig.4G was modified to make all datapoints more visible.

• "These data suggest that type I IFN signaling does not initiate LPO in our model but maintains and amplifies it during prolonged TNF stimulation that, eventually, may lead to cell death". Data for a short term TNF stimulation are not shown, however, so it might impact also on the initiation of LPO.

• The overall conclusion drawn from Fig. 3 and 4 is not really clear with regard that IFN does not initiate LPO. Where is that shown? Data on earlier stimulation time points should be added to make this clear.

Author: We demonstrated ROS production (new Suppl.Fig.3G) and the rate of LPO biosynthesis (new Suppl.Fig.4E-F) at 6 h post TNF stimulation, while the Ifnb1 superinduction occurs between 12-18 h post TNF stimulation. This temporal separation supports our conclusion that IFN-β superinduction does not initiate LPO. We clarified it in the text:

“Thus, Ifnb1 super-induction and IFN-I pathway hyperactivity in B6.Sst1S macrophages follow the initial LPO production, and maintain and amplify it during prolonged TNF stimulation”. (Previously: These data suggest that type I IFN signaling does not initiate LPO in our model). We also edited the conclusion in this section to explain the hierarchy of the sst1-regulated AOD and IFN-I pathways better:

“Taken together, the above experiments allowed us to reject the hypothesis that IFN-I hyperactivity caused the sst1-dependent AOD dysregulation. In contrast, they established that the hyperactivity of the IFN-I pathway in TNF-stimulated B6.Sst1S macrophages was itself driven by the initial dysregulation of AOD and iron-mediated lipid peroxidation. During prolonged TNF stimulation, however, the IFN-I pathway was upregulated, possibly via ROS/LPO-dependent JNK activation, and acted as a potent amplifier of lipid peroxidation”.

We believe that these additional data and explanation strengthen our conclusions drawn from Figures 3 and 4.

• "A select set of mouse LTR-containing endogenous retroviruses (ERV's) (Jayewickreme et al, 2021), and non-retroviral LINE L1 elements were expressed at a basal level before and after TNF stimulation, but their levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6". This sentence should be revised as the differences between B6 and B6.Sst1S BMDMs seem small and are not there after 48h anymore. Are these mild changes really caused by the mutation or could they result from different housing conditions and/or slowly diverging genetically lines. How many mice were used for the analysis? Is there already heterogeneity between mice from the same line?

Author: We agree with the reviewer that the data presented in Suppl.Fig.4 (Suppl.Fig.5 in the revised version) indicated no increase in single- and double-stranded transposon RNAs in the B6.Sst1S macrophages. The purpose of these experiment was to test the hypothesis that increased transposon expression might be responsible for triggering the superinduction of type I interferon response in TNF-stimulated B6.Sst1S macrophages. In collaboration with a transposon expert Dr. Nelson Lau (co-author of this manuscript) we demonstrated that transposon expression was not increased above the B6 level and, thus, rejected this attractive hypothesis. We explained the purpose of this experiment in the text and adequately described our findings as “the levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6”…and concluded that ” the above analyses allowed us to exclude the overexpression of persistent viral or transposon RNAs as a primary mechanism of the IFN-I pathway hyperactivity” in the sst1-mutant macrophages.

• Fig. 5A: Indeed, it even seems that Myc is upregulated for the mutant BMDMs. Yet, there are only 2 data points for B6 12h. !These experiments need repetitions and quantification and statistics!

Author: We observed these differences in c-Myc mRNA levels by independent methods: RNAseq and qRT-PCR. The qRT-PCR experiments were repeated 3 times. A representative experiment in Fig.5A shows 3 data points for each condition. We reformatted the panel to make all data points clearly visible.

• Fig. 5B: Why would the protein level decrease in the controls over 6h of additional cultivation? Is this caused by fresh M-CSF? In this case maybe cells should be left to settle for one day before stimulating them to properly compare c-Myc induction. Comment on two c-Myc bands is needed. At 12h only the upper one seems increased for TNF stimulated mutant BMDMs compared to B6 BMDMs.

Author: We agree with the reviewer’s point that cells need to be rested after media change that contains fresh CSF-1. Indeed, in Suppl.Fig.6C, we show that after media change containing 10% L929 supernatant (a source of CSF1) there is an increase in c-Myc protein levels that takes approximately 12 hours to return to baseline.

Our protocol includes resting period of 18 – 24 h after medium change before TNF stimulation. We updated Methods to highlight this detail. Thus, the increase in c-Myc levels we observe at 12 h of TNF stimulation (Fig.5B) is induced by TNF, not the addition of growth factors, as further discussed in the text.

The two c-Myc bands observed in Fig.5B,I and J, are similar to patterns reported in previous studies that used the same commercial antibodies (PMIDs: 24395249, 24137534, 25351955). Whether they correspond to different c-Myc isoforms or post-translational modifications is unknown.

• Fig. 5A,B: It seems that not all the RNA is translated into protein, as c-Myc at 12h in the mutant BMDMs seems to be lower than at 6h, while the gene expression implicates it vice versa.

Author: In addition to Fig.5B, the time course of Myc protein expression up to 24 h is presented in new panels Fig. 5I-5J. It demonstrates the gradual decrease of Myc protein levels. The observed dissociation between the mRNA and protein levels in the sst1-mutant BMDMs at 12 and 24 h is most likely due to translation inhibition as a result of the development of the integrated stress response, ISR (as shown in our previous publication by Bhattacharya et al., JCI, 2021). Translation of Myc is known to be particularly sensitive to the ISR (PMID18551192, PMID25079319, PMID28490664). Perhaps, the IFN-driven ISR may serve as a backup mechanism for Myc downregulation. We are planning to investigate these regulatory mechanisms in greater detail in the future.

• Fig. 5J: Indeed, the inhibitor seems to cause the downregulation of the proteins. Explanation?

Author: This experiment was repeated twice and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as had been previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we rejected the hypotghesis that JNK activity might have a major role in c-Myc upregulation in sst1 mutant macrophages.

• "TNF stimulation tended to reduce the LPO accumulation in the B6 macrophages and to increase it in the B6.Sst1S ones" However, this is not apparent in Sup. Fig. 6B. Here it seems that there might be a significant increase.

Author: Suppl.Fig.6B (currently Suppl.Fig.7B) shows the 4-HNE accumulation at day 3 post infection. The data obtained after 5 days of Mtb infection are shown in Fig.6A. We clarified this in the text: “By day 5 post infection, TNF stimulation induced significant LPO accumulation only in the B6.Sst1S macrophages (Fig.6A)”.

• Fig. 6B: Mtb and 4-HNE should be shown in two different channels in order to really assign each staining correctly.

What time point is this? Are the mycobacteria cleared at MOI1, since it looks that there are fewer than that? How does this look like for the B6 BMDMs? Are there even less mycobacteria?

Author: We included B6 infection data to the updated Fig.6B and added Suppl.Fig.7C and 7D that address this reviewer’s comment. The data represent day 5 of Mtb infection as indicated in the updated Fig.6B and Suppl.Fig.7C and 7D legends. New Suppl.Fig.7D shows quantification of replicating Mtb using Mtb replication reporter stain expressing single strand DNA binding protein GFP fusion, as described in Methods. We observed fewer Mtb and a lower percentage of replicating Mtb in B6 macrophages, but we did not observe a complete Mtb elimination in either background.

We used red fluorescence for both Mtb::mCherry and 4-HNE staining to clearly visualize the SSB-GFP puncta in replicating Mtb DNA. In the revised manuscript, we have included the relevant channels in Suppl. Fig.7C and D to demonstrate clearly distinct patterns of Mtb::mCherry and 4-HNE signals. We did not aim to quantify the 4-HNE signal intensity in this experiment. For the 4-HNE quantification we use Mtb that expressed no reporter proteins (Fig.6A-B and Suppl.Fig.7A-B).

• Fig 6E: In the context of survival a viability staining needs to be included, as well as the data from day 0. Then it needs to be analyzed whether cell numbers remain the same from D0 or if there is a change.

Author: We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death to exclude artifacts due to cell loss. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

"The 3D imaging demonstrated that YFP-positive cells were restricted to the lesions, but did not strictly co-localize with intracellular Mtb, i.e. the Ifnb promoter activity was triggered by inflammatory stimuli, but not by the direct recognition of intracellular bacteria. We validated the IFNb reporter findings using in situ hybridization with the Ifnb probe, as well as anti-GFP antibody staining (Suppl.Fig.8B - E)." The colocalization is not present within the tissue sections. It seems that the reporter line does not show the same staining pattern in vivo as the IFNß probe or the anti GFP antibody staining. The reporter line has to be tested for the specificity of the staining. Furthermore, to state that it was restricted to the lesions, an uninvolved tissue area needs to be depicted.

Author: The Ifnb secreting cells are notoriously difficult to detect in vivo using direct staining of the protein. Therefore, lineage tracing of reporter expression are used as surrogates. The Ifnb reporter used in our study has been developed by the Locksley laboratory (Scheu et al., PNAS, 2008, PMID: 19088190) and has been validated in many independent studies. The reporter mice express the YFP protein under the control of the Ifnb1 promoter. The YFP protein accumulates within the cells, while Ifnb protein is rapidly secreted and does not accumulate in the producing cells in appreciable amounts. Also, the kinetics of YFP protein degradation is much slower as compared to the endogenous Ifnb1 mRNA that was detected using in situ hybridization. Thus, there is no precise spatiotemporal coincidence of these readouts in Ifnb expressing cells in vivo. However, this methodology more closely reflect the Ifnb expressing cells in vivo, as compared to a Cre-lox mediated lineage tracing approach. In the revised manuscript we demonstrate that both YFP and mRNA signals partially overlap (Suppl.Fig.12B). In Suppl.Fig.12B. we also included a new panel showing no YFP expression in the uninvolved area of the reporter mice infected with Mtb. The YFP expression by activated macrophages is demonstrated by co-staining with Iba1- and iNOS-specific antibodies (new Fig.7D and Suppl.Fig.13A). Our specificity control also included TB lesions in mice that do not carry the YFP reporter and did not express the YFP signal, as reported elsewhere (Yabaji et al., BioRxiv, https://doi.org/10.1101/2023.10.17.562695).

• Are paucibacillary and multibacillary lesions different within the same animal or does one animal have one lesion phenotype? If that is the case, what is causing the differences between mice? Bacterial counts for the mice are required.

Author: The heterogeneity of pulmonary TB lesions has been widely acknowledged in clinic and highlighted in recent experimental studies. In our model of chronic pulmonary TB (described in detail in Yabaji et al., https://doi.org/10.1101/2025.02.28.640830 and https://doi.org/10.1101/2023.10.17.562695) the development of pulmonary TB lesions is not synchronized, i.e. the lesions are heterogeneous between the animals and within individual animals at the same timepoint. Therefore, we performed a lesion stratification where individual lesions were classified by a certified veterinary pathologist in a blinded manner based on their morphology (H&E) and acid fast staining of the bacteria, as depicted in Suppl.Fig.8.

• "Among the IFN-inducible genes upregulated in paucibacillary lesions were Ifi44l, a recently described negative regulator of IFN-I that enhances control of Mtb in human macrophages (DeDiego et al, 2019; Jiang et al, 2021) and Ciita, a regulator of MHC class II inducible by IFNy, but not IFN-I (Suppl.Table 8 and Suppl.Fig.10 D-E)." Why is Sup. Fig. 10 D, E referred to? The figure legend is also not clear, e.g. what means "upregulated in a subset of IFN-inducible genes"? Input for the hallmarks needs to be defined.

Author: These data is now presented in Suppl.Fig.11 and following the reviewer’s comment, we moved reference to panels 11D – E up to previous paragraph in the main text, where it naturally belongs . We also edited the figure legend to refer to the list of IFN-inducible genes compiled from the literature that is discussed in the text. We appreciate the reviewer’s suggestion that helped us improve the text clarity. The inputs for the Hallmark pathway analysis are presented in Suppl.Tables 7 and 8, as described in the text.

• Fig. 7C: Single channel pictures are required as it is hard to see the differences in staining with so many markers. Why is there no iNOS expression in the bottom row? What does the rectangle indicate on the bottom right? As black is chosen for DAPI, it is not visible at all. In case the signal is needed a visible a color should be chosen.

Author: We thoroughly revised this figure to address the reviewer’s concern about the lack of clarity. We provide individual channels for each marker in Fig.7D – E and Suppl.Fig.13F. We have to use DAPI in these presentation in gray scale to better visualize other markers.

• "In the advanced lesions these markers were primarily expressed by activated macrophages (Iba1+) expressing iNOS and/or Ifny (YFP+)(Fig.7D)" Iba1 is needed in the quantification. Based on the images, iNOS seems to be highly produced in Iba1 negative cells. Which cells do produce it then? Flow cytometry data for this quantification are required. This would allow you to specifically check which cells express the markers and allow for a more precise analysis of double positive cells.

Author: Currently these data demonstrating the co-localization of stress markers phospho-c-Jun and Chac1 with YFP are presented in Fig.7E (images) and Suppl.Fig.13D (quantification). The co-localization of stress markers phospho-cJun and Chac1 with iNOS is presented in Suppl.Fig.13F (images) and Suppl.Fig.13E (quantification). We agree that some iNOS+ cells are Iba1-negative (Fig.7D). We manually quantified percentages of Iba1+iNOS+ double positive cells and demonstrated that they represent the majority of the iNOS+ population(Suppl.Fig.13A). Regarding the required FACS analysis, we focus on spatial approaches because of the heterogeneity of the lesions that would be lost if lungs are dissociated for FACS. We are working on spatial transcriptomics at a single cell resolution that preserves spatial organization of TB lesions to address the reviewer’s comment and will present our results in the future.

• Results part 6: In general, can you please state for each experiment at what time point mice were analyzed? You should include an additional macrophage staining (e.g. MerTK, F4/80), as alveolar macrophages are not staining well for Iba1 and you might therefore miss them in your IF microscopy. It would be very nice if you could perform flow cytometry to really check on the macrophages during infection and distinguish subsets (e.g. alveolar macrophages, interstitial macrophages, monocytes).

Author: We have included the details of time post infection in figure legends for Fig.7, Suppl.Figures 8, 9, 12B, 13, 14A of the revised manuscript. We have performed staining with CD11b, CD206 and CD163 to differentiate the recruited and lung resident macrophages and determined that in chronic pulmonary TB lesions in our model the vast majority of macrophages are recruited CD11b+, but not resident (CD206+ and CD163+) macrophages. These data is presented in another manuscript (Yabaji et al., BioRxiv https://doi.org/10.1101/2023.10.17.562695).

• Spatial sequencing: The manuscript would highly profit from more data on that. It would be very interesting to check for the DEGs and show differential spatial distribution. Expression of marker genes should be inferred to further define macrophage subsets (e.g. alveolar macrophages, interstitial macrophages, recruited macrophages) and see if these subsets behave differently within the same lesion but also between the lesions. Additional bioinformatic approaches might allow you to investigate cell-cell interactions. There is a lot of potential with such a dataset, especially from TB lesions, that would elevate your findings and prove interesting to the TB field.

• "Thus, progression from the Mtb-controlling paucibacillary to non-controlling multibacillary TB lesions in the lungs of TB susceptible mice was mechanistically linked with a pathological state of macrophage activation characterized by escalating stress (as evidenced by the upregulation phospho-cJUN, PKR and Chac1), the upregulation of IFNβ and the IFN-I pathway hyperactivity, with a concurrent reduction of IFNγ responses." To really show the upregulation within macrophages and their activation, a more detailed IF microscopy with the inclusion of additional macrophage markers needs to be provided. Flow cytometry would enable analysis for the differences between alveolar and interstitial macrophages, as well as for monocytes. As however, it seems that the majority of iNOS, as well as the stress associated markers are not produced by Iba1+ cells. Analyzing granulocytes and T lymphocytes should be considered.

Author: We appreciate the reviewer’s suggestion. Indeed, our model provides an excellent opportunity to investigate macrophage heterogeneity and cell interactions within chronic TB lesions. We are working on spatial transcriptomics at a single cell resolution that would address the reviewer’s comment and will present our results in the future.

In agreement with classical literature the overwhelming majority of myeloid cells in chronic pulmonary TB lesions is represented by macrophages. Neutrophils are detected at the necrotic stage, but our study is focused on pre-necrotic stages to reveal the earlier mechanisms pre-disposing to the necrotization. We never observed neutrophils or T cells expressing iNOS in our studies.

• It's mentioned in the method section that controls in the IF staining were only fixed for 10min, while the infected cells were fixed for 30min. Consistency is important as the PFA fixation might impact on the fluorescence signal. Therefore, controls should be repeated with the same fixation time.

Author: We have carefully considered the impact of fixation time on fluorescence and have separately analyzed the non-infected and infected samples to address this concern.

For the non-infected samples, we examined the effect of TNF in both B6 and B6.Sst1S backgrounds, ensuring that a consistent fixation protocol (10 min) was applied across all experiments without Mtb infection.

For the Mtb infection experiments, we employed an optimized fixation protocol (30 min) to ensure that Mtb was killed before handling the plates, which is critical for preserving the integrity of the samples. In this context, we compared B6 and B6.Sst1S samples to evaluate the effects of fixation and Mtb infection on lipid peroxidation (LPO) induction.

We believe this approach balances the need for experimental consistency with the specific requirements for handling infected cells, and we have revised the manuscript to reflect this clarification.

• Reactive oxygen species levels should be determined in B6 and B6.Sst1S BMDMs (stimulated and unstimulated), as they are very important for oxidative stress.

Author: We have conducted experiments to measure ROS production in both B6 and B6.Sst1S BMDMs and demonstrated higher levels of ROS in the susceptible BMDMs after prolonged TNF stimulation (new Fig.3I – J and Suppl. Fig. 3G). Additionally, we have previously published a comparison of ROS production between B6 and B6.Sst1S by FACS (PMID: 33301427), which also supports the findings presented here.

• Sup. Fig 2C: The inclusion of an unstimulated control would be advisable in order to evaluate if there are already difference in the beginning.

Author: We have included the untreated control to the Suppl. Fig. 2C (currently Suppl. Fig. 2D) in the revised manuscript.

• Sup. Fig. 3F: Why is the fold change now lower than in Fig. 4D (fold change of around 28 compared to 120 in 4D)?

Author: The data in Fig.4D (Fig.4E in the revised manuscript) and Suppl.Fig.3F (currently Suppl.Fig.4C) represent separate experiments and this variation between experiments is commonly observed in qRT-PCR that is affected by slight variations in the expression in unsimulated controls used for the normalization and the kinetics of the response. This 2-4 fold difference between same treatments in separate experiments, as compared to 30 – 100 fold and higher induction by TNF does not affect the data interpretation.

• Sup. Fig. 5C, D: The data seems very interesting as you even observe an increase in gene expression. Data for the B6 mice should be evaluated for increase to a similar level as the TNF treated mutants. Data on the viability of the cells are necessary, as they no longer receive M-CSF and might be dying at this point already.

Author: To ensure that the observed effects were not confounded by cytotoxicity, we determined non-toxic concentrations of the CSF1R inhibitors during 48h of incubation and used them in our experiments that lasted for 24h. To address this valid comment, we have included cell viability data in the revised manuscript to confirm that the treatments did not result in cell death (Suppl. Fig. 6D). This experiment rejected our hypothesis that CSF1 driven Myc expression could be involved in the Ifnb superinduction. Other effects of CSF1R inhibitors on type I IFN pathway are intriguing but are beyond the scope of this study.

• Sup. Fig 12: the phospho-c-Jun picture for (P) is not the same as in the merged one with Iba1. Double positive cells are mentioned to be analyzed, but from the staining it appears that P-c-Jun is expressed by other cells. You do not indicate how many replicates were counted and if the P and M lesions were evaluated within the same animal. What does the error bar indicate? It seems unlikely from the plots that the double positive cells are significant. Please provide the p values and statistical analysis.

Author: We thank the reviewer for bringing this inadvertent field replacement in the single phospho-cJun channel to our attention. However, the quantification of Iba1+phospho-cJun+ double positive cells in Suppl.Fig.12 and our conclusions were not affected. In the revised manuscript, images and quantification of phospho-cJun and Iba1 co-expression are shown in new Suppl.Fig.13B and C, respectively. We have also updated the figure legends to denote the number of lesions analyzed and statistical tests. Specifically, lesions from 6–8 mice per group (paucibacillary and multibacillary) were evaluated. Each dot in panels Suppl.Fig.13 represent individual lesions.

• Sup. Fig. 13D (suppl.Fig.15D now): What about the expression of MYC itself? Other parts of the signaling pathway should be analyzed(e.g. IFNb, JNK)?

Author: The difference in MYC mRNA expression tended to be higher in TB patients with poor outcomes, but it was not statistically significant after correction for multiple testing. The upregulation of Myc pathway in the blood transcriptome associated with TB treatment failure most likely reflects greater proportion of immature cells in peripheral blood, possibly due to increased myelopoiesis. Pathway analysis of the differentially expressed genes revealed that treatment failures were associated with the following pathways relevant to this study: NF-kB Signaling, Flt3 Signaling in Hematopoietic Progenitor Cells (indicative of common myeloid progenitor cell proliferation), SAPK/JNK Signaling and Senescence (possibly indicative of oxidative stress). The upregulation of these pathways in human patients with poor TB treatment outcomes correlates with our findings in TB susceptible mice.

• In the mfIHC you he usage of anti-mouse antibodies is mentioned. Pictures of sections incubated with the secondary antibody alone are required to exclude the possibility that the staining is not specific. Especially, as this data is essential to the manuscript and mouse-anti-mouse antibodies are notorious for background noise.

Author: We are well aware of the technical difficulties associated with using mouse on mouse staining. In those cases, we use rabbit anti-mouse isotype specific antibodies specifically developed to avoid non-specific background (Abcam cat#ab133469). Each antibody panel for fluorescent multiplexed IHC is carefully optimized prior to studies. We did not use any primary mouse antibodies in the final version of the manuscript and, hence, removed this mention from the Methods.

• In order to tie the story together, it would be interesting to treat infected mice with an INFAR antibody, as well as perform this experiment with a Myc antibody. According to your data, you might expect the survival of the mice to be increased or bacterial loads to be affected.

Author: In collaboration with the Vance laboratory, we tested effects of type I IFN pathway inhibition in B6.Sst1S mice on TB susceptibility: either type I receptor knockout or blocking antibodies increased their resistance to virulent Mtb (published in Ji et al., 2019; PMID 31611644). Unfortunately, blocking Myc using neutralizing antibodies in vivo is not currently achievable. Specifically blocking Myc using small molecule inhibitors in vivo is notoriously difficult, as recognized in oncology literature. We consider using small molecule inhibitors of either Myc translation or specific pathways downstream of Myc in the future.

• It is surprising that you not even once cite or mention your previous study on bioRxiv considering the similarity of the results and topic (https://doi.org/10.1101/2020.12.14.422743). Is not even your Figure 1I and Figure 2 J, K the same as in that study depicted in Figure 4?

Author: The reviewer refers to the first version of this manuscript uploaded to BioRxiv, but it has never been published. We continued this work and greatly expanded our original observations, as presented in the current manuscript. Therefore, we do not consider the previous version as an independent manuscript and, therefore, do not cite it.

• Please revise spelling of the manuscript and pay attention to write gene names in italics

Author: Thank you, we corrected the gene and protein names according to current nomenclature.

Minor points: - Fig. 1: Please provide some DEGs that explain why you used this resolution for the clustering of the scRNAseq data and that these clusters are truly distinct from each other.

Author: Differential gene expression in clusters is presented in Suppl.Fig.1C (interferon response) and Suppl.Fig.1D (stress markers and interferon response previously established in our studies).

• Fig. 1F: What do the two lines represent (magenta, green)?

Author: The lines indicate pseudotime trajectories of B6 (magenta) and B6.Sst1S (green) BMDMs.

• Fig. 1F, G: Why was cluster 6 excluded?

Author: This cluster was not different between B6 and B6.Sst1S, so it was not useful for drawing the strain-specific trajectories.

• Fig. 1E, G, H: The intensity scales are missing. They are vital to understand the data.

Author: We have included the scale in revised manuscript (Fig.1E,G,H and Suppl.Fig.1C-D).

• Fig. 2G-I: please revise order, as you first refer to Fig. 2H and I

Author: We revised the panels’ order accordingly

• Fig. 5: You say the data represents three samples but at least in D and E you have more. Please revise. Why do you only include at (G) the inhibitor only control?

Author: We added the inhibitor only controls to Fig. 5D - H. We also indicated the number of replicates in the updated Fig.5 legend.

• Figure 7A, Sup. Fig. 8: Are these maximum intensity projection? Or is one z-level from the 3D stack depicted?

Author: The Fig. 7A shows 3D images with all the stacks combined.

• Fig. 7B: What do the white boxes indicate?

Author: We have removed this panel in the revised version and replaced it with better images.

• Sup. Fig. 1A: The legend for the staining is missing

Author: The Suppl. Fig.1A shows the relative proportions of either naïve (R and S) or TNF-stimulated (RT and ST) B6 or B6.Sst1S macrophages within individual single cell clusters depicted in Fig.1B. The color code is shown next to the graph on the right.

• Sup. Fig. 1B: The feature plots are not clear: The legend for the expression levels is missing. What does the heading means?

Author: We updated the headings, as in Fig.1C. The dots represent individual cells expressing Sp110 mRNA (upper panels) and Sp140 mRNA (lower panels).

• Sup. Fig. 3C: The scale bar is barely visible.

Author: We resized the scale bar to make it visible and presented in Suppl. Fig.3E (previously Suppl. Fig.3C).

• Sup. Fig. 3D: There is not figure legend or the legend to C-E is wrong.

• Sup. Fig. 3F, G: You do not state to what the data is relative to.

Author: We identified an error in the Suppl.Fig.3 legend referring to specific panels. The Suppl.Fig.3 legend has been updated accordingly. New panels were added and Suppl.Fig.3-G panels are now Suppl.Fig.4C-D.

• Sup. Fig. 3H: It seems you used a two-way ANOVA, yet state it differently. Please revise the figure legend, as Dunnett's multiple comparison would only check for significances compared to the control.

Author: Following the reviewer’s comment, we repeated statistical analysis to include correction for multiple comparisons and revised the figure and legend accordingly.

• Sup. Fig. 4A, B: It is not clear what the lines depict as the legend is not explained. Names that are not required should be changed to make it clear what is depicted (e.g. "TE@" what does this refer to?)

Author: This previous Sup. Fig 4 is now Sup. Fig. 5. The “TE@” is a leftover label from the bioinformatics pipeline, referring to “Transposable Element”. We apologize for this confusion and have removed these extraneous labels. We have also added transposon names of the LTR (MMLV30 and RTLV4) and L1Md to Suppl.Fig.5A and 5B legend, respectively.

• Sup. 4B: What does the y-scale on the right refer to?

Author: We apologize for the missing label for the y-scale on the right which represents the mRNA expression level for the SetDB1 gene, which has a much lower steady state level than the LINE L1Md, so we plotted two Y-scales to allow both the gene and transposon to be visualized on this graph.

• Sup. 4C: Interpretation of the data is highly hindered by the fact that the scales differ between the B6 and B6.Sst1. The scales are barely visible.

Author: We apologize for the missing labels for the y-scales of these coverage plots, which were originally meant to just show a qualitative picture of the small RNA sequencing that was already quantitated by the total amounts in Sup. 4B. We have added thee auto-scaled Y-scales to Sup. 4C and improved the presentation of this figure.

• Sup. Fig. 5A, B: Is the legend correct? Did you add the antibody for 2 days or is the quantification from day 3?

Author: We recognize that the reviewer refers to Suppl.Fig.6A-B (Suppl.Fig.7A-B in the revised manuscript). We did not add antibodies to live cells. The figure legend describes staining with 4-HNE-specific antibodies 3 days post Mtb infection.

• Sup. Fig. 8A: Are the "early" and "intermediate" lesions from the same time points? What are the definitions for these stages?

Author: We discussed our lesion classification according to histopathology and bacterial loads above. Of note, in the revised manuscript we simplified our classification to denote paucibacillary and multibacillary lesions only. We agree with reviewers that designation lesions as early, intermediate and advanced lesions were based on our assumptions regarding the time course of their progression from low to high bacterial loads.

• Sup. Fig. 8E: You should state that the bottom picture is an enlargement of an area in the top one. Scale bars are missing.

Author: We replaced this panel with clearer images in Suppl.Fig.12B.

• Sup. Fig. 11A: The IF staining is only visible for Iba and iNOS. Please provide single channels in order to make the other staining visible.

Author: Suppl.Fig.11A (now Suppl.Fig.13B) shows the low-magnification images of TB lesions. In the Fig. 7 and Suppl. Fig. 13F of the revised manuscript we provided images for individual markers.

• Sup. Fig. 13A (Suppl.Fig.15A now): Your axis label is not clear. What do the numbers behind the genes indicate? Why did you choose oncogene signatures and not inflammatory markers to check for a correlation with disease outcome?

Author: X axis of Suppl.Fig.15A represent pre-defined molecular signature gene sets MSigDB) in Gene Set Enrichment Analysis (GSEA) database (https://www.gsea-msigdb.org/gsea/msigdb). On Y axis is area under curve (AUC) score for each gene set.

• Sup. 13D(Suppl.Fig.15D now):: Maybe you could reorder the patients, so that the impression is clearer, as right now only the top genes seem to show a diverging gene signature, while the rest gives the impression of an equal distribution.

Author: The Myc upregulated gene set myc_up was identified among top gene sets associated with treatment failure using unbiased ssGSEA algorithm. We agree with the reviewer that not every gene in the myc_up gene set correlates with the treatment outcome. But the association of the gene set is statistically significant, as presented in Suppl.Fig.15B – C.

• The scale bars for many microscopy pictures are missing.

Author: We have included clearly visible scale bars to all the microscopy images in the revised version.

• The black bar plots should be changed (e.g. in color), since the single data points cannot be seen otherwise.
• It would be advisable that a consistent color scheme would be used throughout the manuscript to make it easier to identify similar conditions, as otherwise many different colours are not required and lead right now rather to confusion (e.g. sometimes a black bar refers to BMDMs with and sometimes without TNF stimulation, or B6 BMDMs). Furthermore, plot sizes and fonts should be consistent within the manuscript (including the supplemental data)

Author: We followed this useful suggestion and selected consistent color codes for B6 and B6.Sst1S groups to enhance clarity throughout the revised manuscript.

Within the methods section: - At which concentration did you use the IFNAR antibody and the isotype?

Author: We updated method section by including respective concentrations in the revised manuscript.

• Were mice maintained under SPF conditions? At what age where they used?

Author: Yes, the mice are specific pathogen free. We used 10 - 14 week old mice for Mtb infection.

• The BMDM cultivation is not clear. According to your cited paper you use LCCM but can you provide how much M-CSF it contains? How do you make sure that amounts are the same between experiments and do not vary? You do not mention how you actually obtain this conditioned medium. Is there the possibility of contamination or transferred fibroblasts that would impact on the data analysis? Is LCCM also added during stimulation and inhibitor treatment?

Author: We obtain LCCM by collecting the supernatant from L929 cell line that form confluent monolayer according to well-established protocols for LCCM collection. The supernatants are filtered through 0.22 micron filters to exclude contamination with L929 cells and bacteria. The medium is prepared in 500 ml batches that are sufficient for multiples experiments. Each batch of L929-conditioned medium is tested for biological activity using serial dilutions.

• How was the BCG infection performed? How much bacteria did you use? Which BCG strain was used?

Author: We infected mice with M. bovis BCG Pasteur subcutaneously in the hock using 106 CFU per mouse.

• At what density did you seed the BMDMs for stimulation and inhibitor experiments?

Author: In 96 well plates, we seed 12,000 cells per well and allow the cells to grow for 4 days to reach confluency (approximately 50,000 cells per well). For a 6-well plate, we seed 2.5 × 10^5 cells per well and culture them for 4 days to reach confluency. For a 24-well plate, we seed 50,000 cells per well and keep the cells in media for 4 days before starting any treatments. This ensures that the cells are in a proliferative or near-confluent state before beginning the stimulation or inhibitor treatments. Our detailed protocol is published in STAR Protocols (Yabaji et al., 2022; PMID 35310069).

• What machine did you use to perform the bulk RNA sequencing? How many replicates did you include for the sequencing?

Author: For bulk sequencing we used 3 RNA samples for each condition. The samples were sequenced at Boston University Microarray & Sequencing Resource service using Illumina NextSeq™ 2000 instrument.

• How many replicates were used for the scRNA sequencing? Why is your threshold for the exclusion of mitochondrial DNA so high? A typical threshold of less than 5% has been reported to work well with mouse tissue.

Author: We used one sample per condition. For the mitochondrial cutoff, we usually base it off of the total distribution. There is no "universal" threshold that can be applied to all datasets. Thresholds must be determined empirically.

• You do not mention how many PCAs were considered for the scRNA sequencing analysis.

Author: We considered 50 PCAs, this information was added to Methods

• You should name all the package versions you used for the scRNA sequencing (e.g. for the slingshot, VAM package)

Author: The following package versions were used: Seurat v4.0.4, VAM v1.0.0, Slingshot v2.3.0, SingleCellTK v2.4.1, Celda v1.10.0, we added this information to Methods.

• You mention two batches for the human samples. Can you specify what the two batches are?

Author: Human blood samples were collected at five sites, as described in the updated Methods section and two RNAseq batches were processed separately that required batch correction.

• At which temperature was the IF staining performed?

Author: We performed the IF at 4oC. We included the details in revised version.

Reviewer #2 (Significance (Required)):

Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection. However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

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

Summary The authors use a mouse model designed to be more susceptible to M.tb (addition of sst1 locus) which has granulomatous lesions more similar to human granulomas, making this mouse highly relevant for M.tb pathogenesis studies. Using WT B6 macrophages or sst1B6 macrophages, the authors seek to understand the how the sst1 locus affects macrophage response to prolonged TNFa exposure, which can occur during a pro-inflammatory response in the lungs. Using single cell RNA-seq, revealed clusters of mutant macrophages with upregulated genes associated with oxidative stress responses and IFN-I signaling pathways when treated with TNF compared to WT macs. The authors go on to show that mutant macrophages have decreased NRF2, decreased antioxidant defense genes and less Sp110 and Sp140. Mutant macrophages are also more susceptible to lipid peroxidation and iron-mediated oxidative stress. The IFN-I pathway hyperactivity is caused by the dysregulation of iron storage and antioxidant defense. These mutant macrophages are more susceptible to M.tb infection, showing they are less able to control bacterial growth even in the presence of T cells from BCG vaccinated mice. The transcription factor Myc is more highly expressed in mutant macs during TNF treatment and inhibition Myc led to better control of M.tb growth. Myc is also more abundant in PBMCs from M.tb infected humans with poor outcomes, suggesting that Myc should be further investigated as a target for host-directed therapies for tuberculosis.

Major Comments Isotypes for IF imaging and confocal IF imaging are not listed, or not performed. It is a concern that the microscopy images throughout the manuscript do not have isotype controls for the primary antibodies.

Fig 4 (and later) the anti-IFNAR Ab is used along with the Isotype antibody, Fig 4I does not show the isotype. Use of the isotype antibody is also missing in later figures as well as Fig 3J. Why was this left off as the proper control for the Ab?

Author: We addressed the comment in revised manuscript as described above in summary and responses to reviewers 1 and 2. Isotype controls for IFNAR1 blockade were included in Fig.3M (previously 3J), Fig. 4I, Suppl.Fig.4G (previously Fig.4I), and updated Fig.4C -E, Fig.6L-M, Suppl.Fig.4F -G, 7I.

Conclusions drawn by the authors from some of the WB data are worded strongly, yet by eye the blots don't look as dramatically different as suggested. It would be very helpful to quantify the density of bands when making conclusions. (for example, Fig 4A).

Author: We added the densitometry of Western blot values after normalization above each lane in Fig.2A – C, Fig.3C – D and 3K; Fig.4A – B, Fig5B,C,I,J.

Fig 5A is not described clearly. If the gene expression is normalized to untreated B6 macs, then the level of untreated B6 macs should be 1. In the graph the blue bars are slightly below 1, which would not suggest that levels "initially increased and subsequently downregulated" as stated in the text. It seems like the text describes the protein expression but not the RNA expression. Please check this section and more clearly describe the results.

Author: We appreciate the reviewer’s comment and modified the text to specify the mRNA and protein expression data, as follows:

“We observed that Myc was regulated in an sst1-dependent manner: in TNF-stimulated B6 wild type BMDMs, c-Myc mRNA was downregulated, while in the susceptible macrophages c-Myc mRNA was upregulated (Fig.5A). The c-Myc protein levels were also higher in the B6.Sst1S cells in unstimulated BMDMs and 6 – 12 h of TNF stimulation (Fig.5B)”.

Also, why look at RNA through 24h but protein only through 12h? If c-myc transcripts continue to increase through 24h, it would be interesting to see if protein levels also increase at this later time point.

Author: The time-course of Myc expression up to 24 h is presented in new panels Fig. 5I-5J

It demonstrates the decrease of Myc protein levels at 24 h. In the wild type B6 BMDMs the levels of Myc protein significantly decreased in parallel with the mRNA suppression presented in Fig.5A. In contrast , we observed the dissociation of the mRNA and protein levels in the sst1-mutant BMDMs at 12 and 24 h, most likely, because the mutant macrophages develop integrated stress response (as shown in our previous publication by Bhattacharya et al., JCI, 2021) that is known to inhibit Myc mRNA translation.

Fig 5J the bands look smaller after D-JNK1 treatment at 6 and 12h though in the text is says no change. Quantifying the bands here would be helpful to see if there really is no difference.

Author: This experiment was repeated twice, and the average normalized densitometry values are presented in the updated Fig.5J. The main question addressed in this experiment was whether the hyperactivity of JNK in TNF-stimulated sst1 mutant macrophages contributed to Myc upregulation, as was previously shown in cancer. Comparing effects of JNK inhibition on phospho-cJun and c-Myc protein levels in TNF stimulated B6.Sst1S macrophages (updated Fig.5J), we concluded that JNK did not have a major role in c-Myc upregulation in this context.

Section 4, third paragraph, the conclusion that JNK activation in mutant macs drives pathways downstream of Myc are not supported here. Are there data or other literature from the lab that supports this claim?

Author: This statement was based on evidence from available literature where JNK was shown to activate oncogens, including Myc. In addition, inhibition of Myc in our model upregulated ferritin (Fig.Fig.5C), reduced the labile iron pool, prevented the LPO accumulation (Fig.5D - G) and inhibited stress markers (Fig.5H). However, we do not have direct experimental evidence in our model that Myc inhibition reduces ASK1 and JNK activities. Hence, we removed this statement from the text and plan to investigate this in the future.

Fig 6N Please provide further rationale for the BCG in vivo experiment. It is unclear what the hypothesis was for this experiment.

Author: In the current version BCG vaccination data is presented in Suppl.Fig.14B. We demonstrate that stressed BMDMs do not respond to activation by BCG-specific T cells (Fig.6J) and their unresponsiveness is mediated by type I interferon (Fig.6L and 6M). The observed accumulation of the stressed macrophages in pulmonary TB lesions of the sst1-susceptible mice (Fig.7E, Suppl.Fig.13 and 14A) and the upregulation of type I interferon pathway (Fig.1E,1G, 7C), Suppl.Fig.1C and 11) suggested that the effect of further boosting T lymphocytes using BCG in Mtb-infected mice will be neutralized due to the macrophage unresponsiveness. This experiment provides a novel insight explaining why BCG vaccine may not be efficient against pulmonary TB in susceptible hosts.

The in vitro work is all concerning treatment with TNFa and how this exposure modifies the responses in B6 vs sst1B6 macrophages; however, this is not explored in the in vivo studies. Are there differences in TNFa levels in the pauci- vs multi-bacillary lesions that lead to (or correlate with) the accumulation of peroxidation products in the intralesional macrophages. How to the experiments with TNFa in vitro relate back to how the macrophages are responding in vivo during infection?

Author: Our investigation of mechanisms of necrosis of TB granulomas stems from and supported by in vivo studies as summarized below.

This work started with the characterization necrotic TB granulomas in C3HeB/FeJ mice in vivo followed by a classical forward genetic analysis of susceptibility to virulent Mtb in vivo.

That led to the discovery of the sst1 locus and demonstration that it plays a dominant role in the formation of necrotic TB granulomas in mouse lungs in vivo. Using genetic and immunological approaches we demonstrated that the sst1 susceptibility allele controls macrophage function in vivo (Yan, et al., J.Immunol. 2007) and an aberrant macrophage activation by TNF and increased production of Ifn-b in vitro (He et al. Plos Pathogens, 2013). In collaboration with the Vance lab we demonstrated that the type I IFN receptor inactivation reduced the susceptibility to intracellular bacteria of the sst1-susceptible mice in vivo (Ji et al., Nature Microbiology, 2019). Next, we demonstrated that the Ifnb1 mRNA superinduction results from combined effects of TNF and JNK leading to integrated stress response in vitro (Bhattacharya, JCI, 2021). Thus, our previous work started with extensive characterization of the in vivo phenotype that led to the identification of the underlying macrophage deficiency that allowed for the detailed characterization of the macrophage phenotype in vitro presented in this manuscript. In a separate study, the Sher lab confirmed our conclusions and their in vivo relevance using Bach1 knockout in the sst1-susceptible B6.Sst1S background, where boosting antioxidant defense by Bach1 inactivation resulted in decreased type I interferon pathway activity and reduced granuloma necrosis. We have chosen TNF stimulation for our in vitro studies because this cytokine is most relevant for the formation and maintenance of the integrity of TB granulomas in vivo as shown in mice, non-human primates and humans. Here we demonstrate that although TNF is necessary for host resistance to virulent Mtb, its activity is insufficient for full protection of the susceptible hosts, because of altered macrophages responsiveness to TNF. Thus, our exploration of the necrosis of TB granulomas encompass both in vitro and extensive in vivo studies.

Minor comments Introduction, while well written, is longer than necessary. Consider shortening this section. Throughout figures, many graphs show a fold induction/accumulation/etc, but it is rarely specified what the internal control is for each graph. This needs to be added. Paragraph one, authors use the phrase "the entire IFN pathway was dramatically upregulated..." seems to be an exaggeration. How do you know the "entire" IFN pathway was upregulated in a dramatic fashion?

Author: 1) We shortened the introduction and discussion; 2) verified that figure legends internal controls that were used to calculate fold induction; 3) removed the word “entire” to avoid overinterpretation.

Figures 1E, G and H and supp fig 1C, the heat maps are missing an expression key Section 2 second paragraph refers to figs 2D, E as cytoplasmic in the text, but figure legend and y-axis of 2E show total protein.

Author: The expression keys were added to Fig.1E,G,H, Fig.7C, Suppl.Fig.1C and 1D and Suppl.Fig.11A of the revised manuscript.

Section 3 end of paragraph 1 refers to Fig 3h. Does this also refer to Supp Fig 3E?

Author: Yes, Fig.3H shows microscopy of 4-HNE and Suppl.Fig.3H shows quantification of the image analysis. In the revised manuscript these data are presented in Fig.3H and Suppl.Fig.3F. The text was modified to reflect this change.

Supplemental Fig 3 legend for C-E seems to incorrectly also reference F and G.

Author: We corrected this error in the figure legend. New panels were added to Suppl.Fig.3 and previous Suppl.Fig.3F and G were moved to Suppl.Fig.4 panels C and D of the revise version.

Fig 3K, the p-cJun was inhibited with the JNK inhibitor, however it’s unclear why this was done or the conclusion drawn from this experiment. Use of the JNK inhibitor is not discussed in the text.

Author: The JNK inhibitor was used to confirm that c-Jun phosphorylation in our studies is mediated by JNK and to compare effects of JNK inhibition on phospho-cJun and Myc expression. This experiment demonstrated that the JNK inhibitor effectively inhibited c-Jun phosphorylation but not Myc upregulation, as shown in Fig.5I-J of the revised manuscript.

Fig 4 I and Supp Fig 3 H seem to have been swapped? The graph in Fig 4I matches the images in Supp Fig 3I. Please check.

Author: We reorganized the panels to provide microscopy images and corresponding quantification together in the revised the panels Fig. 4H and Fig. 4I, as well as in Suppl. Fig. 4F and Suppl. Fig. 4G.

Fig 6, it is unclear what % cell number means. Also for bacterial growth, the data are fold change compared to what internal control?

Author: We updated Fig.6 legend to indicate that the cell number percentages were calculated based on the number of cells at Day 0 (immediately after Mtb infection). We routinely use fixable cell death staining to enumerate cell death. Brief protocol containing this information is included in Methods section. The detailed protocol including normalization using BCG spike has been published – Yabaji et al, STAR Protocols, 2022. Here we did not present dead cell percentage as it remained low and we did not observe damage to macrophage monolayers. This allows us to exclude artifacts due to cell loss. The fold change of Mtb was calculated after normalization using Mtb load at Day 0 after infection and washes.

Fig 7B needs an expression key

Author: The expression keys was added to Fig.7C (previously Fig. 7B).

Supp Fig 7 and Supp Fig 8A, what do the arrows indicate?

Author: In Suppl.Fig.8 (previously Suppl.Fig.7) the arrows indicate acid fast bacilli (Mtb).

In figures Fig.7A and Suppl.Fig.9A arrows indicate Mtb expressing fluorescent reporter mCherry. Corresponding figure legends were updated in the revised version.

Supp Fig 9A, two ROI appear to be outlined in white, not just 1 as the legend says Methods:

Author: we updated the figure legend.

Certain items are listed in the Reagents section that are not used in the manuscript, such as necrostatin-1 or Z-VAD-FMK. Please carefully check the methods to ensure extra items or missing items does not occur.

Author: These experiments were performed, but not included in the final manuscript. Hence, we removed the “necrostatin-1 or Z-VAD-FMK” from the reagents section in methods of revised version.

Western blot, method of visualizing/imaging bands is not provided, method of quantifying density is not provided, though this was done for fig 5C and should be performed for the other WBs.

Author: We used GE ImageQuant LAS4000 Multi-Mode Imager to acquire the Western blot images and the densitometric analyses were performed by area quantification using ImageJ. We included this information in the method section. We added the densitometry of Western blot values after normalization above each lane in Fig.2A – C, Fig.3C – D and 3K; Fig.4A – B, Fig5B,C,I,J.

Reviewer #3 (Significance (Required)):

The work of Yabaji et al is of high significance to the field of macrophage biology and M.tb pathogenesis in macrophages. This work builds from previously published work (Bhattacharya 2021) in which the authors first identified the aberrant response induced by TNF in sst1 mutant macrophages. Better understanding how macrophages with the sst1 locus respond not only to bacterial infection but stimulation with relevant ligands such as TNF will aid the field in identifying biomarkers for TB, biomarkers that can suggest a poor outcome vs. "cure" in response to antibiotic treatment or design of host-directed therapies. This work will be of interest to those who study macrophage biology and who study M.tb pathogenesis and tuberculosis in particular. This study expands the knowledge already gained on the sst1 locus to further determine how early macrophage responses are shaped that can ultimately determine disease progression. Strengths of the study include the methodologies, employing both bulk and single cell-RNA seq to answer specific questions. Data are analyze using automated methods (such as HALO) to eliminated bias. The experiments are well planned and designed to determine the mechanisms behind the increased iron-related oxidative stress found in the mutant macrophages following TNF treatment. Also, in vivo studies were performed to validate some of the in vitro work. Examining pauci-bacillary lesions vs multi-bacillary lesions and spatial transcriptomics is a significant strength of this work. The inclusion of human data is another strength of the study, showing increased Myc in humans with poor response to antibiotics for TB. Limitations include the fact that the work is all done with BMDMs. Use of alveolar macrophages from the mice would be a more relevant cell type for M.tb studies. AMs are less inflammatory, therefore treatment with TNF of AMs could result in different results compared to BMDMs. Reviewer's field of expertise: macrophage activation, M.tb pathogenesis in human and mouse models, cell signaling Limitations: not qualified to evaluate single cell or bulk RNA-seq technical analysis/methodology or spatial transcriptomics analysis.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Yabaji et al. investigated the effects of BMDMs stimulated with TNF from both WT and B6.Sst1S mice, which have previously been identified to contain the sst1 locus conferring susceptibility to Mycobacterium tuberculosis. They identified that B6.Sst1S macrophages show a superinduction of IFNß, which might be caused by increased c-Myc expression, expanding on the mechanistic insights made by the same group (Bhattacharya et al. 2021). Furthermore, prolonged TNF stimulation led to oxidative stress, which WT BMDMs could compensate for by the activation of the antioxidant defense via NRF2. On the other hand, B6.Sst1S BMDMs lack the expression of SP110 and SP140, co-activators of NRF2, and were therefore subjected to maintained oxidative stress. Yabaji et al. could link those findings to in vivo studies by correlating the presence of stressed and aberrantly activated macrophages within granulomas to the failure of Mtb control, as well as the progression towards necrosis. As the knowledge regarding Mtb progression and necrosis of granulomas is not yet well understood, findings that might help provide novel therapy options for TB are crucial.

Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

In particular a) important controls are often missing, e.g. T-cells form non-immune mice in Fig. 6J, in F, effectivity of BCG in B6 mice in 6N; b) single experiments are shown throughout the manuscript, in particular western blots and histology without proper quantification and statistics, this is absolutely not acceptable; c) very few repetitions are shown in in vitro experiments, where there is no evidence for limitation in resources (usually not more than 3), it is not clear what "independent experiment means" - i.e. the robustness of the findings is questionable; d) data are often normalized multiple times, e.g. in the case of qPCR, and the methods of normalization are not clear (what house-keeping gene exactly?);

Moreover, experiments regarding IFN I signaling (e.g. short term TNF treatment of BMDMs to analyze LPO, making sure that the reporter mouse for IFNß works in vivo) and c-Myc (e.g. the increase after M-CSF addition might impact on other analysis as well and the experiments should be adjusted to control for this effect; MYC expression in the human samples) should be carefully repeated and evaluated to draw correct conclusions.

In addition, we would like to strongly encourage the authors to more precisely outline the experimental set-ups and figure legends, so that the reader can easily understand and follow them. In other words: The legends are - in part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

Finally, it is necessary that the connection to several overlapping preprints by the same author group is outlined, e.g. to https://www.biorxiv.org/content/10.1101/2020.12.14.422743v1.full.

part very - incomplete. In addition, the authors should be mindful of gene names vs. protein names and italicize where appropriate.

Finally, it is necessary that the connection to several overlapping preprints by the same author group is outlined, e.g. to https://www.biorxiv.org/content/10.1101/2020.12.14.422743v1.full.

Specific comments to the experiments and data:

• Fig. 1E: Evaluation of differences in up- and downregulation between B6 and B6.Sst1S cells should highlight where these cells are within the heatmap, as it is only labelled with the clusters, or it should be depicted differently (in particular for cluster 1 and 2). Furthermore, a more simple labelling of the pathways would increase the readability of the data
• Fig. 2D, E: The staining legend is missing. For the quantification it is not clear what % total means. Is this based on the intensity or area? What do the dots represent in the bar chart? Is one data point pooled from several pictures? If not, the experiments need to be repeated, as three pictures might not be representative for evaluation.
• Fig. 2E: Statistics comparing B6/ B6,SsT1S with TNF (different) is required: Absence of induction is not a proof for a difference!
• Fig. 3E: Positive and negative control need to be depicted in the figure (see legend).
• Fig. 3I: A quantification by flow cytometry or total cell counts are important, as 6% cell death in cell culture is a very modest observation. Otherwise, confocal images of the quantification would be a good addition to judge the specificity of the viability staining.
• Fig. 3I, J: What does one dot represent?
• Fig. 3K,L: For the B6 BMDMs it seems that p-cJun is highly increased at 12h in (L), while it is not in (K). On the other hand, for the B6.Sst1S BMDMs it peaks at 24h in (K), while in (L) it seems to at 12h. According to the data in (L) it seems that p-cJun is rather earlier and stronger activated in B6 BMDMs and has a weakened but prolonged activation in the B6.Sst1S BMDMs, which would not fit with your statement in the text that B6.Sst1S BMDMs show an upregulation. !These experiments need repetitions and quantification and statistiscs!
• Figure 3J: the isotype control for the IFNAR antibody is missing
• Fig. 3L: ASK1 seems to be higher at 12h for the B6 BMDMs and similar for both lines at 24h, which is not fitting to the statement in the text. ("Also, the ASK1 - JNK - cJun stress kinase axis was upregulated in B6.Sst1S macrophages, as compared to B6, after 12 - 36 h of TNF stimulation")
• Fig.4A - C: "IFNAR1 blockade, however, did not increase either the NRF2 and FTL protein levels, or the Fth, Ftl and Gpx1 mRNA levels above those treated with isotype control antibodies" Maybe not above the isotype but it is higher than the TNF alone stimulation at least for NRF2 at 8h and for Ftl at both time points. Why does the isotype already cause stimulation/induction of the cells? !These experiments need repetitions and quantification and statistics!
• Figure 4C and subsequent: How exactly was the experiment done (house-keeping gene)?
• Figure 4D,E: Information on cells used is missing. Why the change in stimulation time? Did it not work after 12h? Then the experiments in A-C should be repeated for 16h.
• Figure 4E: It seems the isotype control itself has already an effect in the reduction of IFNb.
• Figure 4E: It would be helpful to see if these transcripts are actually translated into protein levels, e.g. perform an ELISA. Authors state that IFNAR blockages does not alter the expression but you statistic says otherwise.
• Fig. 4F: To what does the fold induction refer to? If it is again to unstimulated cells, then why is the induction now so much higher than in (E) where it was only 50x (now to 100x).
• Figure 4G: Again to what is the fold induction referring to? It seems your Fer-1 treatment only contains 2 data points. This needs to be fixed.
• Fig. 4H: It seems that the Isotype control antibody had an effect to increase 4-HNE (compared to TNF stimulated only). Was the AB added also at 12h post stimulation? Figure legend should be adjusted.
• Figure 4I: How was the data measured here, i.e. what is depicted? The isotype control is missing. It seems a two-way ANOVA was used, yet it is stated differently. The figure legend should be revised, as Dunnett's multiple comparison would only check for significances compared to the control.
• "These data suggest that type I IFN signaling does not initiate LPO in our model but maintains and amplifies it during prolonged TNF stimulation that, eventually, may lead to cell death". Data for a short term TNF stimulation are not shown, however, so it might impact also on the initiation of LPO.
• The data for Ifnb expression (or better protein level) should be provided for B6 BMDMs as well.
• "A select set of mouse LTR-containing endogenous retroviruses (ERV's) (Jayewickreme et al, 2021), and non-retroviral LINE L1 elements were expressed at a basal level before and after TNF stimulation, but their levels in the B6.Sst1S BMDMs were similar to or lower than those seen in B6". This sentence should be revised as the differences between B6 and B6.Sst1S BMDMs seem small and are not there after 48h anymore. Are these mild changes really caused by the mutation or could they result from different housing conditions and/or slowly diverging genetically lines. How many mice were used for the analysis? Is there already heterogeneity between mice from the same line?
• The overall conclusion drawn from Fig. 3 and 4 is not really clear with regard that IFN does not initiate LPO. Where is that shown? Data on earlier stimulation time points should be added to make this clear.
• Fig. 5A: Indeed, it even seems that Myc is upregulated for the mutant BMDMs. Yet, there are only 2 data points for B6 12h. !These experiments need repetitions and quantification and statistics!
• Fig. 5B: Why would the protein level decrease in the controls over 6h of additional cultivation? Is this caused by fresh M-CSF? In this case maybe cells should be left to settle for one day before stimulating them to properly compare c-Myc induction. Comment on two c-Myc bands is needed. At 12h only the upper one seems increased for TNF stimulated mutant BMDMs compared to B6 BMDMs
• Fig. 5A,B: It seems that not all the RNA is translated into protein, as c-Myc at 12h in the mutant BMDMs seems to be lower than at 6h, while the gene expression implicates it vice versa.
• Fig. 5J: Indeed the inhibitor seems to cause the downregulation of the proteins. Explanation?
• "TNF stimulation tended to reduce the LPO accumulation in the B6 macrophages and to increase it in the B6.Sst1S ones" However, this is not apparent in Sup. Fig. 6B. Here it seems that there might be a significant increase.
• Fig. 6B: Mtb and 4-HNE should be shown in two different channels in order to really assign each staining correctly. What time point is this? Are the mycobacteria cleared at MOI1, since it looks that there are fewer than that? How does this look like for the B6 BMDMs? Are there even less mycobacteria?
• Fig 6E: In the context of survival a viability staining needs to be included, as well as the data from day 0. Then it needs to be analyzed whether cell numbers remain the same from D0 or if there is a change.
• "The 3D imaging demonstrated that YFP-positive cells were restricted to the lesions, but did not strictly co-localize with intracellular Mtb, i.e. the Ifnb promoter activity was triggered by inflammatory stimuli, but not by the direct recognition of intracellular bacteria. We validated the IFNb reporter findings using in situ hybridization with the Ifnb probe, as well as anti-GFP antibody staining (Suppl.Fig.8B - E)." The colocalization is not present within the tissue sections. It seems that the reporter line does not show the same staining pattern in vivo as the IFNß probe or the anti GFP antibody staining. The reporter line has to be tested for the specificity of the staining. Furthermore, to state that it was restricted to the lesions, an uninvolved tissue area needs to be depicted.
• Are paucibacillary and multibacillary lesions different within the same animal or does one animal have one lesion phenotype? If that is the case, what is causing the differences between mice? Bacterial counts for the mice are required.
• "Among the IFN-inducible genes upregulated in paucibacillary lesions were Ifi44l, a recently described negative regulator of IFN-I that enhances control of Mtb in human macrophages (DeDiego et al, 2019; Jiang et al, 2021) and Ciita, a regulator of MHC class II inducible by IFNy, but not IFN-I (Suppl.Table 8 and Suppl.Fig.10 D-E)." Why is Sup. Fig. 10 D, E referred to? The figure legend is also not clear, e.g. what means "upregulated in a subset of IFN-inducible genes"? Input for the hallmarks needs to be defined.
• Fig. 7C: Single channel pictures are required as it is hard to see the differences in staining with so many markers. Why is there no iNOS expression in the bottom row? What does the rectangle indicate on the bottom right? As black is chosen for DAPI, it is not visible at all. In case the signal is needed a visible a color should be chosen.
• "In the advanced lesions these markers were primarily expressed by activated macrophages (Iba1+) expressing iNOS and/or Ifny (YFP+)(Fig.7D)" Iba1 is needed in the quantification. Based on the images, iNOS seems to be highly produced in Iba1 negative cells. Which cells do produce it then? Flow cytometry data for this quantification are required This would allow you to specifically check which cells express the markers and allow for a more precise analysis of double positive cells.
• Results part 6: In general, can you please state for each experiment at what time point mice were analyzed? You should include an additional macrophage staining (e.g. MerTK, F4/80), as alveolar macrophages are not staining well for Iba1 and you might therefore miss them in your IF microscopy. It would be very nice if you could perform flow cytometry to really check on the macrophages during infection and distinguish subsets (e.g. alveolar macrophages, interstitial macrophages, monocytes)
• Spatial sequencing: The manuscript would highly profit from more data on that. It would be very interesting to check for the DEGs and show differential spatial distribution. Expression of marker genes should be inferred to further define macrophage subsets (e.g. alveolar macrophages, interstitial macrophages, recruited macrophages) and see if these subsets behave differently within the same lesion but also between the lesions. Additional bioinformatic approaches might allow you to investigate cell-cell interactions. There is a lot of potential with such a dataset, especially from TB lesions, that would elevate your findings and prove interesting to the TB field.
• "Thus, progression from the Mtb-controlling paucibacillary to non-controlling multibacillary TB lesions in the lungs of TB susceptible mice was mechanistically linked with a pathological state of macrophage activation characterized by escalating stress (as evidenced by the upregulation phospho-cJUN, PKR and Chac1), the upregulation of IFNβ and the IFN-I pathway hyperactivity, with a concurrent reduction of IFNγ responses." To really show the upregulation within macrophages and their activation, a more detailed IF microscopy with the inclusion of additional macrophage markers needs to be provided. Flow cytometry would enable analysis for the differences between alveolar and interstitial macrophages, as well as for monocytes. As however, it seems that the majority of iNOS, as well as the stress associated markers are not produced by Iba1+ cells. Analyzing granulocytes and T lymphocytes should be considered.
• It's mentioned in the method section that controls in the IF staining were only fixed for 10min, while the infected cells were fixed for 30min. Consistency is important as the PFA fixation might impact on the fluorescence signal. Therefore, controls should be repeated with the same fixation time.
• Reactive oxygen species levels should be determined in B6 and B6.Sst1S BMDMs (stimulated and unstimulated), as they are very important for oxidative stress.
• Sup. Fig 2C: The inclusion of an unstimulated control would be advisable in order to evaluate if there are already difference in the beginning.
• Sup. Fig. 3F: Why is the fold change now lower than in Fig. 4D (fold change of around 28 compared to 120 in 4D)?
• Sup. Fig. 5C, D: The data seems very interesting as you even observe an increase in gene expression. Data for the B6 mice should be evaluated for increase to a similar level as the TNF treated mutants. Data on the viability of the cells are necessary, as they no longer receive M-CSF and might be dying at this point already.
• Sup. Fig 12: the P-c-Jun picture for (P) is not the same as in the merged one with Iba1. Double positive cells are mentioned to be analyzed, but from the staining it appears that P-c-Jun is expressed by other cells. You do not indicate how many replicates were counted and if the P and M lesions were evaluated within the same animal. What does the error bar indicate? It seems unlikely from the plots that the double positive cells are significant. Please provide the p values and statistical analysis.
• Sup. Fig. 13D: What about the expression of MYC itself? Other parts of the signaling pathway should be analyzed(e.g. IFNb, JNK)?
• In the mfIHC you he usage of anti-mouse antibodies is mentioned. Pictures of sections incubated with the secondary antibody alone are required to exclude the possibility that the staining is not specific. Especially, as this data is essential to the manuscript and mouse-anti-mouse antibodies are notorious for background noise.
• In order to tie the story together, it would be interesting to treat infected mice with an INFAR antibody, as well as perform this experiment with a Myc antibody. According to your data, you might expect the survival of the mice to be increased or bacterial loads to be affected.
• It is surprising that you not even once cite or mention your previous study on bioRxiv considering the similarity of the results and topic (https://doi.org/10.1101/2020.12.14.422743). Is not even your Figure 1I and Figure 2 J, K the same as in that study depicted in Figure 4?
• Please revise spelling of the manuscript and pay attention to write gene names in italics

Minor points:

• Fig. 1: Please provide some DEGs that explain why you used this resolution for the clustering of the scRNAseq data and that these clusters are truly distinct from each other.
• Fig. 1F: What do the two lines represent (magenta, green)?
• Fig. 1F, G: Why was cluster 6 excluded?
• Fig. 1E, G, H: The intensity scales are missing. They are vital to understand the data.
• Fig. 2G-I: please revise order, as you first refer to Fig. 2H and I
• Fig. 5: You say the data represents three samples but at least in D and E you have more. Please revise. Why do you only include at (G) the inhibitor only control?
• Figure 7A, Sup. Fig. 8: Are these maximum intensity projection? Or is one z-level from the 3D stack depicted?
• Fig. 7B: What do the white boxes indicate?
• Sup. Fig. 1A: The legend for the staining is missing
• Sup. Fig. 1B: The feature plots are not clear: The legend for the expression levels is missing. What does the heading means?
• Sup. Fig. 3C: The scale bar is barely visible.
• Sup. Fig. 3D: There is not figure legend or the legend to C-E is wrong.
• Sup. Fig. 3F, G: You do not state to what the data is relative to.
• Sup. Fig. 3H: It seems you used a two-way ANOVA, yet state it differently. Please revise the figure legend, as Dunnett's multiple comparison would only check for significances compared to the control.
• Sup. Fig. 4A, B: It is not clear what the lines depict as the legend is not explained. Names that are not required should be changed to make it clear what is depicted (e.g. "TE@" what does this refer to?)
• Sup. 4B: What does the y-scale on the right refer to?
• Sup. 4C: Interpretation of the data is highly hindered by the fact that the scales differ between the B6 and B6.Sst1. The scales are barely visible.
• Sup. Fig. 5A, B: Is the legend correct? Did you add the antibody for 2 days or is the quantification from day 3?
• Sup. Fig. 8A: Are the "early" and "intermediate" lesions from the same time points? What are the definitions for these stages?
• Sup. Fig. 8E: You should state that the bottom picture is an enlargement of an area in the top one. Scale bars are missing.
• Sup. Fig. 11A: The IF staining is only visible for Iba and iNOS. Please provide single channels in order to make the other staining visible.
• Sup. Fig. 13A: Your axis label is not clear. What do the numbers behind the genes indicate? Why did you chose oncogene signatures and not inflammatory markers to check for a correlation with disease outcome?

• Sup. 13D: Maybe you could reorder the patients, so that the impression is clearer, as right now only the top genes seem to show a diverging gene signature, while the rest gives the impression of an equal distribution.

• The scale bars for many microscopy pictures are missing.

• The black bar plots should be changed (e.g. in color), since the single data points cannot be seen otherwise.
• It would be advisable that a consistent color scheme would be used throughout the manuscript to make it easier to identify similar conditions, as otherwise many different colours are not required and lead right now rather to confusion (e.g. sometimes a black bar refers to BMDMs with and sometimes without TNF stimulation, or B6 BMDMs). Furthermore, plot sizes and fonts should be consistent within the manuscript (including the supplemental data)

Within the methods section:

• At which concentration did you use the IFNAR antibody and the isotype?
• Were mice maintained under SPF conditions? At what age where they used?
• The BMDM cultivation is not clear. According to your cited paper you use LCCM but can you provide how much M-CSF it contains? How do you make sure that amounts are the same between experiments and do not vary? You do not mention how you actually obtain this conditioned medium. Is there the possibility of contamination or transferred fibroblasts that would impact on the data analysis? Is LCCM also added during stimulation and inhibitor treatment?
• How was the BCG infection performed? How much bacteria did you use? Which BCG strain was used?
• At what density did you seed the BMDMs for stimulation and inhibitor experiments?
• What machine did you use to perform the bulk RNA sequencing? How many replicates did you include for the sequencing?
• How many replicates were used for the scRNA sequencing? Why is your threshold for the exclusion of mitochondrial DNA so high? A typical threshold of less than 5% has been reported to work well with mouse tissue.
• You do not mention how many PCAs were considered for the scRNA sequencing analysis.
• You should name all the package versions you used for the scRNA sequencing (e.g. for the slingshot, VAM package)
• You mention two batches for the human samples. Can you specify what the two batches are?
• At which temperature was the IF staining performed?

Significance

Overall, the manuscript has interesting findings with regard to macrophage responses in Mycobacteria tuberculosis infection.

However, in its current form there are several shortcomings, both with respect to the precision of the experiments and conclusions drawn.

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

Learn more at Review Commons

Reply to the reviewers

1. General Statements

We thank the editor for handling our manuscript and the reviewers for their constructive critiques. We are deeply convinced that the reviewers’ suggestions have substantially raised the quality and possible impact of our manuscript. We also like to thank the reviewers for their judgements that the subject of our manuscript is biologically and clinically significant and of high importance, and that our manuscript might help to increase focus and visibility for affected individuals.

New text passages in the manuscript are colored in red. Below is a point-by-point response to the reviewers’ comments.

2. Point-by-point description of the revisions

Response to reviewer 1 comments

Major comments

Point 1-1

The authors performed qRT-PCR validation for markers of differentiation and hypoxia, with a major absence of VEGF and HIF1a. The paper would be strengthened by mention of these factors, especially by qRT-PCR or Western blot.

We thank the reviewer for the suggestion to include the bona fide hypoxia markers Vegfa and Hif1-alpha. We followed the suggestion and performed qRT-PCR on Vegfa transcripts at each tested condition (Figs. 1A,2A,3A,4A,5A,5D,5I,5N). As Hif1α is rather regulated on protein than on transcript level, we followed the advice to perform Western blots. We analyzed Hif1α protein levels on proliferating cells and quantified by normalization to actin (Figs. 1B,C and 5 B,C).

Point 1-2

Please provide justification of selection 0.5% as their hypoxic condition or perhaps repeat experiments in a less extreme environment to see if their conclusions still hold true.

We admit that our approach to use 0.5% hypoxia was a drastic challenge for the cells. It should be noted, however, that physiologic oxygen levels during pregnancy at times drop to lower than 1% (Hansen et al, 2020; Ng et al, 2017). In the first place, we had used oxygen levels lower than this, because we had wanted to ensure that we can detect responses by bulk RNA-seq with a limited number of samples. As we had many conditions to compare, we did not want to use more than 3-4 samples per condition. The fact that the cells showed normal proliferation underscores the fact that 0.5% O2 per se was not so low that it would be overly stressful to the cells.

Nevertheless, we are very grateful to the reviewer for the suggestion to include a milder hypoxic condition. We chose 2% O2, because this equals the physiological oxygen concentration shortly before the onset of cranial neural crest cell (CNCC) differentiation. We could recapitulate the phenomenon of impaired differentiation to chondrocytes, osteoblasts and smooth muscle cells at these mild hypoxic conditions, as shown by qRT-PCR and immunofluorescence of typical markers (Figs. 5D-R). Moreover, the differentiation-specific induction of the two central hypoxia-attenuated risk genes associated with orofacial clefts that we had identified by our bioinformatic analyses at 0.5% O2 (Boc and Cdo1), was still observable at 2% O2 (Figs. EV6C,D). Interestingly, in some rare cases, the attenuation of induction was lost or not as drastic as in 0.5% O2.

We are convinced that the experiments at 2% O2 strongly increased the relevance of our manuscript, because we thus detected that oxygen levels prevailing shortly before the onset of CNCC differentiation still can influence their differentiation. This leads to the conclusion that only slight decreases of intra-uterine oxygen levels indeed might interfere with correct differentiation of CNCC.

Point 1-3

Standard immunohistochemistry or histology of differentiated cells would strengthen the authors' claims of reduced differentiation under hypoxic conditions, e.g., Alcian blue, alk-phos or Alizarin red, and smooth muscle actin or other indicator.

We are grateful to the reviewer for the suggestion to include stainings of cells, as these stainings visualized the drastic effects of hypoxia on the cells. We performed immunofluorescent stainings against at least one marker protein for each differentiation paradigm. At 0.5% O2, each protein signals were nearly completely absent and cell morphology was disrupted (Figs. 2E,F, 3E, 4E). At 2% O2, we detected some more protein deposition than at 0.5%. Importantly, cells had retained their normal shape at mild hypoxia (Figs. 5H,M,R, EV5A).

Point 1-4

The authors identify a few genes that appear down-regulated in all three differentiation conditions. If it is within the scope of the study, it would strengthen the claim of these genes' function to show the effect of knock-down or knock-out for validation.

We thank the reviewer for the suggestion of gene knock-down or knock-out in order to prove functional relevance of our findings. As this would have been too much effort and beyond the scope of our study, we rather followed the suggestion of reviewer 2 (cf. points 2-6, and 2-8) that headed to the same direction: we mined publicly available sequence data on orofacial development for gene expression or marks of active enhancers. We found robust expression of the two central hypoxia-attenuated OFC risk genes Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells with the help of a single cell RNA-seq dataset (Figs. 7C-E, EV6B).

Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are grateful for the suggestion to circumvent gene knockouts by reviewer 2, as we think these data strongly emphasized the importance of our findings.

Point 1-5

Another major critique lies in the initial claim that proliferation of O9-1 cells is not significantly impacted by hypoxia. In figures 1E-H, photograms of the cells cultured 24 -72 hours and quantifications of live vs dead cells are shown as evidence for this argument. However, the increased density of cells in normoxic conditions may be a confounding variable in this assay. It would be interesting for the researchers to assess the percent of dead vs alive cells between normoxic and hypoxic conditions when the plates reach equivalent densities.

We apologize for the use of image sections from photographs with different cell densities. Of course, as demonstrated by our quantification, cell densities between 0.5% and 21% O2 in total were equal (cf. Figs. 1D,E). We therefore replaced the formerly used sections with new image sections with equal cell numbers.

We thank the reviewer for the suggestion to examine if cell numbers influence cell death rates. We followed this advice by several approaches: first, we seeded cells at different densities, incubated them for 72 h (the same time span where a minimal difference had been detected) and performed live/dead stainings (Fig. EV1B). The seeding density did not affect percentages of dead cells and the values were in the same range as in our initial experiment (Fig. 1J). Moreover, we performed TUNEL stainings of apoptotic cells at different time points to have an additional readout of cell death (Figs. 1K,L). As expected, the percentages of TUNEL-positive cells were identical between hypoxic and normoxic cells at all analyzed time points.

We therefore concluded that hypoxia does not influence the rate of cell death of proliferating CNCC and accordingly specified our wording in the results section.

Point 1-6

At end of Fig 1 section authors attempt to tie phenotypes observed in a cell line in vitro to the complex biological processes. They are not comparable and in vivo models would be better suited for these types of comparisons.

We apologize for the overconfident wording in our manuscript. Of course, our in vitro experiments cannot fully simulate the complex developmental processes taking place in vivo. We therefore changed the text to a more careful formulation. Moreover, we kept the wording in the discussion section that we cannot exclude that in the in vivo situation proliferation of CNCC is also affected by low oxygen levels because nutrients might not be available in such excess as they are in cell culture.

Point 1-7

Fig 2: if qRT-PCR did not show statistically different results between experimental and control groups why move on to bulk RNA seq?

We apologize that the sentence about statistical significance was misleading. What we wanted to express is that there was only a little difference (if any at all) between differentiated cells at 0.5% O2 and proliferating cells at 0.5% O2 or 21% O2. For the sake of clarity and readability, we deleted this misleading sentence.

Point 1-8

Fig 5: hypoxia this intense is going to affect broad range of biological processes and genes. Finding a few genes that are affected in extreme hypoxia that are also risk genes is highly unlikely. How can the authors be assured that these overlaps are actually significant and not just by chance?

We thank the reviewer for the suggestion to test for statistical significance. We tested significance of the overlap of respective gene sets (nsOFC vs. hyp-a; OFC vs. hyp-a) by Fisher’s exact test. We included Venn diagrams depicting the overlap and present the exact p-values (Figs. EV5C,D). In each case where overlap of genes occurred, p-values indicated significance.

Point 1-9

Would appreciate discussion on how examination of neural crest is relevant for OFC, as most animal models of OFC demonstrate the pathogenesis in embryonic epithelium or periderm, not in the neural crest. Defects in neural crest are associated with other congenital craniofacial anomalies such as craniosynostosis or complex (Tessier) clefts, not the typical orofacial cleft. Please revise rationale of study, interpretation of data and Discussion to specifically state how neural crest cells are involved in the pathogenesis of orofacial cleft.

We apologize for not pointing out enough the role of epithelial cells in the emergence of orofacial clefts. We revised our introduction, results and discussion sections in this regard and emphasized the role of epithelial cells. Importantly, we addressed the possible influence of the results gained in CNCC on epithelial cells by analyzing scRNA-seq data with the algorithm CellChat, as suggested by reviewer 2 (cf. point 2-8). We detected several cell communication pathways from CNCC to epithelial cells which contain components that are misexpressed upon hypoxia in our dataset (Figs. 7F-I). Therefore, during hypoxia, these pathways might influence epithelial cells and therefore indirectly cause orofacial clefts. We outlined this possible interplay in the discussion and briefly mentioned it in the abstract.

We have not discussed more strongly the role of CNCC in the emergence of OFC in the revised manuscript, because we did not want to put even more emphasis on this matter. Numerous studies have proven the contribution of cranial neural crest tissue to the emergence of orofacial clefts. This fact is also pointed out in several review articles about orofacial clefts. In most cases, this knowledge was achieved by mouse models, because tissue-specific conditional knockouts are feasible (in contrast to genetic studies on patients), usually via deletion with the Wnt1-Cre driver. Funato et al. give an excellent (but quite old) overview of mouse models in which the neural crest-specific knockout of a gene leads to emergence of OFC and lists 17 genes for which this is the case (Funato et al, 2015). Moreover, several recent studies also report on the emergence of orofacial clefts upon neural crest-specific deletion (Forman et al, 2024; Li et al, 2025). These include genes responsible for DNA methylation (Ulschmid et al, 2024), and a study on subunits of chromatin remodeling complexes that are necessary for correct transcription of their target genes, which was conducted by our group (Gehlen-Breitbach et al, 2023).

Minor comments

__Point 1-10 __

The author should replace "Final proof" in the introduction with "further evidence supporting."

We apologize for the incorrect wording. Of course, it is highly questionable if there is such a thing as final proof in life sciences. We re-phrased the text according to the reviewer’s suggestion.

Point 1-11

Authors are inconsistent when referring to Figures- sometimes they capitalize (i.e. 1J) and other times they leave lower case (i.e. 1i). Needs to be consistent throughout. Figures are not numbered.

We apologize for the inconsistency. We corrected the references to figures. Moreover, we apologize for the missing figure numbers. We also corrected this and included figure numbers.

Point 1-12

In figures authors would sometimes list 21% O2 first then 0.5% O2 or vice versa. (i.e. Fig on page 21 panels I, J, K). Needs to be consistent.

We again apologize for being inconsistent. We corrected the inconsistency in Fig. 1D. Now, 21% O2 is presented before/above 0.5% O2.

Point 1-13

Figures on pages 28, 29, 30 panel J and page 31 panel F: there is no legend on what the scale/measurement is for the difference in expression level other than it ranges from -1 to +3.

We thank the reviewer for the hint. We are aware that from the heatmaps we used one cannot infer relative expression rates of different genes or similar. If we would have considered expression strength of single genes, many of the gene-specific differing expression rates under the different conditions would have been hard to detect, as presentation would have been dominated by the differences in expression rates between genes. We therefore plotted gene-wise scaled expression.

We included an explanation of the procedure in the materials and methods section.

Point 1-14

Will the authors please comment on the one normoxic sample in Figure 1I that did not cluster with the others? Did this meet the standards to merit exclusion as an outlier?

We regret that the default scale of our plot of the principal component analysis is a bit misleading. This is the case because x-axis accounts for 80.3% of variance and y-axis only accounts for 6.1%. Therefore, the sample that might seem as an outlier actually met our standards. Nevertheless, we decided to keep the default scaling as is, in order not to embellish the graph (Fig. 1M).

Point 1-15

The authors refer to DEG as deregulated genes; while not strictly incorrect, the more standard usage is "differentially expressed genes." Please address.

We apologize for the incorrect explanation of the acronym. Of course, this was corrected in the revised manuscript.

Significance

This work on neural crest cells and hypoxia are biologically and clinically significant.

We are deeply grateful to the reviewer for considering our manuscript significant for both biologists and clinicians. We are convinced that the additional data we gathered in the course of the revision has significantly increased the importance of our work. Therefore, we once again express our gratitude to the reviewer for the valuable suggestions.

Response to reviewer 2 comments

Major comments

Point 2-1

The conclusions drawn from the experimental data are carefully formulated for the most part. One of the main concerns is that the cells were subjected to extreme hypoxic conditions, while it may be more biologically relevant to include a condition representing more mild hypoxia (e.g. 10%).

Please refer to the response to point 1-2.

Point 2-2

One of the opening claims regarding severe hypoxia only mildly affecting cell proliferation is not shown clearly, since no mitotic markers have been analyzed (i.e. KI67 or PCNA staining or a simple EdU incorporation assay). Thus, the claim that they assessed cell proliferation is not very convincing, even though cell death was analyzed.

We appreciate the reviewer’s suggestion to include a more thorough analysis of proliferation rates. We followed the advice and performed immunofluorescent stainings against Ki67 (accounting for cells in proliferative state) and phospho-histone H3 (accounting for cells undergoing mitosis). We performed this assay at different time points of culture in order to address the question if cell density might influence proliferation rates (Figs. 1F-H). Neither for Ki67 nor for pHH3 a difference was detected between 21% and 0.5% O2.

We are convinced that these analyses strengthened our initial findings and provide strong evidence that hypoxia does not influence proliferation rates of CNCC.

Point 2-3

Additionally, cellular morphology of the cells could be assessed (brightfield images), since previous studies observed that hypoxia can be an inducive factor in cranial neural crest and driving EMT (Scully et al. 2016; Barriga et al. 2013).

We thank the reviewer’s hint and followed the advice. We analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F). As outlined in the results section, we did not detect a difference in these parameters between 21% and 0.5% O2.

We included the second reference mentioned by the reviewer (Barriga et al, 2013) additionally to Scully et al. 2016 that had already been cited.

Point 2-4

Furthermore, in the RNA seq analysis of chondrogenic fate biased cells the authors draw a conclusion based on the proximity of the samples on the PCA plot, which is not very convincing. More careful analysis of the bulk RNA seq data sets they have generated for key marker genes will be more convincing (for example, a heatmap with selected genes would be a helpful representation).

We apologize for the rash and inaccurate conclusion based on proximity on PCA plots. We are grateful to the reviewer for the suggestion to include heatmaps with selected marker genes. Following this advice, we generated heatmaps on our bulk RNA-seq data with the GO terms specific for each differentiation paradigm (Figs. EV2F, EV3F, EV4F).

We are convinced that these maps are perfect additions to the heatmaps of the 200 top differentially-expressed genes that already had been included in the manuscript (Figs. 2K, 3J, 4J) and helped to strengthen our findings. For chondrocytes and smooth muscle cells, the new, GO-specific heatmaps perfectly recapitulated the phenomenon of hypoxia-attenuated induction. Interestingly, for osteoblasts, about half of the induced genes were hypoxia-attenuated, while the other half was induced stronger than under normoxia. This pointed to gene-specific mechanisms of hypoxia-dependent attenuation of transcription. Moreover, it shed light on a hypoxia-evoked complete dysregulation of transcriptional induction in osteoblasts, as nearly none of the genes was induced similar to normoxia.

__ __

Point 2-5

As mentioned above, a straight-forward and not time consuming experiment (given that it was assessed for a maximum of 72 hrs) would be to repeat the culture of NCCs and stain for mitotic markers, and quantify the number of positively stained cells over total cell numbers. Furthermore, it is not that demanding to add an experimental condition of less severe hypoxia in this assay.

We thank the reviewer for the suggestion and followed the advice (cf. point 2-2). The conducted experiments straightened our results, because the initially detected slight tendency to lower cell numbers at 0.5% O2 could thus be falsified: We did not detect any difference for Ki67 and pHH3 between 0.5% and 21% O2 at any analyzed time point (Figs. 1F-H). Moreover, percentages of dead or apoptotic cells at 0.5% O2 did not vary from 21% (Figs. 1I-L, EV1B). As we could not detect any difference in proliferation between 21% and 0.5% O2, we skipped the analysis of proliferating cells at 2% O2.

Point 2-6

Without underestimating how time consuming this would be, a major lack of experimental validation of the key genes they identify as important across all conditions may be the limitation of the study (this would be the difference between correlation and a probable underlying mechanism). This can be circumvented by more extensive reference to in situ data sets from mouse or existing data sets of single cell and spatial transcriptomics. A suggested targeted knock-down (for example with siRNA, shRNA or CRISPR) to validate a few of the key genes revealed as important could take a few months, with an estimated cost up to 5,000 euros per targeted gene and replicate.

We thank the reviewer for the notion that targeted knockdowns are beyond the scope of our manuscript. We are deeply grateful for the reviewer’s constructive criticism and for the suggestion to analyze publicly available data sets in order to gather data depicting in vivo relevance of our identified central hypoxia-attenuated OFC risk genes Boc, Cdo1 and Actg2 (cf. point 1-4). We detected robust expression of Boc and Cdo1 during human craniofacial development (Fig. 7A) and we identified enhancers that are active in embryonic craniofacial mouse tissue (Fig. 7B). Moreover, we detected expression of both genes during murine craniofacial development in undifferentiated mesenchymal cells, osteoblasts, chondrocytes and smooth muscle cells by reanalysis of a scRNA-seq dataset (Figs. 7C-E, EV6B). This data comprised scRNA-seq of mouse embryonic maxillary prominence at stages E11.5 and E14.5 (Sun et al, 2023).

Thus, we found evidence for the in vivo relevance of Boc and Cdo1 and could rule out a possible important role of Actg2, the third gene we had identified. We therefore are deeply grateful for the suggestion, as we think these data strongly emphasize the importance of our findings.

Point 2-7

On methods, replicates and statistics: The experimental methods and approach are described efficiently and seem reproducible. All biological and technical replicates are of a minimum of N=3 from independent experiments and statistical tests have been run in all cases.

We thank the reviewer for the appreciation of our methodology, descriptions and statistical analyses.

Minor points

Point 2-8

One of the key implications of NCCs in palate formation is interaction with orofacial epithelial cells, which the authors also mention. It may be interesting to check if any signaling pathways involved in this crosstalk are affected under hypoxic conditions in their existing data sets of bulk RNA SEQ. This can be done by using available algorithms such as CellChat (Jin et al. 2021; Jin, Plikus, and Nie 2023), which has been reported to work also in bulk RNA seq data analysis (according to GitHub). The authors could mine the literature for existing RNA sequencing data that include osteoblasts, chondrocytes and epithelial cells (Ozekin, O'Rourke, and Bates 2023; Piña et al. 2023).

We are very grateful to the reviewer for this suggestion. Moreover, we like to thank the reviewer for mentioning exemplary references. We followed the advice by the methodology lined out in results and materials and methods sections: we applied the CellChat algorithm on a scRNA-seq dataset (Pina et al, 2023; Sun et al., 2023) to identify pathways containing components that are hypoxia-attenuated (and associated with a risk for OFC) in our bulk RNA-seq dataset (Figs. 7F-I). We did not use the datasets the reviewer had suggested, because the data were not available for us or the file format was not well-suited for the analysis with CellChat. Importantly, the dataset from Sun et al. has the following advantages over the suggested references: the complete maxillary prominence was used (instead of palatal shelves only), and different time points were included. Thus, we were able to follow the expression of genes of interest at different developmental stages before the onset of differentiation and after (Figs. 7C-E and EV6B). By our approach, we identified several OFC-related pathways that contain hypoxia-attenuated components such as BMP and FGF signaling and deposition of collagen and fibronectin (Figs. 7F-I). Importantly, the named pathways (and others) send outgoing communication patterns to epithelial cells. Therefore, hypoxia-attenuated gene induction in CNCC could influence epithelial cells via these pathways.

We believe that the use of the CellChat algorithm has brought a deeper understanding of how hypoxia can have indirect consequences on the important topic of epithelial cells and thus could also evoke OFC. We therefore once again like to express our gratitude to the reviewer.

Point 2-9

Additionally, another process that may be affected is EMT (epithelial-to-mesenchymal-transition) and is possible to assess by re-analysis of bulk RNA-seq data while focusing on key genes implicated in this process (i.e. E-cadherin, vimentin, EpCAM, Snail, Twist, PRRX1).

We thank the reviewer for the advice. We followed the advice and analyzed cellular morphology by the parameters cell length, total number of pseudopodia, number of filopodia and number of lobopodia (Figs. EV1C-F) (cf. point 2-3). As we did not detect any differences between 21% and 0.5% O2, and because the cells we used for our analyses represent mesenchymal cells, i.e. cells that had already undergone EMT, we did not re-analyze our dataset with the focus on EMT.

Point 2-10

Lastly, when the authors report on the significantly up- or down-regulated genes, it may be interesting to categorize them by ligands, receptors, intracellular molecules and transcription factors (and use separate plots to visualize them). While a big focus of the manuscript are down-regulated genes, less emphasis was given in upregulated genes (other than the response to hypoxia gene module).

We thank the reviewer for the advice. Following this advice, we categorized genes according to Panther protein classes "intercellular signal molecule" (PC00207), "transmembrane signal receptor" (PC00197) and "gene-specific transcriptional regulator" (PC00264) and depicted the results with violin plots (Fig. EV5B). We could not analyze intracellular molecules, because this protein class does not exist in the Panther database. We had not focused on the genes with stronger induction in hypoxic condition, because the number of genes was low in each differentiation paradigm (7 in chondrocytes, less than 30 in osteoblasts, none in smooth muscle cells) and the transcriptional changes were mostly not as drastic as for the attenuated genes. In order to achieve a broader overview of deregulated processes, we now included GO term analyses of genes downregulated during the differentiation regimes both at 21% and 0.5% O2 (Figs. EV2D,E, EV3D,E, EV4D,E).

Point 2-11

The authors are referencing extensively and accurately existing studies in the field and the manuscript is exceptionally well-written, with only a few points of limited clarity or increased complexity. Such an example is when the authors refer to OFC risk genes, because it is not clearly stated how the referenced studies reached their conclusions (for example, are they mouse studies, do they involve mutants, are any of these studies based on GWAS on human cohorts). This matter would significantly improve the flow of the text and highlight the importance of the study and their findings.

We would like to thank the reviewer very much for the appreciation of our scientific writing. We apologize for not explaining exactly how our OFC risk gene lists had been curated. We included this information for both non-syndromic and other OFC risk genes at the respective sites in the results section. Moreover, we included the Human Phenotype Ontology terms that had been used in the search in the materials and methods section.

We thank the reviewer for this suggestion, as we agree that this information significantly highlights the importance of our findings.

Point 2-12

The figures could be redesigned to be more intuitive to interpret. For example, using violin plots and heatmaps, as discussed, and including references or re-analysis/re-use of existing spatial transcriptomics and in situs for marker genes.

In all cases where there is a comparison of gene expression levels, violin plots would be a better representation of up- and down-regulated genes (i.e. selected genes from Fig1K, comparison of gene expression between normoxic and hypoxic NCCs, Fig 2G when analyzing chondrogenesis and the respective analysis for osteoblasts and smooth muscle cells, as well as when comparing the three fate-biasing conditions to identify common genes that are misregulated).

We thank the reviewer for the advice and for the appreciation of the usage of heatmaps (Figs. 2K, 3J, 4J, 6F). Unfortunately, as the number of biological replicates is only three to four, the visualization of gene expression data from our bulk RNA-seq data with violin plots was not intuitive. We therefore retained the heatmaps rather than choosing bar graphs, because they are much clearer when presenting expression data of several to many genes. We included violin plots whenever possible due to high numbers of data points (Figs. EV1C, EV1D, EV1E, EV1F, EV5B). Moreover, we added additional heatmaps to depict transcriptional changes of genes associated with GO terms with the various differentiation regimes (Figs. EV2F, EV3F, EV4F). Unfortunately, we did not detect the three central hypoxia-attenuated genes in spatial transcriptomics data on craniofacial development. But we used scRNA-seq data of different stages of orofacial mouse tissue where we could identify expression of Boc and Cdo1 (cf. points 1-4 and 2-6). These data helped, together with other in vivo data to gain evidence for the in vivo function of Boc and Cdo1 during CNCC differentiation and helped to dismiss Actg2 as another central player.

Significance

Several pieces of evidence have pointed to hypoxia as an environmental factor contributing to congenital orofacial clefts, ranging from studies in mouse to observations in human. The authors are doing an excellent job in putting this information together and the question they are trying to answer is of high importance, given the prevalence of such congenital syndromes.

We are deeply grateful to the reviewer for the appreciation of our work and for classifying our research topic as highly important.

In terms of the methods and model employed, there are some limitations, related to the choice of a mouse cell line over one from human, the severe hypoxia induced (over a more mild), and the conditions of directed differentiation not allowing for simultaneous examination of more complex lineage transitions. The methods as a whole are not that up-to-date, given the single cell and multiplexed transcriptomic advances the last couple of decades, advanced bioinformatics that could be used in combination with in vitro lineage tracing methods.

We thank the reviewer for the honest evaluation of our methods, especially for the constructive suggestions that were given to address our hypotheses with more up-to-date methods and at milder hypoxic conditions. As outlined above, we followed the advice and re-analyzed existing scRNA-seq datasets (cf. points 2-6 and 2-8) and checked our central hypotheses at milder hypoxic conditions (cf. response to point 1-3).

We are deeply convinced that both significantly increased the biological relevance of our results, because we thus (1) gathered evidence for the in vivo function of Boc and Cdo1 and (2) were able to show that the phenomenon of hypoxia-attenuated gene induction still holds true at biologically relevant hypoxic conditions.

The audience this work will reach are neural crest experts, developmental biologists, and potentially clinical doctors. The general public outreach of such a paper is also diverse, as more focus and visibility is required for the individuals affected by those syndromes and their families.

We thank the reviewer for the judgement that our manuscript will not only reach neural crest experts, but also developmental biologists in general and potentially also clinicians. We are very much pleased that the reviewer shares our opinion that affected individuals should be more in the focus of public attention. We like to express our gratitude for the judgement that our manuscript might help to increase focus and visibility for them.

References

Barriga EH, Maxwell PH, Reyes AE, Mayor R (2013) The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition. The Journal of cell biology 201: 759-776, 10.1083/jcb.201212100.

Forman TE, Sajek MP, Larson ED, Mukherjee N, Fantauzzo KA (2024) PDGFRα signaling regulates Srsf3 transcript binding to affect PI3K signaling and endosomal trafficking. Elife 13, 10.7554/eLife.98531.

Funato N, Nakamura M, Yanagisawa H (2015) Molecular basis of cleft palates in mice. World journal of biological chemistry 6: 121-138, 10.4331/wjbc.v6.i3.121.

Gehlen-Breitbach S, Schmid T, Fröb F, Rodrian G, Weider M, Wegner M, Gölz L (2023) The Tip60/Ep400 chromatin remodeling complex impacts basic cellular functions in cranial neural crest-derived tissue during early orofacial development. International Journal of Oral Science 15: 16, 10.1038/s41368-023-00222-7.

Hansen JM, Jones DP, Harris C (2020) The Redox Theory of Development. Antioxid Redox Signal 32: 715-740, 10.1089/ars.2019.7976.

Li D, Tian Y, Vona B, Yu X, Lin J, Ma L, Lou S, Li X, Zhu G, Wang Y et al (2025) A TAF11 variant contributes to non-syndromic cleft lip only through modulating neural crest cell migration. Hum Mol Genet 34: 392-401, 10.1093/hmg/ddae188.

Ng KYB, Mingels R, Morgan H, Macklon N, Cheong Y (2017) In vivo oxygen, temperature and pH dynamics in the female reproductive tract and their importance in human conception: a systematic review. Human Reproduction Update 24: 15-34, 10.1093/humupd/dmx028.

Pina JO, Raju R, Roth DM, Winchester EW, Chattaraj P, Kidwai F, Faucz FR, Iben J, Mitra A, Campbell K et al (2023) Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis. Nature communications 14: 5687, 10.1038/s41467-023-41349-9.

Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q (2023) Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 50: 676-687, 10.1016/j.jgg.2023.02.008.

Ulschmid CM, Sun MR, Jabbarpour CR, Steward AC, Rivera-González KS, Cao J, Martin AA, Barnes M, Wicklund L, Madrid A et al (2024) Disruption of DNA methylation-mediated cranial neural crest proliferation and differentiation causes orofacial clefts in mice. Proc Natl Acad Sci U S A 121: e2317668121, 10.1073/pnas.2317668121.

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

Learn more at Review Commons

Reply to the reviewers

Reviewer #1:

Major Comments:

1. The data in the paper strongly suggests that the new copper shuttles are selective for copper and have faster binding kinetics (Fig 1) than the previous one. However, the data regarding the copper shuttling from the copper(Aβ) peptides is not very convincing. It appears to be due to the Cu effect alone (Fig.3), as the reduction in viability with Cu(II)+ AscH- is almost the same as the Cu(II)(Aβ)+AscH-. To convincingly show that the peptide shuttle can strip copper from (Aβ) peptides, the authors need to show that the copper is bound to the (Aβ) peptide before it is used in the experiment. Rightfully so, the effect of the toxicity of Cu(II)+ AscH- is similar to that of Cu(II)(Aβ16)+AscH-. This is due to the fact that Aβ16 is not toxic to the cells, so therefore there is no compounded effect of Cu and Aβ16 as seen for Cu(II)(Aβ40). As for the toxicity of Cu(II)+ AscH-, it is be similar to Cu(II)(Aβ)+AscH- because Cu(II) will be bound to a weaker ligand in the medium and such loosely bound Cu is also able to produce ROS with AscH- with similar rates as Cu-Ab.

Data from our lab and others have shown that in HEPES solution at pH 7.4, Aβ forms a complex with Cu. The present work is also in line with Cu-binding to Ab, as in Figure 1C (GSH), the rate of Cu withdrawal by the shuttle can only be explained by Cu bound to Ab, as Cu in the buffer binds to the shuttle much faster. Also, the AscH- consumption rate measured in Fig S5D-E are congruent of Cu bound to Ab, unbound Cu has a much faster rate of AscH- consumption (Santoro et al. 2018, doi.org/10.1039/C8CC06040A).

The concentrations of Aβ and Cu used in our experimental condition were determined with a UV-Vis spectrophotometer.

Minor comments:

1. The paper does not cite Figure 1A and some supplementary figures, especially Supp. Fig. 1-2. All the figures and supplementary figures should be cited. This has been rectified for all the concerned figures.

The data presentation in Figures 3B and S8 is confusing."-" signs indicate no addition or the blank box means no addition. Also, the AKH-αR5W4 has no "-" sign in the first bar. For clarity, please indicate the -, +, or no sign means in the figure legends. Also, what does "Batch A" refer to in Figure 3B?

The figures have been modified as suggested by the reviewer.

Page 7, correct (Error! Referencesource not found.Figure 1C).

This has been rectified.

The Giantin staining in Figure 2B is making it hard to visualize ATP7A trafficking. If the Giantin image overlay is removed, it may be easier to see the movement of ATP7A from the perinuclear region to the vesicles.

The images have been modified to better appreciate the ATP7A change in distribution upon the increase in intracellular Cu level. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B

In the introduction, the authors mention, "These molecules have, however, a major pitfall as is seen for Elesclemol, a candidate for Menkes disease treatments 32. The authors cite reference " Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins." The paper showing elesclomol as a candidate for Menkes disease treatments is Guthrie L et al., Elesclomol alleviates Menkes pathology and mortality by escorting Cu to cuproenzymes in mice. Science. 2020.

We thank the reviewer for pointing this out, which was apparently not clearly explained. Our intention here was to show that a major pitfall of shuttles like Elesclomol, as seen in the study by Tsvetkov, P. et al. Science (2022), is cuprotoxicity. The sentence has been clarified and the work of Guthrie L et al is cited for Elesclomol as a candidate for Menkes disease.

Reviewer #2 :

Major issues:

1. This reviewer is not convinced that the authors' experimental system is well suited for studies of glia activation and protective effects. With the exception of a couple of panels it is very hard to see differences. The authors should significantly improve the quality of images in Figure 5 to make this set of data convincing. We thank the reviewer for his/her detailed evaluation and for bringing to light the quality of the image in Figure 5. We have therefore improved the quality of the images by improving the signal to noise ratio to better show the differences between conditions.

Similarly, the quality of giantin staining is low and needs to be improved and more experimental details are needed (see details below).

As stated in our answer to reviewer 1, the images have been modified to better appreciate ATP7A redistribution upon increase of intracellular Cu levels. We have reduced the number of conditions for which images are provided and provided individual staining for clarity. Zoomed images are also provided. The remainder of the conditions are in Figure S7B.

Given that shuttles are found within vesicles, the authors should discuss the mechanism through which Cu is released into the cytosol to trigger ATP7B trafficking.

The mechanism of Cu escape from endosomes remains poorly understood. However, supported by our recent observations that Cu quickly (within 10 min) dissociates from the Cu-shuttle AKH-αR5W4NBD in endosomes (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963), we discuss the potential involvement CTR1/2 and DMT1 (page 16).

There are numerous small writing issues that make paper difficult to read. The authors are encouraged to carefully edit their manuscript.

We thank the reviewer for pointing this out and several errors have been corrected whereas various sentences have been clarified.

Minor issues

* „A solution of monomerized Aβ complex in 10% DMEM (diluted with DMEM salt solution) was prepared in microcentrifuge tubes" - here and further the description of media composition is confusing What is the rest 90%?

This has been rectified. The composition of the salt solution that makes up the 90% has been provided (page 4).

* „Afterwards, AscH- was added to the tubes and vortexed, the mixture was then added to PC12 cells" - concentration of ascorbate is mentioned only once (later in the figure legend) where it can be barely found, also without explaining the choice of concentration. Additionally, ascorbate's product code is not listed. Please, correct.

These points have been rectified.

* Description of the cell (PC12 line) handling conditions is absent (growth medium, passage number used etc) and should be included.

This information is now provided.

* ATP7A delocalization assay. Details for the secondary antibodies are absent (full name (e.g. AlexaFluor 488), manufacturer, code) and should be added.

Missing information has been added.

* page 6: „Next, we investigated the capacity of the shuttles to withdraw Cu(II) from cell culture media, DMEM 10% and DMEM/F12 1:1 (D/F)." Here and further explanation is needed why the mixture of DMEM/F12 is needed (F12 is also not listed in the materials list).

DMEM/F12 is a media that is commercially available used for some cell types, and it has been added to the materials list (page 4).

* Page 7. Legend to the figure 1B: „Conditions: Cu(II)=AKH-αR5W4NBD=DapHH-αR5W4NBD=HDapH-αR5W4NBD= 5 μM, DMEM 10%, D/F 100%, 25{degree sign}C, n=3." - „DMEM/F12" ratio equals to „100%" is confusing, please clarify

This has been clarified.

* Page 8-9. Legend to the Figure 2A. „Similar observations were obtained with 5 different cell cultures." Same remark goes to the legend to supplementary figure 7 ("Similar observations were obtained with at least 3 different cell cultures"). Do the authors mean independent experiments or different cell lines? Please clarify. If different cell lines, consider including these data into the supplement.

Indeed we meant independent experimentations. This has been clarified.

* Page 8-9, figure 2B. Giantin is a cis-golgi marker, which should localize perinuclearly. In the cells shown the signal is diffuse and appears non-specific. Please improve the quality.

We have reduced the number of conditions for which images are provides and are providing individual staining for clarity. Zoomed images are also provided allowing visualization of the typical cis-Golgi distribution of Giantin.

* Page 8-9, figure 2B. ATP7A is shown in green. The authors did not specify the secondary antibody has been used for it. If the secondary antibody used for labeling of ATP7A has green fluorescence then how does one distinguish between the transporter signal and signal of the green fluorescent shuttle? Please provide more details.

We thank the reviewer for pointing this point as we missed to mention this technical issue in the original manuscript. The Cu-shuttles labeled with NBD indeed emit in the green signal, but they are not fixable under our conditions and are washed out during ICC procedure. Accordingly, they do generate any background signal and do not interfere with the ICC as shown by the controls and test conditions (Figure S7B and Figure 2B). This is now mentioned (page 11).

* Page 9 and Figure 2B. Why did authors use Cu(II)EDTA for the experiment? What was the concentration? Please, add this information as well as Cu(II)GTSM treatment conditions to the experiment description in materials and methods.

EDTA is a strong chelator of Cu(II), however due to its negative charge it cannot penetrate the plasma membrane thus importing Cu. It is therefore used as a negative control, to eliminate the speculation of Cu non-specifically crossing the plasma membrane or through a channel.

* Figure 2 and supplementary figure 7. It would be beneficial to have higher magnification images. Please, add them, if possible.

These higher magnification images have been provided.

* Page 11. „In conclusion, the novel Cu(II)-selective peptide shuttles .... capable of instantly preventing ... toxicity on PC12 cells, whereas ... instantly rescue Cu(II)Aβ1-42 toxicity". Authors should be more careful with terminology. According to the materials and methods, the survival assay was carried out after 24h of cells' treatment with the reagents. Effect visible after 24h and „instant rescue" is not the same, Please clarify or modify the wording

In principle, the peptides cannot reverse the production of ROS, however they prevent ROS production. Therefore, for the peptides to have an effect, they have to instantly halt ROS production. This is justified by the novel shuttles being more effective than AKH-αR5W4NBD in preventing toxicity, given we modified just the Cu binding sequence. We have however restricted the use of the term instantly to ROS production.

* Page 13, figure 5, panels C and D. In both quantitations Cu(II) was used as one of the control conditions. Why in panel D the percentage of activated microglial cells (second graphs from right) is several fold higher (appr. 150% vs >500%)?

This variability was observed throughout our set of experiments and could be linked to the quality of the hippocampal slices used. Slight variations in the age of the animals or in the traces of metals in the mediums are likely explanations. However, the different groups that are compared represent experiments performed simultaneously.

* Supplementary Figure S3B. The lowest solid line does not correspond to any color in the legend (please, check and correct). However, by the method of exclusion, one may conclude that it refers to Cu(II)+HDapH-shuttle. What could be a potential explanation for stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper (also supported by the panel D)?

The stronger quenching of this shuttle by binding Cu(II) directly from the spiked media comparing to when it is pre-complexed with copper is not significant.

* In discussion the authors mention that the designed shuttles are prone to degradation in 48 hours. In the viability assays, they treat cells for 24 hours, in the fluorescent and confocal microscopy experiments for one hour or less. What is the lifetime of these shuttle peptides in the cells?

The lifetime of the shuttle peptide in the cells is currently unknown. However, after 24h incubation of PC12 cells with the AKH-αR5W4NBD, DapHH-αR5W4NBD and HDapH-αR5W4NBD, the Cu shuttles lose their punctate distribution and appear diffuse inside the cells. We have recently shown that AKH-αR5W4NBD cycles through different endosomal compartments and eventually reaches the lysosomes where it could be degraded (Okafor et al., 2024, /doi.org/10.3389/fmolb.2024.1355963). Therefore, the diffuse distribution of the fluorescence signal could suggest degradation of the Cu-shuttles.

* From the microscopy observations, the mechanism of entry of apo-shuttles (with no Cu(II) in the complex) and in complex with Cu(II) looks quite different. Namely, in figure S7 the fluorescent signal is very strong in the plasma membrane with significantly less vesicular pattern when compared to figure 2A. It is especially apparent for DapHH shuttle at 15 minutes of incubation. Can authors hypothesize/discuss the reason for these differences?

The difference of the shuttle’s signal in the presence or absence of Cu binding, is due to fluorescence quenching by Cu bound and was at the heart of the design of these shuttles. Hence a strong signal at the plasma membrane is seen in the absence of Cu as these CPP-based shuttles interact strongly with the plasma membrane. However in presence of Cu, they become less visible due to quenching by Cu. Interestingly however, is that when Cu dissociates from the shuttle inside the cells (likely in acid endosomes), this quenching is suppressed and the fluorescence reappears. This is now better explained (page 10).

* Please, show the figures in the supplementary file in the same order as you refer to them.

This has been rectified.

* Introduction. Description of the shuttle peptides: „(3) a cell penetrating peptide (CPP), αR5W4, with sequence RRWWRRRWWR, for cell entry35" - one R is the middle is extra.

This has been rectified.

*Kd units are missing (pages 2, 3 and 15) and should be added.

This has been added.

* Figure 1A is either not referred at all or mislabeled.

* Page 7, Figure 1B: x axis on the second panel (+Mn+) misses a label.

* Page 8. „Upon addition of DapHH-αR5W4NBD or HDapH-αR5W4NBD, an immediate slow-down in ROS production was observed (Figure 1D and S1E), ..." - mislabeled supplementary figure, please, correct.

* Page 11. „...but not in the presence of AKH-αR5W4NBD which required pre-incubation to prevent toxicity (Figure 3AFigure)." Please, correct the reference to the figure.

* Page 11. „This is in line with the faster retrieval ... previously demonstrated in vitro (Figure 1)" - please, specify the panel.

* Supplementary materials and methods, subsection „Retrieval of Cu by peptide shuttles from Aβ", page 2: „The same was done for 10 μM Cu(II)...to give the estimated 100% saturated emission level." - check the spelling of the shuttle species.

* Supplementary Figure S4. By the behavior of AKH-shuttle in the presence of copper and other metals, it looks that panels are shuffled, i.e. panel C looks corresponding to the panel B with DMEM/F12 conditions, whish is also supported by the values in the Table S1. Please, check and correct, if needed.

* Supplementary figure S9, panel A. Apparently, mislabeled images with Abeta1-42 and Cu(II)Abeta1-42. Please, correct.

We apologize for the different issues in referencing figures. This has been rectified.

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

Minor Concerns

I think that authors can add some concepts of general interest on AD, as follows

evidence showed that AD top-line disease-modifying drugs employing monoclonal antibodies (donanemab, lecanemab, and aducanumab) that tag Aβ, based on the 'Amyloid cascade hypothesis', are able to rid the brain of Aβ plaques, but the drug benefits consist in a reduction of 35% of cognitive decline. The remaining disease burden (more than 65%) has no disease-modifying therapeutic options, at the moment. Furthermore, monoclonal antibodies against Aβ have strong side- events (ARIA). On this basis, it could be suggested that removing Aβ plaque might not be sufficient to slow the 100% percentage of clinical decline in AD. This is why the Cu(II) shuttle invention presented by the candidate may represent a valid and concrete means to fight AD, since also meta-analyses demonstrate that Cu and more specifically non-Cp Cu is increased in AD (PMID: 34219710). The authors can add some of these clinical considerations in the Discussion.

There is only a very brief description of the scenario of evidence of the involvement of copper in Alzheimer's, especially from a clinical point of view, I mean the scenario resulting from clinical studies carried out on AD patients. This would have highlighted the unmet medical need to which these new compounds (the Cu shuttles) can provide an answer. At least for a subpopulation of Alzheimer's patients, and we know that there are different subtypes of Alzheimer's disease (for example 10.1016/j.neurobiolaging.2004.04.001, but authors can find others), these Cu(II) selective shuttles could provide beneficial effects. Literature reports about a percentage of AD patients with increased levels of Cu (some papers on this topic e can be easily retrieved,), who may primarily benefit from these compounds. These can be easily identified as it is also characterized by a different biochemical, cognitive, and genetic profile. The current study is timely since AD patients with high Cu can be easily identified since they are characterized by a different biochemical, cognitive, and genetic profile as per recent findings (PMID: 37047347). This information can improve the quality of the manuscript by providing information about the unmet clinical need that this study can answer

We thank the reviewer for his very positive evaluation and for his suggestion that gives more perspective to our work. Accordingly, we have added these parts to the introduction and discussion sections.

Author response:

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

Reviewer #1 (Public Review):

(1) The questions after reading this manuscript are what novel insights have been gained that significantly go beyond what was already known about the interaction of these receptors and, more importantly, what are the physiological implications of these findings? The proposed significance of the results in the last paragraph of the Discussion section is speculative since none of the receptor interactions have been investigated in TNBC cell lines. Moreover, no physiological experiments were conducted using the PRLR and GH knockout T47D cells to provide biological relevance for the receptor heteromers. The proposed role of JAK2 in the cell surface distribution and association of both receptors as stated in the title was only derived from the analysis of box 1 domain receptor mutants. A knockout of JAK2 was not conducted to assess heteromers formation.

We thank the reviewer for these comments. The novel insight is that two different cytokine receptors can interact in an asymmetric, ligand-dependent manner, such that one receptor regulates the other receptor’s surface availability, mediated by JAK2. To our knowledge this has not been reported before. Beyond our observations, there is the question if this could be a much more common regulatory mechanism and if it has therapeutic relevance. However, answering these questions is beyond the scope of this work.

Along the same line, the question regarding the biological relevance of our receptor heteromers and JAK2’s role in cell surface distribution is undoubtfully very important. Studying GHR-PRLR cell surface distributions in JAK2 knockout cells and certain TNBC cell lines as proposed by the reviewer could perhaps be insightful. However, most TNBCs down-regulate PRLR [1], so we would first have to identify TNBC cell lines that actually express PRLR at sufficiently high levels. Moreover, knocking out JAK2 is known to significantly reduce GHR surface availability [2,3], such that the proposed experiment would probably provide only limited insights.

Unfortunately, our team is currently not in the position to perform any experiments (due to lack of funding and shortage of personnel). However, to address the reviewer’s comment as much as possible, we have revised the respective paragraph of the discussion section to emphasize the speculative nature of our statement and have added another paragraph discussing shortcoming and future experiments (see revised manuscript, pages 23-24).

(1) López-Ozuna, V., Hachim, I., Hachim, M. et al. Prolactin Pro-Differentiation Pathway in Triple Negative Breast Cancer: Impact on Prognosis and Potential Therapy. Sci Rep 6, 30934 (2016). https://www.nature.com/articles/srep30934

(2) He, K., Wang, X., Jiang, J., Guan, R., Bernstein, K.E., Sayeski, P.P., Frank, S.J. Janus kinase 2 determinants for growth hormone receptor association, surface assembly, and signaling. Mol Endocrinol. 2003;17(11):2211-27. doi: 10.1210/me.2003-0256. PMID: 12920237.

(3) He, K., Loesch, K., Cowan, J.W., Li, X., Deng, L., Wang, X., Jiang, J., Frank, S.J. Janus Kinase 2 Enhances the Stability of the Mature Growth Hormone Receptor, Endocrinology, Volume 146, Issue 11, 2005, Pages 4755–4765,https://doi.org/10.1210/en.2005-0514

(2) Except for some investigation of γ2A-JAK2 cells, most of the experiments in this study were conducted on a single breast cancer cell line. In terms of rigor and reproducibility, this is somewhat borderline. The CRISPR/Cas9 mutant T47D cells were not used for rescue experiments with the corresponding full-length receptors and the box1 mutants. A missed opportunity is the lack of an investigation correlating the number of receptors with physiological changes upon ligand stimulation (e.g., cellular clustering, proliferation, downstream signaling strength).

We appreciate the reviewer’s comments. While we are confident in the reproducibility of our findings, including those obtained in the T47D cell line, we acknowledge that testing in additional cell lines would have strengthened the generalizability of our results. We also recognize that performing a rescue experiment using our T47D hPRLR or hGHR KO cells would have been valuable. Furthermore, examining physiological changes, such as proliferation rates and downstream signaling responses, would have provided additional insights. Unfortunately, these experiments were not conducted at the time, and we currently lack the resources to carry them out.

(3) An obvious shortcoming of the study that was not discussed seems to be that the main methodology used in this study (super-resolution microscopy) does not distinguish the presence of various isoforms of the PRLR on the cell surface. Is it possible that the ligand stimulation changes the ratio between different isoforms? Which isoforms besides the long form may be involved in heteromers formation, presumably all that can bind JAK2?

This is a very good point. We fully agree with the reviewer that a discussion of the results in the light of different PRLR isoforms is appropriate. We have added information on PRLR isoforms to the Introduction (see revised manuscript, page 2) and Discussion sections (see revised manuscript, pages 23-24).

(4) Changes in the ligand-inducible activation of JAK2 and STAT5 were not investigated in the T47D knockout models for the PRL and GHR. It is also a missed opportunity to use super-resolution microscopy as a validation tool for the knockouts on the single cell level and how it might affect the distribution of the corresponding other receptor that is still expressed.

We thank the reviewer for his comment. We fully agree that such additional experiments could be very valuable. We are sorry but, as already mentioned above, this is not something we are able to address at this stage due to lack of personnel and funding. However, we do hope to address these and other proposed experiments in the future.

(5) Why does the binding of PRL not cause a similar decrease (internalization and downregulation) of the PRLR, and instead, an increase in cell surface localization? This seems to be contrary to previous observations in MCF-7 cells (J Biol Chem. 2005 October 7; 280(40): 33909-33916).

It has been recently reported for GHR that not only JAK2 but also LYN binds to the box1-box2 region, creating competition that results in divergent signaling cascades and affects GHR nanoclustering [1]. So, it is reasonable to assume that similar mechanisms may be at work that regulate PRLR cell surface availability. Differences in cells’ expression of such kinases could perhaps play a role in the perceived inconsistency. Also, Lu et al. [2] studied the downregulation of the long PRLR isoform in response to PRL. All other PRLR isoforms were not detectable in MCF-7 cells. So, differences between MCF-7 and T47D may lead to this perceived contradiction.

At this stage, we can only speculate about the actual reasons for these seemingly contradictory results. However, for full transparency, we are now mentioning this apparent contradiction in the Discussion section (see page 23) and have added the references below.

(1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

https://www.cell.com/cell-reports/pdf/S2211-1247(23)00501-6.pdf

(2) Lu, J.C., Piazza, T.M., Schuler, L.A. Proteasomes mediate prolactin-induced receptor down-regulation and fragment generation in breast cancer cells. J Biol Chem. 2005 Oct 7;280(40):33909-16. doi: 10.1074/jbc.M508118200. PMID: 16103113; PMCID: PMC1976473.

(6) Some figures and illustrations are of poor quality and were put together without paying attention to detail. For example, in Fig 5A, the GHR was cut off, possibly to omit other nonspecific bands, the WB images look 'washed out'. 5B, 5D: the labels are not in one line over the bars, and what is the point of showing all individual data points when the bar graphs with all annotations and SD lines are disappearing? As done for the y2A cells, the illustrations in 5B-5E should indicate what cell lines were used. No loading controls in Fig 5F, is there any protein in the first lane? No loading controls in Fig 6B and 6H.

We thank the reviewer for pointing this out. We have amended Fig. 5A to now show larger crops of the two GHR and PRLR Western Blot images and thus a greater range of proteins present in the extracts. Please note that the bands in the WBs other than what is identified as GHR and PRLR are non-specific and reflect roughly equivalent loading of protein in each lane.

We also made some changes to Figures 5B-5E.

(7) The proximity ligation method was not described in the M&M section of the manuscript.

We thank the reviewer for pointing this out. We have added a description of the PL method to the Methods section.

Reviewer #1 (Recommendations for the Authors):

A final suggestion for future investigations: Instead of focusing on the heteromer formation of the GHR/PRLR which both signal all through the same downstream effectors (JAK2, STAT5), it would have been more cancer-relevant, and perhaps even more interesting, to look for heteromers between the PRLR and receptors of the IL-6 family since it had been shown that PRL can stimulate STAT3, which is a unique feature of cancer cells. If that is the case, this would require a different modality of the interaction between different JAK kinases.

We highly appreciate the reviewer’s recommendation and hope to follow up on it in the near future.

Reviewer #2 (Public Review):

(1) I could not fully evaluate some of the data, mainly because several details on acquisition and analysis are lacking. It would be useful to know what the background signal was in dSTORM and how the authors distinguished the specific signal from unspecific background fluorescence, which can be quite prominent in these experiments. Typically, one would evaluate the signal coming from antibodies randomly bound to a substrate around the cells to determine the switching properties of the dyes in their buffer and the average number of localisations representing one antibody. This would help evaluate if GHR or PRLR appeared as monomers or multimers in the plasma membrane before stimulation, which is currently a matter of debate. It would also provide better support for the model proposed in Figure 8.

We are grateful for the reviewer’s comment. In our experience, the background signal is more relevant in dSTORM when imaging proteins that are located at deeper depths (> 3 μm) above the coverslip surface. In our experiments, cells are attached to the coverslip surface and the proteins being imaged are on the cell membrane. In addition, we employed dSTORM’s TIRF (total internal reflection fluorescence) microscopy mode to image membrane receptor proteins. TIRFM exploits the unique properties of an induced evanescent field in a limited specimen region immediately adjacent to the interface between two media having different refractive indices. It thereby dramatically reduces background by rejecting fluorescence from out-of-focus areas in the detection path and illuminating only the area right near the surface.

Having said that, a few other sources such as auto-fluorescence, scattering, and non-bleached fluorescent molecules close to and distant from the focal plane can contribute to the background signal. We tried to reduce auto-fluorescence by ensuring that cells are grown in phenol-red-free media, imaging is performed in STORM buffer which reduces autofluorescence, and our immunostaining protocol includes a quenching step aside from using blocking buffer with different serum, in addition to BSA. Moreover, we employed extensive washing steps following antibody incubations to eliminate non-specifically bound antibodies. Ensuring that the TIRF illumination field is uniform helps reduce scatter. Additionally, an extended bleach step prior to the acquisition of frames to determine localizations helped further reduce the probability of non-bleached fluorescent molecules.

In short, due to the experimental design we do not expect much background. However, in the future, we will address this concern and estimate background in a subtype dependent manner. To this end we will distinguish two types of background noise: (A) background with a small change between subsequent frames, which mainly consists of auto-fluorescence and non-bleached out-of-focus fluorescent molecules; and (B) background that changes every imaging frame, which is mainly from non-bleached fluorescent molecules near the focal plane. For type (A) background, temporal filters must be used for background estimation [1]; for type (B) background, low-pass filters (e.g., wavelet transform) should be used for background estimation [2].

(1) Hoogendoorn, Crosby, Leyton-Puig, Breedijk, Jalink, Gadella, and Postma (2014). The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation. Scientific reports, 4, 3854. https://doi.org/10.1038/srep03854

(2) Patel, Williamson, Owen, and Cohen (2021). Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM). Bioinformatics, Volume 37, Issue 17, September 2021, Pages 2730–2737, https://doi.org/10.1093/bioinformatics/btab136

(2) Since many of the findings in this work come from the evaluation of localisation clusters, an image showing actual localisations would help support the main conclusions. I believe that the dSTORM images in Figures 1 and 2 are density maps, although this was not explicitly stated. Alexa 568 and Alexa 647 typically give a very different number of localisations, and this is also dependent on the concentration of BME. Did the authors take that into account when interpreting the results and creating the model in Figures 2 and 8?

I believe that including this information is important as findings in this paper heavily rely on the number of localisations detected under different conditions.

Including information on proximity labelling and CRISPR/Cas9 in the methods section would help with the reproducibility of these findings by other groups.

Figures 1 and 2 show Gaussian interpolations of actual localizations, not density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting. In other words, we show reconstructed images based on identifying true localizations using gaussian fitting and some strict parameters to identify true fluorophore blinking. This allowed us to identify true localizations with high confidence and generate a high-resolution image for membrane receptors.

Indeed, Alexa 568 and 647 give different numbers of localization. This is dependent on the intrinsic photo-physics of the fluorophores. Specifically, each fluorophore has a different duty cycle, switching cycle, and survival fraction. However, we note that we focused on capturing the relative changes in receptor numbers over time, before and after stimulation by ligands, not the absolute numbers of surface GHR and PRLR. We are not comparing the absolute numbers of localizations or drawing comparisons for localization numbers between 568 and 647. For all these different conditions/times, the photo-physics for a particular fluorophore remains the same. This allows us to make relative comparisons.

As far as the effect of BME is concerned, the concentration of mercaptoethanol needs to be carefully optimized, as too high a concentration can potentially quench the fluorescence or affect the overall stability of the sample. However, we are using an optimized concentration which has been previously validated across multiple STORM experiments. This makes the concerns relating to the concentration of BME irrelevant to the current experimental design. Besides, the concentration of BME is maintained across all experimental conditions.

We have added information regarding PL and CRISPR/Cas9 for generating hGHR KO and hPRLR KO cells in two new subsections to the Methods section.

Reviewer #2 (Recommendations for the authors):

In the methods please include:<br /> (1) A section with details on proximity ligation assays.

We have added a description of the PL method to the Methods section.

(2) A section on CRISPR/Cas9 technology.

We have added two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR  or hPRLR knockout” to the Methods section.

(3) List the precise composition of the buffer or cite the paper that you followed.

We used the buffer recipe described in this protocol [1] and have added the components with concentrations as well as the following reference to the manuscript.

(1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

(4) Exposure time used for image acquisition to put 40 000 frames in the context of total imaging time and clarify why you decided to take 40 000 images per channel.

Our Nikon Ti2 N-STORM microscope is equipped with an iXon DU-897 Ultra EMCCD camera from Andor (Oxford Instruments). According to the camera’s manufacturer, this camera platform uses a back-illuminated 512 x 512 frame transfer sensor and overclocks readout to 17 MHz, pushing speed performance to 56 fps (in full frame mode). We note that we always tried to acquire STORM images at the maximal frame rate. As for the exposure time, according to the manufacturer it can be as short as 17.8 ms. We would like to emphasize that we did not specify/alter the exposure time.

See also: https://andor.oxinst.com/assets/uploads/products/andor/documents/andor-ixon-ultra-emccd-specifications.pdf

The decision to take 40,000 images per frame was based on our intention to identify the true population of the molecules of interest that are localized and accurately represented in the final reconstruction image. The total number of frames depends on the sample complexity, density of sample labeling and desired resolution. We tested a range of frames between 20,000 and 60,000 and found for our experimental design and output requirements that 40,000 frames provided the best balance between achieving maximal resolution and desired localizations to make consistent and accurate localization estimates across different stimulation conditions compared to basal controls.

(5) The lasers used to switch Alexa 568 and Alexa 647. Were you alternating between the lasers for switching and imaging of dyes? Intermittent and continuous illumination will produce very different unspecific background fluorescence.

Yes, we used an alternating approach for the lasers exciting Alexa 647 and Alexa 568, for both switching and imaging of the dyes.

(6) A paragraph with a detailed description of methods used to differentiate the background fluorescence from the signal.

We have addressed the background fluorescence under Point 1 (Public Review). We have added a paragraph in the Methods section on this issue.

(7) Minor corrections to the text:

It appears as though there is a large difference in the expression level of GHR and PRLR in basal conditions in Figure 1. This can be due to the switching properties of the dyes, which is related to the amount of BME in the buffer, or it can be because there is indeed more PRL. Would the authors be able to comment on this?

We thank the reviewer for this suggestions. According to expression data available online there is indeed more PRLR than GHR in T47D cells. According to CellMiner [1], T47D cells have an RNA-Seq gene expression level log2(FPKM + 1) of 6.814 for PRLR, and 3.587 for GHR, strongly suggesting that there is more PRLR than GHR in basal conditions, matching the reviewer’s interpretation of our images in Fig. 1 (basal). However, we would advise against using STORM images for direct comparisons of receptor expression. First, with TIRF images, we are only looking at the membrane fraction (~150 nm close to the coverslip membrane interface) that is attached to the coverslip. Secondly, as discussed above, our data represent relative cell surface receptor levels that allow for comparison of different conditions (basal vs. stimulation) and does not represent absolute quantifications. Everything is relative and in comparison to controls.

Also, BME is not going to change the level of expression. The differences in growth factor expression as estimated by relative comparison can be attributed to the actual changes in growth factors and is not an artifact of the amount of BME in the buffer or the properties of dyes. These factors are maintained across all experimental conditions and do not influence the final outcome.

(1) https://discover.nci.nih.gov/cellminer/

(8) I would encourage the authors to use unspecific binding to characterize the signal coming from single antibodies bound to the substrate. This would provide a mean number of localizations that a single antibody generates. With this information, one can evaluate how many receptors there are per cluster, which would strengthen the findings and potentially provide additional support for the model presented in Figure 8. It would also explain why the distributions of localisations per cluster in Fig. 3B look very different for hGHR and hPRLR. As the authors point out in the discussion, the results on predimerization of these receptors in basal conditions are conflicting and therefore it is important to shed more light on this topic.

We thank the reviewer for this suggestions. While we are unable to perform this experiment at this stage, we will keep it in mind for future experiments.

(9) Minor corrections to the figures:

Figure 1:

In the legend, please say what representation was used. Are these density maps or another representation? Please provide examples of actual localisations (either as dots or crosses representing the peaks of the Gaussians). Most findings of this work rely on the characterisation of the clusters of localisations and therefore it is of essence to show what the clusters look like. This could potentially go to the supplemental info to minimise additional work. It's very hard to see the puncta in this figure.

If the authors created zoomed regions in each of the images (as in Figure 3), it would be much easier to evaluate the expression level and the extent of colocalisation. Halfway through GHR 3 min green pixels become grey, but this may be the issue with the document that was created. Please check. Either increase the font on the scale bars in this figure or delete it.

As described above, Figure 1 does not show density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting and smoothing.

We have generated zoomed regions. In our files (original as well as pdf) we do not see pixels become grey. We increased the font size above one of the scale bars and removed all others.

Figure 3:

In A, the GHR clusters are colour coded but PRLR are not. Are both DBSCN images? Explain the meaning of colour coding or show it as black and white. Was brightness also increased in the PRLR image? The font on the scale bars is too small. In B, right panels, the font on the axes is too small. In the figure legend explain the meaning of 33.3 and 16.7

In our document, both GHR and PRLR are color coded but the hGHR clusters are certainly bigger and therefore appear brighter than the hPRLR clusters. Both are DBSCAN images. The color coding allows to distinguish different clusters (there is no other meaning). We have kept the color-coding but have added a sentence to the caption addressing this. Brightness was increased in both images of Panel B equally. 33.3 and 16.7 are the median cluster sizes. We have added a sentence to the caption explaining this. We have increased the font on the axes in B (right panels).

Figure 4:

I struggled to see any colocalization in the 2nd and the 3rd image. Please show zoomed-in sections. In the panels B and C, the data are presented as fractions. Is this per cell? My interpretation is that ~80% of PRL clusters also contain GHR.

Is this in agreement with Figures 1 and 2? In Figure 1, PRL 3 min, Merge, colocalization seems much smaller. Could the authors give the total numbers of GHR and PRLR from which the fractions were calculated at least in basal conditions?

We have provided zoom-in views. As for panels B and C, fractions are number of clusters containing both receptors divided by the total number of clusters. We used the same strategy that we had used for calculating the localization changes: We randomly selected 4 ROIs (regions of interest) per cell to calculate fractions and then calculated the average of three different cells from independently repeated experiments. We did not calculate total numbers of GHR/PRLR. The numbers are fractions of cluster numbers.

Moreover, the reviewer interprets results in panels B and C that ~80% of PRLR clusters also contain GHR. We assume the reviewer refers to Basal state. Now, the reviewer’s interpretation is not correct for the following reason: ~80% of clusters have both receptors. How many of the remaining (~20%) clusters have only PRLR or only GHR is not revealed in the panels. Only if 100% of clusters have PRLR, we can conclude that 80% of PRLR clusters also contain GHR.

Also, while Figures 1 and 2 show localization based on dSTORM images, Figure 3 indicates and quantifies co-localization based on proximity ligation assays following DBSCAN analysis using Clus-DoC. We do not think that the results are directly comparable.

Reviewer #3 (Public Review):

(1) The manuscript suffers from a lack of detail, which in places makes it difficult to evaluate the data and would make it very difficult for the results to be replicated by others. In addition, the manuscript would very much benefit from a full discussion of the limitations of the study. For example, the manuscript is written as if there is only one form of the PRLR while the anti-PRLR antibody used for dSTORM would also recognize the intermediate form and short forms 1a and 1b on the T47D cells. Given the very different roles of these other PRLR forms in breast cancer (Dufau, Vonderhaar, Clevenger, Walker and other labs), this limitation should at the very least be discussed. Similarly, the manuscript is written as if Jak2 essentially only signals through STAT5 but Jak2 is involved in multiple other signaling pathways from the multiple PRLRs, including the long form. Also, while there are papers suggesting that PRL can be protective in breast cancer, the majority of publications in this area find that PRL promotes breast cancer. How then would the authors interpret the effect of PRL on GHR in light of all those non-protective results? [Check papers by Hallgeir Rui]

We thank the reviewer for such thoughtful comments. We have added a paragraph in the Discussion section on the limitations of our study, including sole focus on T47D and γ2A-JAK2 cells and lack of PRLR isoform-specific data. Also, we are now mentioning that these isoforms play different roles in breast cancer, citing papers by Dufau, Vonderhaar, Clevenger, and Walker labs.

We did not mean to imply that JAK2 signals only via STAT5 or by only binding the long form. We have made this point clear in the Introduction as well as in our revised Discussion section. Moreover, we have added information and references on JAK2 signaling and PRLR isoform specific signaling.

In our Discussions section we are also mentioning the findings that PRL is promoting breast cancer. We would like to point out that it is well perceivable that PRL is protective in BC by reducing surface hGHR availability but that this effect may depend on JAK2 levels as well as on expression levels of other kinases that competitively bind Box1 and/or Box2 [1]. Besides, could it not be that PRL’s effect is BC stage dependent? In any case, we have emphasized the speculative nature of our statement.

(1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

Reviewer #3 (Recommendations for the authors):

Points for improvement of the manuscript:

(1) Method details -

a) "we utilized CRISPR/Cas9 to generate hPRLR knockout T47D cells ......" Exactly how? Nothing is said under methods. Can we be sure that you knocked out the whole gene?

We have addressed this point by adding two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR or hPRLR knockout” to the Methods section.

b) Some of the Western blots are missing mol wt markers. How specific are the various antibodies used for Westerns? For example, the previous publications are quoted as providing characterization of the antibodies also seem to use just band cutouts and do not show the full molecular weight range of whole cell extracts blotted. Anti-PRLR antibodies are notoriously bad and so this is important.

There is an antibody referred to in Figure 5 that is not listed under "antibodies" in the methods.

We have modified Figure 5a, showing the entire gel as well as molecular weight markers. As for specificity of our antibodies, we used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48, which have been previously tested and used. In addition, we did our own control experiments to ensure specificity. We have added some of our many control results as Supplementary Figures S2 and S3.

We thank the reviewer for noticing the missing antibody in the Methods section. We have now added information about this antibody.

c) There is no description of the proximity ligation assay.

We have addressed this by adding a paragraph on PLA in the Methods section.

d) What is the level of expression of GHR, PRLR, and Jak2 in the gamma2A-JAK2 cells compared to the T47D cells? Artifacts of overexpression are always a worry.

γ2A-JAK2 cell series are over-expressing the receptors. That’s the reason we did not only rely on the observation in γ2A-JAK2 cell lines but also did the experiment in T47D cell lines.

e) There are no concentrations given for components of the dSTORM imaging buffer. On line 380, I think the authors mean alternating lasers not alternatively.

Thank you. Indeed, we meant alternating lasers. We are referring to [1] (the protocol we followed) for information on the imaging buffer.

(1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

f) In general, a read-through to determine whether there is enough detail for others to replicate is required. 4% PFA in what? Do you mean PBS or should it be Dulbecco's PBS etc., etc.?

We prepared a 4% PFA in PBS solution. We mean Dulbecco's PBS.

(2) There are no controls shown or described for the dSTORM. For example, non-specific primary antibody and second antibodies alone for non-specific sticking. Do the second antibodies cross-react with the other primary antibody? Is there only one band when blotting whole cell extracts with the GHR antibody so we can be sure of specificity?

We used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48 (but also tested several other antibodies). While these antibodies have been previously tested and used, we performed additional control experiments to ensure specificity of our primary antibodies and absence of non-specific binding of our secondary antibodies. We have added some of our many control results as Supplementary Figures S2 and S3.

(3) Writing/figures-

a) As discussed in the public review regarding different forms of the PRLR and the presence of other Jak2-dependent signaling

We have added paragraphs on PRLR isoforms and other JAK2-dependent signaling pathways to the Introduction. Also, we have added a paragraph on PRLR isoforms (in the context of our findings) to the Discussion section.

b) What are the units for figure 3c and d?

The figures show numbers of localizations (obtained from fluorophore blinking events). In the figure caption to 3C and 3D, we have specified the unit (i.e. counts).

c) The wheat germ agglutinin stains more than the plasma membrane and so this sentence needs some adjustment.

We thank the reviewer for this comment. We have rephrased this sentence (see caption to Fig. 4).

d) It might be better not to use the term "downregulation" since this is usually associated with expression and not internalization.

While we understand the reviewer’s discomfort with the use of the word “downregulation”, we still think that it best describes the observed effect. Moreover, we would like to note that in the field of receptorology “downregulation” is a specific term for trafficking of cell surface receptors in response to ligands. That said, to address the reviewer’s comment, we are now using the terms “cell surface downregulation” or “downregulation of cell surface [..] receptor” throughout the manuscript in order to explicitly distinguish it from gene downregulation.

e) Line 420 talks about "previous work", a term that usually indicates work from the same lab. My apologies if I am wrong, but the reference doesn't seem to be associated with the authors.

At the end of the sentence containing the phrase “previous work”, we are referring to reference [57], which has Dr. Stuart Frank as senior and corresponding author. Dr. Frank is also a co-corresponding author on this manuscript. While in our opinion, “previous work” does not imply some sort of ownership, we are happy to confirm that one of us was responsible for the work we are referencing.

Reviewing Editor's recommendations:

The reviewers have all provided a very constructive assessment of the work and offered many useful suggestions to improve the manuscript. I'd advise thinking carefully about how many of these can be reasonably addressed. Most will not require further experiments. I consider it essential to improve the methods to ensure others could repeat the work. This includes adding methods for the PLA and including detail about the controls for the dSTORM. The reviewers have offered suggestions about types of controls to include if these have not already been done.

We thank the editor for their recommendations. We have revised the methods section, which now includes a paragraph on PLA as well as on CRISPR/Cas9-based generation of mutant cell lines. We have also added information on the dSTORM buffer to the manuscript. Data of controls indicating antibody specificity (using confocal microscopy) have been added to the manuscript’s supplementary material (see Fig. S2 and S3).

I agree with the reviewers that the different isoforms of the prolactin receptor need to be considered. I think this could be done as an acknowledgment and point of discussion.

We have revised the discussions section and have added a paragraph on the different PRLR isoforms, among others.

For Figure 2E, make it clear in the figure (or at least in legend) that the middle line is the basal condition.

We thank the editor for their comment. We have made changes to Fig 2E and have added a sentence to the legend making it clear that the middle depicts the basal condition.

My biggest concern overall was the fact that this is all largely conducted in a single cell line. This was echoed by at least one of the reviewers. I wonder if you have replicated this in other breast cancer cell lines or mammary epithelial cells? I don't think this is necessary for the current manuscript but would increase confidence if available.

We thank the editor for their comment and fully agree with their assessment. Unfortunately, we have not replicated these experiments in other BC cell lines nor mammary epithelial cells but would certainly want to do so in the near future.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this work, the authors investigate the functional difference between the most commonly expressed form of PTH, and a novel point mutation in PTH identified in a patient with chronic hypocalcemia and hyperphosphatemia. The value of this mutant form of PTH as a potential anabolic agent for bone is investigated alongside PTH(1-84), which is a current anabolic therapy. The authors have achieved the aims of the study.

Strengths:

The work is novel, as it describes the function of a novel, naturally occurring, variant of PTH in terms of its ability to dimerise, to lead to cAMP activation, to increase serum calcium, and its pharmacological action compared to normal PTH.

Recommendations for the authors:

(1) In your response to the reviewers you included a figure. You said it was for the reviewers only. We are *not* including it here. Is that correct or should it be in the Public Reviews?

We apologize for any confusion and appreciate your thorough review. The phrase “data only for reviewers” was intended to indicate that the content was included in the revision based on reviewers’ comments, not in the main text (article). However, we acknowledge that this phrasing may be inappropriate. We are agree to make the figure included in the previous author response of the public reviews. Accordingly, we propose to revise the previous author response as follows:

- Remove "(data only for reviewers)".

-  Correct the typo from "perosteal" to "periosteal".

- “Thank you for your comment. First, we ensured that the bones sampled during the experiment showed no defects, and we carefully separated the femur bones from the mice to preserve their integrity. In the 3-point bending test, PTH treatment significantly increased the maximum load of the femur bone compared to the OVX-control group. Additionally, the maximum load in the PTH treatment group was significantly greater than that observed in the PTH dimer group. Furthermore, structural factors influencing bone strength, such as the periosteal perimeter and the endocortical bone perimeter, were also increased in the PTH treatment group compared to the PTH dimer group.”

(2) Do you mean to always have R⁰ (have a superscript) and RG (never have a superscript) or should they be shown in the same way throughout your paper?

Thank you for your thorough review. Based on previous studies that addressed the conformation of PTH1R, R⁰ is typically shown with a superscript, while RG is not (Hoare et al., 2001; Dean et al., 2006; Okazaki et al., 2008). We have followed this notation and will ensure consistency throughout our paper.

Hoare, S. R., Gardella, T. J., & Usdin, T. B. (2001). Evaluating the signal transduction mechanism of the parathyroid hormone 1 receptor: effect of receptor-G-protein interaction on the ligand binding mechanism and receptor conformation. Journal of Biological Chemistry, 276(11), 7741-7753.

Dean, T., Linglart, A., Mahon, M. J., Bastepe, M., Jüppner, H., Potts Jr, J. T., & Gardella, T. J. (2006). Mechanisms of ligand binding to the parathyroid hormone (PTH)/PTH-related protein receptor: selectivity of a modified PTH (1–15) radioligand for GαS-coupled receptor conformations. Molecular endocrinology, 20(4), 931-943.

Okazaki, M., Ferrandon, S., Vilardaga, J. P., Bouxsein, M. L., Potts Jr, J. T., & Gardella, T. J. (2008). Prolonged signaling at the parathyroid hormone receptor by peptide ligands targeted to a specific receptor conformation. Proceedings of the National Academy of Sciences, 105(43), 16525-16530.

(3) The following grammatical and fact changes and word changes are requested.

We appreciate the thoughtful review and thank you for pointing out the grammatical, factual, and word changes required. We have carefully reviewed and addressed each of these corrections to ensure the paper's accuracy and readability.

We appreciate the reviewers' detailed and constructive reviews. We have addressed all the comments to improve the quality of our paper.

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

Learn more at Review Commons

Reply to the reviewers

We thank the reviewers for their comments and have included substantial new data to strengthen the work by specifically addressing questions regarding the molecular mechanisms driving the proteomic and phenotypic changes observed in these disease models. We have generated a new ganglioside disease model (GM1 gangliosidosis) and demonstrated that the lysosomal exocytosis mechanism identified for GM2 gangliosidosis is a conserved mechanism that alters the PM proteome (see new Figure 5).

We have also carried out substantial additional experimental work to address the question of whether specific protein-lipid interactions drive some of these changes. We have preliminary data supporting this (included below) but we are not confident that these data are robust enough for inclusion in this manuscript. This work required substantial in vitro experiments including the expression and purification of several proteins for use in liposome binding assays. Although these data are promising, they have been challenging to reproduce and we would prefer to develop this work further for inclusion in a subsequent paper.

Although not requested by any reviewers we have also included substantial additional multielectrode array (MEA) data in Figure 4 to further support the phenotypic changes to electrical signalling seen in the Tay Sachs disease model.

We would like to note that even without these new data the reviewers highlighted that the “high-quality data presented significantly advance the field” and that the work “exposes key conceptual novelties” using “new insight” and “new tools” that shed “light on the complex pathophysiology that links lipid accumulation to neuronal dysfunction”. And that this highlights “an underappreciated dimension of these diseases” allowing them to be “understood better thanks to this study”. More generally the reviewers state that the work is of interest to both “clinicians and basic researchers” and is relevant to “broader fields in cellular and neurodegenerative biology”.

Point-by-point description of the revisions

• *

Reviewer 1

Confirmation of Neuronal Differentiation: To confirm neuronal differentiation in their i3N cell model, the authors show qPCR results indicating the expression of mature neuronal markers and the downregulation of stem cell markers by day 14. However, single-cell RNA sequencing (scRNA-seq) could provide a more detailed evaluation of the differentiation process, addressing the fine-grained cell-type composition within the cell population. Depending on the results, the authors might more precisely interpret functional data and assess the possible influence of increased GM2 levels on cell fate decisions.

The accumulation of GM2 may not be identical across all neurons and so it is possible that, although the neuronal populations as a whole display mature differentiation, individual cells may respond differently to the amount of lipid debris. However, there are several technical reasons why obtaining samples for scRNAseq is extremely challenging. By 14 dpi the separation of individual neurons from each other is very difficult as they are in a densely grown and highly attached and interconnected network. Furthermore, the individual neurons have a highly polarized differentiated morphology with long delicate axonal and dendritic projections, that are readily cleaved and lysed in the process of harvesting and dissociation to obtain single cell suspensions for FACS sorting. In neurons, mRNAs are also abundantly localised along the length of their neuritic projections [1], thus these damaged preparations would provide unreliably meaningful data. Alternatively, sufficiently isolated individual neurons show poor survival and do not mature. If these technical difficulties could be overcome, in order to monitor altered differentiation, it would be necessary to determine which timepoint was most relevant to capture differences between day 0 stem cells and day 28 when they are synchronously firing glutamatergic neuron cultures. For this analysis to be robust it would require sample preparation and analysis of multiple stages of the differentiation process. For all the reasons above we cannot address this reviewer’s request.

Mechanistic Links Between Lipid Accumulation and Proteomic Changes: The authors report specific proteome changes upon HEXA/B KO. What are the mechanistic links between lipid accumulation and proteomic changes? Is the overall degradative performance of lysosomes compromised? The authors note that certain proteins, such as TSPANs, can bind directly to GSL headgroups. Clarifying whether the observed proteomic changes result from specific, direct lipid-protein interactions versus indirect effects could strengthen the argument for targeted lipid-mediated proteomic shifts.

In response to these questions, we have carried out substantial additional experimental work testing the lipid interactions of some of the proteins that are most altered in their abundance at the PM. We focussed on the top non-lysosomal proteins as we are proposing that the lysosomal ones are primarily changed due to lysosomal exocytosis, suggesting the non-lysosomal are the best candidates for direct GSL-binding. To robustly identify specific lipid-protein interactions is highly challenging but something we have demonstrated previously [2].

In vitro lipid-binding assays require expression and purification of the proteins of interest to then be used in liposome pulldown experiments using liposomes of defined composition. As we are most interested in the specificity of the headgroup interaction we focussed on producing the extracellular portions of these proteins that would be predicted to bind these headgroups (again this is a strategy we have successfully used previously [2]). We expressed and purified the extracellular domains of three top non-lysosomal hits: CNTNAP4, CNTN5 and NTRK2 (Fig. R1A, provided in attached response document). These purified proteins were used in liposome-binding assays using liposomes composed of different sphingolipids and gangliosides (Fig. R1B). These data demonstrate that the GPI-anchored protein CNTN5 and its potential binding partner CNTNAP4 bind promiscuously to different headgroups. This may be consistent with their being incorporated into GSL-rich membrane microdomains via the GPI-anchor. Interestingly, in this assay NTRK2 demonstrates specific and substantial binding to GM2, with some weaker binding to GD3.

These data support that the increased abundance of NTRK2 at the PM could be driven by direct interactions with the same lipid that is accumulating at the PM. As exciting and compelling as these data are, we have subsequently been unable to repeat this observation for NTRK2. We are unsure why and have tried several different strategies to test this interaction, but at this stage with only an N=1 for this observation we do not feel confident to include these data in the manuscript.

We intend to pursue this further using a range of alternative techniques and protein constructs but this will take substantial additional time and effort that we feel go beyond the scope of this current manuscript.

Additionally, does this phenomenon extend to other sphingolipidoses (e.g., Gaucher disease)? Comparing the proteomes of i3N cells across different sphingolipidoses could reveal whether the accumulation of distinct GSLs produces unique or shared proteomic profiles, highlighting similarities or specificities across lysosomal storage disorders.

We agree with the reviewer that this is an interesting and important question and had intended to do this as follow-up work in a future publication. However, in the interests of addressing this point here, we are including additional data we have generated from a new i3N model of GM1 gangliosidosis. As for the GM2 gangliosidosis models, we used CRISPRi to knockdown GLB1 and have confirmed this KD by q-PCR. We have also profiled the GSL composition and quantified the increased GM1 abundance. We have followed this up with both whole-cell and PM proteomics. We have presented comparative proteomics of the two models and demonstrated that they both result in significant accumulation of lysosomal proteins both in cells and at the PM. This shared proteomic profile is consistent with lysosomal exocytosis being a conserved mechanism driving altered PM composition in these diseases. We have included this work as an additional results section and an additional figure (Figure 5) as well as expanding the discussion. For this analysis we collected mass spec data at 28 dpi based on our observations in the paper that electrical signalling was synchronised at this point (Fig 4). In the text we discuss additional changes in these new WCP data such as the appearance of other trafficking molecules such as Arl8a that further support a lysosomal exocytosis mechanism.

In terms of the unique proteomic profiles of these diseases, the read depth of the PMP data in this case was not sufficient to confidently identify differences between the two gangliosidosis models and therefore we intend to pursue this work with additional LSDs in future studies to be included in a follow-up paper.

In terms of mechanistic links between lipid accumulation and proteome changes, we feel these new data provide substantial additional support that the appearance of lysosomal proteins at the PM is driven by lysosomal exocytosis and have preliminary data supporting that some non-lysosomal protein changes may be driven by altered protein-lipid interactions.

Impact of Increased PM GM2 Levels on Endocytic Pathways: Along similar lines, the authors show differences in the PM proteome and in the representation of specific PM lipid domain-associated proteins. As some of these proteins are turned over by mechanisms involving lipid domain-dependent endocytosis, the authors might want to examine the effect of increased PM GM2 levels on various endocytic pathways.

We thank the reviewer for this suggestion and have attempted assays monitoring endocytosis using several approaches including the uptake of fluorescently labelled bovine serum albumin (DQ-BSA) [3–5]. These endocytosis assays are well established in standard cell lines such as HeLa cells. Despite several attempts by us to get this working in neurons using multiple alternative readouts (microscopy and plate-based fluorescence) we have been unable to measure changes in endocytosis. Exploration of alternative methods to probe Clathrin-independent/dynamin-independent endocytosis (CLIC/GEEC) suggests these pathways are difficult to observe by fluorescence microscopy as there is minimal concentration of cargo proteins during the formation of carriers before endocytosis [6]. As an alternative strategy to probe changes in lipid-domain dependent endocytosis we have analysed the proteomics data for changes in galectins but no changes were identified in the data. We also explored available tools for modulating lysosomal exocytosis and monitoring lysosomal movement including activating TRPML1 to trigger exocytosis and activating ABCA3 to drive more lipid accumulation [7–10]. Similarly to the endocytosis assays above, these were not translatable to neurons in our hands due to a range of challenges including increased toxicity of these drugs on this cell type. We have made a substantial effort to try and address these questions and have conferred with colleagues who have also reported difficulties in establishing these assays in neurons. We are keen to continue to pursue this question but due to the technical challenges we feel this work lies beyond the scope of the current manuscript.

Multifaceted Nature of Gangliosidoses as PM Disorders: The manuscript presents an important perspective by reframing gangliosidoses as multifaceted PM disorders that disrupt neuronal function and membrane composition. By further elaborating on the connection between membrane lipid alterations, neuronal excitability, and synaptic composition, and by exploring the interplay with lysosomal dysfunction, the authors could provide a richer understanding of gangliosidoses and GSL function in general.

We appreciate that the reviewer agrees with us that reframing gangliosidoses as more complex multifaceted diseases is important. We are not sure if there is a request here for more elaboration in the text but based on the new data included in the paper, we have expanded some of the discussion around these points. We are very enthusiastic to continue to probe the connections and interplay as described by the reviewer and this is the focus of our ongoing studies.

Reviewer 2

1. T-tests and one-way ANOVAs were used, but it is not clear if datasets were tested for normality and equal standard deviations. Please add these details. If data are not normal or standard deviations are unequal, other tests will have to be used.

All graphs were checked for normality and variance in standard deviation and for figure 1F, where the data was not normally distributed, a Kruskal-Wallace test was used in place of a one-way ANOVA. All significantly different results are now labelled on graphs and the relevant tests described in the figure legends. This has also all been updated in the Supplementary data.

1. It needs to be clearly explained how many data points were used for statistical analyses and what the data points were. E.g., N=3 independent experiments on 3 different days, each done in n=3 different wells, total n=9. Each well can be considered a biological replicate, but it's of lesser value than the "big Ns" done on different days. The authors can choose different ways of defining their N/n numbers, but it has to be transparent. The bar graphs would ideally display the data points.

All figure legends now clearly explain N and n numbers used in experiments. Individual data points are displayed on qPCR graphs where N and n are mixed, with shapes denoting the biological repeat (N). In addition to clarification in figure legends, N and n numbers are described in the methods sections where appropriate.

For completeness we also include here details of these N/n numbers.

• For the q-PCR experiments, technical triplicates (repeats on the same day, n=3) were carried out for 3 separate biological replicates on different days (N=3). We have changed how these data are plotted to clarify this.
• For the activity assays, N=3 biological replicates were carried out on cell lysates from cultures grown on different days.
• For the microscopy analysis, coverslips from N=3 biological replicates on different days were used. n=2 coverslips per N were used to generate 15 images per N.
• For the glycan analysis, N=3 independent cell pellets were prepared on different days.
• For the proteomics experiments, these were done as N=3 independent cell cultures grown and prepared on different days. Specifically, one of each cell line SCRM, HEXA-1, HEXA-2, HEXB-1 and HEXB-2 were grown and harvested or biotinylated at a time (for WCP or PMP), with repeats on different days. These N=3 were then combined for the ΔHEX-A/B lines to provide N=12 biological repeats for disease cell lines to be compared to N=3 biological repeats for “SCRM” control cell lines.
• For calcium imaging, n=4 wells for each of SCRM, ΔHEXA-1 and ΔHEXB1 were averaged and the mean from each was used to provide n=3 data points across two biological repeats of this experiment, N=2.
• For the MEA data, we now include substantially more data than in the original manuscript (see comments at the top of this document). This is now N=3 biological replicates across n=52 wells over a time period from 38-45 dpi.
• The N/n values and statistical tests have also all been updated in the Supplementary data.

  1. There should be a comment on how statistical power was calculated upfront and if not: how N/n numbers were chosen ("based on similar expts in the past").

N/n numbers, as detailed above, were chosen based on previous experiments by ourselves and others, as well as recommended practice [2,11–15]. Typically, these papers do not describe the statistical power upfront. We have added statements to this effect and relevant references to the methods section of the manuscript.

1. "This suggests that some of the proteins that are accumulating in these diseases are specifically products of lipid accumulation rather than a product of general lysosomal dysfunction. In further support of this, several lysosomal proteins including V-type ATPases (ATP6 family), mannose-6-phosphate receptor (M6PR) and biogenesis of lysosomal organelle complex subunits (BLOC1) are quantified in the WCP but are not increased in abundance." This part is confusing. It seems like the authors observe an accumulation of endolysosomes in general (page 6), but then only certain endolysosomal proteins accumulate - and the authors speculate that this is due to decreased degradation or enhanced translation (mRNA levels are unaffected). This question should be addressed better, ideally experimentally: are endolysosomes accumulating in general or not? And what defines the endolysosomal proteins that accumulate vs. those that don't? How is that regulated?

Recently published work has identified that late endosomes/lysosomes do not possess one composition; they are dynamically remodelled and there is substantial heterogeneity in the composition of different lysosomes [16,17]. While some components, such as LAMP1 and Cathepsin D, are common across all lysosomal compartments there is considerable heterogeneity in the composition of these organelles. These studies also demonstrate that in disease-relevant conditions or upon drug treatment, lysosomes change their protein composition. For example, in a LIPL-4 KO mouse model they observe an increased abundance of Ragulator complex components, similarly to the increase in LAMTOR3 seen in our new 28 dpi WCP data for GM1 and GM2 gangliosidoses. Interestingly, in this study they demonstrate that lysosomal lipolysis leads to bigger changes in lysosomal protein composition than other pro-longevity mechanisms [17]. Another recent paper looking at a different lysosomal storage disease in microglia with accumulating GSLs and cholesterol has also identified abundance changes in a subset of lysosomal proteins including several we observe here including TTYH3, NPC1, PSAP and TSPAN7 [18]. Beyond proteomic analyses, the experimental tools for identifying these different populations are currently very limited, but these published studies support that it is possible to have accumulation of what we define as lysosomes by IF (using LAMP1 or lysotracker) but for the proteomic analysis to identify increased abundance of only a subset of lysosomal proteins.

These papers do not identify or speculate on how these differences are regulated. Analysis of the changes in our WCP as well as the new data for GM1 gangliosidoses support that the proteins that are most changed in response to GSL accumulation are membrane proteins involved in lipid and cholesterol binding and transport (New Fig 2D and 5E and see response below). This specific enrichment suggests that the changes are directly linked to the lipid changes, thus our suggestion that these accumulate due to a need for the cell to process these lipids but also that they may get “trapped” in the membrane whorls such that they are not efficiently degraded.

We have included the references above and a more detailed description of lysosomal heterogeneity into the main text to help address the reviewer’s questions.

1. Fig. 1D: The GO terms are confusing. Why are there more proteins in the category lysosomal membrane than lysosome as a whole? Other categories seem to be overlapping as well.

We apologize for the confusion; this graph does not display protein counts it is the adjusted P values for the enrichment of the term. To make this clearer, the DAVID analysis graphs are now presented in a new format. We present in this new graph the false discovery rate (FDR) (adjusted P value) which is a measure of the significance of whether that GO term is specifically enriched in the dataset. We have also expanded the GO term analysis to include molecular function and biological process descriptors in addition to the cellular component originally described. For full clarity, to the right of each term we include the number of significant hits that have this term, that being the number of proteins that are contributing to this GO term enrichment.

1. Fig. 2C/3A: It'd be good to also show the hits that don't match the expectation/pathways of interest.

We provide a full list in the Supplementary Information of all hits that are considered significant allowing the reader to access this information without having to download the datasets from PRIDE. We did not label all hits in these panels to avoid cluttering the image. In the main text we have focused on those that clearly fall within related categories or pathways as we feel that several “hits” in the same area represents a more compelling and confident assessment of the data. Several of the additional hits not mentioned in the main text do still match the expectations/pathways. For example, one of the top hits not labelled in the WCP is GPR155 (a cholesterol binding protein at the lysosomal membrane) and one of the top unlabelled hits in the PMP data is OPCML (a GPI-anchored protein that clusters in GSL-rich microdomains). There are some, such as KITLG (up in the PMP data), that we don’t currently have a hypothesis for why/how they change, but we are reluctant to describe and speculate upon additional isolated/orphan hits in the main text when these have not been further validated.

1. Fig. 3: It is not intuitive that synaptic proteins in particular would accumulate at the plasma membrane due to the lipid storage defect. Are they mis-trafficked or are they at synaptic membranes? That could, e.g, be addressed by isolating synaptosomes. And why this selectivity for synaptic proteins? Neurons should have more plasma membrane that is not synaptic. And, e.g, the release of lysosomal material should not happen at synapses (and lysosomes should not deliver synaptic proteins to the PM, unless there is a failure to degrade them).

We agree that synapses represent a relatively small proportion of the entire PM of neurons, but synapses are particularly enriched with glycosphingolipids where they affect synaptogenesis and synaptic transmission [19–22]. For these reasons we think that some synaptic proteins are particularly sensitive to these lipid changes as they are localised in GSL-rich membrane microdomains. We have now clarified this point in the text. We have also further clarified that we were not proposing that lysosomal proteins are present at the synapses. We observed that lysosomal proteins are enriched at the PM and this may be more generally across the whole PM, while the changes to synaptic proteins may or may not be localised at the synapse. We apologise for the confusion and have modified the text at the end of the PM proteomics results section to make this clearer.

To try and address experimentally the question of whether these proteins are at synapses, we have attempted synaptosome enrichment. However, lysosomal compartments co-sedimented with synaptosomes during the preparation – LAMP1 staining was enriched in the synaptosome preparations of all samples including SCRM controls. Therefore, we cannot distinguish these compartments which is particularly problematic in this disease model.

(7. Continued) Or is there an effect on synaptic vesicles? Are there more? Do they deliver their cargo more readily? Or is there a failure to do endocytosis of synaptic proteins, and that's why the accumulate? What is the connection between SVs and endolysosomes? More clarity would be good here.

We do think that there is an effect on synaptic vesicles particularly as the SV proteins SYT1 and SV2b are significantly increased in abundance at the PM suggesting they are not being internalized normally. Furthermore, the new WCP data going out to 28 dpi for both GM1 and GM2 gangliosidoses have identified a significant increase in Arl8a which plays a shared role in lysosomal and SV anterograde trafficking [23,24]. Whilst previously thought of as discrete pathways, evidence now suggests that endolysosomal and SV recycling pathways form a continuum with several shared proteins involved in the fusion, trafficking and sorting in both pathways [25]. Arl8a provides a good example of an adaptor protein that functions in both pathways and also when overexpressed results in enhanced neurotransmission consistent with our studies [26]. We have adjusted the discussion text to include a description of the links between SVs and endolysosomal trafficking and the potential shared role Arl8a may be playing in both pathways.

Regarding the question of whether there are more SVs or not, this is hard to determine directly as they are particularly small (~50 nm) and difficult to visualise or specifically stain for using microscopy. Not all SV-associated proteins are increased in the PMP data, for example SNAP25 and several other synaptotagmins are not changed in the 28 dpi data for both gangliosidosis models. We hope in the future to address SV changes more directly with higher resolution imaging such as electron microscopy or cryo-tomography but cannot currently confidently answer these specific questions.

1. Fig. 4: The assumption that there is more synaptic activity because there are more synaptic proteins at the membrane seems to be plausible, but also speculative at this point.

We have modified the text at the end of this results section to highlight that this is a speculative link.

1. The possible contribution of glial cells should at least be discussed.

We mention potential deleterious effects on bystander cells including other neurons, astrocytes and microglia in the second last paragraph of the discussion. In response to this request we have expanded and modified this text.

Minor: there are some typos etc.

Although no specific examples were listed, we have endeavored to find and correct typos, we have also checked for English spelling (not American) throughout.

Reviewer 3

1. Results section, 1st paragraph- to develop disease models- -- Please add cellular models as we already have KO mouse models.

This has been added to the text.

1. It was not clear what was the percentage of mutation success with their CRISPR technique.

The CRISPR method employed here was CRISPRi so there is no mutation of the genome. Instead, inactive/dead-Cas9 is targeted to the promotor/early exon of the HEXA or HEXB gene to inhibit mRNA production. We have included qPCR data to demonstrate the extent of the KD for two different guides to each of these genes in Fig 1.

1. Will the anti-GM2 antibody be available for other researchers? The researcher details needs to be clarified.

The anti-GM2 antibody is not commercial available and was generated by one of the co-authors. We invite scientists with an interest in this antibody to contact the corresponding author for details.

1. Hex activity assay was shown in 1C, but it was not clear that it is MUG or MUGS.

We apologise for this and have relabelled these activity assay graphs and expanded the legend text to clarify how these two substrates were used to distinguish the two different KD lines. We also corrected a small mistake in the methods section.

1. Is there a significance in Figure 2 B, 4A, 4B,4C and 4E?

Based on additional requests from reviewer 2 we have added significance indicators and details of significance tests for several panels in Figures 1-5 including 2B and 4B. For 4A we do not state a significant difference, we use these data to select a timepoint (28 dpi) where all cell lines have synchronous (correlated) signal. The data in Figure 4C and D have been substantially updated and expanded. Analysis of the data in 4C is plotted in 4D where we show significance. For 4E we are stating that the applied stimulation (white triangles) stimulates the HEXA cells every time but the SCRM do not respond to each stimulation. It is not clear how we would quantify this difference and there is no precedent for doing this in the MEA literature or by the Axion company who provided the instrument. We have also included additional references for best practice when analysing MEA data.

REFERENCES

1. Mofatteh M. mRNA localization and local translation in neurons. AIMS Neurosci. 2020;7: 299–310. doi:10.3934/Neuroscience.2020016
2. McKie SJ, Nicholson AS, Smith E, Fawke S, Caroe ER, Williamson JC, et al. Altered plasma membrane abundance of the sulfatide-binding protein NF155 links glycosphingolipid imbalances to demyelination. Proc Natl Acad Sci U S A. 2023;120: e2218823120. doi:10.1073/pnas.2218823120
3. Marwaha R, Sharma M. DQ-Red BSA Trafficking Assay in Cultured Cells to Assess Cargo Delivery to Lysosomes. Bio Protoc. 2017;7: e2571. doi:10.21769/BioProtoc.2571
4. gustavo.parfitt. Lysosome proteolysis analysis with DQ-BSA. 2022 [cited 13 Feb 2025]. Available: https://www.protocols.io/view/lysosome-proteolysis-analysis-with-dq-bsa-cgjxtupn
5. Fernandez-Mosquera L, Yambire KF, Couto R, Pereyra L, Pabis K, Ponsford AH, et al. Mitochondrial respiratory chain deficiency inhibits lysosomal hydrolysis. Autophagy. 2019;15: 1572–1591. doi:10.1080/15548627.2019.1586256
6. Rennick JJ, Johnston APR, Parton RG. Key principles and methods for studying the endocytosis of biological and nanoparticle therapeutics. Nat Nanotechnol. 2021;16: 266–276. doi:10.1038/s41565-021-00858-8
7. Pastore N, Annunziata F, Colonna R, Maffia V, Giuliano T, Custode BM, et al. Increased expression or activation of TRPML1 reduces hepatic storage of toxic Z alpha-1 antitrypsin. Molecular Therapy. 2023;31: 2651–2661. doi:10.1016/j.ymthe.2023.06.018
8. Zhang H, Wang Y, Wang R, Zhang X, Chen H. TRPML1 agonist ML-SA5 mitigates uranium-induced nephrotoxicity via promoting lysosomal exocytosis. Biomedicine & Pharmacotherapy. 2024;181: 117728. doi:10.1016/j.biopha.2024.117728
9. Shen D, Wang X, Li X, Zhang X, Yao Z, Dibble S, et al. Lipid Storage Disorders Block Lysosomal Trafficking By Inhibiting TRP Channel and Calcium Release. Nat Commun. 2012;3: 731. doi:10.1038/ncomms1735
10. Wünkhaus D, Tang R, Nyame K, Laqtom NN, Schweizer M, Scotto Rosato A, et al. TRPML1 activation ameliorates lysosomal phenotypes in CLN3 deficient retinal pigment epithelial cells. Sci Rep. 2024;14: 17469. doi:10.1038/s41598-024-67479-8
11. Zlamalova E, Rodger C, Greco F, Cheers SR, Kleniuk J, Nadadhur AG, et al. Atlastin-1 regulates endosomal tubulation and lysosomal proteolysis in human cortical neurons. Neurobiol Dis. 2024;199: 106556. doi:10.1016/j.nbd.2024.106556
12. Anderson GSF, Ballester-Beltran J, Giotopoulos G, Guerrero JA, Surget S, Williamson JC, et al. Unbiased cell surface proteomics identifies SEMA4A as an effective immunotherapy target for myeloma. Blood. 2022;139: 2471–2482. doi:10.1182/blood.2021015161
13. Mossink B, Verboven AHA, Hugte EJH van, Gunnewiek TMK, Parodi G, Linda K, et al. Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlations in vitro. Stem Cell Reports. 2021;16: 2182–2196. doi:10.1016/j.stemcr.2021.07.001
14. McCready FP, Gordillo-Sampedro S, Pradeepan K, Martinez-Trujillo J, Ellis J. Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders. Biology. 2022;11: 316. doi:10.3390/biology11020316
15. Weaver S, Dube S, Mir A, Qin J, Sun G, Ramakrishnan R, et al. Taking qPCR to a higher level: Analysis of CNV reveals the power of high throughput qPCR to enhance quantitative resolution. Methods. 2010;50: 271–276. doi:10.1016/j.ymeth.2010.01.003
16. Bond C, Hugelier S, Xing J, Sorokina EM, Lakadamyali M. Heterogeneity of late endosome/lysosomes shown by multiplexed DNA-PAINT imaging. J Cell Biol. 2025;224: e202403116. doi:10.1083/jcb.202403116
17. Yu Y, Gao SM, Guan Y, Hu P-W, Zhang Q, Liu J, et al. Organelle proteomic profiling reveals lysosomal heterogeneity in association with longevity. Elife. 2024;13: e85214. doi:10.7554/eLife.85214
18. Yasa S, Butz ES, Colombo A, Chandrachud U, Montore L, Tschirner S, et al. Loss of CLN3 in microglia leads to impaired lipid metabolism and myelin turnover. Commun Biol. 2024;7: 1373. doi:10.1038/s42003-024-07057-w
19. Sipione S, Monyror J, Galleguillos D, Steinberg N, Kadam V. Gangliosides in the Brain: Physiology, Pathophysiology and Therapeutic Applications. Front Neurosci. 2020;14: 572965. doi:10.3389/fnins.2020.572965
20. Svennerholm L. Gangliosides and Synaptic Transmission. In: Svennerholm L, Mandel P, Dreyfus H, Urban P-F, editors. Structure and Function of Gangliosides. Boston, MA: Springer US; 1980. pp. 533–544. doi:10.1007/978-1-4684-7844-0_46
21. Palmano K, Rowan A, Guillermo R, Guan J, McJarrow P. The role of gangliosides in neurodevelopment. Nutrients. 2015;7: 3891–3913. doi:10.3390/nu7053891
22. Hering H, Lin C-C, Sheng M. Lipid rafts in the maintenance of synapses, dendritic spines, and surface AMPA receptor stability. J Neurosci. 2003;23: 3262–3271. doi:10.1523/JNEUROSCI.23-08-03262.2003
23. Rizalar FS, Lucht MT, Petzoldt A, Kong S, Sun J, Vines JH, et al. Phosphatidylinositol 3,5-bisphosphate facilitates axonal vesicle transport and presynapse assembly. Science. 2023;382: 223–230. doi:10.1126/science.adg1075
24. Klassen MP, Wu YE, Maeder CI, Nakae I, Cueva JG, Lehrman EK, et al. An Arf-like small G protein, ARL-8, promotes the axonal transport of presynaptic cargoes by suppressing vesicle aggregation. Neuron. 2010;66: 710–723. doi:10.1016/j.neuron.2010.04.033
25. Ivanova D, Cousin MA. Synaptic Vesicle Recycling and the Endolysosomal System: A Reappraisal of Form and Function. Front Synaptic Neurosci. 2022;14: 826098. doi:10.3389/fnsyn.2022.826098
26. Vukoja A, Rey U, Petzoldt AG, Ott C, Vollweiter D, Quentin C, et al. Presynaptic Biogenesis Requires Axonal Transport of Lysosome-Related Vesicles. Neuron. 2018;99: 1216-1232.e7. doi:10.1016/j.neuron.2018.08.004
27. Saheki Y, De Camilli P. Synaptic Vesicle Endocytosis. Cold Spring Harb Perspect Biol. 2012;4: a005645. doi:10.1101/cshperspect.a005645

Author response:

Reviewer 1:

A primary limitation of this study, acknowledged by the authors, is its reliance on self-reports of participants’ emotional states. Although considerable effort was made to minimize expectation effects, further research is needed to confirm that the observed behavioral changes reflect genuine alterations in emotional states.

Thank you very much for raising this point. We fully agree that self-reported emotional states are inherently subjective and that the ramifications of this need to be clarified in the manuscript. However, we would suggest that the focus on self-report may be a strength rather than a limitation. First, the regularities and rules underlying and determining emotional self-report are of primary importance and interest in their own right, and the work presented here does, we believe, shed light on a rich structure present in multivariate timeseries of subjective self-reports and their response to external inputs. Second, there is no clear definition of what a ”genuine emotion state” might be; particularly if there is a discrepancy with self-reported emotions.

Additionally, the generalizability of the findings to long-term remediation strategies remains an open question.

Yes, we agree that what we have described is limited to a short-term intervention and change.

Whether these changes bear on longer-term changes remains to be assessed. Furthermore, the mechanisms or processes that would support such a maintenance are of substantial interest, and will be the focus of future work.

Second, the statistical analysis, particularly the computational approach, sometimes lacks sufficient detail and refinement. While I will not elaborate on specific points here, one notable issue is the interpretation of the intrinsic matrix (A). The model-free analysis reveals correlations between emotions at a given time or within an emotional state across time points. However, it does not provide evidence to support lagged interactions across states that would justify non-diagonal elements in A. The other result concerning the dynamics matrix only highlights a trend in the dominant eigenvalue, which is difficult to interpret in isolation. The absence of a statistically significant group x intervention interaction furthermore makes this finding a little compelling. This weakens the study’s conclusions about the importance of intrinsic dynamics, as claimed in the title.

We appreciate the reviewer’s detailed feedback on the statistical analysis and interpretation of the intrinsic dynamics matrix. It is true that the model-free analysis as presented focuses on within-state correlations and that we have not provided such model-free evidence for lagged interactions across states. We do note that the model comparison suggested that the intervention caused changes in the full A matrix. This would be unlikely if there had not been meaningful cross-emotion lagged effects. Similarly, inference of the A matrix could have revealed a diagonal matrix, and we preferred not to impose such an assumption a priori, as it is very restrictive. Nevertheless, in the absence of a statistically significant group x intervention interaction, the findings regarding the A matrix are less compelling than those related to the control analyses. While this is likely due to a lack of statistical power, these are important points which we will consider in more detail in the revision.

Finally, to avoid potential misunderstandings of their work, the authors should be more careful about their use of terms pertaining to the control theory and take the time to properly define them. For example, the ”controllability” of emotional states can either denote that those states are more changeable (control theory definition), or, conversely, more tightly regulated (common interpretation, as used in the abstract). This is true for numerous terms (stability, sensitivity, Gramian, etc.) for which no clear definition nor references are provided. Readers unfamiliar with the framework of control theory will likely be at a loss without more guidance.

Thank you for this point. We recognize the potential for misunderstanding due to the dual usage of terms such as ”controllability” and will improve the clarity to avoid any misunderstanding.

Reviewer 2:

Acquiring data online inevitably gives rise to selection and self-selection effects. This needs to be acknowledged clearly. Exacerbating this, participant remuneration seems low at an amount below the minimum or living wage in Western countries (do the authors know where their participants came from?).

Thank you for this point. We certainly agree that different experimental settings can induce different biases, and this is no different for online settings. However, online tasks such as the one used here, have become accepted, and there is now a substantial literature showing that in-lab effects are often well-replicated in online settings (Gillan and Rutledge, 2021) . For the current study, it is not clear that an inperson setting may not induce comparably complex biases, e.g. to do with differences between experimenters. All participants were from the UK. Remuneration rates were comparable to other experimental settings, in keeping with other online studies, UK living wage recommendations, and ultimately determined according to institutional ethical guidance.

Another concern is that the intervention does not simply take place before the second block begins but is ongoing during the whole of the second block in that it is integrated into the phrasing of the task on each trial. It is therefore somewhat misleading to speak of a period ’after the intervention’, and it would have been interesting to assess the effect of this by including a third group where the phrasing does not change, but the floating leaves intervention takes place.

Thank you for this point. We acknowledge that the phrasing of the emotion question in the second block may have influenced the observed effects. Including a third group without the reminder would have provided valuable insights and is an important consideration for future studies. We will acknowledge this limitation.

As mentioned in the Limitations section, observation noise was assumed and not estimated. While this is understandable in this case, the effect of this assumption could have been assessed by simulation with varying levels of observation (and process) noise.

Thank you for this comment. We would like to clarify that both observation noise and process noise were estimated in the analyses. We will ensure this is emphasized better in the revised version to avoid future misunderstandings.

Relatedly, the reliance on formal model comparison is unfortunate since the outcome of such comparisons is easily influenced by slight changes to assumptions such as noise levels. An alternative approach would have been to develop a favoured model based on its suitability to address the research question and its ability, established by simulation, to distill relevant changes of behaviour into reliable parameter estimates.

We agree that model comparison alone is insufficient. This is why we have also included extensive simulations, including posterior predictive checks, and have followed established best-practice procedures (Wilson and Collins, 2019). We have focused on a relatively simple model space to avoid overfitting to the dataset, and hence reduce the risk of spurious findings. While we agree that outcomes will be influenced by underlying assumptions, this would persist with the suggested approach of relying on a favoured model. Simulations themselves rely on predefined structures and noise specifications, which inherently shape parameter recovery and inference. Relying only on a favoured model might risk model misspecification, whereby the model may not actually capture the data, and the parameters intended to capture the intervention effect could be confounded. We will clarify the reasoning behind our approach in the revised version.

The statistical analyses clearly show the limitations of classical statistical testing with highly complex models of the kind the authors (commendably) use. Hunting for statistically significant interactions in a multivariate repeated-measures design relying on inputs from time seriesderived point estimates is a difficult proposition. While the authors make the best of the bad situation they create by using null-hypothesis significance testing, a more promising approach would have been to estimate parameters using a sampler like Stan or PyMC and then draw conclusions based on posterior predictive simulations.

This comment raises several interesting points. First, we agree that the value of classical test on individual parameters within such complex situations is limited. This is why our main focus is on global measures like model comparison. Our use of the classical tests is more to support the understanding of the nature of the data, i.e. they have a more descriptive aim. We will hope to clarify this further in the revision. Second, in terms of sampling, we would like to emphasize that the Kalman filter is both efficient and analytical tractable, making it well-suited to our data and research question. It may have been possible to use sampling to obtain posterior distributions rather than point estimates. However, we did not judge this to be worth the (substantial) additional computational cost.

Reviewer 3:

An interesting but perhaps at present slightly confusing aspect of their described results relates to the ’controllability’ of emotions, which they define as their susceptibility to external inputs. Readers should note this definition is (as I understand it) quite distinct from, and sometimes even orthogonal to, concepts of emotional control in the emotion literature, which refer to intentional control of emotions (by emotion regulation strategies such as distancing). The authors also use this second meaning in the discussion. Because of the centrality of control/controllability (in both meanings) to this paper, at present it is key for readers to bear these dual meanings in mind for juxtaposed results that distancing ”reduces controllability” while causing ”enhanced emotional control”.

We fully agree with the reviewer’s observation that ”controllability” can be interpreted in different ways. we will revise the text to ensure consistent usage and explicitly state the distinction between the control theory definition of controllability and its interpretation in the emotion regulation literature.

As above the authors use an active control - a relaxation intervention - which is extremely closely matched with their active intervention (and a major strength). However, there was an additional difference between the groups (as I currently understand it): ”in the group allocated to the distancing intervention, the phrasing of the question about their feelings in the second video block reminded participants about the intervention, stating: ”You observed your emotions and let them pass like the leaves floating by on the stream.” I do wonder if the effects of distancing also have been partially driven by some degree of reappraisal (considered a separate emotion regulation strategy) since this reminder might have evoked retrospective changes in ratings.

We appreciate this substantial point. While our study was designed to isolate the effects of distancing, we acknowledge that elements of reappraisal may also have influenced the results. We will discuss this in the revised version. Additionally, as noted in our response to Reviewer 2, including a third group without the reminder could have provided valuable information, and we consider this to be an important direction for future research.

Not necessarily a weakness, but an unanswered question is exactly how distancing is producing these effects. As the authors point out, there is a possibility that eye-movement avoidance of the more emotionally salient aspects of scenes could be changing participants’ exposure to the emotions somewhat. Not discussed by the authors, but possibly relevant, is the literature on differences between emotion types on oculomotor avoidance, which could have contributed to differential effects on different emotions.

Thank you very much for these suggestions. It is very true that different emotions can elicit different patterns of oculomotor avoidance, which could have contributed to our observed effects. Research suggests that emotions such as disgust are associated with visual avoidance (Armstrong et al., 2014; Dalmaijer et al., 2021), whereas anxiety and other negative emotions exhibited increased attentional bias after fear conditioning (Kelly and Forsyth, 2009; Pischek-Simpson et al., 2009). It would be very interesting to repeat the experiment with eye-tracking to examine these possibilities. What would be particularly interesting to examine is whether a distancing intervention induces multiple, emotionally-specific behaviours, or not.

References

Armstrong, T., McClenahan, L., Kittle, J., and Olatunji, B. O. (2014). Don’t look now! Oculomotor avoidance as a conditioned disgust response. Emotion (Washington, D.C.), 14(1):95–104.

Dalmaijer, E. S., Lee, A., Leiter, R., Brown, Z., and Armstrong, T. (2021). Forever yuck: Oculomotor avoidance of disgusting stimuli resists habituation. Journal of Experimental Psychology. General, 150(8):1598– 1611.

Gillan, C. M. and Rutledge, R. B. (2021). Smartphones and the Neuroscience of Mental Health. Annual Review of Neuroscience, 44(Volume 44, 2021):129–151. Publisher: Annual Reviews.

Kelly, M. M. and Forsyth, J. P. (2009). Associations between emotional avoidance, anxiety sensitivity, and reactions to an observational fear challenge procedure. Behaviour Research and Therapy, 47(4):331–338. Place: Netherlands Publisher: Elsevier Science.

Pischek-Simpson, L. K., Boschen, M. J., Neumann, D. L., and Waters, A. M. (2009). The development of an attentional bias for angry faces following Pavlovian fear conditioning. Behaviour Research and Therapy, 47(4):322–330.

Wilson, R. C. and Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8:e49547. Publisher: eLife Sciences Publications, Ltd.

Reviewer #1 (Public review):

Summary:

In the manuscript entitled 'A comparative analysis of planarian regeneration specificity reveals tissue polarity contributions of the axial cWnt signalling gradient.' Cleland et al. study the robustness of regenerating a head or a tail in the proper position in two different planarian species (S. mediterranea and G. sinensis). The authors find that the expression of notum, a Wnt inhibitor that is triggered after any cut, shows different dynamics of expression in both planarian species, being more symmetrical in the species that display a higher number of double-headed or Janus heads (G. sinenesis), which they refer to a less robust regeneration. The authors claim that the reduced robustness of G. sinensis regeneration is partially explained by this anterior-posterior symmetric expression of notum, since in S. mediterranea, which shows a 'robust regeneration' it appears asymmetric. So, the first claim of the manuscript is that the symmetry in notum expression could underlie the poor robustness of regenerating a head/tail in small bipolar regenerating planarian fragments.

Then, they analyse the role of a proposed tail-to-head cWnt signalling gradient during the regeneration of heads and tails in the same planarian species. To do so they develop an antibody that allows the quantification of b-catenin activity along the AP axis, together with a pharmacological approach that reduces the pre-existent cWnt gradient without affecting the wound-induced. Through this strategy the authors can demonstrate the slope of the b-catenin activity, which is a very nice result, and that it changes according to the size of the animal. Furthermore, they are able to demonstrate that by reducing the cWnt signalling in the pre-existent tissue, there is an increase in the number of double-headed regenerates (Janus heads) and that it depends on the body size and on the decreasing steepness of the cWnt gradient. This result relies on G. sinensis species since the drug is not so effective in S. mediterranea. Thus, the authors' second claim is that the slope of the cWnt gradient may contribute to head-tail regeneration specificity in planarians.

To conclude, it is proposed that regeneration of the correct identity in each wound depends on multiple cues acting in parallel and that their species-specificity provides variations in the regenerative capability of the different planarian species.

The study has great potential to have a high impact on the regeneration community, since the opportunity to compare mechanisms between close species provides the framework for understanding the essential mechanism of regeneration.

Strengths:

The project has several strengths. The authors are able to reproduce the Janus heads phenotypes described by Morgan TH by analysing different planarian species. This is of great importance in the planarian field, because with the current model species, S. mediterranea, this could not be reproduced. So, these results demonstrate that small planarian fragments do make errors during regeneration, giving rise to double-headed animals, which supports the well-known hypothesis that it exists an anteroposterior gradient underlying anteroposterior identity during regeneration. However, and importantly, it does not occur in all planarian species. So, there are differences between planarian species in the robustness of regeneration and may be in the mechanisms that drive this regeneration. The finding of different behaviours and gene expressions in different planarian species is very interesting and promising in the field of regeneration.

A second strength of the study is the demonstration of the b-catenin1 slope in planarians and how it changes with the animal size, and also the establishment of a method to decrease it in the pre-existent tissue but not in the wound. This strategy allows us to examine specifically the role of the pre-existent cWnt signal, demonstrating that it does have a role in the decision of making head or tail during regeneration, which was an essential question in the field of planarians and animal regeneration.

Weaknesses:

(1) The finding that notum, which is the main head determinant identified in planarians, has a different dynamic in both planarian species is very suggestive. However, the different dynamics of notum expression during regeneration, which is the basis of the subsequent rationale, is not properly demonstrated, nor is its correlation with the robustness in regenerating a proper head/tail identity. Main concerns regarding this point:

a) The authors observe that 'In regenerating S. mediterranea 2 mm trunk pieces cut from 6 mm animals, notum expression was induced predominantly at anterior-facing wounds as early as 6 h post-amputation (Figure 2A), as previously reported (Petersen and Reddien 2011)'. However, in the graphics in Figures 2B and C, the expression of notum at 6h is shown as symmetric. It definitely does not agree with the in situ, with the text, or with the published data. How was it measured? It should be corrected and explained since it is the basis of the subsequent rationale.

b) Then, when measuring notum in G. sinensis the authors conclude: 'Strikingly and in sharp contrast to S. mediterranea, the number of notum expressing cells was nearly identical between anterior and posterior wounds without any discernible A/P asymmetry at any of the examined time points (Figures 2E-F)'. However, in the in situ results of 12 h regenerating G. sinensis, there is a clear difference in notum expression between anterior and posterior wounds. Is it not representative of the image? Again, how exactly the measurements were performed? Are dots or pixels quantified? It is not explained in the text. This is a crucial result that has to be consistent.

c) A more general weakness of this part of the manuscript is that even if the authors demonstrate that in G. sinensis the expression of notum is symmetrical in contrast to S. mediterranea, this is just an observation of 1 species that has symmetrical notum and regenerates less robustly than 1 species that has asymmetrical expression and regenerates more robustly. If they for instance look at the expression of wnt1, maybe they also see differences between both species that could be linked to their different regeneration properties (related to this, see below the comment on wnt1 expression). That is to say, comparing 1 to 1 species cannot give any cause-effect evidence.<br /> Furthermore, the authors rely on the fact that notum inhibition rescues the wild-type phenotype to conclude that is the symmetric expression of notum that underlies the appearance of Janus heads. This is what can be read in the results: 'Significantly, the rescue of wild-type regenerates by notum(RNAi) suggests that the symmetric G. sinensis notum expression contributes to the formation of double-heads and thus to reduced regeneration specificity'; and in the Summary: We found that the reduced regeneration robustness of G. sinensis was partially explained by wound site-symmetric expression of the head determinant notum, which is highly anterior-specific in S. mediterranea.' However, notum RNAi decreases notum in both wounds, so it does not produce an asymmetric expression (at least this is not shown). So, it does not link the symmetry or asymmetry of notum with the appearance of Janus heads.

d) If the authors want to maintain the claim that the symmetry of notum is one of the reasons that explain the increase in Janus head phenotype in G. sinensis, there are several possibilities to test it. For instance:

i) Analyse notum expression in different planarian species and relate its symmetry or asymmetry with the appearance of Janus heads. If the claim is true, the species that are more robust should show more asymmetric expression of notum. This would sustain strongly the first claim, and would really be a breakthrough in the field of regeneration.

ii) Another possibility is a more in-depth analysis of notum expression in the species of the study. If the authors show that larger fragments show fewer Janus heads, and also that it depends on the anteroposterior level of the fragments, they could try to relate the rate of Janus heads with the degree of asymmetry in notum expression in both wounds. For instance, they could analyze notum expression in bipolar regenerating fragments along the anteroposterior axis in both species; it should be more symmetric in G sinenesis, in all fragments, according to Figure 2 L. Or they could analyze notum expression in bipolar regenerating fragments of different sizes, mainly in 1 or 2 mm fragments of big planarians, since they are the fragments analyzed that form or not the Janus heads. In G sinensis the expression of notum should be more symmetrical than in S. mediterranea in these fragments.

iii) The authors could design an experiment to demonstrate that the symmetry in the expression of notum affects the rate of Janus heads. The experiment that the authors show is the rescue of the Janus heads in G. sinensis after notum RNAi. However, notum RNAi suppresses notum in both wounds, thus not making them asymmetric. Furthermore, the rescue could be explained by the posteriorizing effect that notum RNAi has in planarians, as reported by several authors. A possibility could be to inhibit APC, which increases notum expression in S. mediterranea (Petersen and Reddien 2011). If APC RNAi in G. sinenesis produces an increase in notum in both wounds and the rate of Janus heads is not rescued, then it would support the hypothesis that notum symmetry is the cause of the Janus heads. However, if it produces an increase of notum in an asymmetric manner, then the Janus phenotype should be rescued.

(2) The second weakness of the study is related to the methodology used to support the second claim, that the slope of bcatenin1 activity has a role in the decision of regeneration - a head and a tail in the correct tip. The main concerns relate to the specificity of the anti-bcatenin1 antibody and to the broad effect of C59 in the secretion of all Wnts.

a) Raising an antibody against beta-catenin1 that allows the quantification by western blot is a strength of the study, since beta-catenin1 is the key element of the cWnt pathway, and their levels are directly associated with the activation of the pathway. Since this is one of the tools that support the second claim of the study, a characterization of the antibody and additional tests to prove its specificity are required. The authors show a Western blot in which the band intensity decreases after beta-catenin1 inhibition in both species. Further analysis should be shown:<br /> i) Demonstration that the intensity of the band increases after APC or Axin inhibition.<br /> ii) Does the antibody work in immunohistochemistry? It would provide further evidence of the specificity of a nuclear signal could be demonstrated.<br /> iii) Explanation and discussion of the protocol used to analyse the levels of b-catenin1 activity along the anteroposterior axis is required. It has been reported that beta-catenin1 is highly expressed and required in the brain in planarians, and also in the pharynx, and in the sexual organs (Hill and Petersen 2015, Sureda-Gomez et al 2016). How is it then explained the anterior-to-posterior gradient of expression of beta-catenin1 seen in this study in both species? Has the pharynx been removed before the protein extraction? What about the beta-catenin1 activity demonstrated in the brain? Why is it not reflected in the western blot analysis using the antibody? This point should be clarified.

b) The second tool used in the second part of the manuscript is the drug C59, which inhibits Porcupine, a protein required for palmitoylation and secretion of Wnts. Because Porcupine could be required for the secretion of all Wnts, the phenotype obtained with the drug could be the sum of the inhibition of cWNT signal (wnt1 for instances) and non-canonical WNT (as wnt5). This is in fact the phenotype resulting after the inhibition of Wntless in planarians (Adell et al. 2009), which is also required for the secretion of Wnts. Thus, in the phenotypes resulting from C59 treatment the analysis of the nervous system and posterior/anterior markers is required. Looking at the in vivo phenotype it appears that in fact the drug is affecting both canonical and no canonical pathways since the animal with protrusions in the lateral part (Figure 4B-double head, or Supplementary Figure 3A) is very similar to the one reported after Wntless inhibition. In case the phenotypes observed also show non-canonical Wnt inhibition, this should be clearly shown and discussed.

The above-mentioned weaknesses are the most important concerns about the present manuscript. However, there are other concerns related to a further analysis of the phenotypes and the analysis of additional Wnt elements as wnt1, which are essential to complete the study and are directly discussed with the authors.

Reviewer #1 (Public review):

Summary

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

(5) Results:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(6) Methods:

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

Author response:

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

Reviewer #1 (Public Review):

Summary:

This paper uses single-molecule FRET to investigate the molecular basis for the distinct activation mechanisms between 2 GPCR responding to the chemokine CXCL12 : CXCR4, that couples to G-proteins, and ACKR3, which is G-protein independent and displays a higher basal activity.

Strengths:

It nicely combines the state-of-the-art techniques used in the studies of the structural dynamics of GPCR. The receptors are produced from eukaryotic cells, mutated, and labeled with single molecule compatible fluorescent dyes. They are reconstituted in nanodiscs, which maintain an environment as close as possible to the cell membrane, and immobilized through the nanodisc MSP protein, to avoid perturbing the receptor's structural dynamics by the use of an antibody for example.

The smFRET data are analysed using the HHMI technique, and the number of states to be taken into account is evaluated using a Bayesian Information Criterion, which constitutes the state-of-the-art for this task.

The data show convincingly that the activation of the CXCR4 and ACKR3 by an agonist leads to a shift from an ensemble of high FRET states to an ensemble of lower FRET states, consistent with an increase in distance between the TM4 and TM6. The two receptors also appear to explore a different conformational space. A wider distribution of states is observed for ACKR3 as compared to CXCR4, and it shifts in the presence of agonists toward the active states, which correlates well with ACKR3's tendency to be constitutively active. This interpretation is confirmed by the use of the mutation of Y254 to leucine (the corresponding residue in CXCR4), which leads to a conformational distribution that resembles the one observed with CXCR4. It is correlated with a decrease in constitutive activity of ACKR3.

Weaknesses:

Although the data overall support the claims of the authors, there are however some details in the data analysis and interpretation that should be modified, clarified, or discussed in my opinion

Concerning the amplitude of the changes in FRET efficiency: the authors do not provide any structural information on the amplitude of the FRET changes that are expected. To me, it looks like a FRET change from ~0.9 to ~0.1 is very important, for a distance change that is expected to be only a few angstroms concerning the movement of the TM6. Can the authors give an explanation for that? How does this FRET change relate to those observed with other GPCRs modified at the same or equivalent positions on TM4 and TM6?

The large FRET change in our system was initially unexpected. However, the reviewer is mistaken that the expected distance change is only a few angstroms. Crystal structures of the homologous beta2 adrenergic receptor (β₂AR) in inactive and active conformations reveal that the cytoplasmic end of TM6 moves outwards by 16 angstroms during activation (Rasmussen et al., 2011, ref 47).  Consistent with this, smFRET studies of β₂AR labeled in TM4 and TM6 (as here) showed that the donor-acceptor (D-A) distance was 14 angstroms longer in the active conformation (Gregorio et al., ref 38).  Surprisingly, the apparent distance change in our system (calculated for our FRET probes, A555/Cy5, using FPbase.com) is almost 30 angstroms. A possible explanation is that the fluorophore attached to TM6 interacts with lipids within the nanodisc when TM6 moves outwards, which could stretch the fluorophore linker and thereby increase the D-A distance (lipids were absent in the β₂AR study). Such an interaction could also constrain the fluorophore in an unfavorable orientation for energy transfer, also leading to lower than expected FRET efficiencies and inflated distance calculations. Regardless, it is important to emphasize that none of the interpretations or conclusions of our study are based on computed D-A distances. Rather, we resolved different receptor conformations and quantified their relative populations based on the measured FRET efficiency distributions.

Finally, we note that a recent smFRET study of the glucagon receptor (labeled in TM4 and TM6, as here) also revealed a large difference in apparent FRET efficiencies between inactive (E_app = 0.83) and active (E_app = 0.32) conformations (Kumar et al., ref. 39). Thus, the large change in FRET efficiency observed in our study is not unprecedented.

Concerning the intermediate states: the authors observe several intermediate states.

(1) First I am surprised, looking at the time traces, by the dwell times of the transitions between the states, which often last several seconds. Is such a long transition time compatible with what is known about the kinetic activation of these receptors?

We too were surprised by the apparent kinetics of the receptors in our system. However, it was previously noted that purified systems, including nanodiscs, lead to slower activation times for GPCRs compared to cellular membrane systems (Lohse et al, Curr. Opin. Cell Biology, 27, 8792, 2014). Indeed, slow transitions among different FRET states (dwell times in the seconds range) were also observed in recent smFRET studies of the mu opioid receptor (Zhao et al., 2024, ref. 41) and the glucagon receptor (Kumar et al., 2023, ref. 39). These studies are consistent with the observed time scale of the FRET transitions reported here.

(2) Second is it possible that these “intermediate” states correspond to differences in FRET efficiencies, that arise from different photophysical states of the dyes? Alexa555 and Cy5 are Cyanines, that are known to be very sensitive to their local environment. This could lead to different quantum yields and therefore different FRET efficiencies for a similar distance. In addition, the authors use statistical labeling of two cysteines, and have therefore in their experiment a mixture of receptors where the donor and acceptor are switched, and can therefore experience different environments. The authors do not speculate structurally on what these intermediate states could be, which is appreciated, but I think they should nevertheless discuss the potential issue of fluorophore photophysics effects.

The reviewer is correct that the intermediate FRET states could, in principle, arise from a conformational change of the receptor that alters the local environment of the donor and/or acceptor fluorophores, rather than a change in donor-acceptor distance. This caveat is now included in the discussion on Pg. 10:

“In principle, the intermediates in CXCR4 and ACKR3 could represent partial movements of TM6 from the inactive to active conformation or more subtle conformational changes altering the photophysical characteristics of the probes without drastically altering the donor-acceptor distance. Either possibility leads to detectable changes in apparent FRET efficiency and reflect discrete conformational steps on the activation pathway; however, it is not possible to resolve specific structural changes from the data.”

Regarding the second possibility, it is true that our labeling methodology leads to a statistical mixture of labeled species (D on TM6 and A on TM4, D on TM4 and A on TM6). If the photophysical properties of the fluorophores were markedly different for the two labeling orientations, this would produce two different FRET efficiencies for a given receptor conformation. Assuming two receptor conformations, this scenario would produce four distinct FRET states: E₁ (inactive receptor, labeling configuration 1), E₂ (active receptor, labeling configuration 1), E₃ (inactive receptor, labeling configuration 2) and E₄ (active receptor, labeling configuration 2), with two cross peaks in the TDP plots, corresponding to E₁ ↔ E₂ and E₃ ↔ E₄ transitions. Notably, E₂ ↔ E₃ cross peaks would not be present, since states E₂ and E₃ exist on separate molecules. Instead, we see all states inter-connected sequentially, R ↔ R’ ↔ R* in CXCR4 and R ↔ R’ ↔ R*’ ↔ R* in ACKR3 (Fig. 2), suggesting that the resolved FRET states represent interconnected conformational states.

We added the following text to the Results section on Pg. 6:

“Two-dimensional transition density probability (TDP) plots revealed that the three FRET states were connected in a sequential fashion (Figs. 2A & B), indicating that the transitions occurred within the same molecules. Notably, these observations exclude the possibility that the midFRET state arises from different local fluorophore environments (hence FRET efficiencies) for the two possible labeling orientations of the introduced cysteines: assuming two receptor conformations, this model would produce four distinct FRET states, but only two cross peaks in the TDP plot.”

(3) It would also have been nice to discuss whether these types of intermediate states have been observed in other studies by smFRET on GPCR labeled at similar positions.

Intermediate states have also been reported in previous smFRET studies of other GPCRs. For example, in the glucagon receptor (also labeled in TM4 and TM6), a third FRET state (E_app =  0.63) was resolved between the inactive (E_app  = 0.85) and active (E_app  = 0.32) states (Kumar et al., Ref. 39).  Discrete intermediate receptor conformations were also observed in the A_2AR labeled in TM4 and TM6 (Fernandes et al., Ref 40). These examples are now cited in the Discussion.

On line 239: the authors talk about the R↔R' transitions that are more probable. In fact it is more striking that the R'↔R* transition appears in the plot. This transition is a signature of the behavior observed in the presence of an agonist, although IT1t is supposed to be an inverse agonist. This observation is consistent with the unexpected (for an inverse agonist) shift in the FRET histogram distribution. In fact, it appears that all CXCR4 antagonists or inverse agonists have a similar (although smaller) effect than the agonist. Is this related to the fact that these (antagonist or inverse agonist) ligands lead to a conformation that is similar to the agonists, but cannot interact with the G-protein ?? Maybe a very interesting experiment would be here to repeat these measurements in the presence of purified G-protein. G-protein has been shown to lead to a shift of the conformational space explored by GPCR toward the active state (using smFRET on class A and class C GPCR). It would be interesting to explore its role on CXCR4 in the presence of these various ligands. Although I am aware that this experiment might go beyond the scope of this study, I think this point should be discussed nevertheless.

We thank the reviewer for this observation and the possible explanation offered.  In response, we have added the following text to the Results section on Pg. 7:

“The small-molecule ligand IT1t is reported to act as an inverse agonist of CXCR4 (54-56). However, the conformational distribution of CXCR4 showed little change to the overall apparent

FRET profile, although R’ ↔ R* transitions appeared in the TDP plot (Figs. 3A & B, Fig. S8). This suggests that the small molecule does not suppress CXCR4 basal signaling by changing the conformational equilibrium. Instead IT1t appears to increase transition probabilities which may impair G protein coupling by CXCR4.”

We have also added the following text to the Results on Pg. 8:

“Despite the ability of CXCL12_P2G and CXCL12_LRHQ to stabilize the active R* conformation of CXCR4, both variants are known to act as antagonists (20). This suggests that the CXCL12 mutants inhibit CXCR4 coupling to G proteins not by suppressing the active receptor population but rather by increasing the dynamics of the receptor state transitions. Our results suggest that the helical movements considered classic signatures of the active state may not be sufficient for CXCR4 to engage productively with G proteins.”

In addition, we have added the following text to the Discussion on Pg. 11:

“The chemokine variants CXCL12_P2G and CXCL12_LRHQ are reported to act as antagonists of CXCR4 (19, 20), and the small molecule IT1t acts as an inverse agonist (54-56). Surprisingly, none of these ligands inhibit formation of the active R* conformation of CXCR4. In fact, the chemokine variants both stabilize and increase this state to some degree, although less effectively than CXCL12_WT. Thus, the antagonism and inverse agonism of these ligands does not appear to be linked exclusively to receptor conformation, suggesting that the ligands inhibit coupling of G proteins to CXCR4 or disrupt the ligand-receptor-G protein interaction network required for signaling (Fig. S10) (21, 23).  Interestingly, these ligands also increase the probabilities of state-to-state transitions (Figs. 3B & 4B), suggesting that enhanced conformational exchange prevents the receptor from productively engaging G proteins. Similarly, ACKR3 is naturally dynamic and lacks G protein coupling, suggesting a common mechanism of G protein antagonism.”

Finally, we also agree that experiments with G proteins could be informative. In fact, we initiated such experiments during the course of this study.  However, it soon became apparent that significant optimization would be required to identify fluorophore labeling positions that report receptor conformation without inhibiting G protein coupling. Accordingly, we decided that G protein experiments would be the subject of future studies.

However, we added the following text to the Discussion on Pg. 12:

“Future smFRET studies performed in the presence of G proteins should be informative in this regard”.

The authors also mentioned in Figure 6 that the energetic landscape of the receptors is relatively flat ... I do not really agree with this statement. For me, a flat conformational landscape would be one where the receptors are able to switch very rapidly between the states (typically in the submillisecond timescale, which is the timescale of protein domain dynamics). Here, the authors observed that the transition between states is in the second timescale, which for me implies that the transition barrier between the states is relatively high to preclude the fast transitions.

We thank the reviewer for the comment. We have modified the description of the energy landscapes of ACKR3 and CXCR4 in the discussion on Pg. 10 as follows:

“These observations imply that ACKR3 has a relatively flat energy landscape, with similar energy minima for the different conformations, whereas the energy landscape of CXCR4 is more rugged (Fig. 6). For both receptors, the energy barriers between states are sufficiently high that transitions occur relatively slowly with seconds long dwell times (Figs. 1C and S2).”

Reviewer #2 (Public Review):

Summary:

his manuscript uses single-molecule fluorescence resonance energy transfer (smFRET) to identify differences in the molecular mechanisms of CXCR4 and ACKR3, two 7transmembrane receptors that both respond to the chemokine CXCL12 but otherwise have very different signaling profiles. CXCR4 is highly selective for CXCL12 and activates heterotrimeric G proteins. In contrast, ACKR3 is quite promiscuous and does not couple to G proteins, but like most G protein-coupled receptors (GPCRs), it is phosphorylated by GPCR kinases and recruits arrestins. By monitoring FRET between two positions on the intracellular face of the receptor (which highlights the movement of transmembrane helix 6 [TM6], a key hallmark of GPCR activation), the authors show that CXCR4 remains mostly in an inactive-like state until CXCL12 binds and stabilizes a single active-like state. ACKR3 rapidly exchanges among four different conformations even in the absence of ligands, and agonists stabilize multiple activated states.

Strengths:

The core method employed in this paper, smFRET, can reveal dynamic aspects of these receptors (the breadth of conformations explored and the rate of exchange among them) that are not evident from static structures or many other biophysical methods. smFRET has not been broadly employed in studies of GPCRs. Therefore, this manuscript makes important conceptual advances in our understanding of how related GPCRs can vary in their conformational dynamics.

Weaknesses:

(1) The cysteine mutations in ACKR3 required to site-specifically install fluorophores substantially increase its basal and ligand-induced activity. If, as the authors posit, basal activity correlates with conformational heterogeneity, the smFRET data could greatly overestimate the conformational heterogeneity of ACKR3.

The change in basal ACKR3 activity with the Cys introductions are modest in comparison and insignificantly different as determined by extra-sum-of-squares F test (P=0.14).

(2) The probes used cannot reveal conformational changes in other positions besides TM6. GPCRs are known to exhibit loose allosteric coupling, so the conformational distribution observed at TM6 may not fully reflect the global conformational distribution of receptors. This could mask important differences that determine the ability of intracellular transducers to couple to specific receptor conformations.

We agree that the overall conformational landscape of the receptors has not been investigated and we have added this caveat to the discussion on Pg. 12.

“An important caveat is that our study does not report on the dynamics of the other TM helices and H8, some of which are known to participate in arrestin interactions.”

(3) While it is clear that CXCR4 and ACKR3 have very different conformational dynamics, the data do not definitively show that this is the main or only mechanism that contributes to their functional differences. There is little discussion of alternative potential mechanisms.

The main functional difference between CXCR4 and ACRK3 is their effector coupling: CXCR4 couples to G proteins, whereas ACKR3 only couples to arrestins (following phosphorylation of the C-terminal tail by GRKs). As currently noted in the discussion, ACKR3 has many features that may contribute to its lack of G protein coupling, including lack of a well-ordered intracellular pocket due to conformational dynamics, lack of an N-term-ECL3 disulfide, different chemokine binding mode, and the presence of Y257. Steric interference due to different ICL loop structures may also interfere with G protein activation. No one thing has proven to confer ACKR3 with G protein activity including swapping all of the ICLs to those of canonical chemokine receptor, suggesting it is a combination of these different factors. The following has been added to the discussion on Pg. 13 to clearly note that any one feature is unlikely to drive the atypical behavior of ACKR3:

“The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.”

(4) The extent to which conformational heterogeneity is a characteristic feature of ACKRs that contributes to their promiscuity and arrestin bias is unclear. The key residue the authors find promotes ACKR3 conformational heterogeneity is not conserved in most other ACKRs, but alternative mechanisms could generate similar heterogeneity.

Despite the commonalities in the roles of the ACKRs, they all appear to have evolved independently. Thus, we do not believe that all features observed and described for one ACKR will explain the behavior of another. We have carefully avoided expanding our observations to other ACKRs to avoid suggesting common mechanisms.

(5) There are no data to confirm that the two receptors retain the same functional profiles observed in cell-based systems following in vitro manipulations (purification, labeling, nanodisc reconstitution).

We agree this is an important point. All labeled receptors responded to agonist stimulation as expected. As only properly folded receptors are able to make the extensive interactions with ligands necessary for conformational changes (for instance, CXCL12 interacts with all TMs and ECLs), this suggests that the proteins are folded correctly and functional following all manipulations.

Reviewer #3 (Public Review):

Summary:

This is a well-designed and rigorous comparative study of the conformational dynamics of two chemokine receptors, the canonical CXCR4 and the atypical ACKR3, using single-molecule fluorescence spectroscopy. These receptors play a role in cell migration and may be relevant for developing drugs targeting tumor growth in cancers. The authors use single-molecule FRET to obtain distributions of a specific intermolecular distance that changes upon activation of the receptor and track differences between the two receptors in the apo state, and in response to ligands and mutations. The picture emerging is that more dynamic conformations promote more basal activity and more promiscuous coupling of the receptor to effectors.

Strengths:

The study is well designed to test the main hypothesis, the sample preparation and the experiments conducted are sound and the data analysis is rigorous. The technique, smFRET, allows for the detection of several substates, even those that are rarely sampled, and it can provide a "connectivity map" by looking at the transition probabilities between states. The receptors are reconstituted in nanodiscs to create a native-like environment. The examples of raw donor/acceptor intensity traces and FRET traces look convincing and the data analysis is reliable to extract the sub-states of the ensemble. The role of specific residues in creating a more flat conformational landscape in ACKR3 (e.g., Y257 and the C34-C287 bridge) is well documented in the paper.

Weaknesses:

The kinetics side of the analysis is mentioned, but not described and discussed. I am not sure why since the data contains that information. For instance, it is not clear if greater conformational flexibility is accompanied by faster transitions between states or not.

The reviewer is correct that kinetic information is available, in principle, from smFRET experiments. However, a detailed kinetic analysis will require a much larger data set than we currently possess, to adequately sample all possible transitions and the dwell times of each FRET state. We intend to perform such an analysis in the future as more data becomes available. The purpose of this initial study was to explore the conformational landscapes of CXCR4 and ACKR3 and to reveal differences between them. To this end, we have documented major differences in conformational preferences and response to ligands of the two receptors that are likely relevant to their different biological behavior. Future kinetic information will add further detail, but is not expected to alter the conclusions drawn here.

The method to choose the number of states seems reasonable, but the "similarity" of states argument (Figures S4 and S6) is not that clear.

We thank the reviewer for noting a need for further clarification. We qualitatively compared the positions of the various FRET peaks across treatments to gain insight into the consistency of the conformations and avoid splitting real states by overfitting the data. For instance, fitting the ACKR3 treatments with three states leads to three distinct FRET populations for the R’ intermediate. Adding a fourth state results in two intermediates that are fairly well overlapping. In contrast, the two-intermediate model for CXCR4 appears to split the R* state of the CXCL12 treated sample and causes a general shift in both intermediate states to lower FRET values when CXCL12 is present. As we assume that the conformations are consistent throughout the treatments, we conclude that this represents an overfitting artifact and not a novel CXCL12CXCR4 R*’ state. Additional sentences have been added to the supplemental figure legend to better describe the comparative analysis.

“(Top) With the 3-state model, the R’ states for apo-CXCR4 and for CXCL12- and IT1t-bound receptor overlapped well with similar apparent FRET values across all of the tested conditions. In the case of the four-state model, the R*’ (Middle) and R’ (Bottom) states were substantially different across the ligand treatments. In particular, the R*’ state with CXCL12 treatment appears to arise from a splitting of the R* conformation, indicating that the model was overfitting the data.”

Also, the "dynamics" explanation offered for ACKR3's failure to couple and activate G proteins is not very convincing. In other studies, it was shown that activation of GPCRs by agonists leads to an increase in local dynamics around the TM6 labelling site, but that did not prevent G protein coupling and activation.

We agree with the reviewer that any single explanation for ACKR3 bias, including the dynamics argument presented here, is insufficient to fully characterize the ACKR3 responses. As noted by the reviewer, the TM6 movement and dynamics is generally correlated with G protein coupling, whereas other dynamics studies (Wingler et al. Cell 2019) have noted that arrestinbiased ligands do not lead to the same degree of TM6 movement. We have added the following statement to the discussion on Pg. 13:

“The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.” 

Recommendations for the authors:  

Reviewer #1 (Recommendations For The Authors):

I would like to raise a technical point about the calculation and reporting of the FRET efficiency. The authors report the FRET efficiency as E=IA/(IA+ID). There is now a strong recommendation from the FRET community (https://doi.org/10.1038/s41592-018-0085-0) to use the term “FRET efficiency” only when a proper correction procedure of all correction factors has been applied, which is not the case here (gamma factor has not been calculated). The authors should therefore use the term “Apparent FRET Efficiency” and  E_app in all the manuscripts.

Also, it would be nice to indicate directly on the figures whether a ligand that is used is an agonist, antagonist, inverse agonist, etc...

We thank the reviewer for suggesting this clarification in terminology. We now refer to apparent FRET efficiency (or E_app) throughout the manuscript and in the figures. In addition, we have added ligand descriptions to the relevant figures.

Reviewer #2 (Recommendations For The Authors):

(1) M159(4.40)C/Q245(6.28)C ACKR3 appears to have higher constitutive activity than ACKR3 Wt (Fig. S1). While the vehicle point itself is likely not significant due to the error in the Wt, the overall trend is clear and arguably even stronger than the effect of Y257(6.40)L (Fig. S9). While this is an inherent limitation of the method used, it should be clearly acknowledged; the comment in lines 162-164 seems to skirt the issue by only saying that arrestin recruitment is retained. It would be helpful and more rigorous to report the curve fit parameters (basal, E_max, EC50) for the arrestin recruitment experiments and the associated errors/significance (see https://www.graphpad.com/guides/prism/latest/statistics/stat_qa_multiple_comparisons_ after_.htm for a discussion).

The Emin, E_max, and EC50 for M159⁴.⁴⁰C/Q245⁶.²⁸C ACKR3 were compared against the values for WT ACKR3 from Fig. S1 and only the E_max was determined to be significantly different by the extra sum of squares F test. A note has been added to the text to reflect these results on Pg. 5.

“Only the E_max for arrestin recruitment to CXCL12-stimulated ACKR3 was significantly altered by the mutations, while all other pharmacological parameters were the same as for WT receptors.”

(2) The methods do not specify the reactive group of the dyes used for labeling (i.e., AlexaFluor 555-maleimide and Cy5-maleimide?).

We regret the omission and have added the necessary details to the materials and methods.

(3) Were any of the native Cys residues removed from ACKR3 and CXCR4 in the constructs used for smFRET? ACKR3 appears to have two additional Cys residues in the N-terminus besides the one involved in the second disulfide bridge, and these would presumably be solvent-exposed. If so, please specify in the Methods and clarify whether the constructs tested in functional assays included these. (Also, please specify if the human receptors were used.)

No additional cysteine residues were mutated in either receptor. All exposed cysteines are predicted to form disulfides. The residues in the N-terminus that the reviewer alludes to, C21 and C26, form a disulfide (Gustavsson et al. Nature Communications 2017) and are thus protected from our probes. Consistent with these expectations, neither WT CXCR4 nor ACKR3 exhibited significant fluorophore labeling (now mentioned in the text on Pg. 5). The species of origin has been added to the material and methods.

(4) There are a few instances where the data seem to slightly diverge from the proposed models that may be helpful to comment on explicitly in the text:

- Figure 4E (ACKR3/CXCL12(P2G)): As noted in the legend, despite stabilizing R*/R*', CXCL12(P2G) reduces transitions between these states compared to Apo. This is more similar to the effects of VUF16840 (Figure 3D) than the other ACKR3 agonists. The authors note the difference between CXCL12(LHRQ) and CXCL12(P2G) (but not vs Apo) in this regard. There might be some other information here regarding the relative importance of the conformational equilibrium vs transition rates for receptor activity.

Although the TDPs for CXCL12_P2G and VUF16840 are similar, as noted by the reviewer, the overall FRET envelopes are drastically different.

The differences in transition probabilities for R ↔ R’ and R*’ « R* transitions observed in the presence of CXCL12_P2G or CXCL12_LRHQ relative to the apo receptor are now explicitly noted in the Results.

- The conformational distributions of ACKR3 apo and ACKR3 Y257L CXCL12 are very similar (Figure 5A,D). However, there is a substantial difference in the basal activity of WT vs CXCL12stimulated Y257L (Figure S9).

The mutation Y257L appears to promote the highest and lowest FRET states at the expense of the intermediates. Although the distribution appears similar between Apo-WT and CXCL12Y257L, the depopulation of the R’ state may lead to the observed activation in cells.

(5) There are inconsistent statements regarding the compatibility of G protein binding to the "active-like" ACKR3 conformation observed in the authors' previous structures (Yen et al, Sci Adv 2022). In the introduction, the authors seem to be making the case that steric clashes cannot account for its lack of coupling; in the discussion, they seem to consider it a possibility.

The introduction to previous research on the molecular mechanisms governing the lack of ACKR3-G protein coupling was not intended to be all encompassing, but rather to highlight previous efforts to elucidate this process and justify our study of the role  of dynamics. Due to the positions of the probes, we can only comment on the impact on TM6 movements and not other conformational changes. The steric clash reported in Yen et al. was in ICL2 and not directly tested here, so our observations do not preclude changes occurring in this region. We also do not claim that the active-like state resolved in our previous structures matches any specific state isolated here by smFRET.

(6) Line 83-85: "Having excluded other mechanisms we therefore surmised that the inability of ACKR3 to activate G proteins may be due to differences in receptor dynamics."

Line 400-402: "It is possible that the active receptor conformation clashes sterically with the G protein as suggested by docking of G proteins to structures of ACKR3."

As mentioned above, we suspect the mechanisms governing the inability of  ACKR3 to couple to G proteins may be more complex than one particular feature but instead due to a combination of several factors. Accordingly, we have not completely eliminated a contribution of steric hindrance as we described in Yen et al. Sci Adv 2022 and instead include it as a possibility. Following the line highlighted here, we list several alternatives: 

“Alternatively, the receptor dynamics and conformational transitions revealed here may prevent formation of productive contacts between ACKR3 and G protein that are required for coupling, even though G proteins appear to constitutively associate with the receptor.”

And, at the end of the paragraph, we have added the following sentence: 

“The atypical activation of ACKR3 does not appear to be dependent on any singular receptor feature and is likely a combination of several factors.”

(7) If the authors believe that the various ligands/mutations are only altering the distribution/dynamics of the same 3/4 conformations of CXCR4/ACKR3, respectively, is there a reason each FRET efficiency histogram is fit independently instead of constraining the individual components to Gaussian components with the same centroids, and/or globally fitting all datasets for the same receptor?

We performed global analysis across all data sets for each sample and condition. Since the peak positions of the various FRET states recovered in this way were consistent across treatments (Fig. S4,S6), we did not feel it was necessary to perform a further global analysis across all samples for a given receptor.

Reviewer #3 (Recommendations For The Authors):

The manuscript is well-written, the arguments are easy to follow and the figures are helpful and clear. Here are a few questions/suggestions that the authors might want to address before the paper will be published:

(1) Include a table with kinetic rates between states in SI and have a brief discussion in the main text to support the trends observed in transition probabilities.

As noted above, determining rate constants for each of the state-to-state transitions will require a much larger set of experimental smFRET data than is currently available and will be the subject of future studies.

(2) The argument of state similarity (Figure S4 and S6)... why are the profiles not Gaussian, like in the fits on Figures S3 and S5, repectively? I would also suggest that once the number of states is chosen to do a global fit, where the FRET values of a certain sub-state across different conditions for one receptor are shared.

The state distributions presented in Figs. S4 and S6 (as well as throughout the rest of the paper) are derived from HMM fitting of the time traces themselves, and are not constrained to be Gaussian, whereas the GMM analysis in Figs. S3 and S5 are Gaussian fits to the final apparent FRET efficiency histograms.

Similar to our response to Review 2 above, due to the consistency of the fitted peak positions obtained across different conditions for a given sample, we did not feel that further global analysis was necessary.

(3) It is shown FRET changes from ~0.85 in the inactive (closed) state to ~0.25 in the active (open) state. How do these values match the expectations based on crystal structure and dye properties?

As noted in our response to Reviewer 1, translating the apparent FRET values using the assumed Förster distances for A555/Cy5 (per FPbase) suggest a change in D-A distance of ~30 angstroms, whereas the expected change from structures is ~16 Å. We suspect this discrepancy is due to the lipids immediately adjacent to the fluorophores, which may lead to the probes being constrained in an extended position when TM6 moves outwards, thus also reporting the linker length in the distance change. Additionally, such interactions may constrain the donor and acceptor in unfavorable orientations for energy transfer, which would also reduce the FRET efficiency in the active state. Since the calculated D-A distance changes appear too large for GPCR activation, we have opted to not make any structural interpretations. Instead, all of our conclusions are based on resolving individual conformational states and quantifying their relative populations, which is based directly on the measured FRET efficiency distributions, not computed distances.

(4) The results on the effect of CXCL12-P2G on CXCR4 are confusing...despite being an antagonist, this ligand stabilizes the "active state"...I am not sure if the explanation offered is sufficient that the opening of the intracellular cleft is not sufficient to drive the G protein coupling/activation.

We agree that the explanation related to the opening of the intracellular cleft being insufficient to drive G protein coupling/activation is speculative and we have removed that text. We now simply propose that the CXCL12 variants inhibit coupling of G proteins to CXCR4 or disrupt interactions necessary for signaling, as stated in the following text to the results on Pg. 8:

“Despite the ability of CXCL12_P2G and CXCL12_LRHQ to stabilize the active R* conformation of CXCR4, both variants are known to act as antagonists (20). This suggests that the CXCL12 mutants inhibit CXCR4 coupling to G proteins not by suppressing the active receptor population but rather by increasing the dynamics of the receptor state-to-state transitions. Our results suggest that the helical movements considered classic signatures of the active state may not be sufficient for CXCR4 to engage productively with G proteins.”

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

Learn more at Review Commons

Reply to the reviewers

General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

• We thank the reviewers for their useful suggestions regarding how to improve our manuscript.
• Reviewer 3 declared that s/he did not find and evaluate the provided Supplementary Materials. As a result, many of her/his criticisms seem invalid: the requested data, validations etc. were already there in the Supplementary Figures and Tables.
• To avoid confusion, we renamed the transgene that is commonly used as a readout for STAT-activated transcription from 10xStat92E-GFP to 10xStat92E DNA binding site-GFP (please see comments by Reviewer 2 that show how easily one can think that Stat92E protein levels go up because of the misleading name of this transgene).
• One co-author, Martin Csordós was among the authors by mistake. Although first considered, his contribution was not included in either the original or the current manuscript version, so we removed his name from the revised version with his permission.
• We prefer to use colour coding for Sections 2., 3. and 4. in our responses to Reviewer comments rather than splitting the responses to queries in separate sections, because many of our answers contain a mixture of planned experiments (labeled as bold), already available data (labeled as underlined), and *explanations why we think that no additional analyses are necessary* (between asterisks). Data already provided in the original submission but missed by Reviewers has white background in our responses. Reviewer comments

Reviewer 1

Major comments:

R1/1. ”Figure 6E seems to indicate that a subset of Su(var)2-10/PIAS isoforms may bind to ATG8 (directly or indirectly). This leads to the straightforward prediction that this subset should be differentially affected by the selective autophagy at the center of the manuscript. That could be tested to strengthen that point. “

Response:

The Atg8a-binding subset of Su(var)2-10/PIAS isoforms could indeed be differentially affected by selective autophagy__. To test this, we will analyze in vivo Su(var)2-10 isoform abundance on western blots with an anti- Su(var)2-10 antibody in __Atg8aΔ12and ____Atg8aK48A/Y49A (Atg8aLDS) mutants.

Minor comments:

R1/2. “ in Fig S1B,C the colocalization between GFP reporters for STAT92E and AP-1 activity and glia marker does not seem convincing, indicating other cell types may be expressing them as well.”

*Response: *

*The overlap between glia labelling and STAT92E and AP-1 transcriptional readout reporter expression is indeed not complete. First of all, epithelial cells in the wing display both STAT92E and AP-1 activity even in uninjured conditions when glial expression of these reporters is not yet observed. Transcriptional reporter activity outside of the wing nerve was previously indicated in figures with arrowheads, now the epithelium is labeled and the regions containing nerve glia are outlined everywhere. *

The fiber-like reporter expression after injury in the wing nerve could correspond to either glia or axons1–3. Glia in the wing nerve have a filament-like appearance resembling axons in confocal images, even glial nuclei are flat/elongated1. Importantly, STAT92E enhancer-driven GFP also labels the nucleus in expressing cells, as opposed to glially driven mtdTomato that is membrane-tethered (and thus excluded from the nucleus: see Fig. S1B, C). Of note, TRE-GFP and Stat-GFP are not expressed in neurons because the cell bodies and nuclei of wing vein neurons are never GFP-positive, see Fig. 2C, Figs. S1, S4 in Neukomm et al.1 and Figure 1 for Reviewers. We also explain this better now in the revised manuscript (please see the legend of Fig. S1).

Nonetheless, we plan to analyze colocalization of mtdTomato-labeled neurons and TRE-GFP and Stat-GFP around the neuronal cell bodies to unequivocally show their different identities. Additionally, we will include transverse confocal sections of the genotypes in Fig. S1B, C that may better illustrate the colocalization.

Fig. 1 for Reviewers. Neuronal (nSyb+) and Stat92E-GFP+ cell morphology in the L1 vein at the anterior wing margin around the neuronal cell bodies which occupy a stereotypical position at the sensilla1. The location and shape of neuronal nuclei (left panel) are different from Stat-GFP+ cell nuclei (right panel, please see also Fig. S1B, C) based on the circumferential GFP signal. Therefore, cells expressing TRE-GFP and Stat-GFP in injured wing nerves are glia and not neurons.

R1/3. “p.7 Instead of "Su(var)2-10 is mainly nuclear due to its transcriptional repressor and chromatin organizer functions" It may be better to say" .. .consistent with its transcriptional repressor and chromatin organizer functions"”

Response:

We have modified the manuscript accordingly.

R1/4. “It is not clear whether the differences in Su(var)2-10/PIAS accumulation between Atg16 and Atg101 RNAi indicate functional differences of blocking autophagy at different stages or simply differences in RNAi efficiency (Atg16) versus the Atg101 mutant.”

Response:

We have added glial Atg1 (the catalytic subunit of the autophagy initiation complex that also includes Atg101) knockdown experiments that show the same lack of Su(var)2-10 accumulation in uninjured conditions as seen in the Atg101 null mutant (please see Fig. S6C). Please note that Atg16-Atg5-Atg12 dependent conjugation of LC3/Atg8a is involved in various vesicle trafficking pathways in addition to autophagy4–6, alterations of which may perturb baseline Su(var)2-10 levels in uninjured animals.

Significance:

R1/5. “STAT92E-dependent glial upregulation of vir-1, but not Draper, is shown, but consequences for glial functions in nerve injury are not tested.”

Response:

We will test antimicrobial peptide (AMP) expression in glia after nerve injury and whether this is affected by STAT92E and vir-1. Certain AMPs such as Attacin C are known to be regulated by both the Stat and NF-____κΒpathways7, and AMPs can be generally upregulated in response to brain injury8,9. This could serve pathogen clearance functions after defence lines such as the epithelium and blood-brain barrier are compromised. In addition, we will test the recruitment of glial processes into the antennal lobe after olfactory nerve injury in animals with glial STAT92E or vir-1 deficiency. Glial invasion is an adaptive response to axon injury and a first step towards debris clearance10.

R1/6. “experiments indicate a role for Su(var)2-10/PIAS SUMOylation activity in tis autophagic degradation, but it is not clear whether the critical substrata Su(var)2-10/PIAS itself or another protein.”

“binding of Su(var)2-10/PIAS to ATG8 is indicated, but no in vitro experiment performed to test whether this is direct and perhaps SUMOylation dependent.”

Response:

*We aimed to answer this question by using a point mutant form of Su(var)2-10: CTD2, which is unable to properly autoSUMOylate itself11, see Fig. 6D. CTD2 mutant Su(var)2-10 levels increased in S2 cells transfected with the mutant construct relative to the wild-type, similar to lysosome inhibition affecting the wild-type protein level but not the mutant variant. Importantly, wild-type Su(var)2-10 is present in CTD2 mutant Su(var)2-10-transfected cells, which can still SUMOylate other Su(var)2-10 targets. It is thus the intrinsic SUMOylation defect of the CTD2 mutant that results in its impaired degradation. It is firmly established that increased Su(var)2-10/PIAS levels repress STAT92E activity12, mammalian example: Liu et al., 199813, pointing to Su(var)2-10 as the critical substrate for autophagy during STAT92E derepression.*

We will further address this point and investigate if Su(var)2-10 directly binds to Atg8a by in vitro SUMOylation of GST-Su(var)2-10 and subsequent GST pulldown assay with HA-Atg8a. In vitro SUMOylation reaction with purified GST-Su(var)2-10 and negative controls are available via in-house collaboration11. We will incubate the resulting proteins and non-SUMOylated counterparts with in vitro transcribed /translated HA-Atg8a, and interactions will be tested by anti-HA western blotting with quantitative fluorescent LICOR Odyssey CLX detection.

Reviewer 2

Major comments:

R2/1. “The working hypothesis is that upon injury, Su(var)2-10 is degraded by autophagy and, as a consequence, Stat92E induces vir-1 expression.

Could the authors clarify why do Stat92E levels increase upon injury? Does Stat92E stability increase upon ATG mediated Su(var)2-10 degradation? Or does it expression/nuclear translocation change?“

Response:

We did not state that Stat92E levels increase during injury - we only used the 10xStat92E DNA binding site-GFP reporter (we have renamed it as such in our revised manuscript to avoid confusion) that is commonly referred to as 10xStat92E-GFP in the literature14, as a readout for Stat92E-dependent transcription.

To address these questions, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene (https://flybase.org/reports/FBti0147707.htm) to test if STAT92E stability/expression or translocation is altered during injury or upon disruption of selective autophagy. We have already tested this reporter and it is detected in the wing nerve nuclei after injury (Figure 2 for Reviewers, panel A).

As the Atg8aLDS mutation specifically impairs selective autophagy, we will use this mutant and wild-type controls to assess STAT92E::GFP::FLAG abundance on western blots from fly lysates with anti-GFP antibody. To assess STAT92E::GFP::FLAG nuclear translocation as well as stability/expression, we will use independently Atg8aLDS and Su(var)2-10 RNAi in glia to perturb STAT92E -dependent transactivation and visualize glia cell membrane by membrane-tethered tdTomato, glial nuclei by DAPI/anti-Repo and STAT92E with the STAT92E::GFP::FLAG fusion transgene in dissected brains. We can also evaluate STAT92E nuclear translocation with the same genotypes in the injured wing nerve glia. Of note, studies in mammals failed to identify an obvious effect of PIAS1 on STAT1 abundance13, please see Figure 2B from this paper as Figure 2 for Reviewers, panel B. Rather, PIAS family proteins bind tyrosine-phosporylated STAT dimers and impair their DNA binding thereby their transcriptional activation function15.

A.

Proc. Natl. Acad. Sci. USA Vol. 95, pp. 10626–10631

https://doi.org/10.1073/pnas.95.18.10626.

Fig. 2 for Reviewers.

1. Stat92E::GFP::FLAG expression and nuclear appearance in the wing nerve before and after injury
2. Increasing PIAS1 (Su(var)2-10 ortholog) levels does not affect STAT1 abundance in mammalian cells R2/2. “Also, since Su(var) levels increase upon ATG RNAi, independently of injury, do ATG levels increase upon injury? It does not seem to be the case from Fig 6D, but then, if the ATG levels do not increase, how to explain the injury mediated effects of Su(var)2-10? “

Response:

*We have not seen an effect of injury on the rate of autophagic degradation (flux) using the common flux reporter GFP-mCherry -Atg8a in glia after injury (shown in Fig. S2D – not 6D). Also, levels of the typical autophagic cargo p62/Ref(2)P and core autophagy proteins such as Atg12, Atg5, Atg16 do not change after nervous system injury16suggesting no change in general autophagic turnover. *

*An increase in general autophagy would be one option to promote degradation of a given cargo. Just as for the ubiquitin-proteasome system, in selective autophagy the labelling of the cargo/substrate for degradation is a regulated process. Dynamic ubiquitylation of a cargo often promotes its autophagic degradation17. We hypothesize that SUMO may fulfil a similar role in labelling cargo for elimination and this may be promoted by injury in the case of Su(var)2-10, which warrants future studies. *

R2/3. “Su(var)2-10 levels in control and injured wings are different between ATG18RNAi and ATG101 mutant (Fig 5). Could the authors explain the rational for using two ATG mutants? and the meaning of this difference? Also, why comparing data using the RNAi approach and a mutation?”

Response:

This issue was also raised in R1/4 and we refer the Reviewer/Editor to that section for our new Atg1 knockdown data and explanations.

*There is a consensus in the autophagy community that mutants for multiple Atg genes should always be used to ensure that it is indeed canonical autophagy that is affected (because Atg proteins can have non-autophagic roles, as is the case for Atg16 in regulation of phagosome maturation - LAP). *

R2/4. “Fig 6 What is the relevance of the Atg8, Sumo and Su(var)2-10 colocalization at puncta, since there is a lot of colocalization outside the puncta and also lots of Su(var)2-10 or Atg8 labeling that does not colocalize? “

Response:

*Su(var)2-10 orthologs PIAS1-4 localize to the nuclear matrix and certain foci in the chromatin and may play roles in heterochromatin formation, DNA repair, and repression of transposable elements in addition to transcriptional repression18–20. SUMO-modified proteins accumulate in response to PIAS activity in phase-separated foci also referred to as SUMO glue21. We show colocalization of Atg8a with similar Su(var)2-10 and SUMO double positive structures in foci. *

*We do not expect a full overlap between Su(var)2-10 and Atg8a labeling for a number of reasons. First, Su(var)2-10 has many different roles that may not be regulated by autophagy. Second, Atg8a+ autophagosomes in the cytoplasm deliver not only indidivual proteins such as Su(var)2-10 for degradation but also many other cellular components. Third, nuclear Atg8a is implicated in the removal of the Sequoia transcriptional repressor from autophagy genes that is unlikely to involve Su(var)2-1022. Now we include these points in the Discussion section.*

R2/5. “The statement made in the first sentence of the discussion is very strong: 'we have uncovered an activation mechanism for Stat92E', without sufficient supporting evidence.”

Response:

We have rephrased this section as follows:

Here we have uncovered the autophagy-dependent clearance of a direct repressor of the Stat92E transcription factor. This, synergistically with injury-induced Stat92E phosphorylation, may ensure proper Stat92E-dependent responses in glia after nerve injury to promote glial reactivity.

R2/6. “Could the authors validate (some) expression data by in situ hybridization experiments?”

Response:

*Our gene expression data were derived from wing nerve imaging or wing tissue. Unfortunately, in situ hybridization is not feasible in this organ because probes do not penetrate the thick chitin-based cuticule and wax cover of the wing (and the same is true for wing immunostaining).* We do provide independent evidence for vir-1 upregulation in the wing after injury via quantitative PCR (qPCR) in Fig. S5C. To corroborate reporter-based data, we will also analyze drpr in qPCR using wing material after injury at the same time points.

R2/7. “Could the authors validate the RNAi lines molecularly (or refer to published data on these lines?”

Response:

*Almost all RNAi lines have already been validated by qPCR, western blot, or immunostaining in Szabo et al., 202316 and other publications23–25. The only exception is Su(var)2-10JF03384 and we show that it is indistinguishable from the validated Su(var)2-10HMS00750 RNAi line (which causes 95% transcript reduction): it also strongly derepresses STAT activity. These reagents have also been widely used in the community (e.g. https://flybase.org/reports/FBal0242556.htm, https://flybase.org/reports/FBal0233496.htm).*

R2/8. „Clarifying the role of Su(var)2-10 on Stat92E would benefit to the presented work. Does Atg8-Su(var)2-10 binding affect Stat92E accumulation, expression, translocation to the nucleus? Some of these experiments could be obtained in S2 cell transfection assays, if too complex in vivo.”

Response:

As explained in R2/1, we will use an endogenous promoter-driven STAT92E::GFP::FLAG protein-protein fusion transgene to test if STAT92E stability/expression or translocation is altered upon disruption of selectiveautophagy (in Atg8aLDS mutant flies).

R2/9. „Also, what happens to the axons in the mutant conditions described in the manuscript? This would higher the impact of the work, but would require in vivo work with fly stocks containing several transgenes.”

Response:

We have already published in our previous paper, Szabo et al., 202316 that the mutants used in the current study display normal axon morphology__. There are only two mutants that we did not test in that paper: Atg8aLDS and our new Atg8anull and we will examine these remaining two during the revision, __but we already published in the above paper that axons appear normal in Atg8aΔ4, a widely used Atg8a mutant allele.

R2/10. „It has been published that Draper is involved in the response to injury in the adult wing nerve. See for example Neukomm et al (2014). The authors should discuss how this fits with their hypothesis and data. In this respect, Fig S4B, which should support the hypothesis, should be improved. It is rather hard to interpret it.”

Response:

Fig. S3 (draper protein trap-Gal4 driven GFP-RFP reporter expression) and S4B (intronic STAT92E binding site of the draper gene driven GFP-RFP reporter expression) show similar results: drpr is already expressed in wing nerve glia before injury, which is in line with Draper’s crucial role in the injury response because Draper-mediated glial signaling triggers glial reactivity. This has been added to the Discussion.

Minor comments:

R2/11. „Rubicon is also a negative regulator of autophagy (doi:10.1038/s41598-023-44203-6). in (Fig2 B, D) we have a higher GFP intensity in both uninjured and injured, and the difference between Injured/uninjured is less significant compared to control. It is possible that Rubicon KD causes more autophagy leading to a higher activation of Stat92E even in control. I wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”

Response:

Loss of Rubicon could indeed potentially remove more Su(var)2-10 via increased autophagy, leading to higher Stat92E activity. However, there is no statistically significant difference between injured and uninjured controls and injured and uninjured Rubicon knockdown, respectively, in Fig2 B, D (p=0.6975 and >0.9999 for each comparison). We are puzzled by the statement that the reviewer „wouldn't take the results as a proof of canonical autophagy implication and not LC3-associated phagocytosis”. We analyzed Rubicon as a factor critical for LAP and its deficiency does not prevent Stat transcriptional activity following injury unlike the loss of Atg8a, Atg16, Atg13 and Atg5. We will further support this result with a mutant of Atg16 with part of the WD40 domain deleted, because this region is critical for LAP but not for autophagy.16,26,27

R2/12. „The rationale for using both repoGal4 and repoGS is unclear. If, as mentioned, the goal is to avoid developmental defects, repoGS should be consistently used. Especially I don't understand how both were utilized to knock down the same genes, such as Atg16”

Response:

*We had to use repoGS (a drug-inducible Gal4 active in glia) because knocking down Su(var)2-10 with repoGal4 resulted in no viable adult progeny. Su(var)2-10 is an essential gene as opposed to most autophagy genes and its absence results in embryonic lethality24. Thus all Su(var)2-10 silencing experiments were done with repoGS. Similarly, Stat92E is involved in various developmental processes and its loss is embryonic lethal. repoGal4 was used for genes generally not having an adverse effect when absent during development16 in the first two figures. In Fig. 4D, we silenced Atg16 by repoGS because it is one of the controls for testing a genetic epistasis between Su(var)2-10 and Atg16. Please note that we see exactly the same phenotype in case of Atg16 knockdown when using either Gal4 version.* This has been explained in the revised methods section.

R2/13. „In the third paragraph of the introduction, I am confused whether Stat92E regulates drpr of the reverse”

Response:

Upon antennal injury, Drpr receptor binding to phagocytic cargo initiates a positive feedback loop in glial cells to promote its own transcription28. Drpr receptor in the plasma membrane regulates Stat92E and AP-1 activity via signal transduction. Stat92E and AP-1, in turn, increases drpr transcription10,28–30 that will result in more plasma membrane Drpr protein expression. We have explained this more clearly in the revised Introduction.

R2/14. „I cannot find the evidence for vir-1 being expressed in glia and target of Gcm in the refences that have been cited.”

Response:

We apologize for not explaining this better: vir-1 is called CG5453 in Freeman et al., 200331. It is listed in Table 1 as a Gcm target since there is no detectable CG5453 expression in a Gcm null mutant, please see below. We have updated the manuscript with this gene name.

.....

.....

Part of Table 1 from Freeman et al., 200331.

R2/15. „The presence of a Stat92E binding site on the vir-1 promoter has already bene described in the paper from Imler and collaborators, Nature immunology 2005. Actually, if this site is present in their transgenic line, it would help the authors strengthen the argument that Stat92E has a direct role on vir1 (for which they make a very strong statement in the discussion, with no direct evidence).”

Response:

*The evidence that Stat92E may have a direct role in vir-1 transcription in glia comes exactly from the same reporter transgene described by Imler and collaborators in the mentioned paper32. We received this transgenic line from the Imler group and monitored its expression after injury upon depletion of Stat92E (Fig. 3B). It thus contains the studied Stat binding site. This was referenced in the Methods and in all relevant sections of the main text, and we now explicitly state this in the revised text.*

R2/16. „In the Fig S2D, I do not see a lot of GFP+ (Glia) cells. I see more Atg8a in injured 3 dpi regardless of colocalization with glia”

Response:

Fig S2D uses one of the standard assays for autophagic turnover, which we now explain in more detail in the Results section. Basically, the dual tagged GFP::mCherry::Atg8a transgene is expressed in glia, and GFP is quenched in lysosomes after delivery by autophagy while mCherry remains fluorescent. So, in addition to double positive dots (autophagosomes), there are mCherry dots lacking GFP (autolysosomes) if autophagy is functional. All of these dots are in glia but the cell boudaries are not visible.

The images shown are single optical slices. The number of mCherry+ puncta are around 7-8 per field in both uninjured and injured (3 dpi) conditions, but puncta brightness is always variable. Since most mCherry+ puncta were rather bright in the original 3 dpi image, we changed it to a more representative image.

R2/17. „The quantification of the signals is made in a specific region of the wing, I guess throughout the nerve thickness. This could be represented more carefully in a schematic and It would also help defining colocalization in the first figure, by using a transverse section.”

Response:

The quantification method is described in Materials and Methods and we have added that quantification was done on single optical slices. The imaged region is depicted in Fig. S1A, where we indicated the rectangular region used in Fiji for image quantification. We will add transverse sections of wings as suggested.

R2/18. „A number of ATG genes are considered in the manuscript, but the rational for using them is not always clear. Showing a schematic would help clarify this. „

Response:

We have added a table showing the different steps of autophagy where the studied Atg genes/proteins function (now Supplementary Table 1). We also added whether the gene is considered specific for autophagy or can play a role in another process, e.g. LAP. We studied different autophagy genes in line with the assumption that disabling distinct autophagic complexes should produce the same phenotype if this process is indeed autophagy (and not LC3-associated phagocytosis for example).

R2/19. „Fig 7 is not cited and its legend is very short.”

Response:

We have now cited Fig 7 and expanded its legend.

R2/20. „Clarify the color coding in Fig S1E”

Response:

We added that red is injured, black is uninjured.

R2/21. „What is the tandem tagged autophagic fly reporter in fig S2D?”

Response:

This is one of the most common tools to study autophagy, please see the updated explanation above at your first question regarding Fig. S2D.

R2/22. „Add a schematic on the vir-1 isoforms.”

Response:

We have added a a schematic showing the vir-1 isoforms in Fig. S5B.

R2/23. „Fig S6B and Fig 5 relate on the levels of Su(var)2-10 upon Atg16 RNAi, but the scale is not the same, why?”

Response:

*The scales are different because these two images measure different things. Fig. 5 indeed displays quantification of Su(var)2-10 levels in brain glia. However, Fig S6B shows quantification of Stat92E-induced GFP reporter levels (as a proxy of Stat92E transcriptional activity) in the wing nerve upon Atg16 knockdown. *

Reviewer 3

R3/1. „The claim that the negative regulator of Stat92E signaling is removed by selective autophagy, involving selective autophagy receptors different from/in addition to Ref(2)P/p62 is not convincingly shown. This claim probably needs to be softened.”

Response:

*We have rephrased this sentence as follows: *

„These data suggest that selective autophagy is involved in Stat92E-dependent transcriptional activation in glia.”

R3/2. „The reporter that was used (10xSTAT92E-eGFP) is not a dynamic reporter of STAT92E activity. It accumulates in glia and is highly stable. The appropriate reporter to look at dynamic changes would be 10XSTAT92E-dGFP, which has a degradable (unstable) GFP that is required to see dynamic changes even in the CNS. All of the claims about STAT92E regulation use this reporter, so they are questionable.”

Response:

10XSTAT92E-dGFP featuring destabilized GFP could be a more appropriate tool for monitoring dynamic changes in transcription when short term- e.g. few hours - changes are investigated. However, we did not see any expression of 10XSTAT92E-dGFP (we tried 2 different transgenic insertions) in the wing nerve, please see Figure 3 for Reviewers. In the brain, dGFP expression with this reporter is also several times lower than stable GFP, please compare Fig. 4A and B in Doherty et al28.

The use of 10xSTAT92E-eGFP to follow dynamic expression changes is justified by many lines of evidence. First, there is no 10xSTAT92E-EGFP expression in uninjured wing nerves (Fig. S1D,E). Injury induces EGFP expression in the wing nerve with a sustained activation from 1 to 3 dpi (days post injury), and the EGFP expression returns to the baseline by 5 dpi (Fig. S1D, E). Second, the initial Stat-dependent upregulation of drpr and the 10XSTAT92E-dGFP signal in the brain both occur in the first 24 hours after injury and are sustained for 72 hours28 similar to our results with 10xSTAT92E-EGFP ((Fig. S1D,E). These results indicate that the dynamics of 10xSTAT92E-EGFP expression allows monitoring changes in Stat-dependent transcription occurring over days.

Figure 3 for Reviewers. Lack of 10XSTAT92E-dGFP signal in the wing nerve from two independent insertions of the same transgene at the indicated time points after wing injury.

R3/3. „The claim that glial drpr is not upregulated by wing injury and drpr accumulation is not apparently a prerequisite for efficient debris processing within the wing is weak. First, they did not stain for Draper using antibodies, rather they used expression constructs. Dee7 is a promoter that was found to be injury activated in the CNS (were they able to replicate that result? I did not receive the supplemental data), but it might not be the crucial regulator in the periphery. The MIMIC line that was converted is better, but might not represent the full spectrum of regulatory events at the draper locus. Finally, they never actually test for endogenous RNA changes, or use the antibody on westerns. Their lack of evidence is not as compelling as it could be.”

Response:

The__ original Supplemental Material already provides answers for this and subsequent questions of Reviewer 3__. We deposited the Supplemental Material to bioRxiv at the time of the first Review Commons submission and it was/is available at https://www.biorxiv.org/content/10.1101/2024.08.28.610109v2.supplementary-material.

Figs. S3 and S4 show in the wing and the brain (using two different drpr reporters for its transcriptional regulation) that drpr expression does not change much in the wing after nerve injury, as opposed to the brain.

*We did indeed replicate that dee7-Gal4 expression is induced in the brain after antennal injury using UAS- TransTimer (Fig. S4A). In contrast, wing cell nuclei already show expression of both fluorescent proteins in uninjured conditions, and RFP+ nucleus numbers do no change after wing injury (Fig. S4B, C). drpr-Gal4 was generated by conversion of a MiMIC gene trap element into a Gal4 that traps all transcripts. drprMI07659 is in an intron that is common in all drpr isoforms so it should capture the regulation of all transcript isoforms. *

We will further analyze drpr expression via independent methods during the revision: qPCR amplification of a common region of drpr transcripts, and western blot with anti-Drpr antibody to compare injured and uninjured wing material. Of note, we see no upregulation of drpr 2 days after wing injury in our (unpublished) RNAseq results either.

*Unfortunately, immunostaining of the adult wing is not feasible because antibodies do not penetrate the thick chitin-based cuticle and wax cover of the wing.*

R3/4. „The authors claim autophagy contributes to glial reactive states in part by acting on JAK-STAT pathway via regulation of Stat92E. They did not investigate other potential STAT92E targets. Does Atg16 knockdown alter STAT92E expression? Apparently Vir1 is still upregulated in the absence of Atg16 following injury, but they don’t show STAT92E changes.”

Response:

We did investigate other potential STAT92E targets besides vir-1. This is referred to in the text as „*immunity-related gene reporters” and it again can be found in the Supplemental Material (____Supplementary Table 2). None of these genes showed glia-specific upregulation following injury. *

We will investigate STAT92E expression with the STAT92E::GFP::FLAG protein-protein fusion transgene after disrupting autophagy as also suggested by Reviewer 2. Please see our detailed answer to the first comment of Reviewer 2.

*We do not agree with the comment that „Vir1 is still upregulated in the absence of Atg16 following injury” because Fig. 3F,G show that lack of Atg16 abolishes the upregulation of the vir-1 reporter: the change from uninjured to injured becomes statistically not significant and the mean GFP intensities are practically identical. *

R3/5. „The authors claim Su(var)2-10 is an autophagic cargo. They should better characterize Su(var)2-10 degradation and its regulation, and image quality needs to be improved (better images, merged examples, and clearer indication of what they are highlighting. There are many arrows in figures that I don't know what they are pointing to. Much of the labeling in Fig 1 (and others) looks like axons. Could TRE-GFP be turned on in neurons? How did they discriminate?”

Response:

As also explained to Reviewer 1’s last comment, we will carry out experiments to address whether SUMOylated Su(var)2-10 binds Atg8a, which can provide evidence for a direct SUMO-dependent autophagic elimination of Su(var)2-10. Please see our detailed response there.

We will further improve image quality for brain images and we already incorporated new images in Fig. S6. *Merged images were missing only in Fig 5, which we have included in the current version. Arrows and arrowheads were used as described in Figure legends, but instead of those, we now clearly label the epithelium and we outlined the region of wing nerve glia in all images. *

Please see our response to the first minor comment of Reviewer 1 regarding the expression of reporters in wing tissues.

R3/6. „The authors claim interaction of Su(var)2-10 with Atg8a in the nucleus and cytoplasm can trigger autophagic breakdown, involving Su(var)2-10 SUMOylation. The paper would benefit from showing direct SUMOylation of Su(var)2-10 after injury. Is there any way to examine this in vivo?”

Response:

We will test direct SUMOylation of Su(var)2-10 using a recently described method by Andreev et al., 202233. FLAG-GFP-Smt3 (SUMO)____ is expressed under SUMO transcriptional regulation and we will immunoprecipitate FLAG-GFP-SUMO and GFP alone as negative control with GFPTrap beads from lysates of heads subjected to traumatic brain injury that results in glial reactivity16____, and also from uninjured head lysates. We will use anti-____Su(var)2-10 ____western blotting to visualize SUMOylated Su(var)2-10 and whether its levels are modulated by brain injury.

R3/7. „The authors state in discussion "we find that draper is highly expressed in wing nerve glia already in uninjured conditions and it is not further induced by wing transection - indicating high phagocytic capacity in wing glia ... axon debris clearance takes substantially longer in the wing nerve than in antennal lobe glomeruli, thus draper levels may not readily predict actual phagocytic activity in glia". However, they never actually assess this in their experiments. All the conclusions about Draper are made from promoter fusions of integrated reporters, which are imperfect. This conclusion cannot be made.”

Response:

As described in our response to R3/3, we will further test drpr expression changes after wing injury using two independent methods: qPCR and western blot .

We deleted this part from the Discussion that were criticized by the reviewer because these are not important for the main message of our manuscript.

R3/8. „Both STAT92E and Jun are activated by a stress response. Could this be a stress response to disrupting autophagy that is somehow enhance by injury?”

Response:

*Stress responses are indeed relayed by AP-1 and Stat signaling, and impaired autophagy could be a source of stress. We would like to emphasize, though, that the main finding of our manuscript is that disrupting autophagy suppresses Stat-dependent transcription. Autophagy inhibition does not increase Stat signaling in uninjured wing nerves and while control flies upregulate Stat activity upon injury, autophagy-deficient animals fail to do so (Fig. 1). Thus, Stat signaling is not activated by loss of autophagy – it is activated by injury (that is the stress) and Stat activation requires autophagy in this setting.*

R3/9. „Minor:

I don't think that "glially" is a word.”

Response:

Online dictionaries such as Wiktionary list glially as a word, and many scientific articles use it: https://doi.org/10.1016/j.conb.2022.102653, https://doi.org/10.1016/j.yexcr.2013.08.016,https://doi.org/10.1016/j.jpain.2006.04.001*, to give some examples. *

We nonetheless refrain from using it in the updated text.

References

1. Neukomm, L.J., Burdett, T.C., Gonzalez, M.A., Züchner, S., and Freeman, M.R. (2014). Rapid in vivo forward genetic approach for identifying axon death genes in Drosophila. Proc National Acad Sci 111, 9965–9970. https://doi.org/10.1073/pnas.1406230111.
2. Giangrande, A., Murray, M.A., and Palka, J. (1993). Development and organization of glial cells in the peripheral nervous system of Drosophila melanogaster. Development 117, 895–904. https://doi.org/10.1242/dev.117.3.895.
3. Stork, T., Engelen, D., Krudewig, A., Silies, M., Bainton, R.J., and Klämbt, C. (2008). Organization and Function of the Blood–Brain Barrier in Drosophila. J. Neurosci. 28, 587–597. https://doi.org/10.1523/jneurosci.4367-07.2008.
4. Figueras-Novoa, C., Timimi, L., Marcassa, E., Ulferts, R., and Beale, R. (2024). Conjugation of ATG8s to single membranes at a glance. J. Cell Sci. 137, jcs261031. https://doi.org/10.1242/jcs.261031.
5. Galluzzi, L., and Green, D.R. (2019). Autophagy-Independent Functions of the Autophagy Machinery. Cell 177, 1682–1699. https://doi.org/10.1016/j.cell.2019.05.026.
6. Nieto-Torres, J.L., Leidal, A.M., Debnath, J., and Hansen, M. (2021). Beyond Autophagy: The Expanding Roles of ATG8 Proteins. Trends Biochem Sci 46, 673–686. https://doi.org/10.1016/j.tibs.2021.01.004.
7. Huang, Z., Kingsolver, M.B., Avadhanula, V., and Hardy, R.W. (2013). An Antiviral Role for Antimicrobial Peptides during the Arthropod Response to Alphavirus Replication. J. Virol. 87, 4272–4280. https://doi.org/10.1128/jvi.03360-12.
8. Purice, M.D., Ray, A., Münzel, E.J., Pope, B.J., Park, D.J., Speese, S.D., and Logan, M.A. (2017). A novel Drosophila injury model reveals severed axons are cleared through a Draper/MMP-1 signaling cascade. Elife 6, e23611. https://doi.org/10.7554/elife.23611.
9. Alphen, B. van, Stewart, S., Iwanaszko, M., Xu, F., Li, K., Rozenfeld, S., Ramakrishnan, A., Itoh, T.Q., Sisobhan, S., Qin, Z., et al. (2022). Glial immune-related pathways mediate effects of closed head traumatic brain injury on behavior and lethality in Drosophila. Plos Biol 20, e3001456. https://doi.org/10.1371/journal.pbio.3001456.
10. MacDonald, J.M., Beach, M.G., Porpiglia, E., Sheehan, A.E., Watts, R.J., and Freeman, M.R. (2006). The Drosophila Cell Corpse Engulfment Receptor Draper Mediates Glial Clearance of Severed Axons. Neuron 50, 869–881. https://doi.org/10.1016/j.neuron.2006.04.028.
11. Bence, M., Jankovics, F., Kristó, I., Gyetvai, Á., Vértessy, B.G., and Erdélyi, M. (2024). Direct interaction of Su(var)2‐10 via the SIM‐binding site of the Piwi protein is required for transposon silencing in Drosophila melanogaster. FEBS J. 291, 1759–1779. https://doi.org/10.1111/febs.17073.
12. Betz, A., Lampen, N., Martinek, S., Young, M.W., and Darnell, J.E. (2001). A Drosophila PIAS homologue negatively regulates stat92E. Proc. Natl. Acad. Sci. 98, 9563–9568. https://doi.org/10.1073/pnas.171302098.
13. Liu, B., Liao, J., Rao, X., Kushner, S.A., Chung, C.D., Chang, D.D., and Shuai, K. (1998). Inhibition of Stat1-mediated gene activation by PIAS1. Proc. Natl. Acad. Sci. 95, 10626–10631. https://doi.org/10.1073/pnas.95.18.10626.
14. Bach, E.A., Ekas, L.A., Ayala-Camargo, A., Flaherty, M.S., Lee, H., Perrimon, N., and Baeg, G.-H. (2007). GFP reporters detect the activation of the Drosophila JAK/STAT pathway in vivo. Gene Expr Patterns 7, 323–331. https://doi.org/10.1016/j.modgep.2006.08.003.
15. Hu, X., li, J., Fu, M., Zhao, X., and Wang, W. (2021). The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct. Target. Ther. 6, 402. https://doi.org/10.1038/s41392-021-00791-1.
16. Szabó, Á., Vincze, V., Chhatre, A.S., Jipa, A., Bognár, S., Varga, K.E., Banik, P., Harmatos-Ürmösi, A., Neukomm, L.J., and Juhász, G. (2023). LC3-associated phagocytosis promotes glial degradation of axon debris after injury in Drosophila models. Nat. Commun. 14, 3077. https://doi.org/10.1038/s41467-023-38755-4.
17. Goodall, E.A., Kraus, F., and Harper, J.W. (2022). Mechanisms underlying ubiquitin-driven selective mitochondrial and bacterial autophagy. Mol. Cell 82, 1501–1513. https://doi.org/10.1016/j.molcel.2022.03.012.
18. Zhang, T., Yang, H., Zhou, Z., Bai, Y., Wang, J., and Wang, W. (2022). Crosstalk between SUMOylation and ubiquitylation controls DNA end resection by maintaining MRE11 homeostasis on chromatin. Nat. Commun. 13, 5133. https://doi.org/10.1038/s41467-022-32920-x.
19. Chen, Z., Zhang, Y., Guan, Q., Zhang, H., Luo, J., Li, J., Wei, W., Xu, X., Liao, L., Wong, J., et al. (2021). Linking nuclear matrix–localized PIAS1 to chromatin SUMOylation via direct binding of histones H3 and H2A.Z. J. Biol. Chem. 297, 101200. https://doi.org/10.1016/j.jbc.2021.101200.
20. Brown, J.R., Conn, K.L., Wasson, P., Charman, M., Tong, L., Grant, K., McFarlane, S., and Boutell, C. (2016). SUMO Ligase Protein Inhibitor of Activated STAT1 (PIAS1) Is a Constituent Promyelocytic Leukemia Nuclear Body Protein That Contributes to the Intrinsic Antiviral Immune Response to Herpes Simplex Virus 1. J. Virol. 90, 5939–5952. https://doi.org/10.1128/jvi.00426-16.
21. Gutierrez-Morton, E., and Wang, Y. (2024). The role of SUMOylation in biomolecular condensate dynamics and protein localization. Cell Insight 3, 100199. https://doi.org/10.1016/j.cellin.2024.100199.
22. Jacomin, A.-C., Petridi, S., Monaco, M.D., Bhujabal, Z., Jain, A., Mulakkal, N.C., Palara, A., Powell, E.L., Chung, B., Zampronio, C., et al. (2020). Regulation of Expression of Autophagy Genes by Atg8a-Interacting Partners Sequoia, YL-1, and Sir2 in Drosophila. Cell Reports 31, 107695. https://doi.org/10.1016/j.celrep.2020.107695.
23. Maimon, I., Popliker, M., and Gilboa, L. (2014). Without children is required for Stat-mediated zfh1 transcription and for germline stem cell differentiation. Development 141, 2602–2610. https://doi.org/10.1242/dev.109611.
24. Ninova, M., Chen, Y.-C.A., Godneeva, B., Rogers, A.K., Luo, Y., Tóth, K.F., and Aravin, A.A. (2020). Su(var)2-10 and the SUMO Pathway Link piRNA-Guided Target Recognition to Chromatin Silencing. Mol. Cell 77, 556-570.e6. https://doi.org/10.1016/j.molcel.2019.11.012.
25. Pircs, K., Nagy, P., Varga, A., Venkei, Z., Erdi, B., Hegedus, K., and Juhasz, G. (2012). Advantages and Limitations of Different p62-Based Assays for Estimating Autophagic Activity in Drosophila. PLoS ONE 7, e44214. https://doi.org/10.1371/journal.pone.0044214.
26. Fletcher, K., Ulferts, R., Jacquin, E., Veith, T., Gammoh, N., Arasteh, J.M., Mayer, U., Carding, S.R., Wileman, T., Beale, R., et al. (2018). The WD40 domain of ATG16L1 is required for its non‐canonical role in lipidation of LC3 at single membranes. EMBO J 37, e97840. https://doi.org/10.15252/embj.201797840.
27. Rai, S., Arasteh, M., Jefferson, M., Pearson, T., Wang, Y., Zhang, W., Bicsak, B., Divekar, D., Powell, P.P., Nauman, R., et al. (2018). The ATG5-binding and coiled coil domains of ATG16L1 maintain autophagy and tissue homeostasis in mice independently of the WD domain required for LC3-associated phagocytosis. Autophagy 15, 1–14. https://doi.org/10.1080/15548627.2018.1534507.
28. Doherty, J., Sheehan, A.E., Bradshaw, R., Fox, A.N., Lu, T.-Y., and Freeman, M.R. (2014). PI3K Signaling and Stat92E Converge to Modulate Glial Responsiveness to Axonal Injury. PLoS Biol 12, e1001985. https://doi.org/10.1371/journal.pbio.1001985.
29. Logan, M.A., Hackett, R., Doherty, J., Sheehan, A., Speese, S.D., and Freeman, M.R. (2012). Negative regulation of glial engulfment activity by Draper terminates glial responses to axon injury. Nat. Neurosci. 15, 722–730. https://doi.org/10.1038/nn.3066.
30. MacDonald, J.M., Doherty, J., Hackett, R., and Freeman, M.R. (2013). The c-Jun kinase signaling cascade promotes glial engulfment activity through activation of draper and phagocytic function. Cell Death Differ 20, 1140–1148. https://doi.org/10.1038/cdd.2013.30.
31. Freeman, M.R., Delrow, J., Kim, J., Johnson, E., and Doe, C.Q. (2003). Unwrapping Glial Biology Gcm Target Genes Regulating Glial Development, Diversification, and Function. Neuron 38, 567–580. https://doi.org/10.1016/s0896-6273(03)00289-7.
32. Dostert, C., Jouanguy, E., Irving, P., Troxler, L., Galiana-Arnoux, D., Hetru, C., Hoffmann, J.A., and Imler, J.-L. (2005). The Jak-STAT signaling pathway is required but not sufficient for the antiviral response of drosophila. Nat. Immunol. 6, 946–953. https://doi.org/10.1038/ni1237.
33. Andreev, V.I., Yu, C., Wang, J., Schnabl, J., Tirian, L., Gehre, M., Handler, D., Duchek, P., Novatchkova, M., Baumgartner, L., et al. (2022). Panoramix SUMOylation on chromatin connects the piRNA pathway to the cellular heterochromatin machinery. Nat. Struct. Mol. Biol. 29, 130–142. https://doi.org/10.1038/s41594-022-00721-x.

Author response:

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

Reviewer #1 (Public review):

Summary:

Multiple compounds that inhibit ATP-sensitive potassium (KATP) channels also chaperone channels to the surface membrane. The authors used an artificial intelligence (AI)-based virtual screening (AtomNet) to identify novel compounds that exhibit chaperoning effects on trafficking-deficient disease-causing mutant channels. One compound, which they named Aekatperone, acts as a low affinity, reversible inhibitor and effective chaperone. A cryoEM structure of KATP bound to Aekatperone showed that the molecule binds at the canonical inhibitory site.

Strengths and weaknesses:

The details of the AI screening itself are inevitably opaque, but appear to differ from classical virtual screening in not involving any physical docking of test compounds into the target site. The authors mention criteria that were used to limit the number of compounds, so that those with high similarity to known binders and 'sequence identity' (does this mean structural identity) were excluded. The identified molecules contain sulfonylurea-like moieties. How different are they from other sulfonylure4as?

We thank the reviewers for the questions. As part of the library preparation, molecules with greater than 0.5 Tanimoto similarity in ECFP4 space to any known binders of the target protein and its homologs within 70% sequence identity were excluded to increase the possibility of identifying novel hits. After scoring and ranking the molecules by the AtomNet® technology, a diversity clustering was performed using the Butina algorithm (Butina D. Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets, J. Chem. Inf. Comput. Sci. 1999, 39, 747–750) with a Tanimoto similarity cutoff of 0.35 in ECFP4 space to minimize selection of structurally similar scaffolds for the final compound buy-list. We have revised the results and methods sections to make this clear.

Sulfonylureas are defined by their core structure comprising a sulfonyl group (–S(=O)₂) and a urea moiety (–NH–CO–NH–). While some compounds identified in our study contain a sulfonamide group (R-S(=O) ₂-NR₂), they differ structurally from sulfonylureas by lacking the key urea group and by incorporating unique R-group substitutions (we have now added this to Figure 1A legend). For example, compound C27 (Z2068224500) includes a sulfonamide group but not a urea moiety. Likewise, C45 (Aekatperone, Z1620764636) contains a sulfonamide group along with an aromatic, nitrogen-rich heterocyclic ring, but no urea group. Additionally, the R-groups in these compounds are more complex than the simple aromatic or alkyl chains typical of sulfonylureas. They include heterocyclic aromatic systems and nitrogen-rich structures, which likely influence their binding properties and lipophilicity. These structural differences suggest distinct functional and pharmacological profiles as supported by our biochemical and functional studies.

The experimental work confirming that Aekatperone acts to traffic mutant KATP channels to the surface and acts as a low affinity, reversible, inhibitor is comprehensive and clear, with very convincing cell biological and patch-clamp data, as is the cryoEM structural analysis, for which the group are leading experts. In addition to the three positive chaperone-effective molecules, the authors identified a large number of compounds that are predicted binders but apparently have no chaperoning effect. Did any of them have inhibitory action on channels? If so, does this give clues to separating chaperoning from inhibitory effects?

This is an interesting question. Evidence from cryo-EM, biochemical and electrophysiology studies reveal a critical role of Kir6.2 N-terminus in K_ATP channel assembly and gating, and that pharmacological chaperones like glibenclamide, repaglinide, carbamazepine, and now aekatperone exert their chaperoning and inhibitory effects by stabilizing the interaction between Kir6.2 N-terminus and the SUR1-ABC core. This stabilization, while promoting the assembly of Kir6.2 and SUR1 to “chaperone” trafficking-impaired mutant channels to the cell surface, also inhibits the channel by restricting the Kir6.2 C-terminal domain from rotating to an open state. An additional mechanism by which these compounds inhibit channel activity is by preventing SUR1-NBD dimerization, which mediates physiological activation of the channel by MgADP (see review: Driggers CM, Shyng SL. Mechanistic insights on K_ATP channel regulation from cryo-EM structures. J Gen Physiol. 2023 Jan 2;155(1): e202113046, PMID: 36441147). From our compound screening, we did find some compounds that showed mild inhibition of the channel by electrophysiology but no obvious chaperone effects by western blots. It is possible that small chaperoning effects of some compounds showing mild channel inhibition effects were missed due to the lower sensitivity of the western blot assay compared to electrophysiology. Alternatively, these compounds could inhibit channels by preventing SUR1NBD dimerization without stabilizing the Kir6.2 N-terminus, which is required for the chaperone effect based on our model. Unfortunately, we did not find any compounds that show chaperone effects but no channel inhibition effects, which is consistent with our understanding of how this type of K_ATP chaperones work (i.e. by stabilizing Kir6.2 N-terminus interaction with SUR1’s ABC core).

The authors suggest that the novel compound may be a promising therapeutic for treatment of congenital hyperinsulinism due to trafficking defective KATP mutations. Because they are low affinity, reversible, inhibitors. This is a very interesting concept, and perhaps a pulsed dosing regimen would allow trafficking without constant channel inhibition (which otherwise defeats the therapeutic purpose), although it is unclear whether the new compound will offer advantages over earlier low-affinity sulfonylurea inhibitor chaperones. These include tolbutamide which has very similar affinity and effect to Aekatperone. As the authors point out this (as well as other sulfonlyureas) are currently out of favor because of potential adverse cardiovascular effects, but again, it is unclear why Aekatperone should not have the same concerns.

We thank the reviewer for the comments. This is clearly an important question to address in the future. While we have not directly tested the effects of Aekatperone on cardiac functions, we did assess its inhibitory effect on cells expressing the cardiac K_ATP channel isoform (SUR2A/Kir6.2). Our results indicate that Aekatperone exhibits higher sensitivity toward the pancreatic K_ATP channel isoform (SUR1/Kir6.2) compared to the cardiac isoform. However, we acknowledge that Aekatperone could still have cardiotoxic effects through its potential action on other channels, such as the hERG channel.

It is worth noting that tolbutamide, despite its known cardiotoxic effects, does not exert these effects through cardiac K_ATP channel inhibition. This has been demonstrated in studies showing no inhibitory effect of tolbutamide on SUR2A/Kir6.2 channels and on channels formed by Kir6.2 and SUR1 harboring the S1238Y mutation (also shown as S1237Y in some studies using a different SUR1 isoform)--the amino acid substitution found in SUR2A at the corresponding position (Ashfield R, Gribble FM, Ashcroft SJ, Ashcroft FM. Identification of the high-affinity tolbutamide site on the SUR1 subunit of the K_ATP channel. Diabetes. 1999 Jun;48(6):1341-7, PMID: 10342826). This suggests that tolbutamide’s cardiotoxic effects might involve other targets like the hERG channel. Interestingly, tolbutamide contains a hydrophobic tail and aromatic rings that align well with the structural features for hERG interaction (Garrido A, Lepailleur A, Mignani SM, Dallemagne P, Rochais C. hERG toxicity assessment: Useful guidelines for drug design. Eur J Med Chem. 2020 Jun 1;195:112290, PMID: 32283295). In contrast, highaffinity sulfonylureas such as glibenclamide and glimepiride, which have additional benzamide moieties, are associated with lower cardiovascular risks (Douros A, Yin H, Yu OHY, Filion KB, Azoulay L, Suissa S. Pharmacologic Differences of Sulfonylureas and the Risk of Adverse Cardiovascular and Hypoglycemic Events. Diabetes Care. 2017, 40:1506-1513, PMID:

28864502). Given these considerations, a comprehensive assessment of Aekatperone’s potential cardiotoxicity is crucial. Future studies involving in silico modeling, in vitro, and in vivo experiments will be essential to evaluate Aekatperone’s interaction with hERG and other offtarget effects. These efforts will help clarify its safety profile. This point has now been added to the Discussion.

Reviewer #2 (Public review):

Summary:

In their study 'AI-Based Discovery and CryoEM Structural Elucidation of a KATP Channel Pharmacochaperone', ElSheikh and colleagues undertake a computational screening approach to identify candidate drugs that may bind to an identified binding pocket in the SUR1 subunit of

KATP channels. Other KATP channel inhibitors such as glibenclamide have been previously shown to bind in this pocket, and in addition to inhibition KATP channel function, these inhibitors can very effectively rescue cell surface expression of trafficking deficient KATP mutations that cause excessive insulin secretion (Congenital Hyperinsulinism). However, a challenge for their utility for treatment of hyperinsulinism has been that they are powerful inhibitors of the channels that are rescued to the channel surface. In contrast, successful therapeutic pharmacochaperones (eg. CFTR chaperones) permit function of the channels rescued to the cell membrane. Thus, a key criteria for the authors' approach in this case was to identify relatively low affinity compounds that target the glibenclamide binding site (and be washed off) - these could potentially rescue KATP surface expression, but also permit KATP function.

Strengths:

The main findings of the manuscript include:

(1) Computational screening of a large virtual compound library, followed by functional screening of cell surface expression, which identified several potential candidate pharmacochaperones that target the glibenclamide binding site.

(2) Prioritization and functional characterization of Aekatperone as a low affinity KATP inhibitor which can be readily 'washed off' in patch clamp, and cell based efflux assays. Thus the drug clearly rescues cell surface expression, but can be manipulated experimentally to permit function of rescued channels.

(3) Determination of the binding site and dynamics of this candidate drug by cryo-EM, and functional validation of several residues involved in drug sensitivity using mutagenesis and patch clamp.

The experiments are well-conceived and executed, and the study is clearly described. The results of the experiments are very straightforward and clearly support the conclusions drawn by the authors. I found the study to provide important new information about KATP chaperone effects of certain drugs, with interesting considerations in terms of ion channel biology and human disease.

Weaknesses:

I don't have any major criticisms of the study as described, but I had some remaining questions that could be addressed in a revision.

(1) The chaperones can effectively rescue KATP trafficking mutants, but clearly not as strongly as the higher affinity inhibitor glibenclamide. Is this relationship between inhibitory potency, and efficacy of trafficking an intrinsic challenge of the approach? I suspect that it may be an intractable problem in the sense that the inhibitor bound conformation that underlies the chaperone effect cannot be uncoupled from the inhibited gating state. But this might not be true (many partial agonist drugs with low efficacy can be strongly potent, for example). In this case, the approach is really to find a 'happy medium' of a drug that is a weak enough inhibitor to be washed away, but still strong enough to exert some satisfactory chaperone effect. Could some additional clarity be added in the discussion on whether the chaperone and gating effects can be 'uncoupled'.

Thank you for the suggestion. A similar question was raised by Reviewer 1, which was addressed above (public review, point 2). We have now added more discussion to clarify this point.

(2) Based on the western blots in Figure 2B, the rescue of cell surface expression appears to require a higher concentration of AKP compared to the concentration response of channel inhibition (~9 microM in Figure 3, perhaps even more potent in patch clamp in Figure 2C). Could the authors clarify/quantify the concentration response for trafficking rescue?

Thank you for bringing up this observation. Indeed, the pharmacochaperone effects of Aekatperone as well as other previously published K_ATP pharmacochaperones require higher concentrations compared to their inhibitory effects on surface-expressed channels. This difference likely stems from the necessity for these compounds to cross the cell membrane and interact with newly synthesized channels in the endoplasmic reticulum, where the trafficking rescue occurs. We estimate that effective pharmacochaperone activity for Aekatperone can be achieved at concentrations ranging from 50 to 100 µM in cells expressing trafficking-deficient K_ATP channel mutants, higher than that required for inhibition of surface-expressed channels (~9 µM IC50). Future work could focus on medicinal chemistry modifications, for example esterification of Aekatperone (Zhou G. Exploring Ester Prodrugs: A Comprehensive Review of Approaches, Applications, and Methods. Pharmacology & Pharmacy, 2024, 15, 269-284). Once inside the cell, the esters would be cleaved by endogenous esterases to release the active compound, ensuring efficient intracellular delivery. This strategy could potentially improve membrane permeability and bioavailability of the compound, which would lower the required concentrations to achieve desired chaperoning effects.

(3) A future challenge in the application of pharmacochaperones of this type in hyperinsulinism may be the manipulation of chaperone concentration in order to permit function. In experiments it is straightforward to wash off the chaperone, but this would not be the case in an organism. I wondered if the authors had attempted to rescue channel function with diazoxide ine presence of AKP, rather than after washing off (ie. is AKP inhibition insurmountable, or can it be overcome by sufficient diazoxide).

Thank you for raising this important point. We have previously shown (Martin GM et al. Pharmacological Correction of Trafficking Defects in ATP-sensitive Potassium Channels Caused by Sulfonylurea Receptor 1 Mutations. J Biol Chem. 2016, 291: 21971-21983, PMID: 27573238) that diazoxide, which stabilizes K_ATP channels in an open conformation, also reduces physical association between Kir6.2 N-terminus and SUR1 as demonstrated by reduced crosslinking of engineered azido-phenylalanine (an unnatural amino acid) at Kir6.2 N-terminal amino acid 12 position to SUR1. Incubating cells with diazoxide did not rescue the trafficking mutants but actually further reduced the maturation efficiency of trafficking mutants. For this reason, we did not include diazoxide during Aekatperone incubation and instead added diazoxide after Aekatperone washout to potentiate the activity of mutant channels rescued to the cell surface. In vivo, we envision testing alternating Aekatperone and diazoxide dosing to maximize functional rescue of K_ATP trafficking mutants.

(4) Do the authors have any information about the turnover time of KATP after washoff of the chaperone (how stable are the rescued channels at the cell surface)? This is a difficult question to probe when glibenclamide is used as a chaperone, but maybe much simpler to address with a lower affinity chaperone like AKP.

Thank you for your thoughtful comment. While we have not yet tested the duration of rescued K_ATP channels at the cell surface following Aekatperone washout, we have conducted similar studies with carbamazepine (Chen PC et al. Carbamazepine as a novel small molecule corrector of trafficking-impaired ATP-sensitive potassium channels identified in congenital hyperinsulinism. J Biol Chem. 2013, 288: 20942-20954, PMID: 23744072), another compound exhibiting reversible inhibitory and chaperone effects (apparent affinity between glibenclamide and Aekatperone). Our previous findings with carbamazepine showed that in cultured cells its chaperone effects were detectable as early as 1 hour and peaked around 6 hours after treatment. Furthermore, when carbamazepine was removed following a 16-hour treatment, the rescue effect persisted for up to 6 hours post-drug removal. These results provide a potential duration of the surface expression rescue effects of reversible pharmacochaperones.

Reviewer #1 (Recommendations for the authors):

The paper is well-written and comprehensive with only very minor essentially copy-editing needed. That said, it would be good if the authors could answer the main points raised above:

(1) What is the relevant Tanimoto parameters and sequence identity (does this mean structural identity) for the identified compounds?

As we answered above in response to the overall assessment, to facilitate the identification of novel hits, molecules with greater than 0.5 Tanimoto similarity in ECFP4 space to any known binders of the target protein and its homologs within 70% amino acid sequence identity were excluded from the commercial library. Additionally, after scoring and ranking the molecules by the AtomNet® technology, a diversity clustering was performed on the top 30,000 molecules using the Butina algorithm with a Tanimoto similarity cutoff of 0.35 in ECFP4 space to minimize selection of structurally similar scaffolds for the final compound buy-list.

(2) Did any of the identified putative binders have inhibitory action on channels? If so, does this give clues to separating chaperoning from inhibitory effects?

Please see response to the same question in the overall assessment above.

(3) Acknowledge that the identified compounds contain sulfonylurea-like moieties, and address why Aekatperone should (or perhaps does not) offer anything advantage over low affinity sulfonrylureas such as tolbutamide?

Please see response to the same question in the overall assessment above.

Reviewer #2 (Recommendations for the authors):

Thank you for assembling the interesting study, which I felt was well designed and communicated. The diverse approaches used in the study, with consistent findings, were definitely a strength. The core findings are also well distilled in the main body of the text, and although there is quite a lot of supplementary information, I felt that it was presented appropriately and well selected in terms of what would be important for readers hoping to learn more. In addition to the questions described above, I only had a few minor editorial issues that could be fixed related to presentation.

(1) Figure 1B. The colours and resolution of the chemical structures are difficult to see clearly and could be improved.

We have revised the figure accordingly.

(2) This is a minor wording point... first sentence of the discussion describes the drugs as pancreatic-selective, when it would be more clear to describe them as selective for the pancreatic isoform of KATP (Kir6.2/SUR1), or perhaps better as 'exhibiting ~4-5 fold selective for SUR1-containing KATP channels vs. SUR2A or SUR2B'.

We have changed the wording as suggested.

(3) As a curiosity (not necessary to do more experiments), but I am curious if the authors know whether there is any meaningful enhancement of trafficking of WT channels by AKP.

All pharmacochaperones we have identified to date including Aekatperone also slightly enhance WT channel surface expression (10-20%).

Reviewing editor recommendations:

(1) Given the modest resolution of the EM reconstruction, it is perhaps not entirely clear how AKP was assigned to the density observed. Specifically, it would be helpful to include a comparison of an AKP-free map and the current AKP map (filtered to a similar resolution) showing slice views of densities in the region around the inferred binding site. This would be very helpful in ascertaining whether the cryoEM reconstruction is an independent validation of the computational and functional experiments or whether the density inference depends on the additional knowledge.

We appreciate the editor’s suggestion. We have now added a Supplemental Figure (Supplementary Figure 7 in the revised manuscript) that compares our AKP-free cryoEM density deposited previously to the EMDB (EMD-26320) and the AKP-bound cryoEM density from this study, with cryoEM density (filtered to the same resolution) superimposed on the structural model.

(2) It could help to mention in brief what is a probable mechanism of AKP inhibition - that is how after binding of AKP, channel opening is restricted. Is it similar to that of other site A ligands?

Based on the strong Kir6.2 N-terminal cryoEM density observed in our AKP map, AKP most likely inhibits K_ATP channels by trapping the Kir6.2 N-terminus in the central cavity of SUR1’s ABC core thus preventing Kir6.2-C-terminal domain from rotating to an open conformation, similar to other ligands that stabilize the Kir6.2 N-terminus-SUR1 interface by binding to site A (such as tolbutamide and AKP), site B (such as repaglinide), or both site A and site B (such as glibenclamide). We have now included this in the revised Results and Discussion sections.

(3) In the context of the MD simulations, do other site A ligands (which from my understanding bind at a similar site) also exhibit similar flexibility as AKP? If there is information available on the flexibility of ligands of varying affinities, bound to the same site, maybe some correlative inferences can be drawn? However, in MD simulation trajectories it is not entirely uncommon for a ligand to simply get trapped in a local energy well. Since the authors have performed significant analysis of their MD results it could be worth mentioning/discussing such phenomena.

Previously published MD data addressing ligand dynamics, such as glibenclamide in the SUR1 pocket (Walczewska-Szewc K, Nowak W. Photo-Switchable Sulfonylureas Binding to ATPSensitive Potassium Channel Reveal the Mechanism of Light-Controlled Insulin Release. J Phys Chem B. 2021, 125: 13111-13121, PMID: 34825567), indicate a certain degree of flexibility. Unfortunately, we cannot directly compare these results, as the simulations were performed without the KNtp domain in the SUR1 cavity, which partially contributes to ligand stabilization. This is an issue we plan to investigate in the future.

In this study, we ran five independent MD simulations, each 500 ns long, resulting in a total of 2.5 μs of simulation time. Across all replicates, the ligand stayed in the same position, with variations mainly in the dynamics of the blurred segment. Considering the length of the simulations and the consistency across the runs, we believe this binding pose is stable and represents a global (or at least highly stable) energy minimum, consistent with the cryo-EM data.

(4) In electrophysiological assays, 10 uM AKP seems to inhibit all currents (Figure 2), but in the Rb+ flux assay ~10 uM appears to be the IC50. The reason for this difference is not entirely clear and it would help to comment on this.

Thank you for noticing the difference. The initial electrophysiological experiments were conducted using the very small amount of AKP provided to us from Atomwise. We estimated the concentration of the reconstituted AKP the best we could, but the concentration was likely to not be very accurate due to difficulty in handling the very small amount of the AKP powder. Subsequent Rb<sup>+>/sup> efflux experiments were conducted using a different, larger batch of AKP we purchased from Enamine. We have now stated this in the Methods section.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; different types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes. The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.

Major comment: 

[Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in DeltaNotch signaling, could the authors analyze the effect of coupling delay on robustness of the segmentation clock against morphogenetic processes?

We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression affects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 2022. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is sufficient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.

Minor comments: 

-  PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.

We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.

-  page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.

We thank the reviewer for spotting this. This has now been corrected.

-  Figure 3C: Description of black crosses in the panels is required in the figure legend.

Thank you for spotting this. The legend has now been corrected.

-  Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?

We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.

-  In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.

These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a different seed value. This is now reflected in the text on page 12 and in figure 4.

-  The authors analyze the effect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.

We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in  figure 4 supplementary figure 1 and are referred to in the text on page 11.

-  The authors analyze the effect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.

We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.

-  I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.

We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.

-  Figure 5-figure supplement 2: panel labels A, B, C are missing. 

Thank you for bringing this to our attention. These have now been added.

– Figure 5-figure supplement 3: panel labels A, B, C are missing.

Thank you for bringing this to our attention. These have now been added.

Reviewer #1 (Significance):

Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.

We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too.  

In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.

Reviewer #2 (Evidence, reproducibility and clarity):

SUMMARY 

The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.

Methodology: The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes affect the segmentation clock.

Model System: The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.

Key Findings and Conclusions: (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.

MAJOR COMMENTS

(1) The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).

We thank the reviewer for their positive comments about our work

(2) This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cellcell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.

We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space. 

(3) The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.

We support open source coding practices.

(4) In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'off' phase of the clock will ingressing cells be in-phase with their neighbours."  However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853]) Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).

We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence. 

However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.

MINOR COMMENTS 

(1) The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).

We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.

(2) The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.

We are very glad to see that the reviewer found that the manuscript was clearly presented.

(3) The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.

Thank you

(4) Minor suggestions: 

a. Page 26: In the Cell addition sub-section of the Methods section, correct all instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).

We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.

b. Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).

Thank you for the suggstion, we have now done this (see table 1 page 36).

Reviewer #2 (Significance):

GENERAL ASSESSMENT 

This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.

ADVANCE

This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.  However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.

The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.

AUDIENCE

Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.

Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from different vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.

Reviewer #3 (Evidence, reproducibility and clarity): 

In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to different biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy.

Below are the comments I would like the authors to address:

(1) The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the effects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the effect of different parameters in greater detail.

One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with differing cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM that captures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available. 

While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting effect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model. 

Furthermore, it is unclear how one would simulate processes such as compactionextension using a one-dimensional model. The two different scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively different than a one-dimensional scenario where cells are introduced continuously along the spatial axis.

(2) I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?

Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the xaxis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.

(3) While studying the effect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes affected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.

We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the different modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve.  While interesting, this work is however outside the scope of the current manuscript.

(4) While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.

We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression. 

(5) Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?

We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:

The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.

Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript. 

Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.

Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).

(6) The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.

We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31. 

Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016). 

We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).

(7) Figure 3 and the associated text shows no effect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.

Thank you for the suggestion. However, we would argue that the lack of effect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.

Reviewer #3 (Significance): 

The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.

Reviewer #2 (Public review):

Summary

The study investigated whether memory retrieval followed soon by extinction training results in a short-term memory deficit when tested - with a reinstatement test that results in recovery from extinction - soon after extinction training. Experiment 1 documents this phenomenon using a between-subjects design. Experiment 2 used a within-subject control and saw that the effect is also observed in a control condition. In addition, it also revealed that if testing is conducted 6 hours after extinction, there is not effect of retrieval prior to extinction as there is recovery from extinction independently of retrieval prior to extinction. A third Group also revealed that retrieval followed by extinction attenuates reinstatement when the test is conducted 24 hours later, consistent with previous literature. Finally, Experiment 3 used continuous theta-burst stimulation of the dorsolateral prefrontal cortex and assessed whether inhibition of that region (vs a control region) reversed the short-term effect revealed in Experiments 1 and 2. The results of control groups in Experiment 3 replicated the previous findings (short-term effect), and the experimental group revealed that these can be reversed by inhibition of the dorsolateral prefrontal cortex.

Strengths

The work is performed using standard procedures (fear conditioning and continuous theta-burst stimulation) and there is some justification of the sample sizes. The results replicate previous findings - some of which have been difficult to replicate and this needs to be acknowledged - and suggest that the effect can also be observed in a short-term reinstatement test.

The study establishes links between the memory reconsolidation and retrieval-induced forgetting (or memory suppression) literatures. The explanations that have been developed for these are distinct and the current results integrate these, by revealing that the DLPFC activity involved in retrieval-extinction short-term effect. There is thus some novelty in the present results, but numerous questions remain unaddressed.

Weakness

The fear acquisition data is converted to a differential fear SCR and this is what is analysed (early vs late). However, the figure shows the raw SCR values for CS+ and CS- and therefore it is unclear whether acquisition was successful (despite there being an "early" vs "late" effect - no descriptives are provided).

In Experiment 1 (Test results) it is unclear whether the main conclusion stems from a comparison of the test data relative to the last extinction trial ("we defined the fear recovery index as the SCR difference between the first test trial and the last extinction trial for a specific CS") or the difference relative to the CS- ("differential fear recovery index between CS+ and CS-"). It would help the reader assess the data if Fig 1e presents all the indexes (both CS+ and CS-). In addition, there is one sentence which I could not understand "there is no statistical difference between the differential fear recovery indexes between CS+ in the reminder and no reminder groups (P=0.048)". The p value suggests that there is a difference, yet it is not clear what is being compared here. Critically, any index taken as a difference relative to the CS- can indicate recovery of fear to the CS+ or absence of discrimination relative to the CS-, so ideally the authors would want to directly compare responses to the CS+ in the reminder and no-reminder groups. In the absence of such comparison, little can be concluded, in particular if SCR CS- data is different between groups. The latter issue is particularly relevant in Experiment 2, in which the CS- seems to vary between groups during the test and this can obscure the interpretation of the result.

In experiment 1, the findings suggest that there is a benefit of retrieval followed by extinction in a short-term reinstatement test. In Experiment 2, the same effect is observed to a cue which did not undergo retrieval before extinction (CS2+), a result that is interpreted as resulting from cue-independence, rather than a failure to replicate in a within-subjects design the observations of Experiment 1 (between-subjects). Although retrieval-induced forgetting is cue-independent (the effect on items that are suppressed [Rp-] can be observed with an independent probe), it is not clear that the current findings are similar, and thus that the strong parallels made are not warranted. Here, both cues have been extinguished and therefore been equally exposed during the critical stage.

The findings in Experiment 2 suggest that the amnesia reported in Experiment 1 is transient, in that no effect is observed when the test is delayed by 6 hours. The phenomena whereby reactivated memories transition to extinguished memories as a function of the amount of exposure (or number of trials) is completely different from the phenomena observed here. In the former, the manipulation has to do with the number of trials (or total amount of time) that the cues are exposed. In the current Experiment 2, the authors did not manipulate the number of trials but instead the retention interval between extinction and test. The finding reported here is closer to a "Kamin effect", that is the forgetting of learned information which is observed with intervals of intermediate length (Baum, 1968). Because the Kamin effect has been inferred to result from retrieval failure, it is unclear how this can be explained here. There needs to be much more clarity on the explanations to substantiate the conclusions.<br /> There are many results (Ryan et al., 2015) that challenge the framework that the authors base their predictions on (consolidation and reconsolidation theory), therefore these need to be acknowledged. These studies showed that memory can be expressed in the absence of the biological machinery thought to be needed for memory performance. The authors should be careful about statements such as "eliminate fear memores" for which there is little evidence.

The parallels between the current findings and the memory suppression literature are speculated in the general discussion, and there is the conclusion that "the retrieval-extinction procedure might facilitate a spontaneous memory suppression process". Because one of the basic tenets of the memory suppression literature is that it reflects an "active suppression" process, there is no reason to believe that in the current paradigm the same phenomenon is in place, but instead it is "automatic". In other words, the conclusions make strong parallels with the memory suppression (and cognitive control) literature, yet the phenomena that they observed is thought to be passive (or spontaneous/automatic). Ultimately, it is unclear why 10 mins between the reminder and extinction learning will "automatically" suppress fear memories. Further down in the discussion it is argued that "For example, in the well-known retrieval-induced forgetting (RIF) phenomenon, the recall of a stored memory can impair the retention of related long-term memory and this forgetting effect emerges as early as 20 minutes after the retrieval procedure, suggesting memory suppression or inhibition can occur in a more spontaneous and automatic manner". I did not follow with the time delay between manipulation and test (20 mins) would speak about whether the process is controlled or automatic. In addition, the links with the "latent cause" theoretical framework are weak if any. There is little reason to believe that one extinction trial, separated by 10 mins from the rest of extinction trials, may lead participants to learn that extinction and acquisition have been generated by the same latent cause.

Among the many conclusions, one is that the current study uncovers the "mechanism" underlying the short-term effects of retrieval-extinction. There is little in the current report that uncovers the mechanism, even in the most psychological sense of the mechanism, so this needs to be clarified. The same applies to the use of "adaptive".

Whilst I could access the data in the OFS site, I could not make sense of the Matlab files as there is no signposting indicating what data is being shown in the files. Thus, as it stands, there is no way of independently replicating the analyses reported.

The supplemental material shows figures with all participants, but only some statistical analyses are provided, and sometimes these are different from those reported in the main manuscript. For example, the test data in Experiment 1 is analysed with a two-way ANOVA with main effects of group (reminder vs no-reminder) and time (last trial of extinction vs first trial of test) in the main report. The analyses with all participants in the sup mat used a mixed two-way ANOVA with group (reminder vs no reminder) and CS (CS+ vs CS-). This makes it difficult to assess the robustness of the results when including all participants. In addition, in the supplementary materials there are no figures and analyses for Experiment 3.

One of the overarching conclusions is that the "mechanisms" underlying reconsolidation (long term) and memory suppression (short term) phenomena are distinct, but memory suppression phenomena can also be observed after a 7-day retention interval (Storm et al., 2012), which then questions the conclusions achieved by the current study.

References:

Baum, M. (1968). Reversal learning of an avoidance response and the Kamin effect. Journal of Comparative and Physiological Psychology, 66(2), 495.<br /> Chalkia, A., Schroyens, N., Leng, L., Vanhasbroeck, N., Zenses, A. K., Van Oudenhove, L., & Beckers, T. (2020). No persistent attenuation of fear memories in humans: A registered replication of the reactivation-extinction effect. Cortex, 129, 496-509.<br /> Ryan, T. J., Roy, D. S., Pignatelli, M., Arons, A., & Tonegawa, S. (2015). Engram cells retain memory under retrograde amnesia. Science, 348(6238), 1007-1013.<br /> Storm, B. C., Bjork, E. L., & Bjork, R. A. (2012). On the durability of retrieval-induced forgetting. Journal of Cognitive Psychology, 24(5), 617-629.

Comments on revisions:

The authors have revised the manuscript but most of my concerns have remained unaddressed.

(1) There are still no descriptive statistics to substantiate learning in Experiment 1.

(2) In the revised analyses, the authors now show that CS- changes in different groups (for example, Experiment 2) so this means that there is little to conclude from the differential scores because these depend on CS-. It is unclear whether the effects arise from CS+ performance or the differential which is subject to CS- variations.

(3) The notion that suppression is automatic is speculative at best

(4) It still struggle with the parallels between these findings and the "limbo" literature. Here you manipulated the retention interval, whereas in the cited studies the number of extinction (exposure) was varied. These are two completely different phenomena.

(5) My point about the data problematic for the reconsolidation (and consolidation) frameworks is that they observed memory in the absence of the brain substrates that are needed for memory to be observed. The answer did not address this. I do not understand how the latent cause model can explain this, if the only difference is the first ITI. Wouldn't participants fail to integrate extinction with acquisition with a longer ITI?

(6) The materials in the OSF site are the same as before, they haven't ben updated.

(7) Concerning supplementary materials, the robustness tests are intended to prove that you 1) can get the same results by varying the statistical models or 2) you can get the same results when you include all participants. Here authors have done both so this does not help. Also, in the rebuttal letter, they stated "Please note we did not include non-learners in these analyses " which contradicts what is stated in the figure captions "(learners + non learners)"

(8) Finally, the literature suggesting that reconsolidation interference "eliminates" a memory is not substantiated by data nor in line with current theorising, so I invite a revision of these strong claims.

Overall, I conclude that the revised manuscript did not address my main concerns.

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

Learn more at Review Commons

Reply to the reviewers

1. General Statements

We thank the reviewers for their thorough evaluation of this manuscript. We are pleased that overall, they found our work and results valuable for the scientific community. Based on their feedback, we performed additional experiments and made several changes to strengthen the manuscript and expand the target audience.

*All three reviewers pointed out that the manuscript lacked demonstration of OneSABER method applicability across sample types (i.e., its claimed versatility) and other whole-mount systems beyond the Macrostomum lignano flatworm. *

We now include an additional results section with accompanying figures (Figs. 6 and 7) that demonstrate the application of OneSABER in whole-mount samples of another flatworm, the planarian Schmidtea mediterranea (Fig. 6), which is much larger than M. lignano, and in formalin-fixed paraffin-embedded (FFPE) mouse small intestine tissue sections (Fig. 7). We believe that these additional experiments on different sample types demonstrate the versatility of the OneSABER approach.

Please note that two more authors, Jan Freark de Boer and Folkert Kuipers, have been added for their contribution to mouse FFPE sections.

Furthermore, two reviewers asked for an additional main figure with a comparison of the signal strengths between the different OneSABER methods.

We have addressed this comment by including an additional results section and its adjacent figure (Fig. 5), where we provide a comparison of fluorescent signals from the same probes and gene but different OneSABER development methods.

Additionally, to implement the revisions, we modified Fig. 1 and Supplementary Fig. 6 and broadened Supplementary Tables S1-S2, S4-S6.

2. Point-by-point description of the revisions

Reviewer #1

1) “Fig.1 seems to suggest that the protocol for in vitro swapping of 3' concatemers happens in two consecutive PCR steps. I recommend indicating in the figure that the switching can be conducted in a single in vitro reaction.”

We have changed Fig. 1 to make this clearer.

2) “Is it possible to multiplex the switching in one single reaction? For example, perform p27 to p28 and p29 to p30 simultaneously? This will be crucial for the split-probe methodology.”

We did not test it. This should be possible if there is no overlap between the 3’ initiator sequences. However, it seems counterproductive as the elongation efficiencies of switching reactions from the 3’ initiator sequences to another concatemer may vary (Supplementary Fig. S6). Running independent extension/switch reactions and performing equimolar mixing of purified extended probes could be a better solution.

3) “Did the authors encounter any switching hairpins sequence that does not work? If not, can they postulate, what are the requirements for the design of switching sequences.”

The design criteria followed the requirements postulated in the original SABER article and its Supplementary Materials (Kishi et al 2019). All switching hairpins we tested in the pairs of the 3 used 3’ initiator sequences (p27, p28 and p30) worked, but elongation efficiencies varied (see an example in Supplementary Fig. S6).

4) “Is there cross hybridization between the switched and original hairpins? For example, can the authors show that the signals from p27 and p30 do not overlaps?”

The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smedwi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

5) “Can the authors quantify results from the direct, AP, TSA, and HCR? What do you mean by 'narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible'?”

*“I agree with reviewer #2 regarding the lack of comparison to standard SABER.” *

A comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

We have rephrased the sentence for clarity. From “As a result, despite higher intracellular resolution, some narrow anatomical structures like neural chords (syt11) or muscles (tnnt2) seem less visible for the human eye after SABER HCR (Figs. 3, 4).” to “As a result, despite higher intracellular resolution, some fine anatomical structures like neural chords (syt11) or muscles (tnnt2) are less resolved by widefield fluorescence microscopy after SABER HCR FISH compared to SABER TSA FISH”

Reviewer #2

1) “This work is building on standard SABER (a set of PER-extended primary probes that serve as landing pads for secondary fluorescently-labeled readout oligos) and pSABER (the readout oligo carries HRP instead of a dye for downstream TSA). The novelty of the work presented here is introducing additional variations of signal amplification, i.e. by using an hapten-labeled oligo to recruit a tertiary readout probe (antibodies conjugated with HRP or AP) or using SABER in combination with HCR. Since SABER can be seen as the underlying platform and pSABER was (arguably) also already introduced as a new platform by Attar et al. 2023, it seems difficult to introduce OneSABER as yet another new platform, of which standard SABER and pSABER are a part of. The reviewer encourages the authors to overthink the conceptual introduction, which in view of its certainly distinct novel features might allow a clearer distinction to previous work.”

We agree with the reviewer’s comments. We have added additional information in the Introduction section to clarify the novelty and key distinct features of OneSABER that justify its separation from other SABER protocols.

2) “Although the authors take care in tributing prior work, some of the studies are only mentioned in the results section, one of such cases is pSABER by Attar et al. 2023. The close relation between pSABER and SABER TSA (HRP on readout oligo vs. hapten on readout oligo + HRP-conjugated antibody) needs to be better positioned in the introduction, clearly framing earlier work, inspirations drawn etc.. This is in line with my previous point.”

The pSABER preprint article by Attar et al. 2023 (now published in a peer-reviewed journal as Attar et al. 2025) is now mentioned in the Introduction, and its inspirational impact on our research is clearly stated.

3) “Fig. 1 lists the individual modules of the OneSABER platform: i) standard SABER, ii) AP SABER, iii) SABER TSA, iv) pSABER (TSA FISH) (would recommend leaving it with original name when introducing it and include additional explanation in parentheses) and iv) SABER HCR. The main figures feature only AP SABER, SABER TSA and SABER HCR, for standard SABER and pSABER one must look up the SI. Since the authors describe the limited performance of standard SABER for one of their targets of interest (syt11) and since they have tested this target for all five conditions, it would be valuable to include a comparative view of all five platform modules in a single figure for syt11 or even also piwi, which also seems to have been tested for all five. Comparing the signal strength would be useful for the community, at least of each SABER variation compared to standard SABER.”

We agree with the reviewer’s comments. Except for pSABER, a comparison of fluorescence signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5. To make the comparison as objective as possible, all FISH developments were re-done using available “far red” fluorophores, except for pSABER. Unfortunately, our directly labeled HRP oligonucleotides for pSABER lost their activity after a year of storage at +4oC. These conjugated oligonucleotides are very expensive and, given their limited shelf life, we cannot justify ordering a new batch for this experiment. Therefore, we only have the data for pSABER syt11 with FITC green tyramide, which is not comparable to “far red” fluorophore signals. This issue has also been discussed in the main text.

In addition, we have modified Fig. 1, as suggested.

4) “The description of how the authors designed their probes is very detailed and they also provide a nice step-by-step protocol for their individual commands using Oligominer and BLAT software. This reviewer is wondering how the authors chose their PER sequences that they appended to their mined set of homologous in situ hybridization probes (p27,p28,p30). This is a general problem of multiplexed ISH approaches with single-stranded overhang, could the author's comment on potential self-interaction of the appended sequence with the homologous part, which might limit the PER efficiency, or elaborate on their choice?”

As being ourselves novice to SABER when we started our work, we based our selection of the p27, p28, and p30 PER sequences on their multiple co-occurrences in previous publications (Amamoto et al. 2019, doi: 10.7554/eLife.51452; Saka et al. 2019, doi: 10.1038/s41587-019-0207-y; Wang et al. 2020, doi: 10.1016/j.omtm.2020.10.003; Salinas-Saavedra et al. 2023, doi: 10.1016/j.celrep.2023.112687; and Attar et al. 2023, doi: 10.1101/2023.01.30.526264). We did not consider the potential interference between PER concatemers and homologous primary probe-binding sequences. However, as all PER concatemers were specifically designed to lack G nucleotides to keep them from self-annealing (Kishi et al. 2019, doi: 10.1038/s41592-019-0404-0), we assumed that it would also reduce potential annealing to the homologous part of the probe.

5) “Fig.1 and l. 125 describe straightforward in vitro switching of the concatemer sequence for an existing set of primary probes as a central feature of the OneSABER platform. However, the authors to my knowledge do not show such experiments themselves and only cite the original SABER paper by Kishi et al. 2019. This reviewer would be grateful to be pointed toward where in Kishi et al. 2019 this was demonstrated, however in view of this central part of the swopping scheme in the OneSABER platform an experiment showing this swopping is missing.”

In the article by Kishi et al. 2019, concatemer switching/swapping is termed as “primer remapping”. We found this term confusing because it does not describe the essence of the reaction. The in situ hybridization results with swapped primary probes are shown in Fig. 6B (multiplexed HCR in S. mediterranea). All probes were originally designed using a p27 PER initiator. We swapped Smed-vit-1 with p30 and Smewi-1 with p28. We also updated Fig. S6, by adding the second section (B) showing the in vitro results after concatemer swapping, as well as hybridization specificity of the secondary imager probes.

6) “the description of Table S6 could use additional information in the legend such that the reader does not have to scroll down to Section S1 to retrieve the information (PER reaction, gel conditions, ladder is dsDNA, what are the individual bands)”

Probably, the reviewer meant Fig. S6. We now wrote a more detailed caption for the figure and extended it with a second panel (B) to illustrate the results of 3’ concatemer swapping.

7) “the manuscript features an extensive set of resources in main body, supplementary materials and protocols. It is important and usually not merited sufficiently making the effort to compare orthogonal approaches for a given aim. This reviewer particularly appreciates the detailed strengths & weaknesses discussion in Table S6.”

We thank the reviewer for the appreciation of our work.

8) “Minor comments:

-Definitions should be consistent, in Fig. 1 all approaches are defined with FISH added, but this definition is not followed consistently in the main text.”

These definitions are now made consistent throughout the text.

9) “Optional:

-The authors describe several newly developed optimization steps during sample preparation for M. lignano ISH experiments compared to established ones. If the data exists, they include a supplementary figure showing improvements of optimized protocol steps”

As almost every step and the buffer recipes were different from the original ISH protocol by Pfister et al. (2007) because of the use of liquid-exchange columns, different probes, and development chemistry, we believe that a comparison would be excessive. We think that the key difference points are already substantially highlighted in the results section.

Reviewer #3

1) “Despite including a whole figure (Figure 1) featuring the operation scheme of the OneSABER platform, the figure as well as the associated text fall short with respect to clearly stating the advantage of the different aspects of the platform. Consider a clearer and more thorough explanation of the different aspects of the platfrom.”

Details on the advantages and disadvantages of using different OneSABER methods in terms of their experimental application and cost efficiency are described in Supplementary Tables S4-S6 of the submitted manuscript. However, we agree that the description in Fig. 1 was too concise and also did not refer to these tables. We have expanded the description in Fig. 1.

2) “Related to the first comment: A more detailed description of the similarities and/or differences of this platform relative to similar applications such as the study by Hall et al, 2024”

The mere point of mentioning the preprint of Hall et al. 2024 (now peer-reviewed, https://doi.org/10.1016/j.celrep.2024.114892) was to acknowledge that in M. lignano the HCR technology has been previously applied (although only once), while all other previously published works on M. lignano utilized canonical antisense RNA probes colorimetric in situ hybridization. We have extensively mentioned the HCR approach and its working principles throughout the submitted manuscript.

3) “The authors describe the probes used as short, synthetic DNA probes targeting short RNA transcripts. Are these probes Oligopaints (Beliveau et al, 2015)? Why is that not more clearly stated in the text?”

Oligopaints use oligo libraries as a renewable source of FISH probes, and these libraries are amplified with fluorophore-conjugated PCR primers. We used synthetic DNA probes directly. In this sense, our probe sets are not oligopaints. However, we used the OligoMiner pipeline of Oligopaints for the design of the probes, and thus used the same tiling strategy as oligopaints. We believe that this has been explained in the manuscript. Please refer to comment 4 of Reviewer 2.

4) “Line 105, p5: The authors state that the number of probes depends on the target RNA length and its expression strength. This data should be in the main text and described in detail since it is a major aspect of the platform design.”

We believe that this statement is common sense, as one cannot design more than 5x 30-50 bp probes for 200 nt transcripts, while for a 2000 bp mRNA, the theoretical limit is ~50 probes. Similarly, weakly expressed genes (regardless of their length) would require either more probes to reach the detection threshold or stronger amplification through choice of concatemer length and/or signal developing techniques. We have rephrased this sentence in the main text to reflect this.

5) “Figure 2 showcases one of the most compelling data supporting the versatility of the platform. Can the signals in each panel be quantified and compared to 1. Published Ab staining? Is there a clear correlation in the intensity of the signals? 2. Between Vector Blue and NBT? 3. Chemical staining and FISH signals?”

Since M. lignano is a relatively new model, there are no published antibody stainings for M. lignano genes used in this study. Furthermore, colorimetric precipitate methods are not quantitative but rather qualitative, because their signal strength is proportional to both the target RNA level and the development time; thus, signals from weakly expressed transcripts can be “boosted” simply by longer development. Therefore, a correct quantitative comparison with colorimetric methods, as requested by the reviewer, was not possible. However, with some corrections on fluorophore differences and animal-to-animal variability, it is possible to roughly compare peak saturation intensities for FISH methods if the experiments are designed for this aim. We performed these experiments, and a comparison of fluorescent signals from the same probes/genes but different OneSABER development methods is shown in Fig. 5.

Minor comments:

6) “The whole mount images and signals are often diffuse, can they be visualized using a DIC where the morphology of the organism is clearer?”

We are unsure which images appear to be diffused to the reviewer. The other reviewers have not pointed out similar issues. Perhaps the question resolves once full-resolution uncompressed images are uploaded.

7) “In order to support the claim that this is a universal approach for whole-mount staining, can the authors show an example of applicability to C. elegans?”

This is now addressed. We included two additional results sections with two accompanying figures (Figs. 6 and 7) that demonstrate OneSABER’s application in whole-mount samples of a much larger than M. lignano model flatworm, the planarian Schmidtea mediterranea (Fig. 6), as well as in formalin-fixed paraffin-embedded (FFPE) small intestine tissue sections of a mouse model (Fig. 7).

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

The authors used a subset of a very large, previously generated 16S dataset to:<br /> (1) Assess age-associated features; and (2) develop a fecal microbiome clock, based on an extensive longitudinal sampling of wild baboons for which near-exact chronological age is known. They further seek to understand deviation from age-expected patterns and uncover if and why some individuals have an older or younger microbiome than expected, and the health and longevity implications of such variation. Overall, the authors compellingly achieved their goals of discovering age-associated microbiome features and developing a fecal microbiome clock. They also showed clear and exciting evidence for sex and rank-associated variation in the pace of gut microbiome aging and impacts of seasonality on microbiome age in females. These data add to a growing understanding of modifiers of the pace of age in primates, and links among different biological indicators of age, with implications for understanding and contextualizing human variation. However, in the current version, there are gaps in the analyses with respect to the social environment, and in comparisons with other biological indicators of age. Despite this, I anticipate this work will be impactful, generate new areas of inquiry, and fuel additional comparative studies.

Thank you for the supportive comments and constructive reviews.

Strengths:

The major strengths of the paper are the size and sampling depth of the study population, including the ability to characterize the social and physical environments, and the application of recent and exciting methods to characterize the microbiome clock. An additional strength was the ability of the authors to compare and contrast the relative age-predictive power of the fecal microbiome clock to other biological methods of age estimation available for the study population (dental wear, blood cell parameters, methylation data). Furthermore, the writing and support materials are clear, informative and visually appealing.

Weaknesses:

It seems clear that more could be done in the area of drawing comparisons among the microbiome clock and other metrics of biological age, given the extensive data available for the study population. It was confusing to see this goal (i.e. "(i) to test whether microbiome age is correlated with other hallmarks of biological age in this population"), listed as a future direction, when the authors began this process here and have the data to do more; it would add to the impact of the paper to see this more extensively developed.

Comparing the microbiome clock to other metrics of biological age in our population is a high priority (these other metrics of biological age are in Table S5 and include epigenetic age measured in blood, the non-invasive physiology and behavior clock (NPB clock), dentine exposure, body mass index, and blood cell counts (Galbany et al. 2011; Altmann et al. 2010; Jayashankar et al. 2003; Weibel et al. 2024; Anderson et al. 2021)). However, we have opted to test these relationships in a separate manuscript. We made this decision because of the complexity of the analytical task: these metrics were not necessarily collected on the same subjects, and when they were, each metric was often measured at a different age for a given animal. Further, two of the metrics (microbiome clock and NPB clock) are measured longitudinally within subjects but on different time scales (the NPB clock is measured annually while microbiome age is measured in individual samples). The other metrics are cross-sectional. Testing the correlations between them will require exploration of how subject inclusion and time scale affect the relationships between metrics.

We now explain the complexity of this analysis in the discussion in lines 447-450. In addition, we have added the NPB clock (Weibel et al. 2024) to the text in lines 260-262 and to Table S5.

An additional weakness of the current set of analyses is that the authors did not explore the impact of current social network connectedness on microbiome parameters, despite the landmark finding from members of this authorship studying the same population that "Social networks predict gut microbiome composition in wild baboons" published here in eLife some years ago. While a mother's social connectedness is included as a parameter of early life adversity, overall the authors focus strongly on social dominance rank, without discussion of that parameter's impact on social network size or directly assessing it.

Thank you for raising this important point, which was not well explained in our manuscript. We find that the signatures of social group membership and social network proximity are only detectable our population for samples collected close in time. All of the samples analyzed in  Tung et al. 2015 (“Social networks predict gut microbiome composition in wild baboons”) were collected within six weeks of each other. By contrast, the data set analyzed here spans 14 years, with very few samples from close social partners collected close in time. Hence, the effects of social group membership and social proximity are weak or undetectable. We described these findings in Grieneisen et al. 2021 and Bjork et al. 2022, and we now explain this logic on line 530, which states, “We did not model individual social network position because prior analyses of this data set find no evidence that close social partners have more similar gut microbiomes, probably because we lack samples from close social partners sampled close in time (Grieneisen et al. 2021; Björk et al. 2022).”

We do find small effects of social group membership, which is included as a random effect in our models of how each microbiome feature is associated with host age (line 529) and our models predicting microbiome Dage (line 606; Table S6).

Reviewer #2 (Public review):

Summary:

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights into biological aging. Although 'microbiota age' holds potential as a proxy of biological age, it comes with limitations considering the gut microbial community can be influenced by various non-age related factors, and various age-related stressors may not manifest in changes in the gut microbiota. The work would benefit from a more comprehensive discussion, that includes the limitations of the study and what these mean to the interpretation of the results.

We agree and have text to the discussion that expands on the limitations of this study and what those limitations mean for the interpretation of the results. For instance, lines 395-400 read, “Despite the relative accuracy of the baboon microbiome clock compared to similar clocks in humans, our clock has several limitations. First, the clock’s ability to predict  individual age is lower than for age clocks based on patterns of DNA methylation—both for humans and baboons (Horvath 2013; Marioni et al. 2015; Chen et al. 2016; Binder et al. 2018; Anderson et al. 2021). One reason for this difference may be that gut microbiomes can be influenced by several non-age-related factors, including social group membership, seasonal changes in resource use, and fluctuations in microbial communities in the environment”

In addition, lines 405-411 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

Strengths:

The dataset this study is based on is impressive, and can reveal various insights into biological ageing and beyond. The analysis implemented is extensive and high-level.

Weaknesses:

The key weakness is the use of microbiota age instead of e.g., DNA-methylation-based epigenetic age as a proxy of biological ageing, for reasons stated in the summary. DNA methylation levels can be measured from faecal samples, and as such epigenetic clocks too can be non-invasive. I will provide authors a list of minor edits to improve the read, to provide more details on Methods, and to make sure study limitations are discussed comprehensively.

Thank you for this point. In response, we have deleted the text from the discussion that stated that non-invasive sampling is an advantage of microbiome clocks. In addition, we now propose a non-invasive epigenetic clock from fecal samples as an important future direction for our population (see line 450).

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Abstract - The opening 2 sentences are not especially original or reflective of the potential value/ premise of the study. Members of this team have themselves measured variation in biological age in many different ways, and the implication that measuring a microbiome clock is easy or straightforward is not compelling. This paper is very interesting and provides unique insight, but I think overall there is a missed opportunity in the abstract to emphasize this, given the innovative science presented here. Furthermore, the last 2 sentences of the abstract are especially interesting - but missing a final statement on the broader significance of research outside of baboons.

We appreciate these comments and have revised the Abstract accordingly. The introductory sentences now read, “Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age.” (lines 31-34). The last two sentences of the abstract now read, “Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.” (lines 40-43).

If possible, it would be highly useful to present some comments on concordance in patterns at different levels. Are all ASVs assessed at both the family and genus levels? Do they follow similar patterns when assessed at different levels? What can we learn about the system by looking at different levels of taxonomic assignment?

The section on relationships between host age and individual microbiome features is already lengthy, so we have not added an analysis of concordance between different taxonomic levels. However, we added a justification for why we tested for age signatures in different levels of taxa to line 171, which reads, “We tested these different taxonomic levels in order to learn whether the degree to which coarse and fine-grained designations categories were associated with host age.”

To calculate the delta age - please clarify if this was done at the level of years, as suggested in Figure 3C, or at the level of months or portion months, etc?

Delta age is measured in years. This is now clarified in lines 294, 295, and 578.

Spelling mistake in table S12, cell B4 (Octovber)

Thank you. This typo has been corrected.

Given the start intro with vertebrates, the second paragraph needs some tweaking to be appropriate. Perhaps, "At least among mammals, one valuable marker of biological aging may lie in the composition and dynamics of the mammalian gut microbiome (7-10)." Or simply remove "mammalian".

We have updated this sentence based on your suggestions in line 54. It reads, “In mammals, one valuable marker of biological aging may lie in the composition and dynamics of the gut microbiome (Claesson et al. 2012; Heintz and Mair 2014; O’Toole and Jeffery 2015; Sadoughi et al. 2022).”

A rewrite at the end of the introduction is needed to avoid the almost direct repetition in lines 115-118 and 129-131 (including lit cited). One potentially effective way to approach this is to keep the predictions in the earlier paragraph and then more clearly center the approach and the overarching results statement in the latter paragraph. (I.e., "we find that season and social rank have stronger effects on microbiome age than early life events. Further, microbiome age does not predict host development or mortality.").

Thank you for pointing this out. We have re-organized the predictions in the introduction based on your suggestion. The alternative “recency effects” model now appears in the paragraph that starts in line 110. The final paragraph then centers on the overall approach and the results statement (lines 128-140)

Be clear in each case where taxon-level trends are discussed if it's at Family, Genus, or other level. It's there most, but not all, of the time.

We have gone through the text and clarified what taxa or microbiome feature was the subject of our analyses in any places where this was not clear.

In the legend for Figure 2, add clarification for how values to right versus left of the centered value should be interpreted with respect to age (e.g. "values to x of the center are more abundant in older individuals").

We now clarify in Figure 2C and 2D that “Positive values are more abundant in older hosts”.

Figure 3 - Are Panels A, B, and C all needed - can the value for all individuals not also be overlaid in the panel showing sex differences and the same point showing individuals with "old" and "young" microbiomes be added in the same plot if it was slightly larger?

We agree and have simplified Figure 3. We reduced the number of panels from three to two, and we added the information about how to calculate delta age to Panel A. We also moved the equation from the top of Panel C to the bottom right of Panel A.

Reviewer #2 (Recommendations for the authors):

Dasari et al present an interesting study investigating the use of 'microbiota age' as an alternative to other measures of 'biological age'. The study provides several curious insights which in principle warrant publication. However, I do think the manuscript should be carefully revised. Below I list some minor revisions that should be implemented. Importantly, the authors should discuss in the Discussion the pros and cons of using 'microbiota age' as a proxy of 'biological age'. Further, the authors should provide more information on Methods, to make sure the study can be replicated.

Thank you for these important points. Based on your comments and those of the first reviewer, we have expanded our discussion of the limitations of using microbiota age as a proxy for biological age (see edits to the paragraph starting in line 395).

We have also expanded our methods around sample collection, DNA extraction, and sequencing to describe our sampling methods, strategies to mitigate and address possible contamination, and batch effects. See lines 483-490 and our citations to the original papers where these methods are described in detail.

(1) Lines 85-99: I think this paragraph could be revisited to make the assumptions clearer. For instance, the last sentence is currently a little confusing: are authors expecting males to exhibit old-for-age microbiomes already during the juvenile period?

This prediction has been clarified. Line 96 now reads, “Hence, we predicted that adult male baboons would exhibit gut microbiomes that are old-for-age, compared to adult females (by contrast, we expected no sex effects on microbiome age in juvenile baboons).”

(2) Lines 118-121: Could the authors discuss this assumption in relation to what has been observed e.g., in humans in terms of delays in gut microbiome development? Delayed/accelerated gut microbiome development has been studied before, so this assumption would be stronger if related to what we know from previous studies.

This comment refers to the sentence which originally stated, “However, we also expected that some sources of early life adversity might be linked to young-for-age gut microbiota. For instance, maternal social isolation might delay gut microbiome development due to less frequent microbial exposures from conspecifics.” We have slightly expanded the text here (line 117) to explain our logic. We now include citations for our predictions. We did not include a detailed discussion of prior literature on microbiome development in the interest of keeping the same level of detail across all sections on our predictions.

(3) As the authors discuss, various adversities can lead to old-for-age but also young-for-age microbiome composition. This should be discussed in the limitations.

We agree. This is now discussed in the sentence starting at line 371, which reads, “…deviations from microbiome age predictions are explained by socio-environmental conditions experienced by individual hosts, especially recent conditions, although the effect sizes are small and are not always directionally consistent.” In addition, the text starting at line 405 now reads, “Third, the relationships between potential socio-environmental drivers of biological aging and the resulting biological age predictions were inconsistent. For instance, some sources of early life adversity were linked to old-for-age gut microbiomes (e.g., males born into large social groups), while others were linked to young-for-age microbiomes (e.g., males who experienced maternal social isolation or early life drought), or were unrelated to gut microbiome age (e.g., males who experienced maternal loss; any source of early life adversity in females).”

(4) In various places, e.g., lines 129-131, it is a little unclear at what chronological age authors are expecting microbiota to appear young/old-for-age.

This sentence was removed while responding to the comments from the first reviewer.

(5) Lines 132-133: this statement could be backed by stating that this is because the gut microbiota can change rapidly e.g., when diet changes (or whatever the authors think could be behind this).

We have added an expository sentence at line 123, including new citations. This sentence reads, “Indeed, gut microbiomes are highly dynamic and can change rapidly in response to host diet or other aspects of host physiology, behavior, or environments”.

We now cite:

· Hicks, A.L., et al. (2018). Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nature Communications 9, 1786.

· Kolodny, O., et al. (2019). Coordinated change at the colony level in fruit bat fur microbiomes through time. Nature Ecology & Evolution 3, 116-124.

· Risely, A., et al. (2021) Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat Commun 12, 6017.

(6) Lines 135-137: current or past season and social rank? This paragraph introduces the idea that it could be past rather than current socio-environmental factors that might predict microbiota age, so the authors should clarify this sentence.

We have clarified the information in this sentence. line 135 now reads, “In general, our results support the idea that a baboon’s current socio-environmental conditions, especially their current social rank and the season of sampling, have stronger effects on microbiome age than early life events—many of which occurred many years prior to sampling.”

(7) Lines 136-137: this sentence could include some kind of a conclusion of this finding. What might this mean?

We have added a sentence at line 138, which speculates that, “…the dynamism of the gut microbiome may often overwhelm and erase early life effects on gut microbiome age.”

(8) Use 'microbiota' or 'microbiome' across the manuscript; currently, the terms are used interchangeably. I don't have a strong opinion on this, although typically 'microbiota' is used when data comes from 16S rRNA.

We have updated the text to replace any instance of “microbiota” with “microbiome”. We use the term microbiome in the sense of this definition from the National Human Genome Research Institute, which defines a microbiome as “the community of microorganisms (such as fungi, bacteria and viruses) that exists in a particular environment”.

(9) Figure 1 legend: make sure to unify formatting; e.g., present sample sizes as N= or n=, rather than both, and either include or do not include commas in 4-digit values (sample sizes).

We have checked the formatting related to sample sizes and the use of commas in 4-digits in the main text and supplement. The formats are now consistent.

(10) Line 166: relative abundances surely?

Following Gloor et al. (2017), our analyses use centered log-ratio (CLR) transformations of read counts, which is the recommended approach for compositional data such as 16S rRNA amplicon read counts. CLR transformations are scale-invariant, so the same ratio is obtained in a sample with few read versus many reads. We now cite Gloor et al. (2017) at line 169 and in the methods in line 517, which reads “centered log ratio (CLR) transformed abundances (i.e., read counts) of each microbial phyla (n=30), family (n=290), genus (n=747), and amplicon sequence variance (ASV) detected in >25% of samples (n=358). CLR transformations are a recommended approach for addressing the compositional nature of 16S rRNA amplicon read count data (Gloor et al. 2017).”  

(11) Lines 167-172: were technical factors, e.g., read depth or sequencing batch, included as random effects?

Thank you for catching this oversight in the text. We did model sequencing depth and batch effects. The sentence starting at line 173 now reads, “For each of these 1,440 features, we tested its association with host age by running linear mixed effects models that included linear and quadratic effects of host age and four other fixed effects: sequencing depth, the season of sample collection (wet or dry), the average maximum temperature for the month prior to sample collection, and the total rainfall in the month prior to sample collection (Grieneisen et al. 2021; Björk et al. 2022; Tung et al. 2015). Baboon identity, social group membership, hydrological year of sampling, and sequencing plate (as a batch effect) were modeled as random effects.”

(12) Lines 175-180: When discussing how these alpha diversity results relate to previous findings, the authors should be clear about whether they talk about weighted or non-weighted measures of alpha diversity. - also maybe this should be included in the discussion rather than the results? Please consider this when revisiting the manuscript (see how it reads after edits).

Richness is the only unweighted metric, which we now clarify in line 181. We opted to retain the interpretation in the text in its original location to maintain the emphasis in the discussion on the microbiome clock results.

(13) Table S1 is very hard to interpret in the provided PDF format as columns are not presented side-by-side. It is currently hard to check model output for e.g., specific families. This needs to be revisited.

We agree. We believe that eLife’s submission portal automatically generates a PDF for any supplementary item. However, we also include the supplementary tables as an Excel workbook which has the columns presented side-by-side.

(14) Line 184: taxa meaning what? Unclear what authors refer to with this sentence, taxa across taxonomic levels, or ASVs, or what does the 51.6% refer to?

We have edited line 191 to clarify that this sentence refers to taxa at all taxonomic levels (phyla to ASVs).

(15) Line 191: a punctuation mark missing after ref (81).

We have added the missing period at the end of this sentence.

(16) Lines 189-197: this should go into the discussion in my opinion.

We have opted to retain this interpretation, now at line 183.

(17) Lines 215-219: Not sure what this means; do the authors mean features were not restricted to age-associated taxa, ie also e.g., diversity and other taxa-independent patterns were included? If so, the rest of the highlighted lines should be revisited to make this clear, currently to me it is very unclear what 'These could include features that are not strongly age-correlated in isolation' means. Currently, that sounds like some features included were only age-associated in combination with other features, but unclear how this relates to taxa-dependency/taxa-independency.

We agree this was not clear. We have revised line 224 to read, “We included all 9,575 microbiome features in our age predictions, as opposed to just those that were statistically significantly associated with age because removing these non-significant features could exclude features that contribute to age prediction via interactions with other taxa.”

(18) Line 403-407: There is now a paper showing epigenetic clocks can be built with faecal samples, so this argument is not valid. Please revisit in light of this publication: https://onlinelibrary.wiley.com/doi/epdf/10.1111/mec.17330

Thank you for bringing this paper to our attention. We deleted the text that describes epigenetic clocks as invasive, and we now cite this paper in line 450, which reads, “We also hope to measure epigenetic age in fecal samples, leveraging methods developed in Hanski et al. 2024.”

(19) Line 427: a punctuation mark/semicolon missing before However.

We have corrected this typo.

(20) Lines 419-428: I don't quite understand this speculation. Why would the priority of access to food lead to an old-looking gut microbiome? This paragraph needs stronger arguments, currently unclear and also not super convincing.

We agree this was confusing. We have revised this text to clarify the explanation. The text starting at line 424 now reads, “This outcome points towards a shared driver of high social status in shaping gut microbiome age in both males and females. While it is difficult to identify a plausible shared driver, one benefit shared by both high-ranking males and females is priority of access to food. This access may result in fewer foraging disruptions and a higher quality, more stable diet. At the same time, prior research in Amboseli suggests that as animals age, their diets become more canalized and less variable (Grieneisen et al. 2021). Hence aging and priority of access to food might both be associated with dietary stability and old-for-age microbiomes. However, this explanation is speculative and more work is needed to understand the relationship between rank and microbiome age.”

(21) Line 434: remove 'be'.

We have corrected this typo.

(22) Line 478: add information on how samples were collected; e.g., were samples collected from the ground? How was cross-contamination with soil microbiota minimised? Were samples taken from the inner part of depositions? These factors can influence microbiota samples quite drastically so detailed info is needed. Also what does homogenisation mean in this context? How soon were samples freeze-dried after sample collection?

We have expanded our methods with respect to sample collection. This text starts in line 483 and reads, “Samples were collected from the ground within 15 minutes of defecation. For each sample, approximately 20 g of feces was collected into a paper cup, homogenized by stirring with a wooden tongue depressor, and a 5 g aliquot of the homogenized sample was transferred to a tube containing 95% ethanol. While a small amount of soil was typically present on the outside of the fecal sample, mammalian feces contains 1000 times the number of microbial cells in a typical soil sample (Sender, Fuchs, and Milo 2016; Raynaud and Nunan 2014), which overwhelms the signal of soil bacteria in our analyses (Grieneisen et al. 2021). Samples were transported from the field in Amboseli to a lab in Nairobi, freeze-dried, and then sifted to remove plant matter prior to long term storage at -80°C.”

(23) Line 480 onwards: were negative controls included in extraction batches? Were samples randomised into extraction batches?

Yes, we included extraction blanks. These are now described in lines 495-500. This text reads, “We included one extraction blank per batch, which had significantly lower DNA concentrations than sample wells (t-test; t=-50, p < 2.2x10-16; Grieneisen et al. 2021). We also included technical replicates, which were the same fecal sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.”

(24) Were extraction, library prep, and sequencing negative controls included? Is data available?

We included extraction blanks (described above) and technical replicates, which were the same sample sequenced across multiple extraction and library preparation batches. Technical replicates from different batches clustered with each other rather than with their batch, indicating that true biological differences between samples are larger than batch effects.

We have updated the data availability statement to read, “All data for these analyses are available on Dryad at https://doi.org/10.5061/dryad.b2rbnzspv. The 16S rRNA gene sequencing data are deposited on EBI-ENA (project ERP119849) and Qiita (study 12949). Code is available at the following GitHub repository: https://github.com/maunadasari/Dasari_etal-GutMicrobiomeAge”.

(25) Line 562: how were corrected microbiome delta ages calculated? Currently, the authors state x, y and z factors were corrected for, but it is unclear how this was done.

The paragraph starting at line 577 describes how microbiome delta age was calculated. We have made only a few changes to this text because we were not sure which aspects of these methods confused the reviewer. However, briefly, we calculated sample-specific microbiome Dage in years as the difference between a sample’s microbial age estimate, age_m from the microbiome clock, and the host’s chronological age in years at the time of sample collection, age_c. Higher microbiome Dages indicate old-for-age microbiomes, as age_m > age_c, and lower values (which are often negative) indicate a young-for-age microbiome, where age_c > age_m (see Figure 3).

(26) Line 579: typo 'as'.

We have corrected this typo.

Works Cited

Altmann, Jeanne, Laurence Gesquiere, Jordi Galbany, Patrick O Onyango, and Susan C Alberts. 2010. “Life History Context of Reproductive Aging in a Wild Primate Model.” Annals of the New York Academy of Sciences 1204:127–38. https://doi.org/10.1111/j.1749-6632.2010.05531.x.

Anderson, Jordan A, Rachel A Johnston, Amanda J Lea, Fernando A Campos, Tawni N Voyles, Mercy Y Akinyi, Susan C Alberts, Elizabeth A Archie, and Jenny Tung. 2021. “High Social Status Males Experience Accelerated Epigenetic Aging in Wild Baboons.” Edited by George H Perry. eLife 10 (April):e66128. https://doi.org/10.7554/eLife.66128.

Binder, Alexandra M., Camila Corvalan, Verónica Mericq, Ana Pereira, José Luis Santos, Steve Horvath, John Shepherd, and Karin B. Michels. 2018. “Faster Ticking Rate of the Epigenetic Clock Is Associated with Faster Pubertal Development in Girls.” Epigenetics 13 (1): 85–94. https://doi.org/10.1080/15592294.2017.1414127.

Björk, Johannes R., Mauna R. Dasari, Kim Roche, Laura Grieneisen, Trevor J. Gould, Jean-Christophe Grenier, Vania Yotova, et al. 2022. “Synchrony and Idiosyncrasy in the Gut Microbiome of Wild Baboons.” Nature Ecology & Evolution, June, 1–10. https://doi.org/10.1038/s41559-022-01773-4.

Chen, Brian H., Riccardo E. Marioni, Elena Colicino, Marjolein J. Peters, Cavin K. Ward-Caviness, Pei-Chien Tsai, Nicholas S. Roetker, et al. 2016. “DNA Methylation-Based Measures of Biological Age: Meta-Analysis Predicting Time to Death.” Aging (Albany NY) 8 (9): 1844–59. https://doi.org/10.18632/aging.101020.

Claesson, Marcus J., Ian B. Jeffery, Susana Conde, Susan E. Power, Eibhlís M. O’Connor, Siobhán Cusack, Hugh M. B. Harris, et al. 2012. “Gut Microbiota Composition Correlates with Diet and Health in the Elderly.” Nature 488 (7410): 178–84. https://doi.org/10.1038/nature11319.

Galbany, Jordi, Jeanne Altmann, Alejandro Pérez-Pérez, and Susan C. Alberts. 2011. “Age and Individual Foraging Behavior Predict Tooth Wear in Amboseli Baboons.” American Journal of Physical Anthropology 144 (1): 51–59. https://doi.org/10.1002/ajpa.21368.

Gloor, Gregory B., Jean M. Macklaim, Vera Pawlowsky-Glahn, and Juan J. Egozcue. 2017. “Microbiome Datasets Are Compositional: And This Is Not Optional.” Frontiers in Microbiology 8. https://doi.org/10.3389/fmicb.2017.02224.

Grieneisen, Laura E., Mauna Dasari, Trevor J. Gould, Johannes R. Björk, Jean-Christophe Grenier, Vania Yotova, David Jansen, et al. 2021. “Gut Microbiome Heritability Is Nearly Universal but Environmentally Contingent.” Science 373 (6551): 181–86. https://doi.org/10.1126/science.aba5483.

Hanski, Eveliina, Susan Joseph, Aura Raulo, Klara M. Wanelik, Áine O’Toole, Sarah C. L. Knowles, and Tom J. Little. 2024. “Epigenetic Age Estimation of Wild Mice Using Faecal Samples.” Molecular Ecology 33 (8): e17330. https://doi.org/10.1111/mec.17330.

Heintz, Caroline, and William Mair. 2014. “You Are What You Host: Microbiome Modulation of the Aging Process.” Cell 156 (3): 408–11. http://dx.doi.org/10.1016/j.cell.2014.01.025.

Horvath, Steve. 2013. “DNA Methylation Age of Human Tissues and Cell Types.” Genome Biology 14 (10): R115. https://doi.org/10.1186/gb-2013-14-10-r115.

Jayashankar, Lakshmi, Kathleen M. Brasky, John A. Ward, and Roberta Attanasio. 2003. “Lymphocyte Modulation in a Baboon Model of Immunosenescence.” Clinical and Vaccine Immunology 10 (5): 870–75. https://doi.org/10.1128/CDLI.10.5.870-875.2003.

Marioni, Riccardo E., Sonia Shah, Allan F. McRae, Brian H. Chen, Elena Colicino, Sarah E. Harris, Jude Gibson, et al. 2015. “DNA Methylation Age of Blood Predicts All-Cause Mortality in Later Life.” Genome Biology 16 (1): 25. https://doi.org/10.1186/s13059-015-0584-6.

O’Toole, Paul W., and Ian B. Jeffery. 2015. “Gut Microbiota and Aging.” Science 350 (6265): 1214–15. https://doi.org/10.1126/science.aac8469.

Raynaud, Xavier, and Naoise Nunan. 2014. “Spatial Ecology of Bacteria at the Microscale in Soil.” PLOS ONE 9 (1): e87217. https://doi.org/10.1371/journal.pone.0087217.

Sadoughi, Baptiste, Dominik Schneider, Rolf Daniel, Oliver Schülke, and Julia Ostner. 2022. “Aging Gut Microbiota of Wild Macaques Are Equally Diverse, Less Stable, but Progressively Personalized.” Microbiome 10 (1): 95. https://doi.org/10.1186/s40168-022-01283-2.

Sender, Ron, Shai Fuchs, and Ron Milo. 2016. “Revised Estimates for the Number of Human and Bacteria Cells in the Body.” PLoS Biology 14 (8): e1002533. https://doi.org/10.1371/journal.pbio.1002533.

Tung, J, L B Barreiro, M B Burns, J C Grenier, J Lynch, L E Grieneisen, J Altmann, S C Alberts, R Blekhman, and E A Archie. 2015. “Social Networks Predict Gut Microbiome Composition in Wild Baboons.” Elife 4. https://doi.org/10.7554/eLife.05224.

Weibel, Chelsea J., Mauna R. Dasari, David A. Jansen, Laurence R. Gesquiere, Raphael S. Mututua, J. Kinyua Warutere, Long’ida I. Siodi, Susan C. Alberts, Jenny Tung, and Elizabeth A. Archie. 2024. “Using Non-Invasive Behavioral and Physiological Data to Measure Biological Age in Wild Baboons.” GeroScience 46 (5): 4059–74. https://doi.org/10.1007/s11357-024-01157-5.

Author response:

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

Reviewer #1 (Public review):

This paper examines the role of MLCK (myosin light chain kinase) and MLCP (myosin light chain phosphatase) in axon regeneration. Using loss-of-function approaches based on small molecule inhibitors and siRNA knockdown, the authors explore axon regeneration in cell culture and in animal models from central and peripheral nervous systems. Their evidence shows that MLCK activity facilitates axon extension/regeneration, while MLCP prevents it.

Major concerns:

(1) In the title, authors indicate that the observed effects from loss-of-function of MLCK/MLCP take place via F-actin redistribution in the growth cone. However, there are no experiments showing a causal effect between changes in axon growth mediated by MLCK/MLCP and F-actin redistribution.

Thank you for your comments. We revised the title of our manuscript to “MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin”. (line 3)

(2) The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. MLCK may regulate axon growth independent of NMII activity. However, this has very important implications for the understanding not only on how NMII works and affects axon extension, but also in trying to understand what MLCP is doing. One wonders if MLCP actions, which are opposite of MLCK, also independent of NMII activity? The authors, in the discussion section, try to find an explanation for this finding, but I consider it fails since the whole rationale of the manuscript is still around how MLCK and MLCP affect NMII phosphorylation.

Thank you for your comments. Although both MLCK and MLCP regulate the activity of NMII, it has been reported that they also govern domain-specific spatial control of actin-based motility in the growth cone. Specifically, MLCK activity is essential for arc translocation and retrograde flow within the P domain, while MLCP appears to specifically modulate arc movement and associated myosin II contractility in the T zone and C domain (Ref). Therefore, it is proposed that the regulatory mechanisms of MLCK and MLCP are highly complex during the process of axon growth. 

[Ref]：Xiao-Feng Zhang, Andrew W Schaefer, Dylan T Burnette, Vincent T Schoonderwoert, Paul Forscher. Rho-dependent contractile responses in the neuronal growth cone are independent of classical peripheral retrograde actin flow. Neuron. 2003 Dec 4;40(5):931-44.

What follows is a discussion of the merits and limitations of different claims of the manuscript in light of the evidence presented.

(1) Using western blot and immunohistochemical analyses, authors first show that MLCK expression is increased in DRG sensory neurons following peripheral axotomy, concomitant to an increase in MLC phosphorylation, suggesting a causal effect (Figure 1). The authors claim that it is common that axon growth-promoting genes are upregulated. It would have been interesting at this point to study in this scenario the regulation of MLCP.

We thank Reviewer for the positive comment on our manuscript.

(2) Using DRG cultures and sciatic nerve crush in the context of MLCK inhibition (ML-7) and down-regulation, authors conclude that MLCK activity is required for mammalian peripheral axon regeneration both in vitro and in vivo (Figure 2). In parallel, the authors show that these treatments affect as expected the phosphorylation levels of MLC.

The in vitro evidence is of standard methods and convincing. However, here, as well as in all other experiments using siRNAs, no Control siRNAs were used. Authors do show that the target protein is downregulated, and they can follow transfected cells with GFP. Still, it should be noted that the standard control for these experiments has not been done.

Thank you for your comments. We utilized scrambled siRNA as a control. I sincerely apologize for the oversight in the manuscript; although we mentioned that scrambled siRNA was used as a control in the figure legends, we failed to clearly articulate this important information in the methods section. We have revised the manuscript accordingly. (line 87, line 549, line, line 562, line 568).

(3) The authors then examined the role of the phosphatase MLCP in axon growth during regeneration. The authors first use a known MLCP blocker, phorbol 12,13-dibutyrate (PDBu), to show that is able to increase the levels of p-MLC, with a concomitant increase in the extent of axon regrowth of DRG neurons, both in permissive as well as non-permissive substrates. The authors repeat the experiments using the knockdown of MYPT1, a key component of the MLC-phosphatase, and again can observe a growth-promoting effect (Figure 3).

The authors further show evidence for the growth-enhancing effect in vivo, in nerve crush experiments. The evidence in vivo deserves more evidence and experimental details (see comment 2). A key weakness of the data was mentioned previously: no control siARN was used.

Thank you for your comments. As mentioned above, we used scramble siRNA as control in vivo experiment as well.

(4) In the next set of experiments (presented in Figure 4) authors extend the previous observations in primary cultures from the CNS. For that, they use cortical and hippocampal cultures, and pharmacological and genetic loss-of-function using the above-mentioned strategies. The expected results were obtained in both CNS neurons: inhibition or knockdown of the kinase decreases axon growth, whereas inhibition or knockdown of the phosphatase increases growth. A main weakness in this set is that drugs were used from the beginning of the experiment, and hence, they would also affect axon specification. As pointed in Materials and Method (lines 143-145) authors counted as "axons" neurites longer than twice the diameter of the cell soma, and hence would not affect the variable measured. In any case, to be sure one is only affecting axon extension in these cells, the drugs should have been used after axon specification and maturation, which occurs at least after 5 DIV.

Thank you for your comments. We acknowledge that the early administration of drugs can lead to unintended effects on neuronal polarization and axon formation. However, in line with our previous publication, we focused exclusively on measuring the longest length of the axon. To quantify axon length, we selected neurons exhibiting an axonal process exceeding twice the diameter of their cell body and measured the longest axon from 100 neurons for each condition (Ref 1, Ref 2). Consequently, we believe that drug administration at the onset of cell culture influences axon formation; however, it does not significantly affect the drug's impact on axon length.

[Ref 1]: Chang-Mei Liu, Rui-Ying Wang, Saijilafu, Zhong-Xian Jiao, Bo-Yin Zhang, Feng-Quan Zhou. MicroRNA-138 and SIRT1 form a mutual negative feedback loop to regulate mammalian axon regeneration. Genes Dev. 2013 Jul 1;27(13):1473-83.

[Ref 2]: Eun-Mi Hur, Saijilafu, Byoung Dae Lee, Seong-Jin Kim, Wen-Lin Xu, Feng-Quan Zhou. GSK3 controls axon growth via CLASP-mediated regulation of growth cone microtubules. Genes Dev. 2011 Sep 15;25(18):1968-81.

(5) In Figure 7, the authors a local cytoskeletal action of the drug, but the evidence provided does not differentiate between a localized action of the drugs and a localized cell activity.

We appreciate the reviewer’s insightful comments and have revised our title to “MLCK/MLCP Regulates mammalian axon regeneration and redistributes growth cone F-actin.” Furthermore, we have made corresponding revisions to the manuscript (line31, line 73).

References:

(1) Eun-Mi Hur 1, In Hong Yang, Deok-Ho Kim, Justin Byun, Saijilafu, Wen-Lin Xu, Philip R Nicovich, Raymond Cheong, Andre Levchenko, Nitish Thakor, Feng-Quan Zhou. 2011. Engineering neuronal growth cones to promote axon regeneration over inhibitory molecules. Proc Natl Acad Sci U S A. 2011 Mar 22;108(12):5057-62. doi: 10.1073/pnas.1011258108.

(2) Garrido-Casado M, Asensio-Juárez G, Talayero VC, Vicente-Manzanares M. 2024. Engines of change: Nonmuscle myosin II in mechanobiology. Curr Opin Cell Biol. 2024 Apr;87:102344. doi: 10.1016/j.ceb.2024.102344.

(3) Karen A Newell-Litwa 1, Rick Horwitz 2, Marcelo L Lamers. 2015. Non-muscle myosin II in disease: mechanisms and therapeutic opportunities. Dis Model Mech. 2015 Dec;8(12):1495-515. doi: 10.1242/dmm.022103.

Reviewer #2 (Public review):

Summary:

Saijilafu et al. demonstrate that MLCK/MLCP proteins promote axonal regeneration in both the central nervous system (CNS) and peripheral nervous system (PNS) using primary cultures of adult DRG neurons, hippocampal and cortical neurons, as well as in vivo experiments involving sciatic nerve injury, spinal cord injury, and optic nerve crush. The authors show that axon regrowth is possible across different contexts through genetic and pharmacological manipulation of these proteins. Additionally, they propose that MLCK/MLCP may regulate F-actin reorganization in the growth cone, which is significant as it suggests a novel strategy for promoting axonal regeneration.

Strengths:

This manuscript presents a wide range of experimental models to address its hypothesis and biological question. Notably, the use of multiple in vivo models significantly enhances the overall validity of the study.

We thank Reviewer for the positive comment on our manuscript.

Weaknesses:

- The authors previously published that blocking myosin II activity stimulates axonal growth and that MLCK activates myosin II. The present work shows that inhibiting MLCK blocks axonal regeneration while blocking MLCP (the protein that dephosphorylates myosin II) produces the opposite effect. Although this contradiction is discussed, no new evidence has been added to the manuscript to clarify this mechanism or address the remaining questions. Critical unresolved questions include: what happens to myosin II expression when both MLCK and MLCP are inhibited? If MLCK/MLCP are acting through an independent mechanism, what would that mechanism be?

- In the discussion, the authors mention the existence of two myosin II isoforms with opposing effects on axonal growth. Still, there is no evidence in the manuscript to support this point.

- It is also unclear how MLCK/MLCP acts on the actin cytoskeleton. The authors suggest that proteins such as ADF/cofilin, Arp 2/3, Eps8, Profilin, Myosin II, and Myosin V could regulate changes in F-actin dynamics. However, this study provides no experimental evidence to determine which proteins may be involved in the mechanism.

Thank you for your comments. Axon growth is an exceptionally intricate process, facilitated by the coordinated regulation of gene expression in the soma, axonal transport along the shaft, and the assembly of cytoskeletal elements and membrane proteins at the growth cone. In this paper, our results primarily demonstrate that MLCK/MLCP plays a crucial role in regulating mammalian axon regeneration and redistributing F-actin within the growth cone; however, we did not investigate which specific proteins act downstream of MLCK/MLCP during axon regeneration.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

- A title more suitable for the evidence shown can be: MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin.

Thank you for your comments. We revised the title of our manuscript to“MLCK/MLCP regulates mammalian axon regeneration and redistributes the growth cone F-actin” (line 3).

-In figure 3, It would be useful to indicate in the figure legend, that the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site.

Thank you for your comments. We revised Figure 3 legend that indicates the red arrow is pointing to a suture that was performed during surgery to mark clearly the injury site (line 571-572).

- The following is a concern raised in the previous round, and that the response by the authors was so complete and accurate that I consider it would be useful to include it in the discussion section.

Thank you for your comments. We included those contents in the discussion section of our revised manuscript (line 348-354, line 355-359).

The author combines MLCK inhibitors with Bleb (Figure 6), trying to verify if both pairs of inhibitors act on the same target/pathway. The rationale is wrong for at least two reasons.

a- Because both lines of evidence point to contrasting actions of NMII on axon growth, one approach could never "rescue" the other.

Reply by authors in R1：If MLCK regulates axon growth through the activation of Myosin, the inhibitory effect of ML-7 (an MLCK inhibitor) on axon growth might be influenced by Bleb, a NMII inhibitor. However, our findings reveal that the combination of Bleb and ML-7 does not alter the rate of axon outgrowth compared to ML-7 alone. This suggests that the roles of ML-7 and Bleb in axon growth are independent. It means MLCK may regulate axon growth independent of NMII activity.

b- Because the approaches target different steps on NMII activation, one could never "prevent" or rescue the other. For example, for Bleb to provide a phenotype, it should find any p-MLC, because it is only that form of MLC that is capable of inhibiting its ATPase site. In light of this, it is not surprising that Bleb is unable to exert any action in a situation where there is no p-MLC (ML-7, which by inhibiting the kinase drives the levels of p-MLC to zero, Figure 4A). Hence, the results are not possible to validate in the current general interpretation of the authors. (See 'major concern').

Reply by authors in R1: The reported mechanism of blebbistatin is not through competition with the ATP binding site of myosin. Instead, it selectively binds to the ATPase intermediate state associated with ADP and inorganic phosphate, which decelerates the phosphate release. Importantly, blebbistatin does not impede myosin's interaction with actin or the ATP-triggered disassociation of actomyosin. It rather inhibits the myosin head when it forms a product complex with a reduced affinity for actin. This indicates that blebbistatin functions by stabilizing a particular myosin intermediate state that is independent of the phosphorylation status of myosin light chain (MLC).

[Ref] Kovács M, Tóth J et al. Mechanism of blebbistatin inhibition of myosin II. J Biol Chem. 2004 Aug 20;279(34):35557-63.

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

Learn more at Review Commons

Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

In this manuscript, the authors highlight the importance of the Golgi apparatus during SARS-CoV-2 infection. Specifically, using different compounds able to alter Golgi structure and function, the authors show a strong reduction in SARS-CoV-2 infection rate. In particular it is interesting to observe that treatments of 24 hrs with BFA strongly impair viral infection, highlithing the importance of Golgi function for this virus. Albeit the time of treatment is different. this observation is in contrast with previous studies on related coronaviruses (Ghosh et al., 2020) that did not observe any effect upon treatment with BFA. This might imply that SARS-CoV-2 relies more on conventional trafficking pathways respect to other coronaviruses which, under certain conditions, favour different trafficking routes.

We thank the reviewer for the positive comments. Indeed, our results with BFA treatment for 24 hours are inconsistent with previous studies based on the prototype coronavirus MHV (Ghosh et al., 2020). To validate this observation, we have now performed new experiments with BFA treatment for 4, 6, and 8 hours, matching the time points used in the previous study (Ghosh et al, 2020). Our new results show that BFA treatment at these early time points significantly inhibits SARS-CoV-2 assembly and secretion, as measured by immunoblotting and TCID50 assays, without reducing intracellular viral RNA levels, which serve as a marker of genome replication. This implies that Golgi function and an intact ER-to-Golgi trafficking route are required for SARS-CoV-2 assembly and secretion. These new results are now presented as new Fig. 2C-H.

The authors additionally observed that viral infection increases TGN46 levels while decreasing GRASP55 levels. To dissect the role of TGN46 and GRASPR55, the authors performed several infection studies in cells in which the levels of the two proteins were modulated either by overexpression (GRASP55) and/or siRNA-mediated knock-down (GRASP55 and TGN46). Those approaches suggest that GRASPR55 overexpression, a protein essential for Golgi stack formation, decelerates viral trafficking and inhibits viral assembly while its depletion reverses the effects. On the other hand, TGN46 knock-down impairs viral trafficking but not assembly. Overall the study clearly shows the importance of the Golgi during SARS-CoV-2 and also shows that modulation of those two factors affect viral infection.

We appreciate the reviewer's accurate summary of our work and positive comments.

However the claims that specifically the trafficking (TGN46) and trafficking and assembly (GRASP55) are not fully substantiated. Regarding GRASP55, the authors state that viral infection decreases GRASPR55 levels and this results in Golgi fragmentation. However GRASPR55 levels decrease is shown at 24 hrs post infection while Golgi fragmentation occurs as early as 5 hrs. Thus there might be no direct casual effect between the two effects.

We agree with the reviewer that GRASP55 downregulation is unlikely to be the only reason for Golgi fragmentation in the infected cells. In our results, 5- or 8-hour post infection caused only mild Golgi fragmentation (Fig. S6D), while 24 hours post infection led to severe Golgi fragmentation. On the other hand, GRASP55 is likely to play a relevant role as SARS-CoV-2 induced Golgi fragmentation can be partially rescued by exogenous GRASP55 expression (Fig S6C). We have modified the text in lines 303-305 accordingly to acknowledge the possibility that other factors also contribute to Golgi fragmentation in infected cells.

Additionally, the authors show that overexpression of GRASP55 rescue Golgi fragmentation, as observed by imaging, however is not clear if only infected cells where quantified and if they had the same level of infection.

Yes, only infected cells with either GFP or GRASP55-GFP expression were quantified. The viral infection rate was significantly lower in GRASP55-GFP expressing cells compared to GFP expressing cells (Fig 5A-B).

The authors exclude and effect on entry based on experiment on Spike expressing pseudovirus in 293-ACE2, however they also clearly observe reduction of ACE2 on the membrane of GRASPR55 expressing cells (Fig S6B). Thus how can they explain this discrepancy and how ca defect in entry can be fully marked out in these cell lines?

We thank the reviewer for pointing this out. This discrepancy is likely due to the different systems used in the two experiments.

In the pseudovirus entry assay, ACE2 was exogenously expressed in 293T cells and GRASP55 expression did not show any effect on the viral entry efficiency. In contrast, Huh7-ACE2 cells were selected for a high surface expression of ACE2. While GRASP55 expression reduces surface ACE2 levels as shown in our cell surface biotinylation assay, we believe that the surface ACE2 levels in GRASP55-expressing cells remain sufficient to support viral entry. To further investigate whether GRASP55 expression affects viral entry using authentic SARS-CoV-2, we performed RT-qPCR analysis of intracellular RNA level of the spike, N, and RdRp in both GFP and GRASP55-GFP expressing cells 4 hours post infection (new Fig 5D). Our results show that GRASP55 expression does not affect SARS-CoV-2 entry efficiency, even though it reduces ACE2 surface expression levels.

It is not clear to which process the authors refer to when they write about "viral trafficking". Is it virion trafficking or viral proteins trafficking? The two process are linked but are not the same. This oversemplification can be misleading. For instance the authors show that overexpression of GRASP55 decreases Spike protein on the plasma membrane and its depletion increases S protein incorporation into psudoviruses. However it was shown that in infected cells S protein is mainly retained at the ERGIC by M and E (Boson et al., 2021) where viral assembly occurs. Thus an increase in S trafficking on the PM does not correlate with an increase in virion trafficking,

We agree with the reviewer that our use of the term "viral trafficking" is imprecise and we have changed this throughout the manuscript to be more specific. S trafficking to the PM may not necessarily be equal to an increase in virion trafficking and thus have rephrased these terms in our writing accordingly.

We acknowledge that our cell surface biotinylation assay results only demonstrate that GRASP55 overexpression slows down spike protein trafficking to the PM. We have accordingly also examined viral protein and infectious particle secretion into the culture medium as a more direct readout of virion trafficking (new Fig 2E, 2H, 6K, and 7P).

Finally, we have removed all of the data describing spike incorporation into pseudoviruses as we acknowledge that plasma membrane assembly of lentiviruses is not a good model for SARS-CoV-2 assembly.

...and ultimately, the data provided do not fully support the authors claim on a modulation of "virion trafficking" in response to GRASP or TGN46 changes, since no experiments clearly show a change in virions secretion.

In response to the above comment, we provide the following clarification: Our Western blotting, TCID50 assay, and plaque assay results collectively demonstrate that SARS-CoV-2 virion secretion is reduced in GRASP55 expressing cells (new Fig 5E-M) and in TGN46-depleted cells (new Fig 7F-H, 7L-N). Conversely, viral assembly and secretion appear to be increased in GRASP55-depleted cells (new Fig 6A, 6E-I) at 24 hpi. Furthermore, within a single viral secretion cycle (10 hpi), GRASP55 depletion increased viral secretion (new Fig 6K), while TGN46 depletion reduced viral secretion (new Fig 7P). These findings strongly support the conclusion that GRASP55 and TGN46 modulate viral secretion.

Importantly, the authors do not rule out potential effects of their perturbations on genome replication. The only experiment that they perform in this direction is presented in Fig. S7B, where the authors show similar percentage of infected cells at early stage upon silecing of GRASPR55. The experiment suggests that productive entry is similar in these conditions, but quantification of intracellular viral genome could exclude a change in viral replication. If no changes in viral replication are observed, the authors could verify an increase in particles secretion by collecting supernatants from the early time points and performing plaque assays and quantification of viral genomes by qRT-PCR, to prove that modulation of GRASPR55 indeed promote SARS-CoV-2 trafficking.

We thank the reviewer for the excellent suggestions. In response, we performed RT-qPCR analysis in GRASP55-expressing and TGN46-depleted cells at 4 hpi to compare the viral genome replication process. Additionally, we performed western blotting analysis and released viral titer assay of the culture media from both GRASP55-depleted and TGN46-depleted cells at 10 hpi to investigate virion release. Our new results show that GRASP55 depletion increases viral secretion (new Fig. 6K), while TGN46 depletion reduces viral secretion (new Fig. 7P). Furthermore, GRASP55 expression and TGN46 depletion do not perturb viral genome replication (new Fig. 5D and new Fig. 7R).

Finally, whenever reduction of viral infection is observed upon cell partubation, a robust analysis of cell viability should be presented to exclude pleiotropic effects. Expecially in presence of multiple pertubation that might affect cell metabolism. The authors should carefully control cell viability and growth in response to depletion of TGN46 and GRASP55.

We thank the reviewer for the excellent suggestions, which were also pointed out by reviewer #3. To address this, we performed the LDH cytotoxicity assay under SARS-CoV-2 infection conditions with TGN46 depletion and GRASP55 depletion/expression (new Fig. 5C, 6L, 7Q). Our new results show that no significant cell death was induced by TGN46 depletion, GRASP55 depletion/expression, or other perturbations.

Minor: show data on viability of the drug and add the relative section in Material and Methods.

We performed LDH assays of SARS-CoV-2 infected Huh7-ACE2 cells treated with 9 small molecules, and LDH release levels were similar across all treatments (new Fig. S3C). Additionally, a CellTiter Glo viability assay of 293T-ACE2 cells did not show any significant effect of cell viability with small molecule treatment (new Fig S3F). Detailed descriptions of these assays have been included in the Material and Methods section.

Figure 3A: should read spike and not nucleocapsid eported for SARS-CoV-2

Fig. 3A labeling is correct - cells were labeled with antibodies for GRASP65 (rabbit) and for nucleocapsid (mouse).

Lack of inhibition with camostat correlates with lack of TMPRSS2 in the Huh7. The sentence seems to be too general while in this case the effect is clearly cell specific. Similarly, the importance of the lysosome in viral entry is restricted to cells lacking TMPRSS2 and cannot be generalized since CQ, for example, does not work in Calu-3 cells that express TMPRSS2 cells.

We agree with the reviewer and have added one sentence: The relative smaller effect of camostat mesylate observed here, compared to previous studies (Hoffmann et al, 2021), might be due to the use of different cell lines across studies in lines 182-184. We also discussed the discrepancy of CQ treatment between our Huh7-ACE2 cells and Calu-3 cells (Hoffmann et al, 2020) in lines 466-473.

Typo: Fig S3B - Y axis should reat viral not vrial

Thank you - we have corrected this.

S3C: concentrations of the compound used in the assay should be reported. Was a viability assay performed also in the 293T-ACE2 cell line?

We thank the reviewer for the suggestion. We have added the concentration information to the legend in Fig. S3E "Cell entry assay of 293T or 293T-ACE2 cells by SARS-CoV-2 Spike pseudotyped lentivirus for 24h in the presence of indicated molecules at the same concentrations as in Fig. 2A." Additionally, we performed a CellTiter Glo assay to assess the viability of 293T-ACE2 cells treated with the 9 molecules. The results demonstrate that treatment with these 9 molecules does not alter cell viability (Fig. S3F).

Significance

Overall, the major strenght of the manuscript is that it has clarified the importance of the Golgi during SARS-CoV-2 infection. The drugs screening demonstrate that for SARS-CoV-2 the conventional secretion seems to have major role respect to other secretory routes observed for other coronaviruses. Also it is clear that the two factors identified by the authors have a role in viral infection, however the major limitation is that the authors failed to clearly highlight which step/s of the viral life cycle are modulated upon GRASP55 and TGN46 perturbatio. Expecially the claims on "trafficking" is not fully substantiated, since the only experiment in this direction is the transport of Spike protein on the plasma membrane upon GRASPR55 overexpression. It is risky to conclude that the trafficking of a single protein reflect the intracellular trafficking of the virions.

Several of the finding presented in the first part of the manuscript have been already previously reported (for example the fragmentation of the Golgi upon SARS-CoV-2 infection), however the role of GRASP55 and TGN46 in SARS-CoV-2 infection has been reported here for the first time. This manuscript can be of interest for a broad audience considering the topic (cell biology, host-pathogen interactions and molecular virology)

My expertise reside in the field of molecular virology, expecially in the contest of the mechanisms of viral replication and host-pathogen interactions.

We thank the reviewer for the overall positive comments and excellent suggestions. We hope that our new results have convincingly demonstrated that viral trafficking is regulated by GRASP55 and TGN46.

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

Summary: In this study, Zhang and colleagues address the impact on SARS-CoV-2 infection on the morphology of the Golgi apparatus and convincingly demonstrate a fragmentation of this organelle in infected cells. Conversely, they show that the modulation of TGN46 or GRASP55 expressions, two components of this organelle impact SARS_CoV-2 replication. By monitoring the relative levels of viral Spike and nucleocapsid in the cell supernatants, they conclude that GRASP55 regulates particle assembly and trafficking while TGN46 controls only secretion. The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were robustly quantified. I believe that this study is potentially interesting and relevant for the SARS-CoV-2 community since providing an extensive characterization of the interplay between SARS-CoV-2 and the Golgi apparatus.

We thank the reviewer for the positive comments.

However, as described below, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery used for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study, especially without clear molecular mechanisms about the interplay between SARS-CoV-2 proteins and TNG46/GRASP55.

We rephrased some sentences following the reviewer's suggestions. Although it was believed that SARS-CoV-2 is assembled at the ERGIC, there has been significant controversy surrounding the virion secretion pathway. Our results strongly support that SARS-CoV-2 virions traffic through the Golgi apparatus and that an intact ER-to-Golgi trafficking pathway is essential for SARS-CoV-2 assembly and secretion. Manipulation of two Golgi-resident proteins, GRASP55 and TGN46, significantly regulates SARS-CoV-2 secretion. Interestingly, GRASP55 regulates both assembly and secretion of SARS-CoV-2, while TGN46 exclusively modulates viral secretion. This is consistent with their subcellular localization, as GRASP55 is localized to the medial/trans Golgi, whereas TGN46 is localized to the TGN. We hope that our new experimental results (Figs. 2C-H, 5C-D, 6J-L, and 7O-R) have addressed all concerns from the reviewer. Identification of downstream protein targets involved in TGN46/GRASP55-mediated modulation of SARS-CoV-2 trafficking will be the focus of our future studies.

Major comments: -All the assays have been performed in liver-derived Huh7 cells (overexpressing SARS-CoV-2 receptor) ACE2 (for infection) or kidney 293 cells (for pseudotyped HIV entry assays). However, no conclusion was validated in lung-derived cells (like A549-ACE2, Calu-3 or primary cells), which would be important since the respiratory tract is the main target of SARS-CoV-2

In our study, Huh7-ACE2 cells are sorted for the high expression of endogenous ACE2 protein, and we did not overexpress ACE2 protein. Also, the liver has been reported to be a site of SARS-CoV-2 infection in humans (Barnes, 2022). We did use A549 and Calu-3 cells in pilot experiments; A549 cells displayed infection rates that were too low for our purposes, and Calu-3 cells showed both low infection rates and relatively disorganized Golgi in the absence of viral infection. We were able to add new IF results from Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). We also confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig S5K-N).

-Fig2: The impact of the drugs on replication was assessed by measuring the % of infected cells. At 24 hpi, I am unsure about what this value is supposed to measure (the whole life cyle, intracellular replication or spread?), especially since it is not indicated when the drugs were added to the cells. Was it during, before or after the infection? This information should be provided.

Fig. 2 refers to infection, not replication. We agree that infection encompasses multiple steps of the viral cycle. In our experiments, cells were treated with the drugs immediately before viral infection. We have added the information into the Fig. 2 legend.

If the "Golgi" drugs impact egress only (as inferred by the genetic modulation phenotypes), I would expect that at this early time point, the % of infection would not drastically change (as well as intracellular RNA) but that the extracellular infectious titers would decrease. Plaque assays (or TCID50 assays) and RT-qPCR on intracellular viral RNA should be conducted to better understand the impact of drug treatments.

This is a great suggestion! As the reviewer expected, our new BFA time-point assay shows that at early time points, the intracellular RNA levels for S, N and RdRp are not reduced. However, the extracellular N protein (measured by WB) and virial titer (measured by TCID50 assay), which serve as readouts for virion secretion, are significantly decreased (new Fig. 2C-H).

On page 10, it is said that the virus makes three cycles of replication within 24 hours following infection. On what data is this based? This seems a lot. If this is true (and shown in Huh7-ACE2 cells), does the assay of figure 2 measure spread in general? More importantly, despite mentioned, the cell viability data are not provided. It is important to show them to ensure that these concentrations of drugs are not toxic at the tested concentrations.

It has been reported that a single cycle of SARS-CoV-2 infection is approximately 8 hours (Eymieux et al, 2021). Therefore, Fig. 2 represents a multicycle infection, reflecting a composite measure of viral infection and spread. Under the microscope, we did not observe dramatic cell death at the tested concentration. To further assess cytotoxicity, we performed a cell toxicity assay for the 9 small molecules that inhibit viral infection of Huh7-ACE2 cells. The results show that no or minor cell death was observed with all these compounds (Fig. S3C).

-I appreciated the extensive confocal microscopy analysis performed by the authors, which seems of high quality and overall, very convincing. They clearly show that SARS-CoV-2 infection induces the fragmentation of the Golgi apparatus although it was reported by others before as mentioned by the authors.

We thank the reviewer for the positive comments. We agree that Golgi fragmentation was observed during SARS-CoV-2 infection, as we mentioned. However, our study provides a comprehensive and systematic analysis of the entire host cell endomembrane system in the response to viral infection.

However, it was hard for me to make the functional link between these data and those related to GRASP55 and TGN46 overexpression/knockdown. First, the authors should assess the morphology of the Golgi apparatus in Huh7-ACE2 when GRASP55 is knocked down/out or when TGN46 is overexpressed. Second, in these 2 conditions that favor replication, it should be assessed whether this correlates with Golgi fragmentation. Even if this was probably shown before, it is relevant to show that these genetic modulations induce Golgi reshaping in this particular cell type by confocal microscopy (and ideally electron microscopy).

Thank you for the suggestion. We performed IF analysis to assess Golgi morphology in Huh7-ACE2 cells under conditions of GRASP55 knockdown or TGN46 overexpression. Our results show that GRASP55 depletion disrupts Golgi structure (Fig. S7D), whereas TGN46 expression does not significantly alter the Golgi morphology (Fig. S8D).

-The fact that GRASP55-GFP expression decreases in 293T the cell surface levels of ACE2, the receptor of Spike (Fig S6), raises concern that the effect of GRASP55 is not specific to the virus and suggests that the whole secretory pathway is altered, while an impairment of virus entry should be expected in this cell line. Is there a similar trend in Huh7-ACE2?

Reviewer 1 raised a similar question regarding viral entry efficiency. Fig. S6B, performed in Huh7-ACE2 cells, shows that GRASP55-GFP expression also decreases ACE2 surface level in these cells. To further assess whether GRASP55 expression affects viral entry, we performed RT-qPCR analysis of viral RNA at early time points of infection. We found that authentic SARS-CoV-2 entry efficiency was not altered by GRASP55 expression (new Fig. 5D). Although GRASP55 overexpression does alter the secretory pathway, we want to point out that SARS-CoV-2 infection downregulates endogenous GRASP55 expression. We have used GRASP55 overexpression as a probe to assess the effects of GRASP55 on the secretory pathway and on SARS-CoV-2 virion trafficking, but this does not actually reflect what is observed in SARS-CoV-2 infection.

In addition to addressing the functionality of the secretory machinery in Huh7-ACE2, it would be relevant to repeat the cell surface labelling in the context of pseudotyped virus production with other viral envelopes such as VSV G protein or HIV gp41/gp120. If the phenotype is specific to Spike trafficking, the cell surface abundance of these alternative viral proteins should not be impacted by GRASP55 overexpression. Otherwise, this would indicate a general effect of on the secretory pathway. Besides, since HIV Gag is directed directly to the plasma membrane during particle assembly without entering the secretory pathway, I am not convinced that upstream alteration on nucleocapsid assembly at the ERGIC should be excluded. Indeed, changes on the S/N ratios are generally mild and I feel that this cannot explain the phenotypes in the extracellular infectious titers.

We have removed the original figure because we acknowledge that HIV Gag is directed directly to the plasma membrane, which is different from the trafficking of SARS-CoV-2 spike protein. We appreciate the reviewer's recognition of the difference in extracellular infectious titers between GFP and G55-GFP expressing cells. We hypothesize that GRASP55 expression not only reduces the number of spikes on each virion but also inhibits the secretion of SARS-CoV-2, resulting in a significantly lower extracellular infectious titer. We agree that it would be interesting to test whether GRASP55 expression affects viral production with other viral envelopes. However, this is beyond the scope of the current study and represents a promising direction for future research.

More generally, the comparison between trafficking and assembly should be better assessed and not simply based on extracellular N and S levels. It was hard to see the differences between the two in terms of phenotypes. The authors should at least measure the intracellular infectivity upon TGN46 and GRASP55 knock/down and overexpression as well as intracellular vRNA abundance as a readout of RNA replication (which is anticipated to remain unchanged).

We thank the reviewer for the valuable suggestions. We performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 depletion (new Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi). Also, we found that GRASP55 depletion increased intracellular infectivity (new Fig. 6J) while TGN46 depletion did not affect intracellular infectivity (new Fig. 7O), suggesting that GRASP55 modulates viral assembly but TGN46 does not.

-Finally, mechanistic insight about the viral determinants regulating the morphology of the Golgi would significantly strengthen the study.

Fig S6 shows that S expression decreases ACE2 surface levels? If so, could some S mutants be tested? Does it correlate with Golgi fragmentation? Do other viral structural proteins contribute to Golgi morphological alterations?

We thank the reviewer for the suggestions. These are indeed interesting experiments, but we believe that investigating viral determinants of Golgi fragmentation should be pursued by future studies.

In the same line of idea, how GRASP55 and TGN46 regulate replication. The link with Golgi morphology is unclear. Are these proteins hijacked by SARS-COV-2?

Our new data in this revised manuscript more clearly define the stages in the viral infection cycle that are modulated by GRASP55 and TGN46. New Fig. 5D and Fig. 7R show that neither GRASP55 nor TGN46 affects viral entry or early viral replication. However, GRASP55 perturbation modulates viral assembly and secretion, while TGN46 perturbation affects virion secretion but not assembly. Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. The mechanisms by which GRASP55 and TGN46 are hijacked by SARS-CoV-2 will be explored in the future studies.

Page 13 mentions some relevant mutants that could be assessed in this context and provide mechanistic insights.

It would be interesting to investigate the effects of GRASP55 mutants or specific domains on SARS-CoV-2 trafficking, which we plan to explore in future studies.

Minor comments: -The signal of calreticulin in Fig. S1 is too low to appreciate it distribution.

We have increased the intensity of calreticulin staining for both uninfected and infected cells in parallel in Fig. S1. Thank you.

-Fig 4K, Q: The differences in LC3 forms levels are not convincing. These results do not allow to draw any conclusion about autophagy, especially considering that this was done at steady-state and that the autophagic flux was not measured. Indeed, a bafilomycin A treatment control would be required to measure the real induction of autophagosomes. Lysosomal degradation inhibition allows the detection of LC3 accumulation.

We agree that additional experiments are needed to demonstrate autophagic flux alteration by SARS-CoV-2. We observed an increase in LC3II/LC3I ratio in infected cells at steady state and did not explore this further, since this is not our main focus of this study. Therefore, we have removed the LC3 blots and quantification from Figs. 4 and S5.

-In the GRASP55 overexpression and TGN46 knockdown studies, associated cell viability should be measured to control that that these genetic manipulations do not induce any cytotoxicity which may impact viral replication.

We appreciate the reviewer's suggestions. We performed the LDH cytotoxicity assay under SARS-CoV-2 infection with TGN46 depletion or GRASP55 expression. Our new results show that TGN46 depletion or GRASP55 depletion/expression did not induce significant cell death (Figs. 5C, 6L, and 7Q).

-The authors should test the impact of GRASP55 and GRASP65 knock-out on SARS-CoV-2 replication

Investigating the genetic GRASP55 knockout effect on SARS-CoV-2 replication would be valuable. However, ACE2 protein expression in our Huh7-ACE2 cells decreases with cell passages, making knockout construction on this background impractical due to low ACE2 levels and poor viral infection rates. We believe that both our GRASP55 overexpression and depletion assays sufficiently support its role in SARS-CoV-2 trafficking. Future studies will explore GRASP55 knockout in different cell lines.

-The authors should provide more details about the USA-WA1/2020 isolate in the Methods section. Is it related to the "Wuhan" strain or the variant which spread globally in early 2020 (with D614G mutation in Spike).

USA-WA1/2020 was isolated from an oropharyngeal swab from a patient who returned from China and developed COVID-19 on January 19, 2020, in Washington, USA. It is related to the "Wuhan" strain but does not have D614G mutation in spike. Additional details have been added to the Methods section.

-Fig 8: The combined modulation of GRASP55 and TGN46 expressions does not really seem additive to me since a 70% decrease of either protein modulation is observed while the combined condition brings this value to 75% in TCID50 assays. This does not bring much insight to the study in my opinion. I would suggest that the authors consider removing this figure.

We agree with the reviewer's recommendation and have removed Fig. 8.

Reviewer #2 (Significance (Required)):

General assessment and advance: The study was generally well performed, and the quality of the microscopy and western blot data is good. It was appreciated that all the phenotypes were quantified extensively. However, I have some concerns regarding the interpretations of some of the key conclusions. Moreover, the fact that it was already described by several groups that Golgi is a key machinery for SARS-CoV-2 virion assembly (ERGIC) and secretion dampens my enthusiasm about the study. In addition, the antiviral activity of several tested drugs was also reported elsewhere. A clear mechanism of how SARS-CoV-2 induces a fragmentation of the Golgi would strengthen the study. In the same line of idea, it is unclear how TGN46 and GRASP55 regulate the late steps of the life cycle. The link between SARS-CoV-2-induced Golgi fragmentation and TGN46/GRASP55 is unclear. In my opinion, the data did not allow to clearly discriminate between virion assembly and egress. I was not convinced that it was not simply due to a general disruption of the secretory pathway (as attested by ACE2 down regulation upon GRASP55 overexpression).

Targeted audience: This study will be of high interest for molecular virologists (not only working on SARS-CoV-2) but could be very well fit into the scope of molecular/cell biology-focused generalist journals

Reviewer expertise: Molecular virology, virus-host interactions (especially involving membranous organelles), SARS-CoV-2, RNA viruses

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

Summary:

Zhang et al. demonstrated in this study that the Golgi apparatus and many other organelles are disturbed by SARS-CoV-2 infection. They focused on the Golgi apparatus and especially on TGN46 and GRASP55 which are both affected differently in their level of expression by the SARS-CoV-2 infection. TGN46 is overexpressed while GRASP55 is decreased in expression. Through different methods overexpression or depletion, the authors nicely demonstrated that modulation of both proteins either increased or decreased particles production. They demonstrated that in absence of GRASP55, SARS-CoV-2 release is increased in the medium. On the contrary, depletion of TGN46 decreases the secretion of SARS-CoV-2 particles.

We thank the reviewer for the accurate summary of our work.

Major comments:

Figure 1: The authors demonstrated that SARS-CoV-2 expression affected the morphology of multiple organelles. Although the results are clear, my concern was that the MOI=1 was really high which indeed would affect the whole cell. To have a less drastic effect on the cell, I would suggest realizing the visualization of some organelles (Golgi, EEA1, Rab7 for example) at a lower MOI=0.1. In addition, it would be nice to verify with a live-dead assay with the MOI=1 if after 24h the cells are still alive, which will confirm that these disturbances are not caused by cells in process of dying.

We thank the reviewer for the excellent suggestions. Investigating how SARS-CoV-2 reshapes subcellular organelles at low MOI (e.g., 0.1) and at different time points would be interesting but is beyond the scope of our study. However, we have performed LDH assay at MOI=1, 2 and 3 for 24 hours to assess cell death. Our results show that LDH release was similar across these conditions (Fig. S5R). We also performed RT-qPCR analysis of Spike, N, and RdRp at early time points of infection. The new results show that neither GRASP55 expression (new Fig. 5D) nor TGN46 expression (Fig. 7R) affects viral RNA abundance at an early infection timepoint (4 hpi).

Figure 2: The results indicated in that panel are really nice. However, the addition of a virus with drugs could increase the proportion of cell death. For the Figure 2C, I propose that the author use a LDH assay to prove that the decrease in infection is not caused by cell death. In addition, a RT-qPCR would be more appropriate to indicate the infection rate and support the microscopy data.

We thank the reviewer for the positive feedback and suggestions. As recommended, we performed an LDH assay to assess cytotoxicity under 9 small molecules treatment of infected cells. Additionally, we performed RT-qPCR analysis for the BFA time-point treatment assay. No significant cell death was observed under these conditions (new Figs. 2D, and S3C).

Figure 3: The authors should have been consistent and add spike instead of nucleocapsid for GalT. According to the figures, Spike seemed to co-localize more with GM130 than Golgin 245. Data analysis of colocalization between Spike and GM130 should be performed to complete the observation. Are no colocalizations of Spike observed with the other Golgi markers?

We agree with the reviewer that it was ideal if spike and GalT were co-stained. Unfortunately, both our spike antibody and GalT antibody are from rabbit, so co-staining could not be done as GM130/spike. We performed colocalization analysis between Spike and GM130, and the results show that GRASP55 expression did enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F). We only co-stained spike with GM130 and Golgin-245 due to the antibody availability.

Figure 4K: While all the experiments were performed at MOI=1, why is the authors using MOI=2 for the immunoblots. Did they have a different result in protein expression for MOI=1 in HuH cells? if so they should show a blot indicating this result.

We did not perform WB to assess protein expression at MOI=1, but our cell toxicity assay showed that there is no significant difference between MOI=2 and MOI=1.

Figure 5: Viral infection should be indicated using RT-qPCR data analysis to support the microscopy observations.

We performed RT-qPCR analysis (new Figs. 2F, 5D, and 7R) and found that BFA treatment did not reduce viral RNA levels at all three time points. Also, GRASP55 expression and TGN46 depletion did not inhibit viral genome RNA levels within one viral infection cycle. Additionally, our new TCID50 assay results support our microscope observation (new Fig. 7O-P). Thanks for the suggestion.

Figure 6: The authors should look at the trafficking of ACE2 and TfR in case of GRASP55 depletion like they did in case of GRASP55 overexpression. It could demonstrate if the virus is using trafficking pathways that are common to the one used by some host receptors to reach the plasma membrane.

Thanks for the excellent suggestion. We performed cell surface biotinylation assay of control and GRASP55-depleted cells. We found that ACE2 and TfR receptor displayed a similar reduction on the cell surface (Fig. S7C), consistent with previous findings that GRASP55 depletion induced Golgi fragmentation and accelerated global conventional protein secretion.

Figure 7: Viral infection assay should also be performed by RT-qPCR. Figure 7H: The immunoblots conditions were performed at MOI=3 this time. The authors should indicate why they did not keep the same MOI conditions. In that case, they should use an intracellular marker for their medium experiment to prove that they isolated proteins that are secreted and not simply released from dead cells. I will also suggest to show LDH assay at MOI=2 and 3 to monitor cell death. Is the Golgi fragmented when GRASP 55 is overexpressed in presence of the virus? Microscopy observations should be performed to reply to this question as it will support their model. The authors suggest that GRASP55 overexpression decreases spike incorporation inside the virion. Can they observe if Spike still colocalizes with GM130 when GRASP55 is overexpressed?

We showed that TGN46 depletion inhibits viral infection by both IF and WB. We further confirmed this through TCID50 assay for both cells and media (new Fig. 7O-P), strengthening our hypothesis.

As we described above, we performed morphological analysis at MOI=1 so that we could observe a significant number of infected cells but minimize cell toxicity. We performed immunoblotting (in Fig. 7H) at MOI=3 to get a good viral infection rate.

As suggested, we also performed LDH assay at MOI=2 and 3 to monitor cell death (new Fig. S2O). Fig. S6C shows that GRASP55 overexpression in the presence of the virus partially rescues Golgi fragmentation. GRASP55 expression did also enhance Spike and GM130 colocalization to some extent (new Fig. S6E-F).

Minor comments:

Figure 1P in the text: Considering that Rab7 up-regulation is equal to "growth of late endosome" is an overstatement. Rab7 is cytosolic at its inactive state and at the endosome at its active state. The authors would have to prove this statement by monitoring an increased quantity of Rab7 at the endosomes which is not enough by just monitoring protein intensity by microscopy. As Rab7 is also localized in lysosomes, and the authors used Lamp2 as a lysosomal marker, it is strange that the area of these structures is not increased. The authors should replace the term "growth" by "an increase in the area of their vesicles".

We did observe less but larger LAMP2 puncta in the infected cells. We agree with the reviewer and rephrased "growth" by an increase in the area of their vesicles". Thank you for the excellent suggestions.

Figure 1Q-T: The observations described in the text did not match the quantification, the area of lysosomes is not significantly different from the non-infected conditions.

In Fig. 1Q-T, we did observe fewer but larger LAMP2 puncta in the infected cells, which was consistent with our quantification, i.e., fewer puncta (Fig. 1R), but each punctum was larger (Fig. 1S), and total area was similar.

Figure 8: In the text, it is mentioned that there is "a dramatic reduction of spike and N in the lysate in GRASP55-expressing and TGN46 depleted cells". However, the quantification indicated that the decrease in N and S content is non-significant. Can the authors precise what was the sample of comparison in the text (siControl versus siTGN46 or siTGN46+GFP versus siTGN46+GFP-GRASP55)?

The decrease in N and S content is significant with the lysate sample comparison (siControl versus siTGN46; siControl+GFP versus siTGN46+GFP; siTGN46+GFP versus siTGN46+GFP-GRASP55). We have now removed this Figure following Reviewer #2's suggestion, since the results are consistent with single protein manipulation and more experiments are needed to confirm whether there is an additive effect.

**Referee cross-commenting**

I agree with most of the concerns of the other reviewers. I do also consider that they should have done their study on cells expressing naturally ACE2. However, at this stage, it will be a lot of work to perform all of their study in a more relevant cell type. The authors should repeat some of their key experiments in lung-derived cell types, to determine if GRASP55 and TGN46 have the same effect on SARS-CoV-2 virion secretion/production.

We thank the reviewer for the suggestions and understanding. As we mentioned before, our study utilizes Huh7-ACE2 cells, which are sorted for the high expression of endogenous ACE2 protein, without ACE2 overexpression. Actually, we also tested A549 and Calu-3 cells. While A549 cells displayed very low infection rate, Calu-3 cells displayed disorganized Golgi without viral infection. However, we did perform immunofluorescence assays in Calu-3 cells. Consistent with our findings in Huh7-ACE2 cells, SARS-CoV-2 infection disrupts Golgi structure and alters protein levels of TGN46 and GRASP55 in Calu3 cells (new Fig. S5R-W). Also, others have reported that liver can be a target for SARS-CoV-2 infection in humans. Furthermore, we confirmed GRASP55 downregulation and TGN46 upregulation in VeroE6 cells (Fig. S6K-N).

Reviewer #3 (Significance (Required)):

The study identified two Golgi proteins (TGN46 and GRASP55) that are involved in modulating the release of SARS-CoV-2 particles from the cells. As these proteins are also acting on general secretion of host proteins to the plasma membrane, the effect on SARS-CoV-2 release could just be indirect. However, it does not change the informative points of the study raised by Zhang et al. It highlights really well how the host trafficking pathway could be diverted for the purpose of the virus, which is to produce particles to maintain its survival.

Strengths: The authors performed a precise and well quantified study. Observing how SARS-CoV-2 impacts host organelles morphology and uses host trafficking proteins to produce particles, brings more clarity on some unclear parts of the life cycle of the virus. In addition, it exposes new targets for therapeutic studies.

We thank the reviewer for the positive comments.

Weakness: The paper is mostly based on microscopy analysis and need some other methods to support their data. The paper lacks some molecular mechanisms explaining the clear role of GRASP55 and TGN46 in particle production or assembly.

In the revised version, we incorporated RT-qPCR assay, cell cytotoxicity assay, and BFA time-point treatment assay. Notably, we added intracellular and extracellular viral titer assays to more precisely distinguish between effects on virion assembly and virion secretion. We also confirmed the key observation that SARS-CoV-2 infection modulates GRASP55 and TGN46 expression in the Calu-3 lung cell line. Additionally, our early time-point results clearly support the role of GRASP55 and TGN46 in viral trafficking.

• Audience: The paper will be interesting for basic research for a virology and cell biology audience.
• Field of expertise with a few keywords: Virology and host cell trafficking.

References

Barnes E (2022) Infection of liver hepatocytes with SARS-CoV-2. Nat Metab 4: 301-302

Bekier ME, 2nd, Wang L, Li J, Huang H, Tang D, Zhang X, Wang Y (2017) Knockout of the Golgi stacking proteins GRASP55 and GRASP65 impairs Golgi structure and function. Mol Biol Cell 28: 2833-2842

Eymieux S, Rouille Y, Terrier O, Seron K, Blanchard E, Rosa-Calatrava M, Dubuisson J, Belouzard S, Roingeard P (2021) Ultrastructural modifications induced by SARS-CoV-2 in Vero cells: a kinetic analysis of viral factory formation, viral particle morphogenesis and virion release. Cell Mol Life Sci 78: 3565-3576

Ghosh S, Dellibovi-Ragheb TA, Kerviel A, Pak E, Qiu Q, Fisher M, Takvorian PM, Bleck C, Hsu VW, Fehr AR et al (2020) beta-Coronaviruses Use Lysosomes for Egress Instead of the Biosynthetic Secretory Pathway. Cell 183: 1520-1535 e1514

Hoffmann M, Hofmann-Winkler H, Smith JC, Kruger N, Arora P, Sorensen LK, Sogaard OS, Hasselstrom JB, Winkler M, Hempel T et al (2021) Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity. EBioMedicine 65: 103255

Hoffmann M, Mosbauer K, Hofmann-Winkler H, Kaul A, Kleine-Weber H, Kruger N, Gassen NC, Muller MA, Drosten C, Pohlmann S (2020) Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature 585: 588-590

Xiang Y, Wang Y (2010) GRASP55 and GRASP65 play complementary and essential roles in Golgi cisternal stacking. J Cell Biol 188: 237-251

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary of what the authors were trying to achieve:

In this manuscript, the authors investigated the role of β-CTF on synaptic function and memory. They report that β-CTF can trigger the loss of synapses in neurons that were transiently transfected in cultured hippocampal slices and that this synapse loss occurs independently of Aβ. They confirmed previous research (Kim et al, Molecular Psychiatry, 2016) that β-CTF-induced cellular toxicity occurs through a mechanism involving a hexapeptide domain (YENPTY) in β-CTF that induces endosomal dysfunction. Although the current study also explores the role of β-CTF in synaptic and memory function in the brain using mice chronically expressing β-CTF, the studies are inconclusive because potential effects of Aβ generated by γ-secretase cleavage of β-CTF were not considered. Based on their findings, the authors suggest developing therapies to treat Alzheimer's disease by targeting β-CTF, but did not address the lack of clinical improvement in trials of several different BACE1 inhibitors, which target β-CTF by preventing its formation.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Major strengths and weaknesses of the methods and results:

The conclusions of the in vitro experiments using cultured hippocampal slices were well supported by the data, but aspects of the in vivo experiments and proteomic studies need additional clarification.

(1) In contrast to the in vitro experiments in which a γ-secretase inhibitor was used to exclude possible effects of Aβ, this possibility was not examined in in-vivo experiments assessing synapse loss and function (Figure 3) and cognitive function (Figure 4). The absence of plaque formation (Figure 4B) is not sufficient to exclude the possibility that Aβ is involved. The potential involvement of Aβ is an important consideration given the 4-month duration of protein expression in the in vivo studies.

We appreciate the reviewer for raising this question. While our current data did not exclude the potential involvement of Aβ-induced toxicity in the synaptic and cognitive dysfunction observed in mice overexpressing β-CTF, addressing this directly remains challenging. Treatment with γ-secretase inhibitors could potentially shed light on this issue. However, treatments with γ-secretase inhibitors are known to lead to brain dysfunction by itself likely due to its blockade of the γ-cleavage of other essential molecules, such as Notch[1, 2]. Therefore, this approach is unlikely to provide a clear answer, which prevents us from pursuing it further experimentally in vivo. We hope the reviewer understands this limitation. We have included additional discussion (page 14 of the revised manuscript) to highlight this question.

(2) The possibility that the results of the proteomic studies conducted in primary cultured hippocampal neurons depend in part on Aβ was also not taken into consideration.

We thank the reviewer for raising this question. In the revised manuscript, we examined the protein levels of synaptic proteins after treatment with γ-secretase inhibitors and found that the levels of certain synaptic proteins were further reduced in neurons expressing β-CTF (Supplementary figure 5A-B). These results do not support Aβ as a major contributor of the proteomic changes induced by β-CTF.

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

The authors' use of sparse expression to examine the role of β-CTF on spine loss could be a useful general tool for examining synapses in brain tissue.

We thank the reviewer for these comments.

Additional context that might help readers interpret or understand the significance of the work:

The discovery of BACE1 stimulated an international effort to develop BACE1 inhibitors to treat Alzheimer's disease. BACE1 inhibitors block the formation of β-CTF which, in turn, prevents the formation of Aβ and other fragments. Unfortunately, BACE1 inhibitors not only did not improve cognition in patients with Alzheimer's disease, they appeared to worsen it, suggesting that producing β-CTF actually facilitates learning and memory. Therefore, it seems unlikely that the disruptive effects of β-CTF on endosomes plays a significant role in human disease. Insights from the authors that shed further light on this issue would be welcome.

Response: We would like to express our gratitude to the reviewer for raising this question. It remains puzzling why BACE1 inhibition has failed to yield benefits in AD patients, while amyloid clearance via Aβ antibodies are able to slow down disease progression. One possible explanation is that pharmacological inhibition of BACE1 may not be as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to the clearance of existing amyloid plaques[3], whereas its pharmacological inhibition prevents the formation of new plaques but does not deplete the existing ones[4]. We think the negative results of BACE1 inhibitors in clinical trials may not be sufficient to rule out the potential contribution of β-CTF to AD pathogenesis. Given that cognitive function continues to deteriorate rapidly in plaque-free patients after 1.5 years of treatment with Aβ antibodies in phase three clinical studies[5], it is important to consider the potential role of other Aβ-related fragments in AD pathogenesis, such as β-CTF. We included further discussion in the revised manuscript (page 15 of the revised manuscript) to discusss this question.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigate the potential role of other cleavage products of amyloid precursor protein (APP) in neurodegeneration. They combine in vitro and in vivo experiments, revealing that β-CTF, a product cleaved by BACE1, promotes synaptic loss independently of Aβ. Furthermore, they suggest that β-CTF may interact with Rab5, leading to endosomal dysfunction and contributing to the loss of synaptic proteins.

We would like to thank the reviewer for his/her suggestions. We have addressed the specific comments in following sections.

Weaknesses:

Most experiments were conducted in vitro using overexpressed β-CTF. Additionally, the study does not elucidate the mechanisms by which β-CTF disrupts endosomal function and induces synaptic degeneration.

We would like to thank the reviewer for this comment. While a significant portion of our experiments were conducted in vitro, the main findings were also confirmed in vivo (Figure 3 and 4). Repeating all the experiments in vivo would be challenging and may not be possible because of technical difficulties. Regarding the use of overexpressed β-CTF, we acknowledge that this represents a common limitation in neurodegenerative disease studies. These diseases progress slowly over decades in patients. To model this progression in cell or mouse models within a time frame feasible for research, overexpression of certain proteins is often inevitable. Since β-CTF levels are elevated in AD patients[6], its overexpression is not a irrelevant approach to investigate its potential effects.

We did not further investigate the mechanisms by which β-CTF disrupted endosomal function because our preliminary results align with previous findings that could explain its mechanism. Kim et al. demonstrated that β-CTF recruits APPL1 (a Rab5 effector) via the YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. However, this paper did not show whether this Rab5 overactivation-induced endosomal dysfunction leads to any damages in synapses. In our study, we observed that co-expression of Rab5_S34N with β-CTF effectively mitigated β-CTF-induced spine loss in hippocampal slice cultures (Figures 6L-M), indicating that Rab5 overactivation-induced endosomal dysfunction contributed to β-CTF-induced spine loss. We included further discussion in the revised manuscript to clarify this (page 15 of the revised manuscript).

Reviewer #3 (Public Review):

Summary:

Most previous studies have focused on the contributions of Abeta and amyloid plaques in the neuronal degeneration associated with Alzheimer's disease, especially in the context of impaired synaptic transmission and plasticity which underlies the impaired cognitive functions, a hallmark in AD. But processes independent of Abeta and plaques are much less explored, and to some extent, the contributions of these processes are less well understood. Luo et all addressed this important question with an array of approaches, and their findings generally support the contribution of beta-CTF-dependent but non-Abeta-dependent process to the impaired synaptic properties in the neurons. Interestingly, the above process appears to operate in a cell-autonomous manner. This cell-autonomous effect of beta-CTF as reported here may facilitate our understanding of some potentially important cellular processes related to neurodegeneration. Although these findings are valuable, it is key to understand the probability of this process occurring in a more natural condition, such as when this process occurs in many neurons at the same time. This will put the authors' findings into a context for a better understanding of their contribution to either physiological or pathological processes, such as Alzheimer's. The experiments and results using the cell system are quite solid, but the in vivo results are incomplete and hence less convincing (see below). The mechanistic analysis is interesting but primitive and does not add much more weight to the significance. Hence, further efforts from the authors are required to clarify and solidify their results, in order to provide a complete picture and support for the authors' conclusions.

We would like to thank the reviewer for the suggestions. We have addressed the specific comments in following sections.

Strengths:

(1) The authors have addressed an interesting and potentially important question

(2) The analysis using the cell system is solid and provides strong support for the authors' major conclusions. This analysis has used various technical approaches to support the authors' conclusions from different aspects and most of these results are consistent with each other.

We would like to thank the reviewer for these comments.

Weaknesses:

(1) The relevance of the authors' major findings to the pathology, especially the Abeta-dependent processes is less clear, and hence the importance of these findings may be limited.

We would like to thank the reviewer for this question. Phase 3 clinical trial data from Aβ antibodies show that cognitive function continues to decline rapidly, even in plaque-free patients, after 1.5 years of treatment[5]. This suggests that plaque-independent mechanisms may drive AD progression. Therefore, it is crucial to consider the potential contributions of other Aβ species or related fragments, such as alternative forms of Aβ and β-CTF. While it is early to predict how much β-CTF contributes to AD progression, it is notable that β-CTF induced synaptic deficits in mice, which recapitulates a key pathological feature of AD. Ultimately, the contribution of β-CTF in AD pathogenesis can only be tested through clinical studies in the future.

(2) In vivo analysis is incomplete, with certain caveats in the experimental procedures and some of the results need to be further explored to confirm the findings.

We would like to thank the reviewer for this suggestion. We have corrected these caveats in the revised manuscript.

(3) The mechanistic analysis is rather primitive and does not add further significance.

We would like to thank the reviewer for this comment. We did not delve further into the underlying mechanisms because our analysis indicates that Rab5 overactivation-induced endosomal dysfunction underlies β-CTF-induced synaptic dysfunction, which is consistent with another study and has been addressed in our study[6]. We hope the reviewer could understand that our focus in this paper is on how β-CTF triggers synaptic deficits, which is why we did not investigate the mechanisms of β-CTF-induced endosomal dysfunction further.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Suggestions for improved or additional experiments, data, or analyses:

(1) In Figures 4H, 4J, 4K and Supplemental Figures 3C, 3E, and 3G, it was unclear whether a repeated measures 2-way ANOVA, rather than a 2-way ANOVA, followed by appropriate post-hoc analyses was used to strengthen the conclusion that there were significant effects in the behavioral tests.

We appreciate the reviewer for raising this point and apologize for the lack of clear description in the manuscript. In those figures mentioned above, we use a repeated measures 2-way ANOVA to analyze the data by Graphpad Prism. In Figure 4H, fear conditioning tests were conducted. The same cohort of mice were used in the baseline, contextual and cued tests. Firstly, baseline freezing was tested; then these mice underwent tone and foot shock training, followed by contextual test and cued test. So, a repeated measures 2-way ANOVA is more appropriate for the experiment.

In water T maze tests (Figure 4J and K), the same cohort of mice were trained and tested each day. So, it’s also appropriate to use a repeated measures 2-way ANOVA.

In Supplementary figure 3C, 3E and 3G, OFT was conducted. In this experiment, the locomotion of the same cohort of mice were recorded. Also, it’s appropriate to use a repeated measures 2-way ANOVA.

Clearer description for these experiments has been provided in the revised manuscript.

(2) Including gender analyses would be helpful.

The mice we used in this study were all males.

Minor corrections to text and figures:

(1) Quantitative analyses in Figures 5A-C, 5H, 6G, 6H, and Supplementary Figures 4 and 5C would be helpful.

We have provided quantitative analysis of these results (Figure 5D, 5J, 6K, Supplementary figure 4D, 5F) mentioned above in the revised manuscript.

(2) Percent correct (%) in Figures 4J and 4K should be labeled as 0, 50, and 100 instead of 0.0, 0.5, and 1.0.

We would like to thank the reviewer for pointing out this. We have made corrections in the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

In the study conducted by Luo et al, it was observed that the fragment of amyloid precursor protein (APP) cleaved by beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), known as β-CTF, plays a crucial role in synaptic damage. The study found increasing expression of β-CTF in neurons could induce synapse loss both in vitro and in vivo, independent of Aβ. Mechanistically, they explored how β-CTF could interfere with the endosome system by interacting with RAB5. While this study is intriguing, there are several points that warrant further investigation:

(1) The study involved overexpressing β-CTF in neurons. It would be valuable to know if the levels of β-CTF are similarly increased in Alzheimer's disease (AD) patients or AD mouse models.

We would like to thank the reviewer for the suggestion. It’s reported β-CTF levels were significantly elevated in the AD cerebral cortex[6]. Most AD mouse models are human APP transgenic mouse models with elevated β-CTF levels[7].

(2) The study noted that β-CTF in neurons is a membranal fragment, but the overexpressed β-CTF was not located in the membrane. It is important to ascertain whether the membranal β-CTF and cytoplasmic β-CTF lead to synapse loss in a similar manner.

We apologize for not clearly explaining the localization of β-CTF in the original manuscript. β-CTF is produced from APP through β-cleavage, a process that occurs in organelles such as endo-lysosomes[8]. The overexpressed β-CTF is also primarily localized in the endo-lysosomal systems (Figure 5C and Supplementary figure 4C), similar to those generated by APP cleavage.

(3) The study found a significant decrease in GluA1, a subunit of AMPA receptors, due to β-CTF. It would be beneficial to investigate whether there are systematic alterations in NMDA receptors, including GluN2A and GluN2B.

We would like to express our gratitude to the reviewer for bringing up this question. The protein levels of GluN2A and GluN2B are also reduced in neurons expressing β-CTF (Figure 6E-F)

(4) The study showed a significant decrease in the frequency of miniature excitatory postsynaptic currents (mEPSC), indicating disrupted presynaptic vesicle neurotransmitter release. It would be pertinent to test whether the expression level of the presynaptic SNARE complex, which is required for vesicle release, is altered by β-CTF.

We would like to express our gratitude to the reviewer for bringing up this question. The protein level of the presynaptic SNARE complex, such as VAMP2, is also reduced in neurons expressing β-CTF (Figure 6E, G).

(5) Since AMPA receptors are glutamate receptors, it is important to determine whether the ability of glutamate release is altered by β-CTF. In vivo studies using a glutamate sensor should be conducted to examine glutamate release.

We would like to express our gratitude to the reviewer for this suggestion. It will be interesting to use glutamate sensors to assess the ability of glutamate release in the future.

(6) The quality of immunostaining associated with Figures 4B and 4C was noted to be suboptimal.

We apologize for the suboptimal quality of these images. The immunostaining in Figures 4B and 4C were captured using the stitching function of a confocal microscope to display larger areas, including the entire hemisphere and hippocampus. We have reprocessed the images to obtain higher-quality versions.

(7) It would be insightful to investigate whether treatment with a BACE1 inhibitor in the study could reverse synaptic deficits mediated by β-CTF.

We would like to thank the reviewer for this sggestion. In Figure 1I-M, we constructed an APP mutant (APP_MV), which cannot be cleaved by BACE1 to produce β-CTF and Aβ but has no impact on β’-cleavage. When co-expressed with BACE1, APP_MV failed to induce spine loss, supporting the effect of β-CTF. We think these results domonstrate that β-CTF underlies the synaptic deficits. It would be interesting to test the effects of BACE1 inhibition in the future.

(8) Considering the potential implications for therapeutics, it is worth exploring whether extremely low levels of β-CTF have beneficial effects in regulating synaptic function or promoting synaptogenesis at a physiological level.

We would like to thank the reviewer for raising this question. We found that when the plasmid amount was reduced to 1/8 of the original dose, β-CTF no longer induced a decrease in dendritic spine density (Supplementary figure 2E-F). It’s reported APP-Swedish mutation in familial AD increased synapse numbers and synaptic transmission, whereas inhibition of BACE1 lowered synapse numbers, suppressed synaptic transmission in wild type neurons, suggesting that at physiological level, β-CTF might be synaptogenic[9].

(9) The molecular mechanism through which β-CTF interferes with Rab5 function should be elucidated.

We would like to thank the reviewer for raising this question. Kim et al have elucidated the mechanism through which β-CTF interferes with Rab5 function. β-CTF recruited APPL1 (a Rab5 effector) via YENPTY motif to Rab5 endosomes, where it stabilizes active GTP-Rab5, leading to pathologically accelerated endocytosis, endosome swelling and selectively impaired transport of Rab5 endosomes[6]. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

(10) The study could compare the role of β-CTF and Aβ in neurodegeneration in AD mouse models.

We would like to thank the reviewer for raising this point. While it is easier to dissect the role of Aβ and β-CTF in vitro, some of the critical tools are not applicabe in vivo, such as γ-secretase inhibitors, which lead to severe side effects because of their inhibition on other γ substrates[1, 2]. Therefore it will be difficult to deomonstrate their different roles in vivo. There are studies showing that β-CTF accumulation precedes Aβ deposition in model mice and mediates Aβ independent intracellular pathologies[10, 11], consistent with our results.

(11) Based on the findings, it would be valuable to discuss possible explanations for the failure of most BACE1 inhibitors in recent clinical trials for humans.

Response: We would like to express our gratitude to the reviewer for raising this recommendation. It is a big puzzle why BACE1 inhibition failed to provide beneficial effects in AD patients whereas clearance of amyloid by Aβ antibodies could slow down the AD progress. One potential answer is that pharmacological inhibition of BACE1 might be not as effective as its genetic removal. Indeed, genetic depletion of BACE1 leads to clearance of existing amyloid plaques[3], whereas pharmacological inhibition of BACE1 could not stop growth of existing plaques, although it prevents formation of new plaques[4]. The negative result of BACE1 inhibitors might not be sufficient to exclude the possibility that β-CTF could also contribute to the AD pathogenesis. We have included additional discussion for this question in the revised manuscript (page 15 of the revised manuscript).

Reviewer #3 (Recommendations For The Authors):

Major:

(1) The cell experiments were performed at DIV 9, do the authors know whether at this age, the neurons are still developing and spine density has not reached a pleated yet? If so, the observed effect may reflect the impact on development and/or maturation, rather than on the mature neurons. The authors should be more specific about this issue.

We would like to thank the reviewer for pointing out this question. These slice cultures were made from 1-week-old rats. DIV 9 is about two weeks old. These neurons are still developing and spine density has not reached a plateau yet[12]. In addition, we also investigated the effects of β-CTF on the synapses of mature neurons in two-month-old mice (Figure 3). So we think the observed effect reflects the impact on both immature and mature neurons.

(2) mEPSCs shown in Figure 3D were of small amplitudes, perhaps also indicating that these synapses are not yet mature.

In Figure 3D, the mEPSC results were obtained from pyramidal neurons in the CA1 region of two-month-old mice. At the age of two months, neurotransmitter levels and synaptic density have reached adult levels[13].

(3) There was no data on the spine density or mEPSCs in the mice OE b-CTF, hence it is unclear whether a primary impact of this manipulation (b-CTF effect) on the synaptic transmission still occurs in vivo.

In Figure 3, we examined the density of dendritic spines and mEPSCs from CA1 pyramidal neurons infected with lentivirus expressing β-CTF in mice and showed that those neurons expressing additional amount of β-CTF exhibited lower spine density and less mEPSCs, supporting that β-CTF also damaged synaptic transmission in vivo.

(4) OE of b-CTF should lead to the production of Abeta, although this may not lead to the formation of significant plaques. How do the authors know whether their findings on behavioral and cognitive impairments were not largely mediated by Abeta, which has been widely reported by previous studies?

We would like to thank the reviewer for pointing out this question. Indeed, our in vivo data could not exclude the potential involvement of Aβ in the pathology, despite the absence of amyloid plaque formation. It will be difficult to demonstrate this question in vivo because of the severe side effects from γ inhibition.

(5) Figure 4H, the freezing level in the cued fear conditioning was very high, likely saturated; this may mask a potential reduction in the b-CTF OE mice (there is a hint for that in the results). The authors should repeat the experiments using less strong footshock strength (hence resulting in less freezing, <70%).

We would like to express our gratitude to the reviewer for bringing up this question. The contextual fear conditioning test assesses hippocampal function, while the cued fear conditioning test assesses amygdala function. We hope the reviewer understands that our primary goal is to assess hippocampus-related functions in this experiment and we did see a significant difference between GFP and β-CTF groups. Therefore, we think the intensity of footshock we used was suitable to serve the primary purpose of this experiment.

(6) Why was the deficit in the Morris water maze in the b-CTF OE mice only significant in the training phase?

We would like to thank the reviewer for rasing this question and apologize for not describing the test clearly. This is a water T maze test, not Morris water maze test.

To make the behavioral paradigm of the water T maze test easier to understand, we have provided a more detailed description of the methods in the new version of the manuscript.

The acquisition phase of the Water T Maze (WTM) evaluates spatial learning and memory, where mice use spatial cues in the environment to navigate to a hidden platform and escape from water, while the reversal learning measures cognitive flexibility in which mice must learn a new location of the hidden platform[14]. In reversal learning task (Figure 4J-K), the learning curves of the two groups of mice did not show any significant differences, indicating that the expression of β-CTF only damages spatial learning and memory but not cognitive flexibility. This is consistent with a previous report using APP/PS1 mice[15].

(7) Will the altered Rab5 in the b-CTF OE condition also affect the level of other proteins?

We would like to express our gratitude to the reviewer for raising this interesting question.  Expression of Rab5_S34N in β-CTF-expressing neurons did not alter the levels of synapse-related proteins that were reduced in these neurons (Supplementary figure 5G-H), suggesting Rab5 overactivation did not contribute to these protein expression changes induced by β-CTF.

(8) How do the authors reconcile their findings with the well-established findings that Abeta affects synaptic transmission and spine density? Do they think these two processes may occur simultaneously in the neurons, or, one process may dominate in the other?

APP, Aβ, and presenilins have been extensively studied in mouse models, providing convincing evidence that high Aβ concentrations are toxic to synapses[16]. Moreover, addition of Aβ to murine cultured neurons or brain slices is toxic to synapses[17]. However, Aβ-induced synaptotoxicity was not observed in our study. A major difference between our study and others is that our study used a isolated expression system that apply Aβ only to individual neurons surrounded by neurons without excessive amount of Aβ, whereas the rest studies generally apply Aβ to all the neurons. Therefore, we predict that Aβ does not lead to synaptic deficits from individual neurons in cell autonomous manners, whereas β-CTF does. Aβ and β-CTF represent two parallel pathways of action. Additional discussion for this question has been included in the revised manuscript (page 14 of the revised manuscript).

Minor:

Fig 2F-G, "prevent" rather than "reverse"?

We would like to thank the reviewer for pointing this out. We have made corrections in the revised manuscript.

Reference:

(1) GüNER G, LICHTENTHALER S F. The substrate repertoire of γ-secretase/presenilin [J]. Seminars in cell & developmental biology, 2020, 105: 27-42.

(2) DOODY R S, RAMAN R, FARLOW M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease [J]. The New England journal of medicine, 2013, 369(4): 341-50.

(3) HU X, DAS B, HOU H, et al. BACE1 deletion in the adult mouse reverses preformed amyloid deposition and improves cognitive functions [J]. The Journal of experimental medicine, 2018, 215(3): 927-40.

(4) PETERS F, SALIHOGLU H, RODRIGUES E, et al. BACE1 inhibition more effectively suppresses initiation than progression of β-amyloid pathology [J]. Acta neuropathologica, 2018, 135(5): 695-710.

(5) SIMS J R, ZIMMER J A, EVANS C D, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial [J]. Jama, 2023, 330(6): 512-27.

(6) KIM S, SATO Y, MOHAN P S, et al. Evidence that the rab5 effector APPL1 mediates APP-βCTF-induced dysfunction of endosomes in Down syndrome and Alzheimer's disease [J]. Molecular psychiatry, 2016, 21(5): 707-16.

(7) MONDRAGóN-RODRíGUEZ S, GU N, MANSEAU F, et al. Alzheimer's Transgenic Model Is Characterized by Very Early Brain Network Alterations and β-CTF Fragment Accumulation: Reversal by β-Secretase Inhibition [J]. Frontiers in cellular neuroscience, 2018, 12: 121.

(8) ZHANG X, SONG W. The role of APP and BACE1 trafficking in APP processing and amyloid-β generation [J]. Alzheimer's research & therapy, 2013, 5(5): 46.

(9) ZHOU B, LU J G, SIDDU A, et al. Synaptogenic effect of APP-Swedish mutation in familial Alzheimer's disease [J]. Science translational medicine, 2022, 14(667): eabn9380.

(10) LAURITZEN I, PARDOSSI-PIQUARD R, BAUER C, et al. The β-secretase-derived C-terminal fragment of βAPP, C99, but not Aβ, is a key contributor to early intraneuronal lesions in triple-transgenic mouse hippocampus [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2012, 32(46): 16243-1655a.

(11) KAUR G, PAWLIK M, GANDY S E, et al. Lysosomal dysfunction in the brain of a mouse model with intraneuronal accumulation of carboxyl terminal fragments of the amyloid precursor protein [J]. Molecular psychiatry, 2017, 22(7): 981-9.

(12) HARRIS K M, JENSEN F E, TSAO B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 1992, 12(7): 2685-705.

(13) SEMPLE B D, BLOMGREN K, GIMLIN K, et al. Brain development in rodents and humans: Identifying benchmarks of maturation and vulnerability to injury across species [J]. Progress in neurobiology, 2013, 106-107: 1-16.

(14) GUARIGLIA S R, CHADMAN K K. Water T-maze: a useful assay for determination of repetitive behaviors in mice [J]. Journal of neuroscience methods, 2013, 220(1): 24-9.

(15) ZOU C, MIFFLIN L, HU Z, et al. Reduction of mNAT1/hNAT2 Contributes to Cerebral Endothelial Necroptosis and Aβ Accumulation in Alzheimer's Disease [J]. Cell reports, 2020, 33(10): 108447.

(16) CHAPMAN P F, WHITE G L, JONES M W, et al. Impaired synaptic plasticity and learning in aged amyloid precursor protein transgenic mice [J]. Nature neuroscience, 1999, 2(3): 271-6.

(17) WANG Z, JACKSON R J, HONG W, et al. Human Brain-Derived Aβ Oligomers Bind to Synapses and Disrupt Synaptic Activity in a Manner That Requires APP [J]. The Journal of neuroscience : the official journal of the Society for Neuroscience, 2017, 37(49): 11947-66.

If these steps are put in place and everyone adapts, accepts, and lives by them, I think it can work. Trust has to be built and there has to be a mutual level of respect for each. There can not be hidden agendas and there should not be a grouping of individuals, i.e. all blacks are criminals or all cops are bad.

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript presents evidence of ’vocal style’ in sperm whale vocal clans. Vocal style was defined as specific patterns in the way that rhythmic codas were produced, providing a fine-scale means of comparing coda variations. Vocal style effectively distinguished clans similar to the way in which vocal repertoires are typically employed. For non-identity codas, vocal style was found to be more similar among clans with more geographic overlap. This suggests the presence of social transmission across sympatric clans while maintaining clan vocal identity.

Strengths:

This is a well-executed study that contributes exciting new insights into cultural vocal learning in sperm whales. The methodology is sound and appropriate for the research question, building on previous work and ground-truthing much of their theories. The use of the Dominica dataset to validate their method lends strength to the concept of vocal style and its application more broadly to the Pacific dataset. The results are framed well in the context of previous works and clearly explain what novel insights the results provide to the current understanding of sperm whale vocal clans. The discussion does an overall great job of outlining why horizontal social learning is the best explanation for the results found.

Weaknesses:

The primary issues with the manuscript are in the technical nature of the writing and a lack of clarity at times with certain terminology. For example, several tree figures are presented and ’distance’ between trees is key to the results, yet ’distance’ is not clearly defined in a way for someone unfamiliar with Markov chains to understand. However, these are issues that can easily be dealt with through minor revisions with a view towards making the manuscript more accessible to a general audience.

I also feel that the discussion could focus a bit more on the broader implications - specifically what the developed methods and results might imply about cultural transmission in other species. This is specifically mentioned in the abstract but not really delved into in detail during the discussion.

We are grateful for the Reviewer’s recognition of the study’s contributions to understanding cultural vocal learning in sperm whales. In response to the concerns regarding clarity and accessibility, we have revised the manuscript to improve the definition of key concepts, such as the notion of “distance” between subcoda trees. This adjustment ensures clarity for readers unfamiliar with the technical details of Markov chains. Additionally, we have expanded the discussion to highlight broader implications of our findings, particularly their relevance to understanding cultural transmission in other species, as suggested.

Reviewer #2 (Public review):

Summary:

The current article presents a new type of analytical approach to the sequential organisation of whale coda units.

Strengths:

The detailed description of the internal temporal structure of whale codas is something that has been thus far lacking.

Weaknesses:

It is unclear how the insight gained from these analyses differs or adds to the voluminous available literature on how codas varies between whale groups and populations. It provides new details, but what new aspects have been learned, or what features of variation seem to be only revealed by this new approach? The theoretical basis and concepts of the paper are problematical and indeed, hamper potentially the insights into whale communication that the methods could offer. Some aspects of the results are also overstated.

We appreciate the Reviewer’s acknowledgment of the novelty in describing the internal temporal structure of whale codas. Regarding the concern about the unique contributions of this approach, we have further emphasized in the revised manuscript how our methodology reveals previously uncharacterized dimensions of coda structure. Specifically, our work highlights how non-identity codas, which have received limited attention, play a significant role in inter-clan acoustic interactions. By leveraging Variable Length Markov Chains, we provide a nuanced understanding of coda subunits that complements existing studies and demonstrates the value of this analytical approach.

Reviewer #3 (Public review):

Summary:

The study presented by Leitao et al., represents an important advancement in comprehending the social learning processes of sperm whales across various communicative and socio-cultural contexts. The authors introduce the concept of ”vocal style” as an addition to the previously established notion of ”vocal repertoire,” thereby enhancing our understanding of sperm whale vocal identity.

Strengths:

A key finding of this research is the correlation between the similarity of clan vocal styles for non-ID codas and spatial overlap (while no change occurs for ID codas), suggesting that social learning plays a crucial role in shaping symbolic cultural boundaries among sperm whale populations. This work holds great appeal for researchers interested in animal cultures and communication. It is poised to attract a broad audience, including scholars studying animal communication and social learning processes across diverse species, particularly cetaceans.

Weaknesses:

In terms of terminology, while the authors use the term ”saying” to describe whale vocalizations, it may be more conservative to employ terms like ”vocalize” or ”whale speech” throughout the manuscript. This approach aligns with the distinction between human speech and other forms of animal communication, as outlined in prior research (Hockett, 1960; Cheney & Seyfarth, 1998; Hauser et al., 2002; Pinker & Jackendoff, 2005; Tomasello, 2010).

We thank the Reviewer for recognizing the importance of our findings and their appeal to broader audiences interested in animal cultures and communication. In response to the suggestion regarding terminology, we have adopted a more conservative language to align with distinctions between human and non-human communication systems. For example, terms like “vocalize” and “vocal repertoire” are used in place of anthropomorphic terms such as “saying”. This ensures consistency with established conventions while maintaining clarity for a broad readership.

Reviewer #1 (Recommendations):

Comment 1

Lines 11-13: As mentioned above, the implications for comparing communication systems and cultural transmission in other species isn’t really discussed much and I think it’s a really interesting component of the study’s broader implications.

Thank you for the comment.

Action - We added a few more sentences to the discussion regarding this.

Comment 2

Figure 1: More information on the figure of these trees would help. What do the connecting lines represent? What do the plain black dots and the black dot with the white dot represent? Especially since the ”distance between trees” is a key result, it’s important that someone unfamiliar with Markov chains can understand the basics of how this is calculated and what it represents. It is explained in the methods, but a brief explanation here would make the results and the figure a lot clearer since the methods are the last section of the manuscript.

These were omitted as we believed that attempting to introduce the mathematical structure and the methodology to compare two instances, in a figure caption, would have caused more ambiguity than necessary.

Action - Added an informal introduction to these concepts on the figure caption. Also added a pointer to the Supplementary Materials.

Comment 3

Table 1: A definition of dICIs should be included here.

Added the definition of discrete ICI to the table.

Comment 4

Figure 2: The placement of the figures is a bit confusing because they are quite far from the text that references them.

We thank the reviewer for pointing this out, we tried to edit the manuscript to improve this issue, but this part of the editing is more within the journal’s powers than our own.

Action - Moved images closes to the corresponding text in manuscript.

Comment 5

Line 117: Probabilistic distance needs to be briefly explained earlier when you first mention distance (see Lines 11-13 comments).

Action - Clarifications added in the caption of figure 1. as per comment on Lines 11-13

Comment 6

Figure 4: Is order considered in these pairwise comparisons? It looks like there are two dots for each pairwise comparison. Additionally, why is the overlap different in these two comparisons? For example, short:four-plus has an overlap of 0.6, while four-plus:short has an overlap of 0.95.

The x-axis of the plots in Figure 4 is geographical clan overlap. This is calculated as per (Hersh et al., 2022) and is described in our Methods (see “Measuring clan overlap” section). Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger ( 0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We have now added a reference to that section of the Methods in our Figure 4 caption and include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 7

Figure 4: I think the reference should be Hersh et al. [11].

Thank you for catching this.

Action - Reference corrected

Comment 8

Line 227: What aspect of your analysis looked at how often codas were produced? You mention coda frequency, but it is unclear how this was incorporated into your analysis. If this is included in the methods, the language is a bit too technical to easily parse it out.

Indeed here we are referencing the results of the paper mentioned in the previous line. We do not look at coda production frequency.

Action - Added citation to paper that actually performs this analysis.

Comment 9

Lines 253-255: I think you could dig into this a little more, as ”there is currently no evidence” is not the most convincing argument that something is not a driver. Perhaps expanding on the latter sentence that clans are recognizable across oceans basins would be helpful. Does this suggest that clans with similar geographic overlap experience diverse environmental conditions across ocean basins? If so, this might better strengthen your argument against environmental drivers.

Thank you for pointing this out. We feel that the next sentence highlights that clans are recognizable across environmental variation from one side to the other of the ocean basin, which supports the inductive reasoning that codas do not vary systematically with environment. However, we have edited these sentences for clarity.

Comment 10

Lines 311-314: It would also be interesting to look at vocal style across non-ID coda types. Are some more similar to each other across clans than others? Perhaps vocal style can further distinguish types of non-ID codas.

In supplementary Materials 3.4.2 and 3.5 we highlight our results when the codas are separated by coda type summarized in Table S4. We do compare the vocal style across non-ID coda types across clans and within the same clan. The results however are aggregated to highlight the differences in style between the clans and a a coda type-only comparison is not shown.

Comment 11

Lines 390-392: I’m assuming this is why pairwise comparisons were directional (i.e., there was both an A:B and a B:A comparison)? Can you speak to why A:B and B:A comparisons can have such different overlap values?

Given two clans—for example, the Four-Plus and the Short clan—spatial overlap is calculated twice: as the proportion of the Four-Plus clan’s repertoires that were recorded within 1,000 km of at least one of the Short clan’s repertoires, and as the proportion of the Short clan’s repertoires that were recorded within 1,000 km of at least one of the Four-Plus clan’s repertoires.

Order is important in these pairwise comparisons and generates an asymmetric matrix because the clans have different spatial extents. A clan found in only one small region might overlap completely with a clan that spans the Pacific Ocean, while the opposite is not true. For example, the Short clan spans the Pacific Ocean while the Four-Plus clan has been documented over a smaller area (but that smaller area overlaps extensively with the Short clan range). That is why the value is smaller (0.6) when considering how much of the Short clan’s range is shared with the Four-Plus clan, and larger (0.95) when considering how much of the Four-Plus clan’s range is shared with the Short clan.

Action - We now include the clan spatial overlap matrix as a supplemental table (Table S5).

Comment 13

Line 56: Can you briefly explain what memory means in the context of Markov chains?

We provide an explanation of the meaning of memory in the Methods section on ”Variable length Markov Chains”. Briefly, the memory in this case means how many states in the past of the Markov chain’s current state are required to predict the next transition of the chain itself. Standard Markov chains “look” back only one time step, while k-th order Markov chains look back k steps. In our case, there was no reason to assume that the memory required to predict different sequences of states (interclick intervals) should be the same across all sequences, and thus we adopted the formalism of variable length Markov chains, that allow for different levels of memory across the system.

Comment 14

Supplementary Figure S3: Like in the main manuscript, briefly explain or remind us what the blank nodes and the yellow nodes are.

Action - Clarified that the orange node represents the root of the tree in the figures.

Comment 15

Supplementary Figure S7: Put the letters before the dataset name.

Action - Done.

Comment 16

Supplementary Figure S10: Unclear what ’inner vs outer’ means.

One specifies comparisons across clans (outer) and the other within the same clan (inner)

Action - Added clarification on the caption of Figure S10

Comment 17

Supplementary Figure S14: Include a-c labels in the figure itself.

Action - Labels added to figure

Comment 18

Supplementary Figure S14: The information about the nodes is what needs to be included earlier and in the main body when discussing the trees.

Action - Added the explanation earlier in the text and in the main body

Reviewer #2 (Recommendations):

Comment 19

Line 22: ”Symbolic” and ”Arbitrary” are not synonyms. Please see the comment above.

We agree. Here, we make the point that the evolution of symbolic markers of group identity can be explained from what are initially arbitrary, and meaningless, signals (see [L1, L2]). Our point being that any vocalization, any coda, could have become selected for as an identity coda, and to become symbolic, and evolve to play a key role in cultural group formation and in-group favoritism because they enable a community of individuals to solve the problem of with whom to collaborate. The specific coda itself does not affect collaborative pay offs, but group specific differences in behavior can, as such the coda is arguably symbolic; as it is observable and recognizable, and can serve as a means for social assortment even when the behavioural differences are not. This can explain the means by which the social segregation which is observed among behaviorally distinct clans of sperm whales. However, in this manuscript, we do not extend this discussion of existing literature and have attempted to concisely describe this in a couple of lines, which clearly do a disservice to the large body of literature on the evolution of symbolic markers and human ethnic groups. We have added some citations to this section so that the reader may follow up should they disagree with out brief introductory statements.

Action - Added citations and pointers to the literature.

Comment 20

Line 24: The authors’ terminology around ”markers”, ”arbitrary”, ”symbolic” is unnecessarily confusing and mystifying, giving the impression these terms are interchangeable. They are not. These terms are an integral and long-established part of key definitions in signal theory. Term use should be followed accordingly. The observation that whale vocal signals vary per population does not necessarily mean that they function as a social tag. The word ”dog” varies per population but its use relates to an animal, not the population that utters the word. ”Dog” is not ”symbolic” of England, English-speaking populations or the English language. Furthermore, the function of whale vocal signals is extremely challenging to determine. In the best conditions, researchers can pin the signal’s context, this is distinct from signal’s function and further even for the signal’s meaning. How exactly the authors determine that whale vocal signals are arbitrary is, thus, perplexing given that this would require a detailed description and understanding of who is producing the song, when, towards whom, and how the receivers react, none of which the authors have and without which no claim on the signals’ function can be made. This terminological laxness and the sensu latu in extremis to various terms in an unjustified, unnecessary and unhelpful.

We use these terms as established in Hersh et al 2022 and the works leading up to it over the last 20 years in the study of sperm whales. These are often derived from definitions by Boyd and Richerson’s work on culture in humans and animals along with evolution of symbolic markers both in theory and in humans. We agree with the reviewer that these are difficult to establish in non-humans, whales or otherwise, but feel strongly that the accumulating evidence provides strong support for the function of these signals as symbolic markers of cultural groups, and that they likely evolved from initially arbitrary calls which were a part of the vocal repertoire (similar to the process and selective environment in Efferson et al. [L1] and McElreath et al. [L2]). We feel that we do not use these terms interchangeably here, and have inherited their use from definitions from anthropology. The work presented here uses terminology built across two decades of work in cetacean, and sperm whale, culture. And do not feel that these terms should be omitted here.

Comment 21

Lines 21-27: Overly broad and hazy paragraph.

We hope the replies above and our changes satisfy this comment and clarify the text.

Comment 22

Figure 1 legend: What are ”memory structures”? Unjustified descriptor.

The phrase was chosen to make draw some intuition on the variation of context length in variable length markov models.

Action - Re-worded from memory structures to statistical properties

Comment 23

Line 30: Omit ”finite”.

Action - Omitted.

Comment 24

Line 31: Please define and distinguish ”rhythm” and ”tempo”. Also see comment above, rhythm and tempo definitions require the use of IOIs.

We disagree with the reviewer’s claims here. In our research specifically, and for sperm whale research generally, coda inter-click intervals (ICIs) are calculated as the time between the start of the first click and the start of the subsequent click. This makes ICIs identical to inter-onset intervals (IOIs) under all definitions we are aware of. For example, Burchardt and Knornschild [L3] define IOIs as such: “In a sequence of acoustic signals, the time span between the start of an element and the next element, comprising the element duration and the following gap duration”. We now include a sentence making this point.

Regardless, we disagree on a more fundamental level with the statement that unless researchers quantify inter-onset intervals (IOIs), they cannot make any claims about rhythm. There are many studies that investigate rhythmic aspects of human and animal vocalizations without using IOIs [L4–L7]. If the duration of sound elements of interest is relatively constant (as is the case for sperm whale clicks), then rhythm analyses can still be meaningfully conducted on inter-call intervals (the silent intervals between calls).

For sperm whales, coda rhythm is defined by the relative ICIs standardized by their total duration. These can be clustered into discrete, defined rhythm types based on characteristic ICI patterns. Coda tempo is relative to the total duration of the coda itself. This can also be clustered into discrete tempo types across all coda durations as well (see [L8]).

Action - We added a sentence specifying that in this case we can use both ICIs and IOIs because of the standardized length of a single click.

Comment 25

Line 36: Are there non-vocalized codas to require the disambiguation here?

No, we have omitted for clarity.

Comment 26

Line 44: ”Higher” than which other social group class?

Sperm whales live in a multi-level social organization. Clans are a “higher” level of social organization than the social “units” which we define in line 40. Clans are made up of all units which share similar production repertoire of codas.

Action - We have added ’above social units’ on line 44 to make this clear.

Comment 27

Line 47: The use of “symbolic” continues to be enigmatic, even if authors are taking in this classification from other researchers. In signal theory (semiotics), not all biomarkers are necessarily symbols. I advise the authors to avoid the use of the term colloquially and instead adopt the definition used in the research field within which the study falls in.

There is ample examples of the use of ”symbolic” when referring to markers of in-group membership both in human and non-human cultures.Our choice to use the term “symbolic” is based on a previous study [L9] that found quantitative evidence that sperm whale identity codas function as symbolic markers of cultural identity, at least for Pacific Ocean clans. The full reasoning behind why the authors used the term “symbolic markers” is given in that paper, but briefly, they found evidence that identity coda usage becomes more distinct as clan overlap increases, while non-identity coda usage does not change. This matches theoretical and empirical work on human symbolic markers[L1, L2, L10, L11].

Action - We retain the use of the term here, as defined in the works cited, and based on its prior usage in the study of both human and non-human cultures.

Comment 28

Line 50: This statement is not technically accurate. The use of a signal as a marker by individuals can only be determined by how individuals ”interpret” and react to that signal - e.g., via playback experiments - it cannot be determined by how different populations use and produce the signals.

We respectfully disagree. While we agree that the optimal situation would be that of playback, the contextual use can provide insight into the functional use of signals; as can expected patterns of use and variation, as was tested in the papers we cite. However, this argument is not the scope nor the synthesis of this paper. These statements are supported by existing published works, as cited, and we encourage the reviewer to take exception with those papers.

Comment 29

Line 69: ”Meaningful speech characteristics”??? These terms do not logically or technically follow the previous statement. Why not stay faithful to the results and state that the method used seems to be valid and reliable because it confirms former studies and methods?

Action - Reworded to better underline the method’s results with previous studies

Comment 30

Lines 72-74: This statement doesn’t seem to accurately capture/explain/resume the difference between ID and non-ID codas.

We are not sure what the reviewer is referring to in this case. The sentence in this case was meant to explain the different relations that ID/non-ID codas have with clan sympatry.

Comment 31

Line 75: The information provided in the few previous sentences does not allow the reader to understand why these results support the notion that cultural transmission and social learning occurs between clans.

We conclude out introduction with a brief summary of our overall findings, which we then use the rest of the manuscript to support these statements.

Comment 32

Table 1: So far, the authors refer to their analyses as capturing the ”rhythm” of whale clicks. Consequently, it is not readily clear at this point why the authors rely on ”ICIs” (inter click intervals) instead of the ”universal” measure used across taxa to capture the rhythm of signal sequences - IOIs (inter onset intervals). If ICIs are the same measure as IOIs, why not use the common term, instead of creating a new term name? Alternatively, if ICIs are not equivalent to IOIs, then arguably the analyses do not capture the ”rhythm” of whale clicks, as claimed by the authors. Any rhythmic claim will need to be based on IOI measures. In animal behaviour, stereotyped is primarily used to describe pathological, dysfunctional behaviour. I suggest the use of other adjective, such as ”regular”, ”repetitive”, ”recurring”, ”predictable”. Another deviation from typical terminology: ”usage frequency” -¿ ”production rate”. Why is a clan a ”higher-order” level of social organization? This requires explanation, at least a mention, of what are the ”lower-order” levels. To the non-expert reader, there is a logical circularity/gap here: Clans are said to produce clan-specific codas, and then, it is said that codas are used to delineate clans. Either one deduces, or one infers, but not both. This raises the question, are clans confirmed by any other means than codas?

We are not creating a “new term name”: inter-click interval (ICI) is the standard terminology used in odontocete (toothed whale) research. We take the reviewer’s point that some readers will not be coming to our paper with that background, however, and now explicitly point out that ICI is synonymous with IOI for sperm whales. Please see our response to your earlier comment for more on this point.

Comment 33

Line 92: Unclear term, ”sub-sequence”. Fig. 1B doesn’t seem to readily help disambiguate the meaning of the term.

In fact reference to Fig. 1B is misplaced as it does not refer to the text. A sub-sequence is simply a contiguous subset of a coda, a subset of it.

Action - Removed ambiguous reference to Fig. 1B

Comment 34

Line 94: How does the use of ”sequence” compare here with ”sub-sequence” above?

In fact its the same situation although the previous comment highlighted a source of ambiguity.

Action - Reworded the sentence to be less confusing.

Comment 35

Line 95: Signal sequences don’t ”contain” memory, they require memory for processing.

Action - Rephrased from “sequences contain memory” to “states depend on previous sequences of varying length”.

Comment 36

Lines 95-97: The analogy with human language seems forced, combinatorics in any given species are expected to entail different transitions between unit/unit-sequences.

Thank you for the comment. Indeed, the purpose of the analogy is to illustrate how variable length Markov Chains work (which have been shown to be good at discerning even accents of the same language). We used human language as an analogy to provide the readers’ with a more intuitive understanding of the results.

Action - Revised paragraph to read: “Despite we do not have direct evidence of unitary blocks in sperm whale communication, on can imagine this effect similarly to what happens with words (e.g., a word beginning with “re” can continue in more ways than one starting with “zy”).”

Comment 37

Line 97: Unclear which possibility is this.

Action - Made the wording clearer.

Comment 38

Line 99: Invocation of memory, although common in the use of Markov chains, in inadequate here given that the research did not study how individuals perceived or processed click sequences, only how individual produced click sequences. If the authors are referring to the cognitive load imposed by producing clicks sequences, terms such as ”sequence planning” will be more accurate.

Here, we use the term “fixed-memory” in relation to the definition of a variable length Markov model. We feel that, in this section of the manuscript, the context is clear that it is a mathematical definition and in no way invokes the biological idea of memory or cognition. It is rather standard to use memory to describe the order of Markov chains. Swapping words in the definition of mathematical objects when the context is clear seems to cause unnecessary ambiguity.

Action - We clarified this in the manuscript (see comments above).

Reviewer #3 (Recommendations):

Comment 39

Line 16: Add ”broadly defined” as there are many other more restricted definitions (see for example Tomasello 1999; 2009). Tomasello M (1999) The cultural origins of human cognition. Harvard University Press, Cambridge Tomasello M (2009) The question of chimpanzee culture, plus postscript (chimpanzee culture 2009). In: Laland KN, Galef BG (eds) The question of animal culture. Harvard University Press, Cambridge, pp 198-221.

Thanks for the clarification.

Action - We added the term “broadly” and added the last reference.

Comment 40

Line 22: Is all stable social learned behavior that becomes idiosyncratic and ”distinguishable” considered symbolic markers? If not, consider adding ”potentially.”

No, but the evolution of cultural groups with differing behavior can reorganize the selective environment in such a way that it can favour an in-group bias that was not initially advantageous to individuals and lead to a preference towards others who share an overt symbolic marker that initially had no meaning and a random frequency in both populations. That is to say, even randomly assigned trivial groups can evolve arbitrary symbolic markers through in-group favouritism once behavioural differences exist even in the absence of any history of rivalry, conflict, or competition between groups. See for example [L1, L2].

Comment 41

Table 1: Identity codas are defined as a ”Subset of coda types most frequently used by a sperm whale clan; canonically used to define vocal clans.” Therefore, I infer that an identity coda is not exclusively used by a specific clan and may be utilized by other clans, albeit less frequently. If this is the case, what criteria determine the frequency of usage for a coda to be categorized as an identity or non-identity coda? Does the criteria used to differentiate between ID and non-ID codas reflect the observed differences in micro changes between the two and within clans?

The methods for this categorization are defined, discussed, and justified in previous work in [L9, L12]. We feel its outside the scope of this paper to review these details here in this manuscript. However, the differences between vocal styles discussed here and the frequency production repertoires which allow for the definition of identity codas are on different scales. The differences between identity and non-identity codas are not the observed differences in vocal style reported here.

Comment 42

Table 1: The definition of vocal style states that it ”Encodes the rhythmic variations within codas.” However, if rhythm changes, does the type of coda change as well? Typically, in musical terms, the component that maintains the structure of a rhythm is ”tempo,” not ”rhythm.” How much microvariation is acceptable to maintain the same rhythm, and when do these variations constitute a new rhythm?

Thank you for raising this important point about the relationship between rhythmic variations and coda categorization. In our definition, ”vocal style” refers to subtle, micro-level variations in the rhythmic structure of codas that do not alter their overarching categorical identity. These microvariations are akin to ”tempo” changes in musical terms, which can modify the expression of a rhythm without fundamentally altering its structure.

The threshold at which microvariations constitute a new rhythm, and thus a new coda type, remains an open question and is a limitation of current analytical approaches. In our study, we used established classification methods to group codas into types, treating variations within these groups as part of the same rhythm. Future work could refine these thresholds to better distinguish between meaningful rhythmic variation and the emergence of new coda types.

Comment 43

Table 1: Change ”say” to ”vocalize” (similarly as used in line 273 for humpback whales ”vocalizations”).

Thanks.

Action - Done.

Comment 44

Lines 33-35 and Figure 1-C: Can a lay listener discern the microvariations within each coda type by ear? Consider including sound samples of individual rhythmic microvariations for the same coda type pattern (e.g., Four plus, Palindrome, Plus One, Regular) to provide readers/listeners with an impression of their detectability. If authors considered too much or redundant Supplemental material at least give a sound sample for each the 4 subcodas modeled structures examples of 4R2 coda variations depicted in Figure 1-C so the reader can have an acoustic impression of them.

We do not think that human listeners would be able to all of the variation detected here. However, this does not mean that it is not important variation for the whales. Human observers being able to classify call variation aurally shouldn’t be seen as a bar representing important biological variation for non-human species, given that their hearing and vocal production systems have evolved independently. Importantly, ’Four Plus’,’Palindrome’, etc are names of Clans; sympatric, but socially segregated, communities of whale families, which share a distinct vocal dialect of coda types. These clans each have have distinguishable coda dialects made up of dozens of coda types (and delineated based on identity codas), these are not names/categorical coda types themselves.

Action - We now provide audio samples of all coda types listed in Figure 1B in the paper’s Github repository.

Comment 45

Line 69: As stated above, it may be confusing to refer to it as ”speech.” I suggest adding something like: ”Our method does capture one essential characteristic of human speech: phonology.” Reply 45.—Thank you for drawing our attention to this.

Action - We removed the word “speech” from the manuscript, using “communication” and/or “vocalization” depending on the context.

Comment 46

Line 111-112: Consider adding a sound sample of the variation of the 4R2 coda type that can be vocalized as BCC but also as CBB as supplementary data.

What the reviewer has correctly observed is that the traditional categorical coda type ’names’ do not capture the variation within a type by rhythm nor by tempo.

Action - We have added samples of all coda types listed in Figure 1B in the paper’s Github repo.

Comment 47

Figure 3: Include a sound sample for each of the 7 coda types in Figure 1B (”specific vocal repertoires”) to illustrate the set of coda types used and their associated usage frequencies, or at least for each of the 7 coda types in Figure 3 and tables S1 and S2.

Sperm whales in the Eastern Caribbean produce dozens of rhythm types across at least five categorical tempo types [L8, L13]. The coda types represented in Figure 1B do not demonstrate all the variability inherent in the sperm whales’ vocal dialect. Importantly, Figure 3, as well as table S1 and S2, refer to clan-level dialects not specific individual coda types.

Action - We added sound samples for each coda rhythm type listed in Figure 1B to the Github repository.

Comment 48

Lines 184-190: It is unclear what human analogy term is used for ID codas. This needs clarification.

We are not making an analogy in humans for the role of ID vs non-ID codas, but only providing the example of accents as changes in vocalization (style) without a change in the actual words used (repertoire).

Action - We tried to make it clearer in the manuscript.

Comment 49

Line 190: Change ”whale speech” to ”whale vocalizations.”

Thanks.

Action - Done.

Comment 50

Figure 4: Correct citation number Hersh ”10” to Hersh ”11.”

Thanks.

Action - Fixed the reference.

Comment 51

Lines 224-232: Clarify whether the reference to how spatial overlap affects the frequency of ID codas refers to shared ID codas between clans or the production frequency of each coda within the total repertoire of codas.

The similarity between ID coda repertoires we are referring to there is based on the ID codas of both clans.

More details on the comparison can be found in [L9].

Action - We added a sentence explaining the comparison is made using the joint set of ID codas.

Comment 52

Lines 240-241: What are non-ID codas vocal cues for?

Non-ID codas likely serve as flexible, context-dependent signals that facilitate group coordination, convey environmental or social context, and promote social learning, especially in mixed-clan or overlapping habitats. Their variability suggests multifunctional roles shaped by ecological and social pressures.

Comment 53

Lines 267-268: It’s unclear whether non-ID coda vocal styles are genetically inherited or not, as argued in lines 257-258.

We did not intend to argue that non-ID coda vocal styles are genetically inherited. Instead, we aimed to present a hypothetical consideration: if non-ID coda vocal styles were genetically inherited, one would expect a direct correlation between vocal style similarity and genetic relatedness. This hypothetical framework was introduced to strengthen our argument that the observed patterns are unlikely to be explained by genetic inheritance, as such correlations have not been observed. While we acknowledge that we lack definitive proof to rule out genetic influences entirely, the evidence available strongly suggests that social learning, rather than genetic transmission, is the more plausible mechanism.

Action - Clarified in manuscript.

Comment 54

Line 277: Can males mate with females from different clans?

Yes, genetic evidence shows that males may even switch ocean basins.

Action - We have clarified that we mean the female members of units from different clans have only rarely been observed to interact at sea between clans.

Comment 55

Lines 287-292: Consider discussing the difference between controlled/voluntary and automatic/involuntary imitation and their implications for cultural selection and social learning (see Heyes 2011; 2012). Heyes, C. (2011). Automatic imitation. Psychological bulletin, 137(3), 463. Heyes, C. (2012). What’s social about social learning?. Journal of comparative psychology, 126(2), 193.

Thank you for your insightful comment regarding this. The distinction between controlled/voluntary and automatic/involuntary imitation, as highlighted by Heyes [L14, L15], provides a potentially valuable framework for interpreting social learning mechanisms in sperm whales. Automatic imitation refers to reflexive, often unconscious mimicry driven by perceptual or motor coupling, while controlled imitation involves deliberate and goal-directed efforts to replicate behaviors. Both forms likely play complementary roles in the cultural transmission observed in sperm whales.

This dual-process perspective highlights the potential for cultural selection to act at different levels. Automatic imitation may drive convergence in shared environments, promoting acoustic homogeneity and facilitating inter-clan communication. In contrast, controlled imitation ensures the preservation of clan-specific vocal traditions, maintaining cultural diversity. This interplay between automatic and controlled processes could reflect a balancing act between cultural assimilation and differentiation, underscoring the adaptive value of these mechanisms in dynamic social and ecological contexts.

Action - We have incorporated a short discussion of this distinction and its implications for our findings in the Discussion. Additionally, we have cited [L14, L15] to provide theoretical grounding for this interpretation.

Comment 56

Methods: Consider integrating the paragraph from lines 319-321 into lines 28-35 and eliminate redundant information.

Thanks.

Action - We implemented the suggestion, removing the first paragraph of the Dataset description and integrating the information when we introduce the concepts of codas and clicks.

[L1] C. Efferson, R. Lalive, and E. Fehr, Science 321, 1844 (2008).

[L2] R. McElreath, R. Boyd, and P. Richerson, Curr. Anthropol. 44, 122 (2003).

[L3] L. S. Burchardt and M. Knornschild, PLoS Computational Biology 16, e1007755 (2020).

[L4] A. Ravignani and K. de Reus, Evolutionary Bioinformatics 15, 1176934318823558 (2019).

[L5] C. T. Kello, S. D. Bella, B. Med´ e, and R. Balasubramaniam, Journal of the Royal Society Interface 14, 20170231 (2017).

[L6] D. Gerhard, Canadian Acoustics 31, 22 (2003).

[L7] N. Mathevon, C. Casey, C. Reichmuth, and I. Charrier, Current Biology 27, 2352 (2017).

[L8] P. Sharma, S. Gero, R. Payne, D. F. Gruber, D. Rus, A. Torralba, and J. Andreas, Nature Communications 15, 3617 (2024).

[L9] T. A. Hersh, S. Gero, L. Rendell, M. Cantor, L. Weilgart, M. Amano, S. M. Dawson, E. Slooten, C. M. Johnson, I. Kerr, et al., Proc. Natl. Acad. Sci. 119, e2201692119 (2022).

[L10] R. Boyd and P. J. Richerson, Cult Anthropol 2, 65 (1987). [L11] E. Cohen, Curr. Anthropol. 53, 588 (2012).

[L12] T. A. Hersh, S. Gero, L. Rendell, and H. Whitehead, Methods Ecol. Evol. 12, 1668 (2021), ISSN 2041-210X, 2041-210X.

[L13] S. Gero, A. Bøttcher, H. Whitehead, and P. T. Madsen, R. Soc. Open Sci. 3, 160061 (2016).

[L14] C. Heyes, Psychological Bulletin 137, 463 (2011).

[L15] C. Heyes, Journal of Comparative Psychology 126, 193 (2012).

Author response:

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

Reviewer #1 (Public review):

Summary:

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

Strengths:

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

Weaknesses：

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

Thanks for the comment. The quantification results of c-Fos signal have been provided in the main text and figures.  

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

Thanks for raising this point. We did indeed monitor the core body temperature during stimulation of POA-PVT pathway, but we did not observe any significant changes. We have included this finding in the revised manuscript.

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

Figure 3G does compare the Day 1 preheat to Day22 preheat, and the difference was significant. The wording has been corrected to avoid confusion. Also, we have modified Figure 3D to 3H in our revised manuscript to improve the clarity of these plots.

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

Thanks for bringing out this issue. We acknowledge that using AA1-EGFP (a different serotype and reporter compared to the AAV9-ChR2-mCherry) as our control virus is not ideal. But based on our own prior experiments, we observed no significant differences in baseline behaviors between animals injected with AAV1 and AAV9 EYFP as well as control mice without virus injection. Therefore, we believe that the baseline behaviors of the animals were unaffected.

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

Thanks for the comment. We agree with the reviewer that the area under the curve (AUC) is an alternative readout for measurement of the strength of calcium response. However, the reason why we chose the maximum z-score is based on the observation that we found POA recipient pPVT neurons after chronic heat treatment exhibited a higher calcium peak corresponding to certain behavioral performances when compared to pre-heat conditions. We thus applied the maximum z-score as a representative way to describe the neuronal activity changes of mice during certain behaviors before and after chronic heat treatment. The other consideration is that we want to reflect that POA recipient pPVT neurons become more sensitive and easier to be activated after chronic heat exposure under the same stressful situations compared to control mice. The maximum z score represented by peak in combination with particular behavioral performances is considered more suitable to highlight our findings in this study.

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

Thanks for the comment and suggestion. We had tried both methods and the statistical results were similar. As suggested, we have updated Fig 5V, as well as Fig. 5H and 5O by comparing animal average in our revised manuscript.

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

Thanks for the valuable suggestion, which would enhance the conclusion. We will consider adopting this approach in future investigations into this question.

Reviewer #2 (Public review):

Summary

The study by Cao et al. highlights an interesting and important aspect of heat- and thermal biology: the effect of repetitive, long-term heat exposure and its impact on brain function.

Even though peripheral, sensory temperature sensors and afferent neuronal pathways conveying acute temperature information to the CNS have been well established, it is largely unknown how persistent, long-term temperature stimuli interact with and shape CNS function, and how these thermally-induced CNS alterations modulate efferent pathways to change physiology and behavior. This study is therefore not only novel but, given global warming, also timely.

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

Strengths

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

Thanks for pointing out the need for clarification. Our optogenetic inhibition had been conducted for 21 days during the heat exposure period (90 mins) for each mouse. And to avoid the light-induced heating effect, we applied the cyclical mode of 3 minutes’ light on and 3 minutes’ light off only during the process of heat exposure but not other time. The detailed description has been supplemented in the Method part of our revised manuscript.

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

Thanks for pointing out this study that suggests similar plasticity of POA neurons under long-term heat exposure serving a different purpose. We have included this information in our discussion as well.  

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

Thanks for raising these important questions. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

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

We agree with the reviewer that the results of the suggested experiment would further strengthen our hypothesis. We will try to confirm this in future studies.

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

Based on our observations, we did not find increased depression parameters after acute heat stress in our experiments (data not shown), which was consistent with other two previous studies (Beas et al., 2018; Zhang et al., 2021). It appears that acute heat stress is more associated with anxiety-like behavior and may not be sufficient to induce depression-like phenotypes in rodents, aligning with our observation during experiments.

Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

Zhang GW, Shen L, Tao C, Jung AH, Peng B, Li Z, Zhang LI, Whit Tao HZ (2021) Medial preoptic area antagonistically mediates stress-induced anxiety and parental behavior Nat Neurosci 24:516-528.

Weaknesses/suggestions for improvement.

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

Thanks for the suggestion. We have modified the introduction in our revised manuscript to make it more balanced.

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

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

Thanks for pointing out the important aspect regarding the different heat exposure paradigms and their potential impacts. We have incorporated these points into both the Introduction and Discussion sections of the revised manuscript.

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

Thanks for the suggestion, and we have clarified the procedure in the revised manuscript.

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

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

Thanks for the comments. For Figure 2-figure supplement 1, the baseline c-Fos activity in pPVT were indeed measured from mouse under room temperature. Observed activity may be attributed to the diverse functions that the pPVT is responsible for. Compared to the heat-exposed group, we observed significant increases in c-Fos signals, suggesting the effect of heat exposure.

For Figure 2-figure supplement 2, through targeted injection of AAV1-Cre into the POA, we achieved selective expression of Cre-dependent ChR2-mCherry in pPVT neurons receiving POA inputs. Following heat exposure, we observed substantial colocalization between heat-induced c-Fos expression (green signal) and ChR2-mCherry-labeled neurons (red signal) in the pPVT. This extensive overlap indicates that POA-recipient pPVT neurons are predominantly heat-responsive and likely mediate the behavioral alterations induced by chronic heat exposure. We have validated these signals and included updated quantification in our revised manuscript.

For Fig 2H, we specifically patched those neurons that were surrounded by red fluorescence under the microscope, ensuring that the patched neurons had a high likelihood of being innervated from POA. This is why all 63 recorded pPVT neurons were found to receive direct synaptic input from the POA.

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

Thanks for the suggestion. We have supplemented this part in our revised manuscript.

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

Thank you for pointing out this issue. We acknowledge that we included AAV1-EGFP when recording the GCaMP8F signal to assist in the post-verification of the accuracy of the injection site. But we also collected recording data from mice with AAV1-Cre without EGFP injected into POA and Cre-dependent GCaMP8F in pPVT, albert in a smaller number. We did not observe any obvious differences in the change in calcium signal between these two virus strategies, suggesting that the sensitivity of the GCaMP signals was not significantly affected by the increased baseline fluorescence due to EGFP.

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

Thanks for the comment. For this female encounter test, we strictly followed the protocol by Ago Y, et al., (2015). During this test, both the strange male and female mice were placed into a wired cup (which is made up of mental wire entanglement and the size for each hole is 0.5 cm [L] x 0.5 cm [W]), which successfully prevented large body contact and the mating behavior but only innate sex-motivated moving around the cup. We have supplemented the details in the method part of our revised manuscript.

Ago Y, Hasebe S, Nishiyama S, Oka S, Onaka Y, Hashimoto H, Takuma K, Matsuda T (2015) The Female Encounter Test: A Novel Method for Evaluating Reward-Seeking Behavior or Motivation in Mice Int J Neuropsychopharmacol 18: pyv062.

Reviewer #3 (Public review):

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

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

We also share similar considerations as the reviewer and indeed have conducted experiments to explore this possibility. Our findings suggest that the POA-pPVT pathway may also mediate behavioral changes induced by other chronic stress, e.g. chronic restraint stress. Nevertheless, given the well-known prominent role of POA neurons in heat perception, we do believe that the POA-pPVT has a specialized role in mediating chronic heat induced changes. The role of this pathway in other stress-related responses will need a more comprehensive study in the future.

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

Thanks for the suggestion. We have modified the description of “emotion” to “emotional valence” in various places throughout the revised manuscript.

(3) Related to the role of POA input to pPVT,

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

For point (a), we acknowledge that pPVT neurons receiving POA inputs may also receive projections from other brain regions. While these additional inputs warrant investigation, they fall beyond the scope of our current study and represent promising directions for future research. Notably, compared to other well-characterized regions such as the amygdala and ventral hippocampus, the pPVT receives particularly robust projections from hypothalamic nuclei (Beas et al., 2018). Our optogenetic inhibition of POA-recipient pPVT neurons during chronic heat exposure effectively prevented the influence of POA excitatory projections on pPVT neurons. Furthermore, selective optogenetic activation of POA excitatory terminals within the pPVT was sufficient to induce similar behavioral abnormalities in mice, strongly supporting the causal role of POA inputs in mediating chronic heat exposure-induced behavioral alterations.

Beas BS, Wright BJ, Skirzewski M, Leng Y, Hyun JH, Koita O, Ringelberg N, Kwon HB, Buonanno A, Penzo MA (2018) The locus coeruleus drives disinhibition in the midline thalamus via a dopaminergic mechanism Nat Neurosci 21:963-973.

Regarding point (b), we acknowledge certain limitations in our in vitro patch-clamp recordings when attributing increased pPVT neuronal excitability to enhanced presynaptic POA inputs. Nevertheless, our brain slice recordings clearly demonstrated heightened excitability of pPVT neurons following chronic heat exposure. This finding was further corroborated by our in vivo fiber photometry recordings specifically targeting POA-recipient pPVT neurons, which confirmed that the increased pPVT neuronal activity was indeed modulated by POA inputs. The causal relationship was strengthened by our observation that optogenetic activation of POA excitatory terminals within the pPVT reproduced behavioral abnormalities similar to those observed in chronic heat-exposed mice. Additionally, our inability to induce circuit-specific LTP in the POA-pPVT pathway suggests that these synapses were already potentiated and saturated, reflecting enhanced excitatory inputs from the POA to pPVT. Collectively, these findings support our conclusion that increased excitatory projections from the POA to pPVT likely represent a key mechanism underlying chronic heat exposure-induced behavioral alterations in mice.

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

Thanks for the comment and suggestion. We acknowledge that there are both excitatory and inhibitory projections from POA to pPVT. Although it is not our primary focus, we are aware of the substantial inhibitory inputs from POA to pPVT which suggests an important function. However, we do not think that this pathway, which would exert an opposite effect on POA-recipient pPVT neurons compared to the excitatory input, contributes to the long-term effect of chronic heat exposure. This is due to the increased, rather than decreased, excitability of the neurons. There is a possibility that this inhibitory input serves as a short-term inhibitory control for other purpose. Further work is needed to fully address this question.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have a number of suggested minor edits that would improve the readability and interpretation of figures for the reader. In many figures, there are places where it is unclear what is being tested, and making minor changes would make the manuscript flow more easily for the reader:

(1) The authors could add additional details about the behavior paradigms in the Figures, especially Figure 1. How long was the chronic heat exposure for? At what temperature? What is the length of time between the end of heat exposure and the start of behaviors? What was the schedule of testing for EPM and social behaviors? Was it all on the same day or on different days? These details will make it easier for the reader to understand the behavior tests.

We have revised our experimental scheme, especially Figure 1, and added more detailed descriptions in the method section. The modifications have also been applied to the other figures.

(2) In Figures 1J and 1K, it is a bit unclear what is being shown in the right panel, since there are no axes or labels to interpret what is being plotted.

We have added body kinetics (purple dot) in the left panel of Figure 1J and 1K to align with the right panels, and we have updated our descriptions in the figure legend.

(3) In general, Figure 1 would benefit from more headers/labels or schematics to demonstrate what is being tested (for example, it's unclear that forced swim, tail suspension, open field, aggression, sucrose preference, or acoustic startle are being studied unless the reader looks at the figure legend in depth. Simple schematics or titles for each panel would help.

We have added the abbreviated titles for each panel of Figure 1 to help readers to better understand what was being tested.

(4) Figure 2A would benefit from edits to the schematic so that it is clear that heat exposure is being done before the animal is sacrificed and cFos is stained.

We have revised the text to clarify that heat exposure occurred before the animal was sacrificed and c-Fos was stained.

(5) Figure 2D: would help if the quantification of overlap of cFos and rabies was shown in the figure in addition to reporting it in the text (84%).

We have added quantification in Figure 2D.

(6) The supplemental data in Figure 2 - Supplemental Figure 1 showing increased Fos in PVT and POA after heat exposure would actually help if it was in main Figure 2 so that the reader can more clearly see the rationale for choosing the POA-PVT circuit. But this is a matter of preference and up to the author where they want to show this data.

Thanks for the suggestion. But considering the layout and space, we will prefer to retain this part in Figure 2-supplemental figure 1.

(7) Figure 3 would benefit from a behavior schematic illustrating the time course of the experiment and what the heat exposure protocol is for each day (how many minutes heat 'on' vs 'off', the temperature of heat, etc). Also, what is different about day 22 that makes it chronic heat vs day 21? Currently, it is a bit hard to understand the protocol.

We have added the temperature and time of chronic heat exposure in the schematic of Figure 3. The “day 22” represented the time point after chronic heat exposure. And we measured the calcium activity of POA recipient pPVT neurons on day 22 to compare with day 1 to demonstrate that the activity changes of POA recipient pPVT neurons after chronic heat exposure.

(8) Figure 3D, it is unclear what the difference is between the Day 1 data on the left and Day 1 data on the right. Same with Figure 3H, unclear what the difference is between the left and the right.

The left panel and right panel reflect different parameters: frequency /min (left) and amplitude (△F/F) for Figure 3D-3H. By doing this, we want to reflect the dynamic activity changes of POA recipient pPVT neurons throughout chronic heat exposure process. Now, all figures in panel 3D to 3H have been revised to make them clearer in meaning.

(9) Figure 4A would benefit from schematics showing the stimulation protocol for chronic optogenetics (how many days? Frequency? Duration of time? Etc)

We have added detailed schematics in our Figure 4A.

Reviewer #2 (Recommendations for the authors)

(1) It is interesting that social behavior appears to be reduced upon long-term heat exposure but not after acute heat exposure. Interaction of animals, such as huddling, can be used by animals as a form of behavioral thermoregulation in cold environments and heat may drive animals apart to allow for better heat dissipation. The social interaction measured here is not huddling (because, I assume, the animals are separated by a divider?) but is this form of behavior measured here related to huddling/"social thermoregulation"? This could be discussed.

Our behavioral tests were performed at room temperature. Even though huddling is a type of social behavior, based on our observation, the tested mouse was actively revolving around the mental cap, suggesting this type of behavior is not related to huddling/social thermoregulation type of social behavior.

(2) Line 113: The statement "Chronic treatment did not change body temperature" should be clarified/rephrased because 90 minutes of 38 degrees centigrade exposure to heat will increase the body temperature of mice. It would be helpful if the authors made clear that they measure body temperature before the heat stimulus (and not during the heat stimulus), which is now only obvious if one digs into the methods section.

We have revised the text and clarified that body temperature was measured before the heat stimulus in the revised manuscript.

(3) Figure 1J and K: for the non-experts, these graphs are difficult to interpret, some more explanation is needed (what exactly is measured ?). We believe that the term "arousal" may not be justified in this context because the authors have not measured sleep patterns (EEG and EMG) to show that the mice arouse from a sleep (or sleep-like) stage; the authors may consider changing the terminology, e.g. something along the lines of "agitation" or "activity".

We have further elaborated the meaning of Figure 1J and K in our revised manuscript. The acoustic startle response is a well-recognized behavioral parameter reflecting arousal levels in rodent model. The more agitation in response to stimulus, the higher the arousal levels in mice. We have used the term “agitation” to describe mice’s performance in the acoustic startle response test.

Reviewer #3 (Recommendations for the authors):

(1) The authors suggest in the introduction of the manuscript that the HPA axis and other multifaceted factors may influence emotional changes caused by heat stress (lines 63-78). However, there are no experiments or discussions on how the POA-pPVT circuit interacts with these factors. In line with the study's proposed direction in the introduction section, it would be valuable to explore, or at least discuss, whether and how the POA-pPVT circuit interacts with the HPA axis or other neural circuits known to regulate emotional and stress responses. Alternatively, the reviewer suggests revising the content of the introduction to align with the focus of the study.

Although POA is known to possibly interact with the HPA axis via its connection with the paraventricular nucleus of the hypothalamus, there is hardly any evidence for the pPVT. Thus, we prefer not to speculate this question, which remains open, in our current manuscript.

(2) In Figure 5, the authors report that pPVT neurons that receive projections from the POA exhibited increased responses to stressful situations following chronic heat exposure. However, considering the long pre- and post-recording time gap of approximately three weeks, the additional expression of GCaMP protein over time could potentially account for the increased signal. Therefore, the reviewer recommends including a control group without heat exposure to rule out this possibility.

We have included Figure 3-figure supplement 1 in our manuscript to exclude the effect of expression of GCaMP protein over time on the recording of calcium signal.

(3) Related to Figure 2, a) Please include quantification data of the overlap between retrogradely labeled and c-Fos-expressing POA neurons, which can be presented as a bar graph in Figure 2. This would be beneficial for readers to estimate how many warm-activated POA neurons connected to the pPVT are actively engaged under these conditions.

In the revised manuscript, we have included the quantification analysis in Figure 2.

b) The images in Figure 2 - Figure Supplement 1 seem to degrade in quality when magnified, making it difficult to discern finer details. Higher-resolution images would greatly improve the clarity and help in accurately visualizing the c-Fos expression patterns in the POA and pPVT regions.

We have changed our images of Figure 2-figure supplement 1 to higher-resolution in the revised manuscript.

c) The c-Fos images in Figure 2D and Figure 2 - Figure Supplement 2C appear unusual in that the c-Fos signal seems to fill the entire cell, whereas c-Fos protein is localized to the nucleus. Could the authors clarify whether this image accurately represents c-Fos staining or if there might be an issue with the staining or imaging process?

We are confident that the green signals in both Figure 2D and Figure 2-figure supplement 2C, which did not occupy the whole cell body, have already accurately reflected the c-Fos and that they were nucleus staining. We have updated the amplified picture in Figure 2D.

d) In Supplemental Figure 2B, the square marking the region of interest should be clearly explained in the figure legend to ensure that readers can fully understand the context and focus of the image.

We have further modified our figure legend in Figure 2-figure supplement 1 in our revised manuscript.

many sources used throughout. could show not much original research.

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

Learn more at Review Commons

Reply to the reviewers

Reviewer 1

Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?

We have not tested whether HAC1 mRNA is processed in S. cerevisiae.

In addition, as requested by the reviewers, we reassessed our RNA-seq data and compared it with data from (Kimmig et al., 2012) (UPR activation in S. pombe), which added a new layer of data that reinforces the differences between the transcriptomic responses induced by HU and DIA and the canonical UPR. The following information is now included in the paper (page 26, highlighted in blue):

“We further compared our transcriptomic data with that obtained by Kimmig et al. from DTT- treated S. pombe cells. When we compared the genes that were downregulated in our conditions with the ones described by Kimmig et al. (FC≤-1), we found no similarities between HU treatment (75 mM HU for 150 minutes) and UPR-induced downregulation, and only three genes ( ist2, efn1 and xpa1) all of them encode for transmembrane proteins, were common with DIA treatment (3 mM DIA for 60 minutes). Additionally, ist2 and xpa1, but not efn1, are considered Ire1-dependent downregulated genes and are located in the ER. These results show that HU- or DIA- induced transcriptomic programs are different from UPR, as they do not heavily rely on mRNA decay and favor gene overexpression. Interestingly, we found similarities between genes showed to be upregulated more that twofold by DTT in Kimmig et al., and HU and DIA conditions. When the two N-Cap-inducing conditions were compared with DTT, we found eight common upregulated genes (frp1, plr1, SPCC663.08c, srx1, gst2, str3, caf5 and hsp16) mostly involved in reduction processes and the chaperone Hsp16 which suggests folding stress”.

Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.

We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics. In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B). In addition, we are currently performing mass spectrometry experiment to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).

What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?

We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.

Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).

In this regard, the following information has been added to the manuscript (page 30, highlighted in blue):

“We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype”.

Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?

DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, in this revised version of the manuscript we have included data showing that guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding accumulate in cytoplasmic foci upon HU or DIA treatment that colocalize with Hsp104 (Figure 4I-J, pages 23-24 and 29), which demonstrate that cytoplasmic chaperone foci contain misfolded proteins. We have also tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules.

Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?

To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.

Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.

Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.

To fully rule out this possibility, we have tested whether these Hsp104-aggregates colocalized with ER transmembrane proteins Rtn1 and Yop1, and with Gma12-GFP that marks the Golgi apparatus. In none of the cases the Hsp104-containing aggregates colocalized or were surrounded by membranes. This information will be added to the final version of the manuscript.

With respect to autophagy, we have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.

To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.

To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.

On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.

Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.

As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.

Minor issues

1. __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
2. 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
3. __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed

Reviewer 2

Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?

We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.

We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and/or alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.

Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.

This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.

We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.

These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 22.

Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?

As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 21.

We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 18).

Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).

Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.

Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).

Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?

Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This likely decreases the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.

To further test this idea, we analyzed the number and size of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. As expected, we found an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions. This information has now been added to the paper (Figure 4K, pages 23-24 and 29).

On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes cytoplasmic protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.

As this question had also been raised by reviewer 1, we further explored the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:

• We tested whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments and, indeed, Hsp104-containing aggregates colocalize with Guk1-9-GFP and Rho1.C17R-GFP. This information has now been added to the paper (Figure 4I-J, pages 23-24 and 29).
• We tested whether these foci were membrane bound with several ER transmembrane proteins (Tts1, Yop1, Rtn1) and integral membrane protein Ish1, and in none of the cases we detected membranes surrounding the aggregates. This information will be included in the final version of the paper.
• We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
• We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 29).

The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.

We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers’ concerns.

Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.

To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.

Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.

Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.

We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress “protects” cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).

Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).

We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.

Minor points

1. __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.

__ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.

Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.

__ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__

We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.

__ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__

We have included pertinent clarification in the new discussion.

Reviewer 3

Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.

Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.

With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we reassessed our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA.

Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.

We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.

In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (pages 20-21).

Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.

Thapsigargin has been described to be ineffective in yeasts as they lack a (SERCA)‐type Ca2+ pump which is the target of this drug (Strayle et al., 1999). However, deletion of the P5A-type ATPase Cta4, which is required for calcium transport into ER membranes (Lustoza et al., 2011), reduced but did not abolish ER expansion. We also tested the effect of anisomycin. We found that anisomycin in combination with HU or DIA mimicked CHX behavior (ER expansion occurrs in both conditions, exacerbating perinuclear ER expansion in combination with DIA and cortical ER expansion when combined with HU). It is difficult to correlate this result with a role of Ca leakage in ER expansion, as there is no recent information regarding CHX and Ca leakage, although it has been indicated that CHX treatment does not increase cytoplasmic Ca levels (Moses & Kline, 1995). As anisomycin, like CHX, blocks protein synthesis and stabilizes polysomes, what we can conclude from this information is that nascent peptides attached to ribosomes during protein synthesis do promote ER expansion when combined with HU or DIA. This information will be added to the final version of the paper.

Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We have also further explored the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).

Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?

We readdressed this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data by reevaluating our transcriptomic data.. The re-analysis of our RNA-Seq data have allowed us to infer the mechanisms that modulate the ER response to HU or DIA treatment and further separate them from UPR. This information has been added to the paper (page 26). As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment

Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.

We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:

“A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC)≥1 and an adjusted p-valueIn this regard, we are currently performing proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).

We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 30).

Major issue #5. Figure S5 presents weak ER expansion in fibrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.

We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.

Minor concerns

1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.

Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.

2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.

We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).

3) Figures 1B through 1D do not indicate the HU concentration.