Author Response

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

Summary:

In this interesting work, the authors investigated an important topical question: when we see travelling waves in cortical activity, is this due to true wave-like spread, or due to sequentially activated sources? In simulations, it is shown that sequential brain module activation can show up as a travelling wave - even in improved methods such as phase delay maps - and a variety of parameters is investigated. Then, in ex-vivo turtle eye-brain preparations, the authors show that visual cortex waves observable in local field potentials are in fact often better explained as areas D1 and D2 being sequentially activated. This has implications for how we think about travelling wave methodology and relevant analytical tools.

Strengths:

I enjoyed reading the discussion. The authors are careful in their claims, and point out that some phenomena may still indeed be genuine travelling waves, but we should have a higher evidence bar to claim this for a particular process in light of this paper and Zhigalov & Jensen (2023) (ref 44). Given this careful discussion, the claims made are well-supported by the experimental results. The discussion also gives a nice overview of potential options in light of this and future directions.

The illustration of different gaussian covariances leading to very different latency maps was interesting to see.

Furthermore, the methods are detailed and clearly structured and the Supplementary Figures, particularly single trial results, are useful and convincing.

We are glad the reviewer found our manuscript “interesting”, the questions we raise “important”, our claims “well-supported by the experimental results”, and our methods “detailed and clearly structured”.

The details of the sequentially activated Gaussian simulations give some useful results, but the fundamental idea still appears to be "sequential activation is often indistinguishable from a travelling wave", an idea advanced e.g. by Zhigalov & Jensen (2023). It takes a while until the (in my opinion) more intriguing experimental results.

To emphasize the experimental results, we switched between the analytical results and the experimental results. Correspondingly, figure 2 now illustrates the more intriguing experimental results and figure 3 the analytical results. In addition, we added subtitles to the different sections of the results to ease the navigation through the paper and to enable the readers to access the different sections more easily.

One of the key claims is that the spikes are more consistent with two sequentially activated modules rather than a continuous wave (with Fig 3k and 3l key to support this). Whilst this is more consistent, it is worth mentioning that there seems to be stochasticity to this and between-trial variability, especially for spikes.

In the revised manuscript we added the reviewer’s comment about stochasticity, and we discuss its possible origins:

"The transition was also not clear when examining spiking responses in some of the trials (as indicated by high DIP scores, Figure 2K). However, the observation that temporal grouping became more pronounced when using ALSA (a more robust estimate of local excitability) (Figure 2L,N), suggests that high DIP values may result from variability in the spike times of single neurons, and not necessarily from the lack of modular activation. Such issues can be resolved by denser sampling of spiking activity in the tissue."

Recommendations For The Authors:

The eye-cortex turtle preparation is not the most common. I would add more context about how specific the results are to this preparation vs how comparable it is to human data.

We added a sentence explaining the relevance of our preparation: “Finally, while the layered organization of turtle cortex is different than that of mammalian cortex, the basic excitability features of both tissues are similar (Connors and Kriegstein, 1986; Hemberger et al., 2019; Kriegstein and Connors, 1986; Larkum et al., 2008; Shein-Idelson et al., 2017b), and substantial differences in the manner by which field potentials and spikes spread through the tissue are not to be expected.”

Philosophical question: when does a 'module' become small enough for it to count as a travelling wave? More on this could be added to the discussion. I think we are in the very early days for a true understanding of travelling waves, and I wonder if these sequentially activated modules will functionally correspond to the known cortical segregation, or if it varies by area/task.

We agree with the reviewer that macroscopic waves could be composed of smaller modules (or single neurons at the smallest scale). Our results suggest that modular patterns can be classified as wave patterns both at large scales (of brain areas) and smaller scales of local neural circuits. Therefore, we believe it is necessary to make this distinction across different scales. We sharpened this point in the first paragraph of the discussion:

"…We showed that LFP measurements indicative of waves propagating across turtle cortex are underlined by discrete and consecutively activated neuronal populations, and not by a continuously propagating wavefront of spikes (Figure 2). Similarly, activation profiles that resemble continuous travelling waves in EEG simulations can be underlined by consecutive activation of two discrete cortical regions (Figure 1). We replicated these results using an analytical model and demonstrated that a simple scenario of sequentially activated Gaussians can exhibit WLPs with a rich diversity of spatiotemporal profiles (Figure 3). Our results offer insight into the scenarios and conditions for WLP detection by identifying failure points that should be considered when identifying travelling waves and therefore suggest caution when interpreting continuous phase latency maps as microscopically propagating wave patterns. Such failure points may exist both when examining activity at the scale of brain regions (Figure 1) and smaller neural circuits (Figure 2). Therefore, our results suggest that the discrepancy between modular and wave activation should be examined across spatial scales. Specifically, it is not necessarily the case that at the fine grained (single neuron) scale activation patterns are modular, but, following coarse graining, smooth wave patterns emerge. Rather, modular activation may hierarchically exist across scales (Kaiser and Hilgetag, 2010; Meunier et al., 2010) and may be masked by smeared spatial supra-threshold excitability boundaries. Below we discuss these limitations across techniques and their implications.”

I would advise the authors to focus on the experimental data, perhaps by putting the simulations second, and by putting some of the equation details that are in Methods into the Supplementary Information. Whilst the simulation parameter space is well-explored, the fundamental idea of spreading Gaussians is relatively simple, and the current manuscript organization detracted from the main message for me a little bit.”

Following the referee’s suggestion, we switched between the section with experimental data and the one with the analytic model (see response to comment 1). In addition, to ease the reading of the methods, we moved the mathematical derivation and related equations to appendix 1.

Things I thought about that you may also enjoy thinking about: Could we tell something about sequential sources vs travelling waves by the nature of the wave - e.g. shape or dispersion? If some wave properties are conserved whilst travelling, this could be evidence for travelling vs two sources.

This is a wonderful suggestion. We are currently working on a follow up publication with a new approach to do exactly that! We think that this new body of work is outside the scope of this paper.

Could synaptic potentials spread like waves, but spikes more in modular bursts? This would also explain the LFP vs spikes difference - maybe travelling waves of EPSPs are there priming the network, 'looking' for suitable modules to activate, which then activate sequentially. The current discussion is quite spike-focused - could some information be in synaptic potentials after all?

This is an interesting idea with intriguing functional implications. We added this idea to our discussion (see paragraph below). In addition, to emphasize our discussion on synaptic potentials, we reorganized the paragraphs in the discussion to separate between our discussion on sub-threshold excitability (which is mostly synaptic) and supra-threshold excitability which is the focus of the second part of the discussion.

“Variability in responses may also be explained by differences in propagation mechanisms (Ermentrout and Kleinfeld, 2001; Muller et al., 2018; Wu et al., 2008). Several reports suggest that waves are underlined by propagation along axonal collaterals (Muller et al., 2018, 2014). Both the transmembrane voltage-gated currents excited during action potentials as well as the post-synaptic currents along axonal boutons can potentially contribute to measured signals. However, such waves travel at high propagation speeds and are not compatible with the wide diversity of wave velocities and mechanisms of local neuronal interactions (Ermentrout and Kleinfeld, 2001; Feller et al., 1996). An intriguing possibility is that such axonal waves prime neuronal excitability by sub-threshold inputs that later result in modular supra-threshold activation. The ability to experimentally discriminate between axonal inputs and local spiking excitability (e.g. by reporters with different wavelengths) can potentially resolve such discrepancies.

Our turtle cortex results (Figure 2) exemplify how contrasting sub-threshold LFP measurements with supra-threshold spiking measurements can yield different conclusions about the nature of activity spread….”

Author Response:

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

Joint Public Review:

[…] While this does not rule out criticality in the brain, it decidedly weakens the evidence for it, which was based on the following logic: critical systems give rise to power law behavior; power law behavior is observed in cortical networks; therefore, cortical networks operate near a critical point. Given, as shown in this paper, that power laws can arise from noncritical processes, the logic breaks. Moreover, the authors show that criticality does not imply optimal information transmission (one of its proposed functions). This highlights the necessity for more rigorous analyses to affirm criticality in the brain. In particular, it suggests that attention should be focused on the question "does the brain implement a dynamical latent variable model?".

These authors are not the first to show that slowly varying firing rates can give rise to power law behavior (see, for example, Touboul and Destexhe, 2017; Priesemann and Shriki, 2018). However, to our knowledge they are the first to show crackling, and to compute information transmission in the critical state.

We thank the reviewers for their thoughtful assessment of our paper.

We would push back on the assessment that our model ‘has nothing to do with criticality,’ and that we observed ‘signatures of criticality [that] emerge through fundamentally non-critical mechanisms.’ This assessment partially stems from the definition of criticality provided in the Public Comment, that ‘criticality is a very specific set of phenomena in physics in which fundamentally local interactions produce unexpected long-range behavior.’

Our disagreement is largely focused on this definition, which we do not think is a standard definition. Taking the favorite textbook example, the Ising model, criticality is characterized by a set of power-law divergences in thermodynamic quantities (e.g., susceptibility, specific heat, magnetization) at the critical temperature, with exponents of these power laws governed by scaling laws. It is not defined by local interactions. All-to-all Ising model is generally viewed as showing a critical behavior at a certain temperature, even though interactions there are manifestly non-local. It is possible that, by “local” in the definition, the Public Comment meant that interactions are “collective” and among microscopic degrees of freedom. However, that same all-to-all Ising model is mathematically equivalent to the mean-field model, where criticality is achieved through large fluctuations of the mean field, but not through microscopic interactions.

More commonly, criticality is defined by power laws and scaling relationships that emerge at a critical value of a parameter(s) of the system. That is, criticality is defined by its signatures. What is crucial in all such definitions is that this atypical, critical state requires fine tuning. For example, in the textbook example of the Ising model, a parameter (the temperature) must be tuned to a critical value for critical behavior to appear. In the branching process model that generates avalanche criticality, criticality requires tuning m=1. The key result of our paper is that all signatures expected for avalanche criticality (power laws, crackling, and, as shown below, estimates of the branching rate m), and hence the criticality itself, appear without fine-tuning.

As we discussed in our introduction, there are a few other instances of signatures of criticality (and hence of criticality itself) emerging without fine-tuning. The first we are aware of was the demonstration of Zipf’s Law (by Schwab, et al. 2014, and Aitchison et al. 2016), a power-law relationship between rank and frequency of states, which was shown to emerge generically in systems driven by a broadly distributed latent variable. A second example, arising from applications of coarse-graining analysis to neural data (cf., Meshulam et al. 2019; also, Morales et al., 2023), was demonstrated in our earlier paper (Morrell et al. 2021). Thus, here we have a third example: the model in this paper generates signatures of criticality in the statistics of avalanches of activity, and it does so without fine-tuning (cf., Fig. 2-3).

The rate at which these ‘criticality without fine-tuning' examples are piling up may inspire revisiting the requirement of fine-tuning in the definition of criticality, and our ongoing work (Ngampruetikorn et al. 2023) suggests that criticality may be more accurately defined through large fluctuations (variance > 1/N) rather than through fine-tuning or scaling relations.

References:

• Schwab DJ, Nemenman I, Mehta P. “Zipf’s Law and Criticality in Multivariate Data without FineTuning.” Phys Rev Lett. 2014 Aug; doi::101103/PhysRevLett.113.068102,

• Aitchison L, Corradi N, Latham PE. “Zipf’s Law Arising Naturally When There Are Underlying, Unobserved Variables.” PLOS Computational biology. 2016 12; 12(12):1-32. doi:10.1371/journal.pcbi.1005110

• Meshulam L, Gauthier JL, Brody CD, Tank DW, Bialek W. “Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons.” Phys Rev Lett. 2019 Oct; doi: 10.1103/PhysRevLett.123.178103.

• Morales GB, di Santo S, Muñoz MA. “Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics.” Proceedings of the National Academy of Sciences. 2023; 120(9):e2208998120.

• Morrell MC, Sederberg AJ, Nemenman I. “Latent Dynamical Variables Produce Signatures of Spatiotemporal Criticality in Large Biological Systems.” Phys Rev Lett. 2021 Mar; doi: 10.1103/PhysRevLett.126.118302.

• Ngampruetikorn, V., Nemenman, I., Schwab, D., “Extrinsic vs Intrinsic Criticality in Systems with Many Components.” arXiv: arXiv:2309.13898 [physics.bio-ph]

Major comments:

1) For many readers, the essential messages of the paper may not be immediately clear. For example, is the paper criticizing the criticality hypothesis of cortical networks, or does the criticism extend deeper, to the theoretical predictions of "crackling" relationships in physical systems as they can emerge without criticality? Statements like "We show that a system coupled to one or many dynamical latent variables can generate avalanche criticality ..." could be misinterpreted as affirming criticality. A more accurate language is needed; for instance, the paper could state that the model generates relationships observed in critical systems. The paper should provide a clearer conclusion and interpretation of the findings in the context of the criticality hypothesis of cortical dynamics.

Please see the response to the Public Review, above. To clarify the essential message that the dynamical latent variable model produces avalanche criticality without fine-tuning, we have made revisions to the abstract and introduction. This point was already made in the discussion (first sentence).

Key sentences changed in the abstract:

"… We find that populations coupled to multiple latent variables produce critical behavior across a broader parameter range than those coupled to a single, quasi-static latent variable, but in both cases, avalanche criticality is observed without fine-tuning of model parameters. … Our results suggest that avalanche criticality arises in neural systems in which activity is effectively modeled as a population driven by a few dynamical variables and these variables can be inferred from the population activity."

In the introduction, we changed the final sentence to read:

"These results demonstrate how criticality in neural recordings can arise from latent dynamics in neural activity, without need for fine-tuning of network parameters."

2) On lines 97-99, the authors state that "We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from recurrent dynamics locally". This idea is also repeated at the beginning of the Summary section. Perhaps being agnostic isn't such a good idea: it's possible that the recurrent dynamics is in a critical regime, which would just push the problem upstream. Presumably you're thinking of recurrent dynamics with slow timescales that's not critical? Or are you happy if it's in the critical regime? This should be clarified.

We have amended this sentence to clarify that any latent dynamics with large fluctuations would suffice:

”We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from large fluctuations in local recurrent dynamics.”

3) Even though the model in Equation 2 has been described in a previous publication and the Methods section, more details regarding the origin and justification of this model in the context of cortical networks would be helpful in the Results section. Was it chosen just for simplicity, or was there a deeper reason?

This model was chosen for its simplicity: there are no direct interactions between neurons, coupling between neurons and latent variables is random, and simulation is straightforward. More complex latent dynamics or non-random structure in the coupling matrices could have been used, but our aim was to explore this model in the simplest setting possible.

We have revised the Results (“Avalanche scaling in a dynamical latent variable model,” first paragraph) to justify the choice of the model:

"We study a model of a population of neurons that are not coupled to each other directly but are driven by a small number of dynamical latent variables -- that is, slowly changing inputs that are not themselves measured (Fig.~\ref{fig:fig1}A). We are agnostic as to the origin of these inputs: they may be externally driven from other brain areas, or they may arise from large fluctuations in local recurrent dynamics. The model was chosen for its simplicity, and because we have previously shown that this model with at least about five latent variables can produce power laws under the coarse-graining analysis \citep{Morrell2021}."

We have added the following to the beginning of the Methods section expanding on the reasons for this choice:

"We study a model from Morrell 2021, originally constructed as a model of large populations of neurons in mouse hippocampus. Neurons are non-interacting, receiving inputs reflective of place-field selectivity as well as input current arising from a random projection from a small number of dynamical latent variables, representing inputs shared across the population of neurons that are not directly measured or controlled. In the current paper, we incorporate only the latent variables (no place variables), and we assume that every cell is coupled to every latent variable with some randomly drawn coupling strength."

4) The Methods section (paragraph starting on line 340) connects the time scale to actual time scales in neuronal systems, stating that "The timescales of latent variables examined range from about 3 seconds to 3000 seconds, assuming 3-ms bins". While bins of 3 ms are relevant for electrophysiological data from LFPs or high-density EEG/MEG, time scales above 10 seconds are difficult to generate through biophysically clear processes like ionic channels and synaptic transmission. The paper suggests that slow time scales of the latent variables are crucial for obtaining power law behavior resembling criticality. Yet, one way to generate such slow time scales is via critical slowing down, implying that some brain areas providing input to the network under study may operate near criticality. This pushes the problem toward explaining the criticality of those external networks. Hence, discussing potential sources for slow time scales in latent variables is crucial. One possibility you might want to consider is sources external to the organism, which could easily have time scales in the 1-24 hour range.

As the reviewers note, it is a possibility that slow timescales arise from some other brain area in which dynamics are slow due to critical dynamics, but many other plausible sources exist. These include slowly varying sensory stimuli or external sources, as suggested by the reviewers. It is also possible to generate “effective” slow dynamics from non-critical internal sources. One example, from recordings in awake mice, is the slow change in the level of arousal that occurs on the scale of many seconds to minutes. These changes arise from release of neuromodulators that have broad effects on neural populations and correlations in activity (for a focused review, see Poulet and Crochet, 2019).

We have added the following sentence to the Methods section where timescales of latent variables was discussed:

"The timescales of latent variables examined range from about $3$ seconds to $3000$ seconds, assuming $3$-ms bins. Inputs with such timescales may arise from external sources, such as sensory stimuli, or from internal sources, such as changes in physiological state."

5) It is common in neuronal avalanche analysis to calculate the branching parameter using the ratio of events in consecutive bins. Near-critical systems should display values close to 1, especially in simulations without subsampling. Including the estimated values of the branching parameter for the different cases investigated in this study could provide more comprehensive data. While the paper acknowledges that the obtained exponents in the model differ from those in a critical branching process, it would still be beneficial to offer the branching parameter of the observed avalanches for comparison.

The reviewers requested that the branching parameter be computed in our model. We point out that, for the quasi-stationary latent variables (as in Fig. 3), a branching parameter of 1 is expected because the summed activity at time t+k is, on average, equal to the summed activity at time t, regardless of k. Numerics are consistent with this expectation. Following the methodology for an unbiased estimate of the branching parameter from Wilting and Priesemann (2018), we checked an example set of parameters (epsilon = 8, eta = 3) for quasi-stationary latent fields. We found that the naïve (biased) estimate of the branching parameter was 0.94, and that the unbiased estimator was exp(−1.4⋅10−8) ≈ 0.999999986.

For faster time scales, it is no longer true that summed activity is constant over time, as the temporal correlations in activity decay exponentially. Using the five-field simulation from Figure 2, we calculated the branching parameter for several values of tau. The biased estimates of m are 0.76 (𝜏=50), 0.79 (𝜏=500), and 0.79 (𝜏=5000). The corrected estimates are 0.98 (𝜏=50), 0.998 (𝜏=500), and 0.9998 (𝜏=5000).

6) In the Discussion (l 269), the paper suggests potential differences between networks cultured in vitro and in vivo. While significant differences indeed exist, it's worth noting that exponents consistent with a critical branching process have also been observed in vivo (Petermann et al 2009; Hahn et al. 2010), as well as in large-scale human data.

We thank the reviewers for pointing out these studies, and we have added the missing one (Hahn et al. 2010) to our reference list. The following was added to the discussion, in the section “Explaining Experimental Exponents:”

"A subset of the in vivo recordings analyzed from anesthetized cat (Hahn et al. 2010) and macaque monkeys (Petermann et al. 2009) exhibited a size distribution exponent close to 1.5."

Along these lines, we noted two additional studies of high relevance that have been published since our initial submission (Capek et al. 2023, Lombardi et al. 2023), and we have added these references to the discussion of experimental exponents.

Minor comments:

1) The term 'latent variable' should be rigorously explained, as it is likely to be unfamiliar to some readers.

Sentences and clauses have been added to the Introduction, Results and the Methods to clarify the term:

Intro: “Numerous studies have reported relatively low-dimensional structure in the activity of large populations of neurons [refs], which can be modeled by a population of neurons that are broadly and heterogeneously coupled to multiple dynamical latent (i.e., unobserved) variables.”

Results: “We studied a population of neurons that are not coupled to each other directly but are driven by a small number of dynamical latent variables -- that is, slowly changing inputs that are not themselves measured.”

Methods: “Neurons are non-interacting, receiving inputs reflective of place-field selectivity as well as input current reflecting a random projection from a small number of dynamical latent variables, representing inputs shared across the population of neurons that are not directly measured.”

2) There's a relatively important typo in the equations: Eq. 2 and Eq. 6 differ by a minus sign in the exponent. Eqs. 3 and 4 use the plus sign, but epsilon_0 on line 198 uses the minus sign. All very confusing until we figured out what was going on. But easy to fix.

Thank you for catching this. We have made the following corrections:

1) Figures adopted the sign convention that epsilon > 0, with larger values of epsilon decreasing the activity level. Signs in Eqs. 3 and 4 have been corrected to match.

2) Equation 5 was missing a minus sign in front of the Hamiltonian. Restoring this minus sign fixed the discrepancy between 2 and 6.

3) In Eq. 7, the left hand side is zeta'/zeta', which is equal to 1. Maybe it should be zeta'/zeta? Fixed, thank you.

Additional comments:

The authors are free to ignore these; they are meant to improve the paper.

We are extremely grateful for the close reading of our paper and note the actions taken below.

1) We personally would not use the abbreviation DLV; we find abbreviations extremely hard to remember. And DLV is not used that often.

Done, thank you for the suggestion.

2) l 198: epsilon_0 = -log(2^{1/N}-1) was kind of hard to picture -- we had to do a little algebra to make sense of it. Why not write e^{-epsilon_0} = 2^{1/N}-1 \approx log(2)/N, which in turn implies that epsilon_0 ~ log(N)?

Thank you, good point. We have added a sentence now to better explain:

"...which is maximized at $\epsilon_0 = - \log (2^{1/N} - 1)$, independent of $J_i$ and $\eta$. After some algebra, we find that $\epsilon_0 \sim \log N$ for large $N$."

3) Typo on l 202: "We plot P_ava as a function of epsilon in Fig. 4B". 4B --> 4D.

Done

4) It would be easier on the reader if the tables were all in one place. It would be even nicer to put the parameters in the figure captions. Or at least N; that one is kind of important.

Table placement was a Latex issue, which we have now fixed. We also have included links between tables and relevant figures and indicated network size.

5) What's x_i in Eqs. 7 and 8?

We added a sentence of explanation. These are the individual observations of avalanche sizes or durations, depending on what is being fit.

6) The latent variables evolve according to an Ornstein-Uhlenbeck process. But we might equally expect oscillations or non-normal behavior coupling dynamical modes, and these are likely to give different behavior with respect to avalanches. It might be worth commenting on this.

7) The model assumes a normal distribution of the coupling strengths between the latent variables and the binary units. Discussing the potential effects of different types of random coupling could provide interesting insights.

Both 6 and 7 are interesting questions. At this point, we could speculate that the main results would be qualitatively unchanged, provided dynamics are sufficiently slow and that the distribution of coupling strengths is sufficiently broad (that is, there is variance in the coupling matrix across individual neurons). Further studies would be needed to make these statements more precise.

8) In Fig 1, tau_f = 1E4 whereas in Fig 2 tau_f = 5E3. Why the difference?

For Figure 1, we chose a set of parameters that gave clear scaling. In Figure 2, we saw some value in showing more than one example of scaling, hence different parameters for the examples in Fig 2 than Fig 1. Note that the Fig 1 simulations are represented in Fig. 2 G-J, as the 5-field simulation with tau_F = 1e4.

Author Response

eLife assessment

This study presents a valuable finding on a new role of Foxp3+ regulatory T cells in sensory perception, which may have an impact on our understanding of somatosensory perception. The authors identified a previously unappreciated action of enkephalins released by immune cells in the resolution of pain and several upstream signals that can regulate the expression of the proenkephalin gene PENK in Foxp3+ Tregs. However, whereas the generation of transgenic mice with conditional deletion of PENK in Foxp3+ cells and PENK fate-mapping is novel and generates compelling data, they show an incomplete analysis of Tregs in the control and transgenic mice, proper tamoxifen controls nor the role of PENK+ skin T cells to further support their hypothesis. Nonetheless, the study would be of interest to the biologists working in the field of neuroimmunology and inflammation.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors explore mechanisms through which T-regs attenuate acute pain using a heat sensitivity paradigm. Analysis of available transcriptomic data revealed expression on the proenkephalin (Penk) gene in T-regs. The authors explore the contribution of T-reg Penk in the resolution of heat sensitivity.

Strengths:

Investigating the potential role of T-reg Penk in the resolution of acute pain is a strength.

Weaknesses:

The overall experimental design is superficial and lacks sufficient rigor to draw any meaningful conclusions.

For instance:

1) The were no TAM controls. What is the evidence that TAM does not alter heat-sensitive receptors.

Author response : By comparing panel A and C, it appears that heat-sensitivity in controls (blue dots) is slightly different before and after TMX administration, suggesting that heat-sensitive receptors are moderately altered by TMX per se. However, heat sensitivity is increased by two fold in KO animals. Thus, a possible effect of TAM on heat receptors is not responsible for the heat hyperalgesia seen in KO, as shown in figure 4 and S3.

2) There are no controls demonstrating that recombination actually occurred. How do the authors know a single dose of TAM is sufficient?

Author response : these experiments are in progress. Specificity of the deletion will be presented in an updated version of the manuscript in the near future.

3) Why was only heat sensitivity assessed? The behavioral tests are inadequate to derive any meaningful conclusions. Further, why wasn't the behavioral data plotted longitudinally

Author response : We respectfuly point the reviewer to figure S3 where the longitudinal data are presented. New behavorial tests are being performed. The results will be presented in a revised version.

Reviewer #2 (Public Review):

Summary:

The present study addresses the role of enkephalins, which are specifically expressed by regulatory T cells (Treg), in sensory perception in mice. The authors used a combination of transcriptomic databases available online to characterize the molecular signature of Treg. The proenkephalin gene Penk is among the most enriched transcripts, suggesting that Treg plays an analgesic role through the release of endogenous opioids. In addition, in silico analysis suggests that Penk is regulated by the TNFR superfamily; this being experimentally confirmed. Using flow cytometry analysis, the authors then show that Penk is mostly expressed in Treg of the skin and colon, compared to other immune cells. Finally, genetic conditional excision of Penk, selectively in Treg, results in heat hypersensitivity, as assessed by behavior analysis.

Strengths:

The manuscript is clear and reveals a previously unappreciated role of enkephalins, as released by immune cells, in sensory perception. The rationale in this manuscript is easy to follow, and conclusions are well supported by data.

Weaknesses:

The sensory deficit of Penk cKO appears to be quite limited compared to control littermates.

Reviewer #3 (Public Review):

Summary:

Aubert et al investigated the role of PENK in regulatory T cells. Through the mining of publicly available transcriptome data, the authors confirmed that PENK expression is selectively enriched in regulatory but not conventional T cells. Further data mining suggested that OX40, 4-1BB as well as BATF, can regulate PENK expression in Tregs. The authors generated fate-mapping mice to confirm selective PENK expression in Tregs and activated effector T cells in the colon and spleen. Interestingly, transgenic mice with conditional deletion of PENK in Tregs resulted in hypersensitivity to heat, which the authors attributed to heat hyperalgesia.

Strengths:

The generation of transgenic mice with conditional deletion of PENK in foxp3 and PENK fate-mapping is novel and can potentially yield significant findings. The identification of upstream signals that regulate PENK is interesting but unlikely to be the main reason why PENK is predominantly expressed in Tregs as both BATF and TNFR are expressed in effector T cells.

Weaknesses:

There is a lack of direct evidence and detailed analysis of Tregs in the control and transgenic mice to support the authors' hypothesis. PENK was previously reported to be expressed in skin Tregs and play a significant role in regulating skin homeostasis: this should be considered as an alternative mechanism that may explain the changed sensitivity to heat observed in the paper.

Author response : Supplementary figures are being prepared and new results are being collected to show that the KO do not perturb immune and/or skin homeostasis at the time of the experiments. These will be presented in a revised version.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The present study provides a phylogenetic analysis of the size prefrontal areas in primates, aiming to investigate whether relative size of the rostral prefrontal cortex (frontal pole) and dorsolateral prefrontal cortex volume vary according to known ecological or social variables.

I am very much in favor of the general approach taken in this study. Neuroimaging now allows us to obtain more detailed anatomical data in a much larger range of species than ever before and this study shows the questions that can be asked using these types of data. In general, the study is conducted with care, focusing on anatomical precision in definition of the cortical areas and using appropriate statistical techniques, such as PGLS. That said, there are some points where I feel the authors could have taken their care a bit further and, as a result, inform the community even more about what is in their data.

We thank the reviewer for this globally positive evaluation of our work, and we appreciate the advices to improve our manuscript.

The introduction sets up the contrast of 'ecological' (mostly foraging) and social variables of a primate's life that can be reflected in the relative size of brain regions. This debate is for a large part a relic of the literature and the authors themselves state in a number of places that perhaps the contrast is a bit artificial. I feel that they could go further in this. Social behavior could easily be a solution to foraging problems, making them variables that are not in competition, but simply different levels of explanation. This point has been made in some of the recent work by Robin Dunbar and Susanne Shultz.

Thank you for this constructive comment, and we acknowledge that the contrast between social vs ecological brain is relatively marginal here. Based also on the first remark by reviewer 3, we have reformulated the introduction to emphasize what we think is actually more critical: the link between cognitive functions as defined in laboratory conditions and socio-ecological variables measured in natural conditions. And the fact that here, we use brain measures as a potential tool to relate these laboratory vs natural variables through a common scenario. Also, we were already mentioning the potential interaction between social and foraging processes in the discussion, but we are happy to add a reference to recent studies by S. Shultz and R. Dunbar (2022), which is indeed directly relevant. We thank the reviewer for pointing out this literature.

In a similar vein, the hypotheses of relating frontal pole to 'meta-cognition' and dorsolateral PFC to 'working memory' is a dramatic oversimplification of the complexity of cognitive function and does a disservice to the careful approach of the rest of the manuscript.

We agree that the formulation of which functions we were attributing to the distinct brain regions might not have been clear enough, but the functional relation between frontal pole and metacognition in the one hand, and DLPFC and working memory on the other hand, have been firmly established in the literature, both through laboratory studies and through clinical data. Clearly, no single brain region is necessary and sufficient for any cognitive operation, but decades of neuropsychology have demonstrated the differential implication of distinct brain regions in distinct functions, which is all we mean here. We have made a specific point on that topic in the discussion (cf p. 16). We have also reformulated the introduction to clarify that, even if the relation between these regions and their functions (FP/ metacognition; DLPFC/ working memory) was clear in laboratory conditions, it was not clear whether this mapping could be used for real life conditions. And therefore whether that simplification was somehow justified beyond the lab (and the clinics), and whether these neuro-cognitive concepts could be applied to natural conditions, are indeed critical questions that we wanted to address. The central goal of the present study was precisely to evaluate the extent to which this brain/cognition relation could be used to understand more natural behaviors and functions, and we hope that it appears more clearly now.

One can also question the predicted relationship between frontal pole meta-cognition and social abilities versus foraging, as Passingham and Wise show in their 2012 book that it is frontal pole size that correlates with learning ability-an argument that they used to relate this part of the brain to foraging abilities. I would strongly suggest the authors refrain from using such descriptive terms. Why not simply use the names of the variables actually showing significant correlations with relative size of the areas?

We basically agree with the reviewer, and we acknowledge the lack of clarity in the introduction of the previous manuscript. There were indeed lots of ambiguity in what we were referring to as ‘function’, associated with a given brain region. « Function » referred to way to many things! We have reformulated the introduction not only to clarify the different types of functions that were attributed to distinct brain regions in the literature but also to clarify how this study was addressing the question: by trying to articulate concepts from neuroscience laboratory studies with concepts from behavioral ecology and evolution using intuitive scenarios. We hope that the present version of the introduction makes that point clearer.

The major methodological judgements in this paper are of course in the delineation of the frontal pole and dorsolateral prefrontal cortex. As I said above, I appreciate how carefully the authors describe their anatomical procedure, allowing researchers to replicate and extend their work. They are also careful not to relate their regions of interest to precise cytoarchitectonic areas, as such a claim would be impossible to make without more evidence. That said, there is a judgement call made in using the principal sulcus as a boundary defining landmark for FP in monkeys and the superior frontal sulcus in apes. I do not believe that these sulci are homologous. Indeed, the authors themselves go on to argue that dorsolateral prefrontal cortex, where studied using cytoarchitecture, stretches to the fundus of principal sulcus in monkeys, but all the way to the inferior frontal sulcus in apes. That means that using the fundus of PS is not a good landmark.

We thank the reviewer for his kind remarks on our careful descriptions. But then, it is not clear whether our choice of using the principal sulcus as a boundary for FP in monkeys vs the superior frontal sulcus in apes is actually a judgement call. First, and foremost, there is no clear and unambiguous definition of what should be the boundaries of the FP. By contrast with cytoarchitectonic maps, but clearly this is out of reach here. In humans and great apes we used Bludau et al 2014 (i.e. sup frontal sulcus), and in monkeys, we chose a conservative landmark that eliminated area 9, which is traditionally associated with the DLPFC (Petrides, 2005; Petrides et al, 2012; Semendeferi et al, 2001).

Of course, any definition will attract criticism, so the best solution might be to run the analysis multiple times, using different definitions for the areas, and see how this affects results.

Indeed, functional maps indicate that dorsal part of anterior PFC in monkeys is functionally part of FP. But again, cytoarchitectonic maps also indicate that this part of the brain includes BA 9, which is traditionally associated with DLPFC (Petrides, 2005; Petrides et al, 2012). As already pointed out in the discussion, there is a functional continuum between FP and DLPFC and our goal when using PS as dorsal border was to be very conservative and to exclude the ambiguous area. But we agree with the reviewer that given that this decision is arbitrary, it was worth exploring other definitions of the FP volume. So, we did complete a new analysis with a less conservative definition of the FP, to include this ambiguous dorsal area, and it is now included in the supplementary material. Maybe as expected, including the ambiguous area in the FP volume shifted the relation with socio-ecological variables towards the pattern displayed by the DLPFC (ie the influence of population density decreased). The most parsimonious interpretation of this results is that when extending the border of the FP region to cover a part of the brain which might belong to the DLPFC, or which might be somehow functionally intermediate between the 2, the specific relation of the FP with socio-ecological variables decreases. Thus, even if we agree that it was important to conduct this analysis, we believe that it only confirms the difficulty to identify a clear boundary between FP and DLPFC. Again, we have clearly explained throughout the manuscript that we admit the lack of precision in our definitions of the functional brain regions. In that frame, the conservative option seems more appropriate and for the sake of clarity, the results of the additional analysis of a FP volume that includes the ambiguous area is only included in the supplementary material.

If I understand correctly, the PGLS was run separately for the three brain measure (whole brain, FP, DLPFC). However, given that the measures are so highly correlated, is there an argument for an analysis that allows testing on residuals. In other words, to test effects of relative size of FP and DLPFC over and above brain size?

Generally, using residuals as “data” (or pseudo-data) is not recommended in statistical analyses. Two widely cited references from the ecological literature are:

Garcia-Berthou E. (2001) On the Misuse of Residuals in Ecology: Testing Regression Residuals vs. the Analysis of Covariance. Journal of Animal Ecology, 70 (4): 708-711.

Freckleton RP. (2002). On the misuse of residuals in ecology: regression of residuals vs. multiple regression. Journal of Animal Ecology 71: 542–545. https://doi.org/10.1046/ j.1365-2656.2002.00618.x.

The main reason for this recommendation is that residuals are dependent on the fitted model, and thus on the particular sample under consideration and the eventual significant effects that can be inferred.

In the discussion and introduction, the authors discuss how size of the area is a proxy for number of neurons. However, as shown by Herculano-Houzel, this assumption does not hold across species. Across monkeys and apes, for instance, there is a different in how many neurons can be packed per volume of brain. There is even earlier work from Semendeferi showing how frontal pole especially shows distinct neuron-to-volume ratios.

We appreciate the reviewer’s comment, but the references to Herculano-Houzel that we have in mind do indicate that the assumption is legitimate within primates.

Herculano-Houzel et al (2007) show that the neuronal density of the cortex is well conserved across primate species (but only monkeys were studied). The conclusion of that study is that using volumes as a proxy for number of neurons, as a measure of computational capacity, should be avoided between rodents and primates (and as they showed later, even more so with birds, for which neuronal density is higher). BUT within primates, since neuronal densities are conserved, volume is a good predictor of number of neurons. Gabi et al (2016) provide evidence that the neuronal density of the PFC is well conserved between humans and non-human primates, which implies that including humans and great apes in the comparison is legitimate. In addition, the brain regions included in the analysis presumably include very similar architectonic regions (e.g. BA 10 for FP, BA 9/46 for DLPFC), which also suggests that the neuronal density should be relatively well conserved across species. Altogether, we believe that there is sufficient evidence to support the idea that the volume of a PFC region in primates is a good proxy for the number of neurons in that region, and therefore of its computational capacity.

Semendeferi and colleagues (2001) pointed out some differences in cytoarchitectonic properties across parts of the FP and discussed how these properties could 1) be used to identify area 10 across species 2) be associated with distinct computational properties, with the idea that thicker ‘cell body free’ layers would leave more space for establishing connections (across dendrites and axons). This pioneering work, together with more recent imaging studies on functional connectivity (e.g. Sallet et al, 2013) emphasize the critical contribution of connectivity pattern as a tool for comparative anatomy. But unfortunately, as pointed out in the discussion already, this is currently out of reach for us.

We acknowledge the limitations, and to be fair, the notion of computational capacity itself is hard to define operationally. Based on the work of Herculano-Houzel et al, average density is conserved enough across primates (including humans) to justify our approximation. We have tried to define our regions of interest using both anatomical and functional maps and, thanks to the reviewer’s suggestions, we even tried several ways to segment these regions. Functional maps in macaques and humans do not exactly match cytoarchitectonic maps, presumably because functions rely not only upon the cytoarchitectonics but also on connectivity patterns (e.g. Sallet et al, 2013).

In sum, we appreciate the reviewer’s point but feel that, given the current understanding of brain functions and the relative conservation of neuronal density across primate PFC regions, the volume of a PFC region seems to be reasonable proxy for its number of neurons, and therefore its computational capacity. We have added these points to the discussions, and we hope that the reader will be able to get a fair sense of how legitimate is that position, given the literature.

Overall, I think this is a very valuable approach and the study demonstrates what can now be achieved in evolutionary neuroscience. I do believe that they authors can be even more thorough and precise in their measurements and claims.

Reviewer #2 (Public Review):

In the manuscript entitled "Linking the evolution of two prefrontal brain regions to social and foraging challenges in primates" the authors measure the volume of the frontal pole (FP, related to metacognition) and the dorsolateral prefrontal cortex (DLPFC, related to working memory) in 16 primate species to evaluate the influence of socio-ecological factors on the size of these cortical regions. The authors select 11 socio-ecological variables and use a phylogenetic generalized least squares (PGLS) approach to evaluate the joint influence of these socio-ecological variables on the neuro-anatomical variability of FP and DLPFC across the 16 selected primate species; in this way, the authors take into account the phylogenetic relations across primate species in their attempt to discover the influence of socio-ecological variables on FP and DLPF evolution.

The authors run their studies on brains collected from 1920 to 1970 and preserved in formalin solution. Also, they obtained data from the Mussée National d´Histoire Naturelle in Paris and from the Allen Brain Institute in California. The main findings consist in showing that the volume of the FP, the DLPFC, and the Rest of the Brain (ROB) across the 16 selected primate species is related to three socio-ecological variables: body mass, daily traveled distance, and population density. The authors conclude that metacognition and working memory are critical for foraging in primates and that FP volume is more sensitive to social constraints than DLPFC volume.

The topic addressed in the present manuscript is relevant for understanding human brain evolution from the point of view of primate research, which, unfortunately, is a shrinking field in neuroscience.

We must not have been clear enough in our manuscript, because our goal is precisely not to separate humans from other primates. This is why, in contrast to other studies, we have included human and non-human primates in the same models. If our goal had been to study human evolution, we would have included fossil data (endocasts) from the human lineage.

But the experimental design has two major weak points: the absence of lissencephalic primates among the selected species and the delimitation of FP and DLPFC. Also, a general theoretical and experimental frame linking evolution (phylogeny) and development (ontogeny) is lacking.

We admit that lissencephalic species could not be included in this study because we use sulci as key landmarks. We believe that including lissencephalic primates would have introduced a bias and noise in our comparisons, as the delimitations and landmarks would have been different for gyrencephalic and lissencephalic primates. Concerning development, it is simply beyond the scope of our study.

Major comments.

1) Is the brain modular? Is there modularity in brain evolution?: The entire manuscript is organized around the idea that the brain is a mosaic of units that have separate evolutionary trajectories:

"In terms of evolution, the functional heterogeneity of distinct brain regions is captured by the notion of 'mosaic brain', where distinct brain regions could show a specific relation with various socio-ecological challenges, and therefore have relatively separate evolutionary trajectories".

This hypothesis is problematic for several reasons. One of them is that each evolutionary module of the brain mosaic should originate in embryological development from a defined progenitor (or progenitors) domain [see García-Calero and Puelles (2020)]. Also, each evolutionary module should comprise connections with other modules; in the present case, FP and DLPFC have not evolved alone but in concert with, at least, their corresponding thalamic nuclei and striatal sector. Did those nuclei and sectors also expand across the selected primate species? Can the authors relate FP and DLPFC expansion to a shared progenitor domain across the analyzed species? This would be key to proposing homology hypotheses for FP and DLPFC across the selected species. The authors use all the time the comparative approach but never explicitly their criteria for defining homology of the cerebral cortex sectors analyzed.

We do not understand what the referee is referring to with the word ‘module’, and why it relates to development. Same thing for the anatomical relation with subcortical structures. Yes, the identity of distinct functional cortical regions relies upon subcortical inputs during development, but clearly this is neither technically feasible, nor relevant here anyways.

We acknowledge, however, that our definition of functional regions was not precise enough, and we have updated the introduction to clarify that point. In short, we clearly do not want to make a strong case for the functional borders that we chose for the regions of interest here (FP and DLPFC), but rather use those regions as proxies for their corresponding functions as defined in laboratory conditions for a couple of species (rhesus macaques and humans, essentially).

Contemporary developmental biology has showed that the selection of morphological brain features happens within severe developmental constrains. Thus, the authors need a hypothesis linking the evolutionary expansion of FP and DLPFC during development. Otherwise, the claims form the mosaic brain and modularity lack fundamental support.

Once again, we do not think that our definition of modules matches what the reviewer has in mind, i.e. modules defined by populations of neurons that developed together (e.g. visual thalamic neurons innervating visual cortices, themselves innervating visual thalamic neurons). Rather, the notion of mosaic brain refers to the fact that different parts of the brain are susceptible to distinct (but not necessarily exclusive) sources of selective pressures. The extent to which these ‘developmental’ modules are related to ‘evolutionary’ modules is clearly beyond the scope of this paper.

Our goal here was to evaluate the extent to which modules that were defined based on cognitive operations identified in laboratory conditions could be related (across species) to socio-ecological factors as measured in wild animals. Again, we agree that the way these modules/ functional maps were defined in the paper were confusing, and we hope that the new version of the manuscript makes this point clearer.

Also, the authors refer most of the time to brain regions, which is confusing because they are analyzing cerebral cortex regions.

We do not understand why the term ‘brain’ is more confusing than ‘cerebral cortex’, especially for a wide audience.

2) Definition and delimitation of FP and DLPFC: The precedent questions are also related to the definition and parcellation of FP and DLPFC. How homologous cortical sectors are defined across primate species? And then, how are those sectors parcellated?

The authors delimited the FP:

"...according to different criteria: it should match the functional anatomy for known species (macaques and humans, essentially) and be reliable enough to be applied to other species using macroscopic neuroanatomical landmarks".

There is an implicit homology criterion here: two cortical regions in two primate species are homologs if these regions have similar functional anatomy based on cortico-cortical connections. Also, macroscopic neuroanatomical landmarks serve to limit the homologs across species.

This is highly problematic. First, because similar function means analogy and not necessarily homology [for further explanation see Puelles et al. (2019); García-Cabezas et al. (2022)].

We are not sure to follow the Reviewer’s point here. First, it is not clear what would be the evolutionary scenario implied by this comment (evolutionary divergence followed by reversion leading to convergence?). Second, based on the literature, both the DLPFC and the FP display strong similarities between macaques and humans, in terms of connectivity patterns (Sallet et al, 2013), in terms of lesion-induced deficit and in terms of task-related activity (Mansouri et al, 2017). These criteria are usually sufficient to call 2 regions functionally equivalent. We do not see how this explanation is "highly problematic" as it is clearly the most parsimonious based on our current knowledge.

Second, because there are several lissencephalic primate species; in these primates, like marmosets and squirrel monkeys, the whole approach of the authors could not have been implemented. Should we suppose that lissencephalic primates lack FP or DLPFC?

We understand neither the reviewer’s logic, nor the tone. We understand that the reviewer is concerned by the debate on whether some laboratory species are more relevant than others for studying the human prefrontal cortex, but this is clearly not the objective of our work. As explained in the manuscript, we identified FP and DLPFC based on functional maps in humans and laboratory monkeys (macaques), and we used specific gyri as landmarks that could be reliably used in other species. And, as rightfully pointed out by reviewer 1, this is in and off itself not so trivial. Of course, lissencephalic animals could not be studied because we could not find these landmarks, but why would it mean that they do not have a prefrontal cortex? The reviewer implies that species that we did not study do not have a prefrontal cortex, which makes little sense. Standards in the field of comparative anatomy of the PFC, especially when it implies rodents (lissencephalic also) include cytoarchitectonic and connectivity criteria, but obviously we are not in a position to address it here. We have, however, included references to the seminal work of Angela Roberts and collaborator in the discussion on marmosets prefrontal functions, to reinforce the idea that the functional organization is relatively well conserved across all primates (with or without gyri on their brain) (Dias et al, 1996; Roberts et al, 2007).

Do these primates have significantly more simplistic ways of life than gyrencephalic primates? Marmosets and squirrel monkeys have quite small brains; does it imply that they have not experience the influence of socio-ecological factors on the size of FP, DLPFC, and the rest of the brain?

Again, none of this is relevant here, because we could not draw conclusions on species that we cannot study for methodological reasons. The reviewer seems to believe that an absence of evidence is equivalent to an evidence of absence, but we do not.

The authors state that:

"the strong development of executive functions in species with larger prefrontal cortices is related to an absolute increase in number of neurons, rather than in an increase in the ration between the number of neurons in the PFC vs the rest of the brain".

How does it apply to marmosets and squirrel monkeys?

Again, we do not understand the reviewer’s point, since it is widely admitted that lissencephalic monkeys display both a prefrontal cortex and executive functions (again, see the work of Angela Roberts cited above). Our goal here was certainly not to get into the debate of what is the prefrontal cortex in a handful of laboratory species, but to evaluate the relevance of laboratory based neuro-cognitive concepts for understanding primates in general, and in their natural environment.

References:

García-Cabezas MA, Hacker JL, Zikopoulos B (2022) Homology of neocortical areas in rats and primates based on cortical type analysis: an update of the Hypothesis on the Dual Origin of the Neocortex. Brain structure & function Online ahead of print. doi:doi.org/ 10.1007/s00429-022-02548-0

García-Calero E, Puelles L (2020) Histogenetic radial models as aids to understanding complex brain structures: The amygdalar radial model as a recent example. Front Neuroanat 14:590011. doi:10.3389/fnana.2020.590011

Nieuwenhuys R, Puelles L (2016) Towards a New Neuromorphology. doi:10.1007/978-3-319-25693-1

Puelles L, Alonso A, Garcia-Calero E, Martinez-de-la-Torre M (2019) Concentric ring topology of mammalian cortical sectors and relevance for patterning studies. J Comp Neurol 527 (10):1731-1752. doi:10.1002/cne.24650

Reviewer #3 (Public Review):

This is an interesting manuscript that addresses a longstanding debate in evolutionary biology - whether social or ecological factors are primarily responsible for the evolution of the large human brain. To address this, the authors examine the relationship between the size of two prefrontal regions involved in metacognition and working memory (DLPFC and FP) and socioecological variables across 16 primate species. I recommend major revisions to this manuscript due to: 1) a lack of clarity surrounding model construction; and 2) an inappropriate treatment of the relative importance of different predictors (due to a lack of scaling/normalization of predictor variables prior to analysis). My comments are organized by section below:

We thank the reviewer for the globally positive evaluation and for the constructive remarks. Introduction:

• Well written and thorough, but the questions presented could use restructuring.

Again, we thank the reviewer, and we believe that this is coherent with some of the remarks of reviewer 1. We have extensively revised the introduction, toning down the social vs ecological brain issue to focus more on what is the objective of the work (evaluating the relevance of lab based neuro-cognitive concepts for understanding natural behavior in primates).

Methods:

• It is unclear which combinations of models were compared or why only population density and distance travelled tested appear to have been included.

The details of the model comparison analysis were presented as a table in the supplementary material (#3, details of the model comparison data), but we understand that this was not clear enough. We have provided more explanation both in the main manuscript and in the supplements. All variables were considered a priori; however, we proceeded beforehand to an exploratory analyses which led us to exclude some variables because of their lack of resolution (not enough categories for qualitative variables) or strong cross-correlations with other quantitative variables. There were much more than three variables included in the models but the combination of these 3 (body mass, daily traveled distance and population density) best predicted (had the smallest AIC) the size of the brain regions. We provide additional information about these exploratory analyses in the supplementary material, sections 2 and 3.

• Brain size (vs. body size) should be used as a predictor in the models.

We do not understand the theoretical reason for replacing body size by brain size in the models. Brain size is not a socio-ecological variable. And of course, that would be impossible for modeling brain size itself. Or is it that the reviewer suggests to use brain size as a covariate to evaluate the effects of other variables in the model over and above the effect on brain size? But what is the theoretical basis for this?

• It is not appropriate to compare the impact of different predictors using their coefficients if the variables were not scaled prior to analysis.

We thank the Reviewer for this comment; however, standardized coefficients are not unproblematic because their calculations are based on the estimated standard-deviations of the variables which are likely to be affected by sampling (in effect more than the means). We note that the methods of standardized coefficients have attracted several criticisms in the literature (see the References section in https://en.wikipedia.org/wiki/Standardized_coefficient). Nevertheless, we now provide a table with these coefficients which makes an easy comparison for the present study. We also updated tables 1, 2 and 3 to include standardized beta values.

Reviewer #1 (Recommendations For The Authors):

N/A

Reviewer #2 (Recommendations For The Authors):

Contemporary developmental biology has showed that the brain of all mammals, including primates, develops out of a bauplan (or blueprint) made of several fundamental morphological units that have invariant topological relations across species (Nieuwenhuys and Puelles 2016).

At some point in the discussion the authors acknowledge that:

"Our aim here was clearly not to provide a clear identification of anatomical boundaries across brain regions in individual species, as others have done using much finer neuroanatomical methods. Such a fine neuroanatomical characterization appears impossible to carry on for a sample size of species compatible with PGLS".

I do not think it would be impossible to carry such neuroanatomical characterization. It would take time and effort, but it is feasible. Such characterization, if performed within the framework of contemporary developmental biology, would allow for well-founded definition and delineation of cortical sectors across primate species, including lissencephalic ones, and would allow for meaningful homologies and interspecies comparisons.

We do not see how our work would benefit from developmental biology at that point, because it is concerned with evolution, and these are very distinct biological phenomena. We do not understand the reviewer’s focus on lissencephalic species, because they are not so prevalent across primates, and it is unlikely that adding a couple of lissencephalic species will change much to the conclusions.

Minor points:

• Please, format references according to the instructions of the journal.

Ok - done

• The authors could use the same color code across Figures 1, 2, and 3.

Ok – done

• The authors say that group hunting "only occurs in a few primate species", but it also occurs in wolves, whales, and other mammalian species.

We focus on primates here, these other species are irrelevant. Again, this is beside the point.

Reviewer #3 (Recommendations For The Authors):

My comments are organized by section below:

Introduction:

• Well written and thorough

• The two questions presented towards the end of the intro are not clear and do not guide the structure of the methods/results sections. I believe one it would be more appropriate to ask if: 1) the relative proportions of the FP and DLPFC (relative to ROB) are consistent across primates; and 2) if the relative size of these region is best predicted by social and/ or ecological variables. Then, the results sections could be organized according to these questions (current results section 1 = 1; current results sections 2, 3, 4 = 2.1, 2.2, 2.3)

As explained above, we agree with the reviewer that the introduction was somehow misleading and we have edited it extensively. We do not, however, agree with the reviewer regarding the relative (vs absolute) measure. We have discussed this in our response to reviewer 1 regarding the comparison of regional volumes as proxies for number of neurons. The best predictor of the computing capacity of a brain region is its number of neurons, but there is no reason to believe that this capacity should decrease if the rest of the brain increases, as implied by the relative measure that the reviewer proposes. That debate is probably critical in the field of comparative neuroanatomy, and confronting different perspectives would surely be both interesting and insightful, but we feel that it is beyond the scope of the present article.

Methods:

• While the methods are straightforward and generally well described, it is unclear which combinations of models were compared or why only population density and distance travelled tested appear to have been included (in e.g., Fig SI 3.1) even though many more variables were collected.

We agree that this was not clear enough, and we have tried to improve the description of our model comparison approach, both in the main text and in the supplementary material.

• Why was body mass rather than ROB used as a predictor in the models? The authors should instead/also include analyses using ROB (so the analysis is of FP and DLPFC size relative to brain size). Using body mass confounds the analyses since they will be impacted by differences in brain size relative body size.


Again, we have addressed this issue above. First, body size is a socio-ecological variable (if anything, it especially predicts energetic needs and energy expenditure), but ROB is clearly not. We do not see the theoretical relevance of ROB in a socio-ecological model. Second, from a neurobiological point of view, since within primates the volume of a given brain region is directly related to its number of neurons (again, see work of Herculano-Houzel), which is a good proxy for its computing capacity, we do not see the theoretical reason for considering ROB.

• It is not appropriate to compare the impact of different predictors using their coefficients if the variables were not scaled prior to analysis. The authors need to implement this in their approach to make such claims.

We thank the reviewer again for pointing that out. We have addressed this question above.

• Differences across primates in terms of frontal lobe networks throughout the brain should be acknowledged (e.g., Barrett et al. 2020, J Neurosci).

We have added that reference to the discussion, together with other references showing that the difference between human and non-human primates is significant, but essentially quantitative, rather than qualitative (the building blocks are relatively well conserved, but their relative weight differs a lot). Thank you for pointing it out.

I hope the authors find my comments helpful in revising their manuscript.

And we thank again the reviewer for the helpful and constructive comments.

Author Response

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

eLife assessment

This fundamental study identifies the homeodomain transcription factor and suspected autism-candidate gene Meis2 as transcriptional regulators of maturation and end-organ innervation of low-threshold mechanoreceptors (LTMRs) in the dorsal root ganglia (DRG) of mice. For a few years, the view on autism spectrum disorders (ASD) has shifted from a disorder that exclusively affects the brain to a condition that also includes the peripheral somatosensory system, even though our knowledge about the genes involved is incomplete. The study by Desiderio and colleagues is therefore not only scientifically interesting but may also have clinical relevance. The work is convincing, with appropriate and validated methodology in line with current state-of-the-art and the findings contribute both to understanding and potential application.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work examined transcription factor Meis2 in the development of mouse and chick DRG neurons, using a combination of techniques, such as the generation of a new conditional mutant strain of Meis2, behavioral assays, in situ hybridization, transcriptomic study, immunohistochemistry, and electrophysiological (ex vivo skin-nerve preparation) recordings. The authors found that Meis2 was selectively expressed in A fiber LTMRs and that its disruption affects the A-LTMRs' end-organ innervation, transcriptome, electrophysiological properties, and light touch-sensation.

Strengths:

1) The authors utilized a well-designed mouse genetics strategy to generate a mouse model where the Meis2 is selectively ablated from pre- and post-mitotic mouse DRG neurons. They used a combination of readouts, such as in situ hybridization, immunhistochemistry, transcriptomic analysis, skin-nerve preparation, electrophysiological recordings, and behavioral assays to determine the role of Meis2 in mouse DRG afferents.

2) They observed a similar preferential expression of Meis2 in large-diameter DRG neurons during development in chicken, suggesting evolutionarily conserved functions of this transcription factor.

3) Conducted severe behavioral assays to probe the reduction of light-touch sensitivity in mouse glabrous and hairy skin. Their behavioral findings support the idea that the function of Meis2 is essential for the development and/or maturation of LTMRs.

4) RNAseq data provide potential molecular pathways through which Meis2 regulates embryonic target-field innervation.

5) Well-performed electrophysiological study using skin-nerve preparation and recordings from saphenous and tibial nerves to investigate physiological deficits of Meis2 mutant sensory afferents.

6) Nice whole-mount IHC of the hair skin, convincingly showing morphological deficits of Meis2 mutant SA- and RA- LTMRs.

Overall, this manuscript is well-written. The experimental design and data quality are good, and the conclusion from the experimental results is logical.

Weaknesses:

1) Although the authors justify this study for the involvement of Meis2 in Autism and Autism associated disorders, no experiments really investigated Autism-like specific behavior in the Meis2 ablated mice.

Indeed, in the first version of the manuscript, we use current understanding of ASD in mouse models and associated sensory defects to articulate our introduction and discussion. As noticed by reviewer 1, none of our experiments really investigated ASD. To avoid over-interpretation of the data, we have now removed sentences mentioning ASD and related references throughout the manuscript.

2) For mechanical force sensing-related behavioral assays, the authors performed VFH and dynamic cotton swabs for the glabrous skin, and sticky tape on the back (hairy skin) for the hairy skin. A few additional experiments involving glabrous skin plantar surfaces, such as stick tape or flow texture discrimination, would make the conclusion stronger.

We fully agree on that performing more behavioral analysis investigating with more details the primary sensory defects as well as some ASD-related behavior would re-inforce our conclusions. Our behavioral analysis clearly showed a loss of sensitivity in response to mechanical stimuli within the light touch range but not for higher range mechanical or noxious thermal stimuli. While the experiments suggested by the reviewer are interesting and would strengthen our conclusions, they are far from trivial and require large cohorts. Given the current laboratory conditions as stated at the outset, these unfortunately are not within reach.

3) The authors considered von Frey filaments (1 and 1.4 g) as noxious mechanical stimuli (Figure 1E and statement on lines 181-183), which is questionable. Alligator clips or pinpricks are more certain to activate mechanical nociceptors.

To avoid misinterpretation of the higher Von Frey filament tests, we deleted the two following statement in page 7: “In the von Frey test, the thresholds for paw withdrawal were similar between all genotypes when using filaments exerting forces ranging from 1 to 1.4g, which likely reflects the activation of mechanical nociception suggesting that Meis2 gene inactivation did not affect nociceptor function.”. The sentence “… while sparing other somatosensory behaviors” was also deleted.

4) There are disconnections and inconsistencies among findings from morphological characterization, physiological recordings, and behavior assays. For example, Meis2 mutant SA-LTMRs show a deficiency in Merkel cell innervation in the glabrous skin but not in hairy skin. With no clear justification, the authors pooled recordings of SA-LTMRs from both glabrous and hairy skin and found a significant increase in mean vibration threshold. Will the results be significantly different if the data are analyzed separately? In addition, whole-mount IHC of Meissner's corpuscles showed morphological changes, but electrophysiological recordings didn't find significant alternation of RAI LTMRs. What does the morphological change mean then? Since the authors found that Meis2 mice are less sensitive to a dynamic cotton swab, which is usually considered as an RA-LTMR mediated behavior, is the SAI-LTMR deficit here responsible for this behavior? Connections among results from different methods are not clear, and the inconsistency should be discussed.

We thank Reviewer 1 for the careful review of our data and fully agree with the weaknesses identified, weaknesses we were ourselves aware of at the time of submission. In particular on the lack of stronger connections between histological and electrophysiological data. Electrophysiological studies were conducted on a first cohort of mice where we mostly emphasize on WT and Meis2 mutant mice. The goal was to describe differences in electrophysiological properties of identified mechanoreceptors from these two genotypes. While substantial differences between WT and Islet1-Cre mice were not expected, only very few mice with this genotype were examined at that time to confirm this assumption. We fully agree with reviewer 1 that confirming differences in SA-LTMRs responses in the hairy and glabrous at electrophysiological levels would be interesting and worthwhile. It is assumed that the physiological properties of SA-LTMRs from glabrous and hairy skins are equivalent in both skin types. Indeed direct comparisons have been made between glabrous and hairy skin SA-LTMRs revealing that they have equivalent receptor properties (see Walcher et al J Physiol quoted in the manuscript). We had not recorded from a sufficient number of hairy and glabrous skin SA-LTMRs to make any meaningful comparison statistically. When we noticed the dramatic differences in the innervation patterns of Merkel cell complexes between glabrous and hairy skin, we immediately planned a second mice cohort, but as explained in the onset to the Public Review, this cohort was sacrificed due to the pandemic lockdown. However, the obtained dataset clearly shows that in Meis2 mutant mice many SA-LTMRs had similar vibration thresholds to those of wild types.

For Meissner corpuscle, histological analysis evidenced clear morphological differences that could of course be investigated at the level of the dual innervation previously reported by Neubarth et al. It is uncertain whether differences in their electrophysiological responses would be revealed by increasing the number of recorded fibers. For this reason, we clearly stated this limitation in the results section page 7 “There was a tendency for RA-LTMRs in Isl1Cre/+::Meis2LoxP/LoxP mutant mice to fire fewer action potentials to sinusoids and to the ramp phase of a series 2 second duration ramp and hold stimuli, but these differences were not statistically significant (Figure 5B). Nevertheless it is important to point out that an electrical search strategy revealed that many Aβ-fibers did not have mechanosensitive receptive fields. Thus by focusing on LTMRs with a mechanosensitive receptive field, we ignore the fact that fewer fibers are mechanosensitive. This is now more extensively discussed in the discussion section of the manuscript page 13:

“Indeed, the electrophysiology methods used here can only identify sensory afferents that have a mechanosensitive receptive field. Primary afferents that have an axon in the skin but no mechanosensitvity can only be identified with a so-called electrical search protocol (45, 46) which was not used here. It is therefore quite likely that many primary afferents that failed to form endings would not be recorded in these experiments e.g. SA-LTMRs and RA-LTMRs that fail to innervate end-organs (Fig.4-6).”

“From our data, we could not conclude whether SA-LTMR electrophysiological responses are differentially affected in the glabrous versus hairy skin of Meis2 mutant as suggested by histological analysis. Further electrophysiological analysis focused on SA-LTMR selectively innervating the glabrous or hairy skin would be necessary to answer this question. Similarly, the decreased sensitivity of Meis2 mutant mice in the cotton swab assay and the morphological defects of Meissner corpuscles evidenced in histological analysis do not correlate with RA-LTMR electrophysiological responses for which a tendency to decreased responses were however measured. The later might result from an insufficient number of fibers recording, whereas the first may be due of pooling SA-LTMR from both the hairy and glabrous skin.”.

Reviewer #2 (Public Review):

Summary:

Desiderio and colleagues investigated the role of the TALE (three amino acid loop extension) homeodomain transcription factor Meis2 during maturation and target innervation of mechanoreceptors and their sensation to touch. They start with a series of careful in situ hybridizations to examine Meis2 transcript expression in mouse and chick DRGs of different embryonic stages. By this approach, they identify Meis2+ neurons as slowly- and rapidly adapting A-beta LTMRs, respectively. Retrograde tracing experiments in newborn mice confirmed that Meis2-expressing sensory neurons project to the skin, while unilateral limb bud ablations in chick embryos in Ovo showed that these neurons require target-derived signals for survival. The authors further generated a conditional knock-out (cKO) mouse model in which Meis2 is selectively lost in Islet1-expressing, postmitotic neurons in the DRG (IsletCre/+::Meis2flox/flox, abbreviated below as cKO). WT and Islet1Cre/+ littermates served as controls. cKO mice did not exhibit any obvious alteration in volume or cellular composition of the DRGs but showed significantly reduced sensitivity to touch stimuli and various innervation defects to different end-organ targets. RNA-sequencing experiments of E18.5 DRGs taken from WT, Islet1Cre/+, and cKO mice reveal extensive gene expression differences between cKO cells and the two controls, including synaptic proteins and components of the GABAergic signaling system. Gene expression also differed considerably between WT and heterozygous Islet1Cre/+ mice while several of the other parameters tested did not. These findings suggest that Islet1 heterozygosity affects gene expression in sensory neurons but not sensory neuron functionality. However, only some of the parameters tested were assessed for all three genotypes. Histological analysis and electrophysiological recordings shed light on the physiological defects resulting from the loss of Meis2. By immunohistochemical approaches, the authors describe distinct innervation defects in glabrous and hairy skin (reduced innervation of Merkel cells by SA1-LTMRs in glabrous but not hairy skin, reduced complexity of A-beta RA1-LTMs innervating Meissner's corpuscles in glabrous skin, reduced branching and innervation of A-betA RA1-LTMRs in hairy skin). Electrophysiological recordings from ex vivo skin nerve preparations found that several, but not all of these histological defects are matched by altered responses to external stimuli, indicating that compensation may play a considerable role in this system.

Strengths:

This is a well-conducted study that combines different experimental approaches to convincingly show that the transcription factor Meis2 plays an important role in the perception of light touch. The authors describe a new mouse model for compromised touch sensation and identify a number of genes whose expression depends on Meis2 in mouse DRGs. Given that dysbalanced MEIS2 expression in humans has been linked to autism and that autism seems to involve an inappropriate response to light touch, the present study makes a novel and important link between this gene and ASD.

Weaknesses:

The authors make use of different experimental approaches to investigate the role of Meis2 in touch sensation, but the results obtained by these techniques could be connected better. For instance, the authors identify several genes involved in synapse formation, synaptic transmission, neuronal projections, or axon and dendrite maturation that are up- or downregulated upon targeted Meis2 deletion, but it is unresolved whether these chances can in any way explain the histological, electrophysiological, or behavioral deficits observed in cKO animals. The use of two different controls (WT and Islet1Cre/+) is unsatisfactory and it is not clear why some parameters were studied in all three genotypes (WT, Islet1Cre/+ and cKO) and others only in WT and cKO. In addition, Meis2 mutant mice apparently are less responsive to touch, whereas in humans, mutation or genomic deletion involving the MEIS2 gene locus is associated with ASD, a condition that, if anything, is associated with an elevated sensitivity to touch. It would be interesting to know how the authors reconcile these two findings. A minor weakness, the first manuscript suffers from some ambiguities and errors, but these can be easily corrected.

We thank the reviewer for the insightful comments and suggestions.

The use of two different controls (WT and Islet1Cre/+) is unsatisfactory and it is not clear why some parameters were studied in all three genotypes (WT, Islet1Cre/+ and cKO) and others only in WT and cKO.

First, we identified a labelling mistake in figures 4D, 5A and 6A where the control shown are from Islet1+/Cre mice and not from WT as reported in the first version. We apologize for this mistake which has now been corrected. This typographical error does not in any way affect our conclusion, on the contrary, it shows that innervation defects are not the consequence of Islet1 heterozygosity.

The reviewer wonders why for some data both control genotypes are presented, and for some others only one is presented. It is quite possible that genes expression changes happen due to a synergistic effect of both heterozygous Meis2 deletion and heterozygous Islet1 deletion. However, we found no evidence that this led to defects in target-field innervation or to changes in the physiological properties of sensory neurons.

Whereas it could be fairly envisaged that some gene expression is modified due to a synergistic effect of both heterozygous Meis2 deletion and heterozygous deletion of Islet1, several lines of evidence support that the defects in target-field innervation and electrophysiological responses are exclusively due to Meis2 deletion. Previous work on Islet1 specific deletion in DRG sensory neurons opens the possibility that some of the phenotypes we report here are in part due to an effect of Islet1 heterozygous deletion or a synergistic effect to Meis2 homozygous deletion.

1) When Islet1 is conditionally deleted in mice using the Wnt1-Cre strain or at later stages using a tamoxifen inducible-Cre, homozygous pups die a few hours after birth. Early Islet1 deletion results in an increased apoptosis in the DRG, a massive loss of DRG sensory neurons and sensory defects associated to nociceptors mostly and some touch neurons while proprioceptive neurons are spared (Sun et al., 2008 now included in the revised version of the manuscript). There was a decrease in the number of Ntrk1+ and Ntrk2+ neurons whereas Ntrk3+ neurons number appeared normal. When Islet1 is inactivated later in development, the number of Ntrk1+ and Ntrk2+ neurons were normal and only the expression of nociceptor specific markers was decreased. Since neither the DRG volume, nor the number of Ntrk1+, Ntrk2+ and Ntrk3+ neurons are changed in Meis2 cKO using the Islet1-Cre strain, an early significant effect of Islet1 heterozygous deletion is very unlikely.

2) For distal innervation defects, it is clear from the Wnt1-Cre::Meis2 data (Figure 3E) that the distal innervation phenotype occurred while Meis2 is inactivated independently of Islet1 expression.

3) Finally, the lack of differences between WT and Islet+/Cre mice in behavioral assays and in electrophysiological characterization of RA-LTMR of the hairy skin (Figure 6C) and SA-LTMR (Figure 4B and C) argues for a lack of significant consequences of Islet1 heterozygous deletion on these parameters.

4) For bulk RNAseq studies, all datasets has been now re-analyzed following Reviewer 2 specific comments (see below). To avoid misinterpretation of the data, the results are now presented differently (see pages 8 and 9) and more critically discussed (see pages 14 and 15). In particular, we included and discuss references on Islet1 cKO mice.

We also agree with reviewer 2 that our RNAseq study only provides cues on potential genes expression that could impact distal innervation and electrophysiological responses. However, proving which of those genes are fully responsible for the morphological and electrophysiological defects would require extensive mouse genetic investigations such as restoring their normal expression level in a Meis2 mutant context, which is beyond the scope of the present study.

Finally, the reviewer questioned how we could reconcile the lower touch sensitivity in Meis2 mutant mice with the exacerbated touch sensitivity found in ASD patient and mouse models of ASD. As suggested by reviewer 1, our study did not really investigate ASD specifically. Therefore, to avoid over interpretation of the data and to follow Reviewer 1 recommendation, we have removed all references to ASD in the revised version of the manuscript. Indeed, to our knowledge, none of the case reports on Meis2 mutant patients investigated sensory function in general and light touch in particular, maybe because of the severe intellectual disability characterizing these patients.

Reviewer #1 (Recommendations For The Authors):

In addition to the aforesaid suggestions in the section 2, there are some minor issues:

We thank the reviewer for the careful reading and for identifying all these typos. All of them have been corrected in the revised version of the manuscript.

1) There should not be a full stop mark in the title of the article. This has been corrected in the new version of the manuscript.

2) Figure 1C, 1D, please correct the typo "controlateral' to "contralateral".

This has been corrected in the new version of the manuscript.

3) Figure 1D, lower graph, Y-axis, please correct the typo 'umber' to "number".

This has been corrected in the new version of the manuscript.

4) To make it easy for readers, add the names of the behavioral tests on top of the graphs in Fig 1E-H.

The name of behavioral tests is now added to the figure.

5) It would be easier to read the markers' names in IHC and ISH images if they were written outside of image panels. The blue staining color in image 1B could be easily mixed with the background. Suggest change colors.

Markers for IHC and IH images are now written outside the image panel or colors have been change in figure 1 and 2 for better clarity.

6) The font size of Genes' name in Figure 3B is too small and not readable.

Figure 3 has now been changed following Reviewer 2 recommendation. The small font size in Figure 3B is no longer present in the figure.

7) Quantification of Fig 3E (number of fibers innervating each dermal papilla or footpad, for example).

Unfortunately, we did not kept the Wnt1Cre::Meis2LoxP/LoxP strain which prevents further analysis (see onset of the answer to public review).

8) In Figure 4, please arrange IHC images and their quantification results adjacent to each other.

The figure has been reorganized and changes in the result section and figures legend were made accordingly.

9) For consistency, please use either LTMR or LTM (See Figure 4F, 5A, 6C), but not both.

This has been homogenized throughout the manuscript.

10) Add arrows/heads to mark the overlaps in Figure 4D.

Arrows are now added in Figure 4D to point at the overlap between Nefh and CK8 staining.

11) Figure 5A, 6A, Lines 236, 240, 247, 258, 305, 308, 313, 347, and many more in Figure legends: please check in entire manuscript and make the mouse genotype nomenclature (+/Cre?) consistent. In some places, Cre is written in all upper case (Line 657).

This has been homogenized throughout the manuscript.

12) Figure 4G: Histogram color could be darker for better contrast.

The color of the histograms has been changes in figures 6 and 5 for better clarity.

13) Please add the figure number to the Figure 6.

The figure number is now indicated on the figure.

1. Figure 6B: Y-axis typo, correct "Nfeh" to Nefh.

This typo is now corrected.

15) Either explain Figure 2B information before that of Figure 2C (In lines 204-207) in the text or change the figure panel sequence to keep the consistent flow of contents.

The figure has been modified and the panel sequence now follows that of the main text.

16) Line 213 has a typo: change "form" to "from".

This typo is now corrected.

17) Line 423 has a typo. Correct "al" to "all".

This typo is now corrected.

18) Line 625 has a typo. Correct "fo" to "of".

This typo is now corrected.

19) Line 669 has a typo. Correct "Alexa Fluo" to "Fluor".

This typo is now corrected.

20) Line 744: To be consistent in the entire manuscript, write "Nfh" as "Nefh".

This typo is now corrected.

21) 740-749: Please add host names for all primary antibodies, as some are given but some are not for the current version.

We now indicated the host species for all primary antibodies used in the study.

22) Line 751 has a typo: change "a" to "as".

This typo is now corrected.

23) Line 754: what is for 20'?

This typo is now corrected.

24) Line 832: change "day test" to "testing day".

The change has been made.

25) Please mention for how many seconds the VFH was administered on the plantar surface in the method.

A new sentence has been added to the “Von Frey withdrawal test” Methods section (page 30): “During each application, bend filament was maintained for approximately four to five seconds”.

26) For the sticky tape test, in lieu of hind paw attending bouts, wet-dog shake behavior, the authors also found some scratching behaviors. Did they separately quantify these behaviors? It would be interesting to see exactly which behavior significantly reduced after Meis2 inactivation.

Unfortunately, at the time of the design of the sticky tape test, we did not consider separating the behaviors considered as “positive” reactions. As these experiments were not video recorded, we are not able to extract this kind of information without generating new mice cohort and repeating this experiment.

27) Line 344-345: consider rephrasing the sentence.

This sentence has been removed.

Reviewer #2 (Recommendations For The Authors):

This is a beautiful and well-conducted study with all the strengths listed in the paragraphs above. Nevertheless, there are still some open questions, ambiguities in the presentation, and minor errors that I would recommend addressing.

Major Points:

1) The authors performed RNA-seq analysis from E18.5 mouse total DEGs from three different genotypes, WT, Isle1Cre/+ and cKO. Although this approach identified several interesting Meis2-dependent candidate genes, the presentation of the results is confusing, and the publication would gain impact if the RNA-seq results were better connected to the histological, behavioral, and electrophysiological data. Specific concerns:

1.1) The gene expression profiles of WT and Islet1Cre/+ samples are remarkably divergent. According to Yang Development 2006, Islet1-Cre was generated by knocking in Cre into the endogenous Islet1 locus and replacing the Isl1 ATG, hence resulting in a heterozygous null for Islet1. When purely technical derivations can be excluded, the RNAseq results presented here suggest that heterozygous loss of Islet1 causes considerable gene expression changes in the postnatal DRG. For analysis of the RNAseq results, the authors focus on genes that are differentially expressed between one experimental condition (Islet1Cre/+::Meis2flox/flox) and either one of two controls (WT or Islet1Cre/+). Hence, they pool the genes that are differently expressed between cKO and Islet1Cre/+ with the genes that are different between cKO and WT. This approach mixes gene expression differences that result from two different genetic alterations, heterozygosity of Islet1 and targeted deletion of Meis2, respectively. It seems much more logical to compare the results pairwise.

We agree with reviewer 2 that heterozygous deletion of Islet1 causes a significant change in genes expression that seems to very little correlate with any of the phenotypes we investigated in the study. When Islet1 is conditionally deleted in mouse using the Wnt1-cre strain, pups die few hours after birth and display increased apoptosis in the DRG, massive loss of DRG sensory neurons and sensory defects associated to nociceptors mostly and some touch neurons while proprioceptive neurons are spared (Sun et al., 2008 now included in the revised version of the manuscript). There is a decrease numbers of Ntrk1+ and Ntrk2+ neurons whereas the numbers of Ntrk3+ neurons appear normal. Later Isl1 inactivation does not induces changes in number of neurons and does not change Ntrk1 and 2 expressions. As explained in the answer to public reviews, bulk RNAseq data have now been reanalyzed following the reviewer suggestions and presented accordingly in the related figures.

In the study bay Sun et al. they also reported DEGs following Islet1 homozygous deletion, but data on Islet1 heterozygous deletion are not included. However, out of the 60 most dysregulated genes identified in their study, only 6 were differentially expressed in our datasets. Importantly, DEGs in their studies where identified using microarray. In another study, the same group, showed that Brn3a (another transcription factor important for DRG neurons differentiation) and Islet1 exhibit negative epistasis on sensory genes expression (Dykes et al., 2011 now included in the revised version of the manuscript). Thus we cannot rule out that similar rules apply for Islet1 and Meis2. However, given the high diversity of DRG sensory neurons, interpreting our bulk RNAseq analysis in such direction might lead to misinterpretation.

1.2) Along the same line, gene expression changes in Islet1Cre/+ DRGs seem to have little functional consequences, at least in the cases where all three genotypes were analyzed (target dependency (Fig. 1E), behavior (Fig. 1F), innervation (Fig. 4F, 6C)). Why were some parameters measured in all three genotypes and others only for WT and cKO? The authors probably reason that parameters that do not differ between WT and cKO animals will likely also not differ between WT and Islet1Cre/+. But what about parameters that do differ? Considering that the innervation of Merkel cells (Fig. 4E) and Meissner corpuscles (Fig. 5A) differ profoundly between WT and cKO, it would be interesting to know what this innervation looks like in Islet1Cre/+ DRGs. NEFH staining together with CK8 or S100beta from existing tissue sections should easily answer this question.

As explained in the answer for public reviews, there was a mistake in the annotation of the control in figure 4 D and E, and in Fig. 5 that has now been corrected. Concerning target-dependency, those are experiments conducted in chick embryo, and therefore no associated genotype.

1.3) Was a minimum cut-off for gene expression applied? The up-and downregulated genes in Fig. 3B list a number of pseudogenes and predicted genes. A quick (and incomplete) check for their expression in Fig2 Supple Table 1 shows that only a few reads were detected for most of them. With such low expression, even small changes will show up as significant differences.

In our first analysis, a cut-off of 10 reads was applied. As reviewer 2 mentioned, this cut-off included several pseudogenes and predicted genes with low expression for which small changes were significant. We now re-analyzed the dataset using a cut-off of 100 reads. This excluded most of the previous predicted genes and pseudogenes for the analysis and resulted in a much small number of DEGs for each dataset. As recommended by reviewer 2, we also now performed the David analysis separately. These results are now presented in Figure 3 and corresponding supplementary figures.

1.4) Given that bulk RNAseq from whole embryonic DRGs was performed, it would be interesting to know what cell type(s) express the Meis2-dependent transcripts. To address this question, the authors resort to published scRNAseq data by Usoskin Nat Neurosci 2015. They correlate the expression of all 488 DEGs (different between cKO and either WT or Islet1Cre/+) with the expression of Meis2 in the sensory neuron subtypes that were classified in the Usoskin paper. From that they conclude that many Meis2-dependent genes were expressed in the same sensory neuron classes as Meis2 itself. This is not apparent from Fig. 3 Supplementary 2. Neither do the 488 DEGs seem to be in any way enriched in the MEIS2-expressing cell clusters NF2/3/4/5, nor is cluster PEP1 particularly high in Meis2 expression. Immunostaining for MEIS2 together with a few selected DEGs would be a better way to assess co-expression.

We agree with reviewer 2 that the correlation between DEGs and the expression of Meis2 in the sensory neuron subtypes was far from striking. In our opinion, the new analysis shows now a more robust correlation. However, it has to be kept in mind that among DEGs not all are expected to be Meis2 direct target genes and therefore to be enriched in the same Meis2-expressing population. This also hold true for genes that could be de-repressed or induced following Meis2 inactivation. Finally, the scRNAseq by Usoskin et al was performed on adult sensory neurons whereas our bulk RNAseq was performed on E18.5 embryos. Thus, because gene expression in developing sensory neurons is well-known to be highly dynamic, it is not expected that the transcriptional signature of sensory neurons subclasses in E18.5 embryo perfectly matches the transcriptional signature of adult subclasses. Finally, we agree that immunostaining for Meis2 together with few selected DEGs would give a better answer on whether they co-localize or not, but our lack of experience with those antibodies together with the lack of financial support for the proposal precludes achieving this pertinent point.

1.5) The authors identify Gabra1 and Gabra4 as upregulated and Gabrr1 as downregulated genes in MEIS2 cKO animals. Does this reflect a change in GABA-receptor subunit composition in LMTRs?

This is an interesting point. First, in our new analysis, increasing the cut-off to 100 reads excluded Gabrr1 from the DEGs. Based on our results, we cannot conclude whereas Gabra1 and Gabra4 up-regulation reflects a change in GABA receptors composition. However, in the GEO term associated to Gabaergic synapse, whereas Gabra1 and Gabra4 were up-regulated the ionotropic glutamate receptor Grid1 was downregulated, rather claiming for an imbalanced GABA/Glutamate transmission. Finally, the increased GABAR expression in the LTMRs might be expected to increase pre-synaptic inhibition on the LTMR synapses onto target neurons in the dorsal horn, thus decreasing synaptic transmission from these neurons into spinal circuits.

2) The authors assessed SA-LTMR innervating Merkel cells in glabrous and hairy skin by IFC staining for neurofilament H and electrophysiological recordings. Due to the small sample size, they pooled recordings, reasoning that nerves that do not successfully innervate Merkel cells (i.e. cKO glabrous skin) do not evoke electrophysiological responses following a touch stimulus.

2.1) It is undoubtedly true that non-innervating nerves will likely not show electrophysiological responses. However, by pooling the recordings of SA-LTMRs from glabrous and hairy skin, the data obtained from the 20% successful recordings of SA-LTMRs from glabrous cKO skin (according to Fig. 4E, upper panel) will be overrepresented and hence lead to a systematic bias. How many recordings were made from the glabrous and hairy skin of each genotype? In case the number of recordings from cKO/glabrous skin is the limiting factor, does the observed difference in vibration threshold hold true when only recordings from hairy skin are compared?

As explained in the text and in our answers to reviewer 1, data for hairy and glabrous SAMs where initially pooled as no differences between them were expected, and next planned electrophysiological experiments were compromised due to the Covid19 pandemic. We are sorry that at this point, we cannot provide additional experiments to clarify this important point. In addition, as mention

3) From the IFC images shown in Fig. 6A, it is not clear how the authors quantified branch points and innervated hair follicles.

Branch points correspond to every time a nerve split in 2 or more nerves. Innervated follicles correspond to follicles that are entangled by circumferential and/or lanceolate Nefh+ endings.

4) The quality of the data is very high, but there are several ambiguities and errors in their presentation.

We apologize for this mistake. Figure 1 Supplementary 1 that reports data from Cat walk analysis is now appropriately included in the files.

4.2) Fig. 3A is confusing and the figure legend just repeats what is already said in the text. What do yellow, blue, and pink represent?

Figure 3 is now fully remade. Legend is now better indicated in Figure 3A. We hope it is now more clear.

4.3) What genotype do the black, grey, and white boxplots in Fig. 6C Fig. 3 Supplementary 1B correspond to?

The legends were missing for Figure 6C and Figure 3 supplementary 1B. They are now appropriately included.

4.4) Up- and downregulated genes are assigned differently in Fig. 3 and Fig. 3 Supplementary 2. The figure legend of Fig. 3 Suppl 2 lists panel B as up-regulated genes but the same genes are labeled down-regulated in Fig. 3.

We apologize for this previous mistake. Figure 3 and corresponding supplementary figures have been redone in the new version.

4.5) Fig. 3E would benefit from a more detailed description. One can easily appreciate that the neurofilament H staining in the cKO sample is different from that of the WT sample but what exactly can be seen here?

We added the following sentence in the results section: “In WT newborn mice, numerous Nefh+ sensory fibers surround all dermal papillae of the hairy skin and footpad of the glabrous skin, whereas in Wnt1Cre::Meis2LoxP/LoxP littermates, very few Nefh+ sensory fibers are present and they poorly innervate the dermal papillae and footpads.“.

4.6) The figure legend to Fig. 4A is unclear. Does the graph show the sum of all recordings performed? From the text, one would guess that the bars correspond to the cKO samples, but this is not specified. Do the controls correspond to WT, Islet1Cre/+ or a mixture of both? In addition, the graph in the lower panel is labeled % Ab fibers, the figure legend reads % of tap units among Ab fibers.

The graphs show the number of tap units identified among all recorded Afibers. Numbers show the number of tap units over the number of recorded fibers. This as been now reformulated in the last version of the manuscript.

4.7) The abbreviation SAM in figure legends 4F, G is not introduced.

This is now indicated in the figure legend.

4.8) Readers who are not familiar with the traces above the graphs in 4F and 4G will find a more detailed description helpful.

This is now indicated in the figure legend.

4.9) Lines 274-275: Does the statement "Finally, consistent with the lack of neuronal loss in Isl1Cre/+::Meis2LoxP/LoxP, the number of recorded fibers were identical in WT and Isl1Cre/+::Meis2LoxP/LoxP." refer to Fig. 4G? This is not specified in the text.

These data were not included in the first version of the manuscript as we though they were not significantly informative. They just indicate the overall numbers of fibers that were recorded in electrophysiological experiments. The sentence has been now removed in the last version of the manuscript to avoid misunderstanding.

4.10) There is no Fig. 6 supplementary 1.

The typo is now corrected. The corresponding data were in fact in Figure 5 Supplementary 1.

Minor points:

• Gangfuß et al. report that a patient previously diagnosed with a range of neurological deficits including the diagnosis of severe infantile autism is heterozygous mutant for MEIS2. Although this study links MEIS2 gene function to ASD in the wider sense, adding a few additional references will make the link stronger. Examples are Shimojima et al., Hum Genome Var 2017 or Bae et al., Science 2022.

These two references have been now included in the introduction section of the manuscript.

• In some figures (e.g. Fig. 4) the numbering of the panels does not follow the order in which the respective data are mentioned in the text.

Figure 4 is now re-organized so that panels follow the same order as in the results section.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Nitrogen metabolism is of fundamental importance to biology. However, the metabolism and biochemistry of guanidine and guanidine containing compounds, including arginine and homoarginine, have been understudied over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new type of guanidine forming enzyme. It was previously known that 2-oxogluturate oxygenase catalysis in bacteria can produce guanidine via oxidation of arginine. Interestingly, the same enzyme that produces guanidine from arginine also oxidises 2-oxogluturate to give the plant signalling molecule ethylene. Funck et al show that a mechanistically related oxygenase enzyme from plants can also produce guanidine, but instead of using arginine as a substrate, it uses homoarginine. The work will stimulate interest in the cellular roles of homoarginine, a metabolite present in plants and other organisms including humans and, more generally, in the biochemistry and metabolism of guanidines.

1) Significance

Studies on the metabolism and biochemistry of the small nitrogen rich molecule guanidine and related compounds including arginine have been largely ignored over the last few decades. Very few guanidine forming enzymes have been identified. Funck et al define a new guanidine forming enzyme that works by oxidation of homoarginine, a metabolite present in organisms ranging from plants to humans. The new enzyme requires oxygen and 2oxogluturate as cosubstrates and is related, but distinct from a known enzyme that oxidises arginine to produce guanidine, but which can also oxidise 2-oxogluturate to produce the plant signalling molecule ethylene.

Overall, I thought this was an exceptionally well written and interesting manuscript. Although a 2-oxogluturate dependent guanidine forming enzyme is known (EFE), the discovery that a related enzyme oxidises homoarginine is really interesting, especially given the presence of homoarginine in plant seeds. There is more work to be done in terms of functional assignment, but this can be the subject of future studies. I also fully endorse the authors' view that guanidine and related compounds have been massively understudied in recent times. I would like to see the possibility that the new enzyme makes ethylene explored. Congratulations to the authors on a very nice study.

Response: We thank the reviewer for the positive evaluation of our manuscript. In the revised version, we have emphasized more clearly that we found no evidence for ethylene production by the recombinant enzymes. The other suggestions of the reviewer are also considered in the revised version as detailed below.

Reviewer #2 (Public Review):

In this study, Dietmar Funck and colleagues have made a significant breakthrough by identifying three isoforms of plant 2-oxoglutarate-dependent dioxygenases (2-ODD-C23) as homo/arginine-6-hydroxylases, catalyzing the degradation of 6-hydroxyhomoarginine into 2aminoadipate-6-semialdehyde (AASA) and guanidine. This discovery marks the very first confirmation of plant or eukaryotic enzymes capable of guanidine production.

The authors selected three plant 2-ODD-C23 enzymes with the highest sequence similarity to bacterial guanidine-producing (EFE) enzymes. They proceeded to clone and express the recombinant enzymes in E coli, demonstrating capacity of all three Arabidopsis isoforms to produce guanidine. Additionally, by precise biochemical experiments, the authors established these three 2-ODD-C23 enzymes as homoarginine-6-hydroxylases (and arginine-hydroxylase for one of them). Furthermore, the authors utilized transgenic plants expressing GFP fusion proteins to show the cytoplasmic localization of all three 2-ODD-C23 enzymes. Most notably, using T-DNA mutant lines and CRISPR/Cas9-generated lines, along with combinations of them, they demonstrate the guanidine-producing capacity of each enzyme isoform in planta. These results provide robust evidence that these three 2-ODD-C23 Arabidopsis isoforms are indeed homoarginine-6-hydroxylases responsible for guanidine generation.

The findings presented in this manuscript are a significant contribution for our understanding of plant biology, particularly given that this work is the first demonstration of enzymatic guanidine production in eukaryotic cells. However, there are a couple of concerns and potential ways for further investigation that the authors should (consider) incorporate.

Firstly, the observation of cytoplasmic and nuclear GFP signals in the transgenic plants may also indicate cleaved GFP from the fusion proteins. Thus, the authors should perform Western blot analysis to confirm the correct size of the 2-ODD-C23 fusion proteins in the transgenic protoplasts.

Secondly, it may be worth measuring pipecolate (and proline?) levels under biotic stress conditions (particularly those that induce transcript changes of these enzymes, Fig S8). Given the results suggesting a potential regulation of the pathway by biotic stress conditions (eg. meJA), these experiments could provide valuable insights into the physiological role of guanidine-producing enzymes in plants. This additional analysis may give a significance of these enzymes in plant defense mechanisms.

Response: We thank also reviewer 2 for the positive evaluation and useful suggestions. We performed the proposed GFP Western blot, which indeed indicated the presences of both, fulllength fusion proteins and free GFP, which can explain the partial nuclear localization. We fully agree that further experiments with biotic and abiotic stress will be required to determine the physiological function of the 2-ODD-C23 enzymes. However, the list of potential experiments is long and they are beyond the scope of the present manuscript.

Reviewer #1 (Recommendations For The Authors):

Specific points

Overall, I thought this was a very interesting study, comprising biochemical, cellular, and in vivo studies. Of course more could be done on each of these, and likely will be, but I think the assignment of biochemical function is very strong, across all three approaches. The one new experiment I would like to see is a clear demonstration of whether ethylene is produced - unlikely but should be tested.

We had mentioned our failure to detect ethylene production by the plant enzymes in the previous version and have made it more prominent and reliable by including ethylene production as positive control in the new supplementary figure S5.

Abstract

Delete 'hitherto overlooked' - this is implicit 'but is more likely' to 'is likely'?

Agreed and modified

Introduction

Second sentence - what about relevant small molecule primary metabolites including precursors of proteins/nucleic acids.

We modified the sentence accordingly.

Paragraph 2 - maybe also note EFE produces glutamate semi aldehyde, via arginine C-5 oxidation.

Paragraph 2 has been re-phrased according to your suggestion.

Overall, I thought the introduction was exceptionally well written.

Perhaps either in the introduction, or later, note there are other 2OG oxygenases that oxidise arginine/arginine derivatives in various ways, e.g. clavaminate synthase/arginine hydroxylases/desaturases.

We added a sentence mentioning the arginine hydroxylases VioC and OrfP to the introduction and included VioC into the sequence comparison in supplementary figure 2 to show that these enzymes, as well as NapI, are very different from EFE and the plant hydroxylases.

Results

Paragraph 1 - qualify similarity and refer to/give a structurally informed sequence alignment, including EFE

A new supplemental figure S2 was added with sequence identity values and a structurally informed alignment. The text has been modified accordingly.

Paragraph 2 - briefly state method of guanidine analysis

We included a reference to the M&M section and mentioned LC-MS in paragraph 2.

Figure 1 - trivial point - proteins are not expressed/genes are

We have modified the legend to figure 1. However, we would like to point out that terms like “recombinant protein expression” are widely used in the field. A quick search with google Ngram viewer shows that “protein expression” started to appear in the mid-80ies and its use stayed constantly at 1/8th of “gene expression”.

Define errors clearly in all figure legends, clearly defining biological/technical repeats<br /> Page 6 - was the His-tag cleared to ensure no issues with Ni contamination?

We treat individual plants or independent bacterial cultures as biological replicates. Only in the case of enzyme activity assays with NAD(P)H, technical replicates were used and this has been indicated in the legend of figure 6.

Lower case 'p' in pentafluorobenzyl corrected

In Figure 2 make clear the hydroxylated intermediates are not observed

We now use grey color for the intermediates and have put them in brackets. Additionally we state in the figure legend that these intermediates were not detected.

Pages 6-7 - I may have missed this but it's important to investigate what happens to the 2OG. Is succinate the only product or is ethylene also produced? This possibility should also be considered in the plant studies, i.e. is there any evidence for responses related to perturbed ethylene metabolism. The authors consider a signalling role relating to AASA/P6C, but seem to ignore a potential ethylene connection.

As stated above, we checked for ethylene production with negative result. EFE produced 6 times more guanidine than the plant enzymes under the same condition, but even 100-fold lower ethylene production would have been clearly detected.

Page 12 - 'plants have been shown to....' Perhaps note how hydroxy guanidine is made?

We now mention the canavanine-γ-lyase that cleaves canavanine into hydroxyguanidine and homoserine.

Overall, I thought the discussion was good, but perhaps a bit long/too speculative on pages 12/13 and this detracted from the biochemical assignment of the enzyme. I'd suggest shortening the discussion somewhat - the precise roles of the enzyme can be the subject of future work. As indicated above, some discussion on potential links to ethylene would be appreciated.

Since reviewer 2 wanted more (speculative) discussion on the role of the 2-ODD-C23 enzymes and there was no detectable ethylene production, we took the liberty to leave the discussion largely unaltered.

I'd also like to see some more consideration/metabolic analyses of guanidine related metabolism in the genetically modified plants.

Such analyses will certainly be included in future experiments once we get an idea about the physiological role of the 2-ODD-C23 enzymes.

Page 16 - mass spectrometry

Corrected.

Please add a structurally informed sequence alignment with EFE and other 2OG oxygenases acting on arginine/derivatives.

An excerpt of the alignment is now presented in supplementary figure S2.

Reviewer #2 (Recommendations For The Authors):

I would like to see more discussion in the manuscript about the possible interconnection/roles between 2-ODD-C23 guanidine-producing, lysine- ALD1-Pipecolate producing, and proline metabolism pathways during both biotic and abiotic stresses.

Since we were unable to detect pipecolate in any of our plant samples and also our preliminary results with biotic stress did not produce any evidence for a function of the 2ODD-C23 enzymes in the tested defense responses, we would like to postpone such extended discussion until we find a condition where the physiological function of these enzymes is evident.

Fig. 4: Authors should change colors for Col-0, 0.2 HoArg and ctrl? They look too similar in my pdf file.

We changed the colors in figure 4 and hope that the enhanced contrast is maintained during the production of the final version of our article.

Author Response

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

Recommendations for the authors:

The single-mutant and double-mutant crp/rpoB strains were made by co-transduction with a nearby gene deletion (kanR-marked). I couldn't tell from the methods section whether these mutants, e.g., crp-H22N delta-chiA, were compared to wild-type cells or deletion mutants, e.g., delta chiA, in the proteomics experiments. I encourage the authors to explain this more clearly in the methods section, and to briefly mention in the Results section and relevant figure legends that the crp/rpoB mutant strains (and possibly the "wild-type" strains) also have gene deletions. If the comparison "wild-type" strains are fully wild-type (i.e., not deleted for chiA/yjaH), it is especially important to mention this in the Results section and the figure legends since the phenotypic changes could be due to the gene deletions rather than the mutations in crp/rpoB

We appreciate and agree with the editor's suggestion to clarify this point.

Accordingly, we have made the following changes to the text:

p11 L30-34 in the main text:

"The second experiment similarly compared an engineered BW25113 (BW) strain, containing the two regulatory mutations from the compact set (i.e., crp H22N and rpoB A1245V) together with the deletions used to insert them (see methods and DataS1 file), to a “wild type” BW strain (a corresponding knockout strain without the mutations, see methods)."

p28 under Chemostat proteomics experiment L13-16 in methods:

"The starting volume of each bioreactor was 150 ml M9 media supplemented with either 30 mM and 10mM D-xylose for the evolved and ancestor samples or only 10mM D-xylose for BW including compact set mutations and/or the deletions used for their insertions (DataS1 file). The minimal media also included trace elements and vitamin B1 was omitted."

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Sender et al describe a model to estimate what fraction of DNA becomes cell-free DNA in plasma. This is of great interest to the community, as the amount of DNA from a certain tissue (for example, a tumor) that becomes available for detection in the blood has important implications for disease detection.

However, the authors' methods do not consider important variables related to cell-free DNA shedding and storage, and their results may thus be inaccurate. At this stage of the paper, the methods section lacks important detail. Thus, it is difficult to fully assess the manuscript and its results.

Strengths:

The question asked by the authors has potentially important implications for disease diagnosis. Understanding how genomic DNA degrades in the human circulation can guide towards ways to enrich for DNA of interest or may lead to unexpected methods of conserving cell-free DNA. Thus, the question "how much genomic DNA becomes cfDNA" is of great interest to the scientific and medical community. Once the weaknesses of the manuscript are addressed, I believe this manuscript has the potential to be a widely used resource.

Weaknesses:

There are two major weaknesses in how the analysis is presented. First, the methods lack detail. Second, the analysis does not consider key variables in their model.

Issues pertaining to the methods section.

The current manuscript builds a flux model, mostly taking values and results from three previous studies: 1) The amount of cellular turnover by cell type, taken from Sender & Milo, 2021

2) The fractions of various tissues that contribute DNA to the plasma, taken from Moss et al, 2018 and Loyfer et al, 2023

My expertise lies in cell-free DNA, and so I will limit my comments to the manuscripts in (2). Paper by Loyfer et al (additional context):

Loyfer et al is a recent landmark paper that presents a computational method for deconvoluting tissues of origin based on methylation profiles of flow-sorted cell types. Thus, the manuscript provides a well-curated methylation dataset of sorted cell-types. The majority of this manuscript describes the methylation patterns and features of the reference methylomes (bulk, sorted cell types), with a smaller portion devoted to cell-free DNA tissue of origin deconvolution.

I believe the data the authors are retrieving from the Loyfer study are from the 23 healthy plasma cfDNA methylomes analyzed in the study, and not the re-analysis of the 52 COVID-19 samples from Cheng et al (MED 2021).

Paper by Moss et al (additional context):

Moss et al is another landmark paper that predates the Loyfer et al manuscript. The technology used in this study (methylation arrays) is outdated but is an incredible resource for the community. This paper evaluates cfDNA tissues of origin in health and different disease scenarios. Again, I assume the current manuscript only pulled data from healthy patients, although I cannot be sure as it is not described in the methods section.

This manuscript:

The current manuscript takes (I think) the total cfDNA concentration from males and females from the Moss et al manuscript (pooled cfDNA; 2 young male groups, 2 old male groups, 2 young female groups, 2 old female groups, Supplementary Dataset; "total_cfDNA_conc" tab). I believe this is the data used as total cfDNA concentration. It would be beneficial for all readers if the authors clarified this point.

The tissues of origin, in the supplemental dataset ("fraction" tab), presents the data from 8 cell types (erythrocytes, monocytes/macrophages, megakaryocytes, granulocytes, hepatocytes, endothelial cells, lymphocytes, other). The fractions in the spreadsheet do not match the Loyfer or Moss manuscripts for healthy individuals. Thus, I do not know what values the supplementary dataset represents. I also don't know what the deconvolution values are used for the flux model.

The integration of these two methods lack detail. Are the authors here using yields (ie, cfDNA concentrations) from Moss et al, and tissue fractions from Loyfer et al? If so, why? There are more samples in the Loyfer manuscript, so why are the samples from Moss et al. being used? The authors are also selectively ignoring cell-types that are present in healthy individuals (Neurons from Moss et al, 2018). Why?

Appraisal:

At this stage of the manuscript, I think additional evidence and analysis is required to confirm the results in the manuscript.

Impact:

Once the authors present additional analysis to substantiate their results, this manuscript will be highly impactful on the community. The field of liquid biopsies (non-invasive diagnostics) has the potential to revolutionize the medical field (and has already in certain areas, such as prenatal diagnostics). Yet, there is a lack of basic science questions in the field. This manuscript is an important step forward in asking more "basic science" questions that seek to answer a fundamental biological question.

We thank the reviewer for the valuable comments on our analysis. In response to the feedback, we have updated the analysis to address all critical points as described below and revised the text to enhance the clarity of our methodology. One notable improvement to our analysis involved ensuring better alignment between the cohort data for cfDNA plasma concentration and cell turnover estimates. To achieve this, we utilized the total plasma concentration of cfDNA from a study conducted by Meddeb et al. 2019, taking into account the influence of age and sex on these concentrations and specifically focusing on a cohort of relatively young and healthy individuals. Additionally, we considered expected variations related to sex, age, and other pertinent factors, as outlined in the studies by Meddeb et al. 2019 and Madsen et al. 2019.

In addition, we have addressed concerns regarding the technical aspects of cfDNA analysis, providing detailed explanations of their limited impact on our analysis and the resulting conclusions.

Reviewer #2 (Public Review):

Summary:

Cell-free DNA (cfDNA) are short DNA fragments released into the circulation when cells die. Plasma cfDNA level is thought to reflect the degree of cell-death or tissue injury. Indeed, plasma cfDNA is a reliable diagnostic biomarker for multiple diseases, providing insights into disease severity and outcomes. In this manuscript, Dr. Sender and colleagues address a fundamental question: What fraction of DNA released from cell death is detectable as plasma cfDNA? The authors use public data to estimate the amount of DNA produced from dying cells. They also utilize public data to estimate plasma cfDNA levels. Their calculations showed that <10% of DNA released is detectable as plasma cfDNA, the fraction of detectable cfDNA varying by tissue sources. The study demonstrates new and fundamental principles that could improve disease diagnosis and treatment via cfDNA.

Strengths:

1) The experimental approach is resource-mindful taking advantage of publicly available data to estimate the fraction of detectable cfDNA in physiological states. The authors did not assess if the fraction of detectable cfDNA changes in disease conditions. Nonetheless, their pioneering study lays the foundation and provides the methods needed for a similar assessment in disease states.

2) The findings of this study potentially explain discrepancies in measured versus expected tissue-specific cfDNA from some tissues. For example, the gastrointestinal tract is subject to high cell turnover and release of DNA. Yet, only a small fraction of that DNA ends up in plasma as gastrointestinal cfDNA.

3) The study proposes potential mechanisms that could account for the low fraction of detectable cfDNA in plasma relative to DNA released. This includes intracellular or tissue machinery that could "chew up" DNA released from dying cells, allowing only a small fraction to escape into plasma as cfDNA. Could this explain why the gastrointestinal track with an elaborate phagosome machinery contributes a small fraction of plasma cfDNA? Given the role of cfDNA as damage-associated molecular pattern in some diseases, targeting such a machinery may provide novel therapeutic opportunities.

Weaknesses:

In vitro and in vivo studies are needed to validate these findings and define tissue machinery that contribute to cfDNA production. The validation studies should address the following limitations of the study design: -

1) Align the cohorts to estimate DNA production and plasma cfDNA levels. Cellular turnover rate and plasma cfDNA levels vary with age, sex, circadian clock, and other factors (Madsen AT et al, EBioMedicine, 2019). This study estimated DNA production using data abstracted from a homogenous group of healthy control males (Sender & Milo, Nat Med 2021). On the other hand, plasma cfDNA levels were obtained from datasets of more diverse cohort of healthy males and females with a wide range of ages (Loyfer et al. Nature, 2023 and Moss et al., Nat Commun, 2018).

2) "cfDNA fragments are not created equal". Recent studies demonstrate that cfDNA composition vary with disease state. For example, cfDNA GC content, fraction of short fragments, and composition of some genomic elements increase in heart transplant rejection compared to no-rejection state (Agbor-Enoh, Circulation, 2021). The genomic location and disease state may therefore be important factors to consider in these analyses.

3) Alternative sources of DNA production should be considered. Aside from cell death, DNA can be released from cells via active secretion. This and other additional sources of DNA should be considered in future studies. The distinct characteristics of mitochondrial DNA to genomic DNA should also be considered.

We appreciate the reviewer's comments on our analysis. In response to the feedback, we have updated to address key points and revised the text accordingly.

1) We have incorporated several enhancements to improve the coherence of our analysis. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

There was no specific estimate for a cohort of young males in both Meddeb et al. and Loyfer et al.; however, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in literature (Meddeb et al. 2019; Madsen et al. 2019). Thus, we demonstrate that sex and age have a small effect on the cfDNA concentrations and thus are unlikely to alter our conclusions substantially when considering a healthy population. We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion.

2) In this study, we addressed the total amount of cfDNA in healthy individuals without regard to GC content, representation of different genomic regions, or fragment length, as the goal was to understand if cell death rates are fully accounted for by cfDNA concentration. We agree that it will be interesting to study the relative representation of the genome in cfDNA and the processes that determine cfDNA concentration in pathologies beyond the rate of cell death. These topics for future research fall beyond this study's scope.

3) We know only a few specific cases whereby DNA is released from cells that are not dying. These include the release of DNA from erythroblasts and megakaryocytes to generate anucleated erythrocytes and platelets (Moss et al. 2022, cited in our paper) and the release of NETs from neutrophils.

The presence of cfDNA fragments originating from megakaryocytes and erythroblasts indicates the elimination of megakaryocytes and erythroblasts and the birth of erythrocytes and platelets. However, the considerations in the rest of the paper still apply: the concentration of cfDNA from these sources is far lower than expected from the cell turnover rate.

Concerning NETosis: the presence of cfDNA originating in neutrophils that have not died would reduce the concentration of cfDNA from dying neutrophils and thus further increase the discrepancy, which is the topic of our study (under-representation of DNA from dying cells in plasma).

We neglected mitochondrial DNA, as it is not measured in methylation cell-of-origin analysis. Similarly to the argument above, if some of the total DNA measured in plasma is in fact, mitochondrial, this would mean that genomic cfDNA concentration is actually lower than the estimates, meaning that an even smaller fraction of DNA from dying cells is measured in plasma.

Recommendations For The Authors

Reviewer #1 (Recommendations For The Authors):

I think readers would appreciate the authors commenting or addressing the following points, in addition to addressing the concerns I raised about the methods section in the public review:

What variables and considerations did the authors omit in this study?

1) Cell-free DNA is found in virtually every biofluid.

Thus, the fact that cell-free DNA is not present in the plasma does not mean it cannot be detected elsewhere. This also implies that phagocytosis may not be the only factor related to cfDNA not being present in the blood. One example (of many, many others) is neutrophil-derived cell-free DNA, which is present in the urine.

Indeed, dying cells and their DNA can be consumed locally, released into the blood, or shed outside the body. The latter is a function of tissue topology. For example, intestinal epithelial cell turnover releases material to the lumen of the gut (i.e., stool); kidney and bladder cell turnover releases material to urine; and lung epithelium releases material to the air spaces. In these cases, the absence of cfDNA in plasma is expected. However, in cases where tissue topology dictates release to blood, low representation in cfDNA indicates local consumption or a related mechanism. In Figure 1 of the manuscript, we distinguish between tissues according to their topology, labeling organs that shed material to the outside denoted by open circles.

Neutrophil-derived DNA in urine likely represents a local process in the kidney (neutrophils that penetrate the epithelium and fall into the urine). Neutrophils that die elsewhere in the body must release cfDNA to the blood before it can reach the urine. Hence, quantifying plasma cfDNA is a legitimate approach for assessing the relationship between cell death and cfDNA. The revised text clarifies this point. We made revisions to the initial paragraph in the results section and a paragraph within the discussion to provide clarity on this topic:

“Based on atlases of human cell type-specific methylation signatures, Moss et al. and Loyfer et al. analyzed the main cell types contributing to plasma cfDNA. They found the primary sources of plasma cfDNA to be blood cells: granulocytes, megakaryocytes, macrophages, and/or monocytes (the signature could not differentiate between the last two), lymphocytes, and erythrocyte progenitors. Other cells that had detectable contributions are endothelial cells and hepatocytes. Qualitatively, these cells represent most of the leading cell types in cellular turnover, as shown in Sender & Milo 2021 (Sender and Milo 2021). Epithelial cells of the gastrointestinal tract, lung, kidney, bladder, and skin are other cell types that significantly contribute to cellular turnover. Dying cells in these tissues are shed into the gut lumen, the air spaces, the urine, or out of the skin (note that while DNA from gut, lung, and kidney epithelial cells can be found in stool, bronchoalveolar lavage, and urine, the fate of DNA from skin cells is not known). This arrangement may explain why DNA from these cell types is not represented in plasma cfDNA in healthy conditions. Therefore, it appears that cells with high cfDNA plasma levels are those with relatively high turnover that are not being shed out of the body.”

“A comparison between the different types of cells shows a trend in which less DNA flux from cells with higher turnover gets to the bloodstream. In particular, a tiny fraction (1 in 3x104) of DNA from erythroid progenitors arrives at the plasma, indicating an extreme efficiency of the DNA recovery mechanism. Erythroid progenitors are arranged in erythroblastic islands. Up to a few tens of erythroid progenitors surround a single macrophage that collects the nuclei extruded during the erythrocyte maturation process (pyrenocytes) (Chasis and Mohandas 2008). The amount of DNA discarded through the maturation of over 200 billion erythrocytes per day (Sender and Milo 2021) exceeds all other sources of homeostatic discarded DNA. Our findings indicate that the organization of dedicated erythroblastic islands functions highly efficiently regarding DNA utilization. Neutrophils are another high-turnover cell type with a low level of cfDNA. When contemplating the process of NETosis (Vorobjeva and Chernyak 2020), the existence of cfDNA originating from live neutrophils would potentially diminish the concentration of cfDNA released by dying neutrophils, thereby amplifying the observed ratio for this particular cell type. The overall trend of higher turnover resulting in a lower cfDNA to DNA flux ratio may indicate similar design principles, in which the utilization of DNA is better in tissues with higher turnover. However, our analysis is limited to only several cell types (due to cfDNA test and deconvolution sensitivities), and extrapolation to cells with lower cell turnover is problematic.”

2) Effect of biofluid storage.

Cell-free DNA continues to degrade after it is extracted via blood draw. This is not expected to change tissue of origin predictions (although that remains to be shown in the literature), but definitely affects extraction yield. This is not accounted for (or even discussed) in the manuscript. It would be important to understand how this was done for the data presented here.

The paper integrates data from multiple recent studies that adhered to state-of-the-art procedures requiring rapid processing of blood samples. In fact, earlier studies that were not careful to isolate plasma quickly typically reported very high concentrations due to the lysis of leukocytes and artifactual release of genomic DNA. Rapid plasma isolation and DNA extraction typically yield 5ng/ml in healthy donors, as stated in the paper (last paragraph of Results).

3) Batch effects

Batch effects are not discussed here and can affect cfDNA yields.

Our analysis relies on data reported by multiple studies from different groups, which independently results in similar key findings (total concentration of cfDNA and the relative contribution of different tissues). Thus, batch effects are unlikely to affect the calculations markedly.

4) Cell-free DNA extraction kits

Different kits and methods extract cell-free DNA at different quantities. Importantly, much research has been done recently that most kits are not sensitive for ultrashort cell-free DNA (of lengths ~50bp). This may represent most of the DNA present in plasma. This raises an important question: are the yields that are being used in Moss et al (where I presume the total concentration is taken from) accurate? Is there more cell-free DNA that was missed? While the importance of this ultrashort cfDNA has yet to be shown, it is in the blood. Thus, the authors' model may underestimate ratios by not accounting for this. This is mentioned in the discussion, but it is not evident why it was not added into the model.

The Qiagen cfDNA extraction kit can detect 50bp fragments. As shown in the specification sheets of the kit (https://www.qiagen.com/us/products/diagnostics-and-clinical-research/solutions-for -laboratory-developed-tests/qiasymphony-dsp-circulating-dna-kit), urine DNA contains abundant DNA fragments that peak at 50bp. In contrast, plasma cfDNA does not contain such fragments at appreciable concentrations. This suggests that small fragments, 50-150bp long, are not a major component of cfDNA, and thus, our measurements of the total concentration of cfDNA are not dramatically underestimated.

The convention regarding the size distribution of cfDNA fragments is based on extensive evidence using multiple approaches. For example, a study that profiled the DNA released by multiple cell lines in vitro (Aucamp et al. 2017) used another kit for DNA isolation – the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel, Düren, Germany). This kit does extract fragments that are 50bp long (nucleospin-gel-and-pcr-clean-up-mini). Indeed, the DNA released from cultured cells did contain a peak at 50bp, but it was minor compared with the nucleosome-size peak.

More recently, several studies did suggest the presence of ultra-short cfDNA fragments, 50 bp long on average, and concluded that such fragments might be present at a molar concentration that is comparable to that of nucleosome-protected DNA (for example, (Hisano et al. 2021)).

Thus, our model estimates can be off by up to 2-fold (that is, actual cfDNA concentration measured in most studies overlooks the small fragments and thus underestimates the actual concentration of cfDNA by 2-fold). This is incorporated into the revised manuscript.

We note that we cannot exclude the presence of abundant ultra-short DNA fragments (e.g., 10bp long). However, such fragments are not measurable in cfDNA analysis. Thus, we can refine our conclusion and state that only a small fraction of DNA of dying cells appears as measured cfDNA. We included a section in the methods detailing the integration of a potential factor for the short fragments and revised the discussion:

“The overall plasma cfDNA concentration was multiplied by a factor of 1.5 to accommodate for the presence of small fragments of approximately 50 base pairs of cfDNA in the plasma. These fragments are suggested to contribute comparable molar concentrations (Hisano, Ito, and Miura 2021). Despite having approximately one-third of the mass, it is reasonable to presume that these fragments represent a similar number of genomes. This assumption is based on the idea that their source is a broken nucleosome unit, and the fragments represent the portion that was not degraded. Given the restricted data and its interpretation, we consider factors spanning the range of 1 (negligible effect) and 2 (doubling of the amount). The chosen factor, 1.5, is selected as the midpoint within this range of uncertainty.”

“In this study, we report a surprising, dramatic discrepancy between the measured levels of cfDNA in the plasma and the potential DNA flux from dying cells. One hypothetical explanation for that discrepancy is the limited sensitivity of typical cfDNA assays to short DNA fragments, which may contribute a significant fraction of the overall cfDNA mass. Regular cfDNA analysis shows a size distribution concentrated around a length of 165 base pairs (bp). The sizes in ctDNA vary more, but most are longer than 100 bp (Alcaide et al. 2020; Udomruk et al. 2021). Recent studies suggested a significant fraction of single-strand ultrashort fragments (length of 25-60 bp) (Cheng et al. 2022; Hisano, Ito, and Miura 2021). However, the total amount of DNA contained in these fragments is less than or comparable to that of the longer “regular” nucleosome-protected cfDNA fragments (Cheng et al. 2022; Hisano, Ito, and Miura 2021), arguing against ultrashort fragments as a dominant explanation for the “missing” cfDNA material. We integrated the estimate provided by Hisano et al. into our analysis as a modifying factor for both the total concentration and uncertainty of plasma cfDNA. Importantly, this incorporation did not alter the overall conclusions, as the discrepancy between the cfDNA plasma concentration and potential DNA flux remains on the same order of magnitude. We note that we cannot exclude the presence of abundant DNA fragments that are even shorter (e.g., 10bp long) and are not measurable in cfDNA analysis. Thus, our formal conclusion is that only a small fraction of the DNA of dying cells appears as measurable cfDNA.”

5) Health status of samples analyzed.

Health, sex and physical activity affects cfDNA yields. This is not accounted for or discussed in the manuscript.

We incorporated several enhancements to improve our analysis in response to the provided feedback. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

Furthermore, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in the works of (Meddeb et al. 2019; Madsen et al. 2019). Our intent in doing so was to demonstrate that these factors are unlikely to alter our conclusions substantially when considering a healthy population. We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion:

“Our estimates for total plasma cfDNA concentration were derived from the median concentration observed in individuals below 47 years of age (n=52), as reported by (Meddeb et al. 2019). To complement this, we integrated our total concentration estimates with data on the proportion of cfDNA originating from specific cell types, leveraging a plasma methylome deconvolution method described by (Loyfer et al. 2023), which did not provide absolute quantities of cfDNA). To quantify the uncertainty associated with our cfDNA concentration estimates, we employed a methodology that considered several sources of variation. First, we incorporated the confidence interval of the median concentration reported by Meddeb et al. as a measure of uncertainty. Additionally, we accounted for individual-specific and analytic variations based on the study by (Madsen et al. 2019), encompassing factors such as the precise timing of measurements and assay precision. These sources of uncertainty were combined using the approach outlined below.”

“Our current analysis focused on estimating plasma cfDNA concentration and cellular turnover in a cohort of healthy, relatively young individuals. The total plasma cfDNA concentrations were sourced from healthy individuals below 47 years, as reported by (Meddeb et al. 2019). We use data analyzed based on plasma samples from healthy individuals to estimate the proportion of cfDNA originating from specific cell types (Loyfer et al. 2023). These values were then compared to the potential DNA flux resulting from homeostatic cellular turnover, estimated for reference healthy males aged between 20 and 30 (Sender and Milo 2021). In our analysis, we considered various sources of uncertainty, including inter-individual variation, variability in the timing of sample collection, and analytical precision (Madsen et al. 2019; Meddeb et al. 2019). These factors collectively contributed to an uncertainty factor of less than 3. Importantly, this level of uncertainty does not alter our conclusion regarding the relatively small fraction of DNA present in plasma as cfDNA. Furthermore, we acknowledge that age and sex can impact total cfDNA concentration, as demonstrated by (Meddeb et al. 2019), with potential variations of up to 30%. However, as the results of our analysis present a much larger difference, these effects do not change the conclusions drawn from our analysis. Nevertheless, age and health status may influence the proportion of cfDNA originating from specific cell types and their corresponding cellular turnover rates. Consequently, the ratios themselves may vary in the elderly population or individuals with underlying health conditions.”

Reviewer #2 (Recommendations For The Authors):

1) Align the cohorts to estimate DNA production and plasma cfDNA levels. Cellular turnover rate and plasma cfDNA levels vary with age, sex, circadian clock, and other factors (Madsen AT et al, EBioMedicine, 2019). This study estimated DNA production using data abstracted from a homogenous group of healthy control males (Sender & Milo, Nat Med 2021). On the other hand, plasma cfDNA levels were obtained from datasets of more diverse cohort of healthy males and females with a wide range of ages (Loyfer et al. Nature, 2023 and Moss et al., Nat Commun, 2018).

We have incorporated several enhancements to improve the coherence of our analysis. In our revised examination, we drew upon the total plasma concentration of cfDNA, as documented in a study conducted by (Meddeb et al. 2019), while considering the influence of age and sex on these concentrations. To ensure the cohort's alignment, we focus on relatively young and healthy individuals, specifically those below the age of 47. This approach allowed for a more meaningful comparison with the estimated DNA flux from a reference male human aged between 20 and 30 years.

There was no specific estimate for a cohort of young males in both Meddeb et al. and Loyfer et al.; however, we factored in the expected variations stemming from sex, age, and other relevant factors, as elucidated in literature (Meddeb et al. 2019; Madsen et al. 2019). Thus, we demonstrate that sex and age have a small effect on the cfDNA concentrations and thus are unlikely to alter our conclusions substantially when considering a healthy population.

We summarize the changes in the first paragraph, replacing the “Tissue-specific cfDNA concentration” subsection of the method, and the fourth paragraph added to the discussion.

“Our estimates for total plasma cfDNA concentration were derived from the median concentration observed in individuals below 47 years of age (n=52), as reported by (Meddeb et al. 2019). To complement this, we integrated our total concentration estimates with data on the proportion of cfDNA originating from specific cell types, leveraging a plasma methylome deconvolution method described by (Loyfer et al. 2023), which did not provide absolute quantities of cfDNA). To quantify the uncertainty associated with our cfDNA concentration estimates, we employed a methodology that considered several sources of variation. First, we incorporated the confidence interval of the median concentration reported by Meddeb et al. as a measure of uncertainty. Additionally, we accounted for individual-specific and analytic variations based on the study by (Madsen et al. 2019), encompassing factors such as the precise timing of measurements and assay precision. These sources of uncertainty were combined using the approach outlined below.”

“Our current analysis focused on estimating plasma cfDNA concentration and cellular turnover in a cohort of healthy, relatively young individuals. The total plasma cfDNA concentrations were sourced from healthy individuals below 47 years, as reported by (Meddeb et al. 2019). We use data analyzed based on plasma samples from healthy individuals to estimate the proportion of cfDNA originating from specific cell types (Loyfer et al. 2023). These values were then compared to the potential DNA flux resulting from homeostatic cellular turnover, estimated for reference healthy males aged between 20 and 30 (Sender and Milo 2021). In our analysis, we considered various sources of uncertainty, including inter-individual variation, variability in the timing of sample collection, and analytical precision (Madsen et al. 2019; Meddeb et al. 2019). These factors collectively contributed to an uncertainty factor of less than 3. Importantly, this level of uncertainty does not alter our conclusion regarding the relatively small fraction of DNA present in plasma as cfDNA. Furthermore, we acknowledge that age and sex can impact total cfDNA concentration, as demonstrated by (Meddeb et al. 2019), with potential variations of up to 30%. However, as the results of our analysis present a much larger difference, these effects do not change the conclusions drawn from our analysis. Nevertheless, age and health status may influence the proportion of cfDNA originating from specific cell types and their corresponding cellular turnover rates. Consequently, the ratios themselves may vary in the elderly population or individuals with underlying health conditions.”

2) "cfDNA fragments are not created equal". Recent studies demonstrate that cfDNA composition vary with disease state. For example, cfDNA GC content, fraction of short fragments, and composition of some genomic elements increase in heart transplant rejection compared to no-rejection state (Agbor-Enoh, Circulation, 2021). The genomic location and disease state may therefore be important factors to consider in these analyses.

In this study, we addressed the total amount of cfDNA in healthy individuals without regard to GC content, representation of different genomic regions, or fragment length, as the goal was to understand if cell death rates are fully accounted for by cfDNA concentration. We agree that it will be interesting to study the relative representation of the genome in cfDNA and the processes that determine cfDNA concentration in pathologies beyond the rate of cell death. These topics for future research fall beyond this study's scope.

3) Alternative sources of DNA production should be considered. Aside from cell death, DNA can be released from cells via active secretion. This and other additional sources of DNA should be considered in future studies. The distinct characteristics of mitochondrial DNA to genomic DNA should also be considered.

We know only a few specific cases whereby DNA is released from cells that are not dying. These include the release of DNA from erythroblasts and megakaryocytes to generate anucleated erythrocytes and platelets (Moss et al. 2022, cited in our paper) and the release of NETs from neutrophils.

The presence of cfDNA fragments originating from megakaryocytes and erythroblasts indicates the elimination of megakaryocytes and erythroblasts and the birth of erythrocytes and platelets. However, the considerations in the rest of the paper still apply: the concentration of cfDNA from these sources is far lower than expected from the cell turnover rate.

Concerning NETosis: the presence of cfDNA originating in neutrophils that have not died would reduce the concentration of cfDNA from dying neutrophils and thus further increase the discrepancy, which is the topic of our study (under-representation of DNA from dying cells in plasma).

We updated a paragraph in the discussion regarding this issue:

“A comparison between the different types of cells shows a trend in which less DNA flux from cells with higher turnover gets to the bloodstream. In particular, a tiny fraction (1 in 3x104) of DNA from erythroid progenitors arrives at the plasma, indicating an extreme efficiency of the DNA recovery mechanism. Erythroid progenitors are arranged in erythroblastic islands. Up to a few tens of erythroid progenitors surround a single macrophage that collects the nuclei extruded during the erythrocyte maturation process (pyrenocytes) (Chasis and Mohandas 2008). The amount of DNA discarded through the maturation of over 200 billion erythrocytes per day (Sender and Milo 2021) exceeds all other sources of homeostatic discarded DNA. Our findings indicate that the organization of dedicated erythroblastic islands functions highly efficiently regarding DNA utilization. Neutrophils are another high-turnover cell type with a low level of cfDNA. When contemplating the process of NETosis (Vorobjeva and Chernyak 2020), the existence of cfDNA originating from live neutrophils would potentially diminish the concentration of cfDNA released by dying neutrophils, thereby amplifying the observed ratio for this particular cell type. The overall trend of higher turnover resulting in a lower cfDNA to DNA flux ratio may indicate similar design principles, in which the utilization of DNA is better in tissues with higher turnover. However, our analysis is limited to only several cell types (due to cfDNA test and deconvolution sensitivities), and extrapolation to cells with lower cell turnover is problematic.”

We neglected mitochondrial DNA, as it is not measured in methylation cell-of-origin analysis. Similarly to the argument above, if some of the total DNA measured in plasma is in fact mitochondrial, this would mean that genomic cfDNA concentration is actually lower than the estimates, meaning that an even smaller fraction of DNA from dying cells is measured in plasma.

Author Response

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

We would firstly like to thank all reviewers for their comments and support of this manuscript.

Reviewer #1 (Recommendations For The Authors):

No further recommendations.

Reviewer #2 (Recommendations For The Authors):

All of my comments have been sufficiently addressed.

Reviewer #3 (Recommendations For The Authors):

Thanks for responding to my former recommendations constructively. I believe these points have been fully addressed in this new version.

However, I have not seen any comments on the points I raised in my former public review concerning the I-2 dependence of the FonSIX4 cell death. Do you know whether FonSIX4 would trigger cell death in tissues not expressing any I-2?

We are a little confused concerning this comment. I-2 is a different class of resistance protein (NLR) that recognises Avr2 and this is likely to be intracellular. From the previous public review, we believe reviewer 3 may have been asking us to clarify the dependence of I (MM or M82) on FonSIX4 cell death. We have performed these controls by expressing FonSIX4 and associated FonSIX4/Avr1 chimeras in N. benthamiana (with the PR-1 signal peptide for efficient secretion of effectors) and it does not cause cell death in the absence of the I receptor – see S11F Fig. This was not explicitly conveyed in text so we have included the following in text: “Using the N. benthamiana assay we show FonSIX4 is recognised by I receptors from both cultivars (IM82 and iMoneymaker) and cell death is dependent on the presence of IM82 or iMoneymaker (Fig 5B, S11 Fig).”

I still recommend discussing whether the Avr1 residues crucial for Avr activity are in the same structural regions of the C-terminal domain where previous work has identified residues under diversifying selection in symbiotic fungal FOLD proteins.

The region important for recognition does encompass some residues within the structural region identified to be under diversifying selection in FOLD effectors from Rhizophagus irregularis previously reported (two residues within one beta-strand). However, we also see residues that don’t overlap to this area. We also note that the mycFOLD proteins analysed in symbiotic fungi are heavily skewed towards strong structurally similarity with FolSIX6 (similar cysteine spacing within both N and C-domains and structural orientation of the N and C-domains) rather than Avr1. We are under the impression that Avr1 was not included in the analysis of diversifying selection in symbiotic fungal FOLD proteins, it also is unclear to us if close Avr1 homologues are present. With this in mind, and considering our already lengthy discussion (as previously highlighted during reviewer), we have decided not to include further discussion concerning this point.

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

We would like to thank the editor(s) and reviewers for their work concerning our manuscript. Most of the suggested changes were related to text changes which we have incorporated into the revised version. Please find our response to reviewers below.

Reviewer #1 (Recommendations For The Authors):

I only have very minor suggestions for the authors. The first one comes from reading the manuscript and finding it very dense with so many acronyms. This will limit the audience that will read the study and appreciate its impact. This is more noticeable in the Results, with many passages that I would suggest moving to Methodology.

We thank reviewer 1 for their very positive review. We understand that due to the nature of this study, which includes many protein alleles/mutations that were expressed with different boundaries etc., it is difficult to achieve this. Reviewer 2 asked for more details to be provided. We hope we have achieved a nice balance in the revised manuscript.

Something else that would facilitate the reading of the manuscript is the effectors name. The authors use the SIX name or the Avr name for some effectors and it makes it difficult to follow up.

We have tried to make this consistent for Avr1 (SIX4), Avr2 (SIX3) and Avr3 (SIX1). Other SIX effectors are not known Avrs so the SIX names were used.

Reading the manuscript and seeing how in most of the sections the authors used a computational approach followed by an experimental approach, I wonder why Alphafold2-multimer was not used to investigate the interaction between the effector and the receptor?

This is a great suggestion, we have certainly investigated this, however to date there is no experimental evidence to directly support the direct interaction between I and Avr1. Post review, we spent some time trying to capture an interaction using a co-immunoprecipitation approach however to date we have not been able to obtain robust data that support this. We are currently looking to study this utilising protein biophysics/biochemistry but this work will take some time.

Reviewer #2 (Recommendations For The Authors):

We thank reviewer 2 for the very thorough editing and recommendations. We have incorporated all minor text edits below into the manuscript.

Line 43: perhaps "Effector recognition" instead of "Effector detection", to be consistent with line 51?

Line 60: Change to "leads".

Line 79: Italicise Avr2.

Line 94: Add the acronym ETI in parentheses after "effector-triggered immunity".

Line 106: "(Leptosphaeria Avirulence-Supressing)" should be "(Leptosphaeria Avirulence and Supressing)".

Line 112: Change "defined" to "define".

Line 119: Spell out the species name on first use.

Line 205: Glomeromycota is a division rather than a genus. Consistent with Fig 2, it also does not need to italicized.

Line 207: Change "basidiomycete" to "Division Basidiomycota", consistent with Fig 2.

Line 214: Change "alignment of Avr1, Avr3, SIX6 and SIX13" to "alignment of the mature Avr1, Avr3, SIX6 and SIX13 sequences".

Line 324: Change "solved structures" to "solved protein structures".

Line 335: Spell out acronyms like "MS" on first use in figure legends. Also dpi in other figure legends.

Line 341: replace "effector-triggered immunity (ETI)" with "(ETI)" - see comment on Line 94.

Line 370: Change "domains" to "domain".

Line 374: In the title, change "C-terminus" to C-domain", consistent with the rest of the figure legend.

Line 404: Change "(basidiomycetes and ascomycetes)" to "(Basidiomycota and Ascomycota fungi)", consistent with Fig 2C.

Line 416: Change "in" to "by".

Line 427: un-italicize the parentheses.

Line 519: First mention of NLR. Spell out the acronym on first use in main text. S5 and S11 figure titles should be bolded.

Line 852: Replace "@" with "at".

S4 Table: Gene names should be italicised.

S5 Table: Needs to be indicated that the primer sequences are in the 5´-3´ orientation.

With regards to the Agrobacterium tumefaciens-mediated transient expression assays involving co-expression of the Avr1 effector and I immune receptor, the authors need to make clear how many biological replicates were performed as this information is only provided for the ion leakage assay.

We have added these data to the figure legend

Line 57: For me, the text "Fol secretes a limited number of structurally related effectors" reads as Fol secretes structurally related effectors, but very few of them are structurally related. Perhaps it would be better to say that the effector repertoire of Fol is made up of proteins that adopt a limited number of structural folds, or that the effector repertoire can be classified into a reduced set of structural families?

This edit has been incorporated.

Lines 66-67: Subtle re-wording required for "The best-characterized pathosystem is F. oxysporum f. sp. lycopersici (Fol)", as a pathosystem is made up of a pathogen and its host. Perhaps "The best-characterized pathosystem involves F. oxysporum f. sp. lycopersici (Fol) and tomato".

Sentence has been reworded.

Line 113 and throughout: Stick with one of "resistance protein", "receptor", "immune receptor" and "immunity receptor" throughout the manuscript.

We have decided to use both receptor and immunity receptor as not all receptors investigated in the manuscript provide immunity.

Lines 149-150: The title does not fully represent what is shown in the figure. The text "that is unique among fungal effectors" can be deleted as there is nothing in Fig 1 that shows that the fold is unique to fungal effectors.

Figure title has been changed.

Line 173: The RMSD of Avr3 is stated as being 3.7 Å, but in S3 Fig it is stated as being 3.6 Å.

This was a mistake in the main text and has been corrected.

Lines 202-204: This sentence needs to be reworded, as the way that it is written implies that the Diversispora and Rhizophagus genera are in the Ascomycota division. Also, "Ascomycetes" should be changed to "Ascomycota fungi", consistent with Fig 2.

Sentence has been reworded.

Line 233: "Scores above 8". What type of scores? Z-scores?

These are Z-scores. This has been added in text.

Lines 242-246: It is stated that SIX9 and SIX11 share structural similarity to various RNA-binding proteins, but no scores used to make these assessments is given. The scores should be provided in the text.

Z-scores have been added.

Fig 4A: SIX3 should be Avr2, consistent with line 292. The gene names should be italicised in Fig 4A.

SIX3 was changed to Avr2. Gene names have been italicised.

Line 356: Subtle rewording required, as "co-infiltrated with both IM82 and iMoneymaker" implies that you infiltrated with protein rather than Agrobacterium strains.

Sentence has been reworded.

Fig 5A, Fig 5C and Line 380: Light blue is used, but this looks grey. Perhaps change colour, as grey is already used to show the pro-domain in Fig 5A (or simply change the colour used to highlight the pro-domain)?

Colour depicting the C-domain was changed.

Lines 530-531: This text is no longer correct. Rlm4 and Rlm3 are now known to be alleles of Rlm9. See: Haddadi, P., Larkan, N. J., Van deWouw, A., Zhang, Y., Neik, T. X., Beynon, E., ... & Borhan, M. H. (2022). Brassica napus genes Rlm4 and Rlm7, conferring resistance to Leptosphaeria maculans, are alleles of the Rlm9 wall‐associated kinase‐like resistance locus. Plant Biotechnology Journal, 20(7), 1229.

We thank the reviewer for picking this up. This text has been updated.

Line 553: Provide more information on what the PR1 signal peptide is.

More information about the PR1 signal peptide has been added.

Lines 767-781: Descriptions and naming conventions of proteins throughout the figure legend need to be consistent and better reflect their makeup. For example, I think it would be best to put the sequence range after each protein mentioned - e.g. Avr118-242 or Avr159-242 instead of Avr1, PSL1_C37S18-111 instead of PSL1_C37S, etc. Furthermore, it is often stated that a protein is full-length when it lacks a signal peptide - my thought is that if a proteins lack its signal peptide, it is not full-length. The acronym "PD" also needs to be spelled out as "pro-domain (PD)" in the figure legend.

We have incorporated sequence range for proteins that were produced upon first use. Sequence ranges that were modelled in AlphaFold2 were not added in text because they can be found in Supplementary Table 3.

Lines 853-845: It is stated the sizes of proteins are indicated above the chromatogram in S10 Fig, but this is not the case. It is also not clear from S10B Fig that the faint peaks correspond to the peaks in the Fig 4B chromatogram. In S10D Fig, the stick of C58S is difficult to see. Perhaps change the colour or use an arrow/asterisk?

Protein size estimates have been added above the chromatogram. Added text to indicate that the faint peaks correspond to peaks in Fig 4B. Added an asterisk in S10D Fig to identify the location of C58.

S14 Fig is not mentioned/referenced in the main text of the manuscript.

This was a mistake and has been added.

The reference list needs to be updated to accommodate those referenced bioRxiv preprints that have now been published in peer-reviewed journals.

The reference list has been updated.

Reviewer #3 (Recommendations For The Authors):

It would be good to discuss whether the pro-domains affecting virulence or avirulence activity.

Kex2, the protease that cleaves the pro-domain functions in the golgi. We therefore suspect that the pro-domain is removed prior to secretion. For recombinant protein production in E. coli we find that these pro-domains are necessary to obtain soluble protein (doi: 10.1111/nph.17516). As we require the pro-domain for protein production and can not completely removing them from our preps, we cannot perform experiments to test this and subsequently comment further. In a paper that identified SIX effectors in tomato utilising proteomics approach (https://bsppjournals.onlinelibrary.wiley.com/doi/10.1111/j.1364-3703.2007.00384.x), it appears that the pro-domains were not captured in this analysis. This supports the conclusion that they are not associated with the mature/secreted protein.

The authors stated that the C-terminal domain of SIX6 has a single disulfide bond unique to SIX6. Please clarify in which context is it unique: in Fusarium or across all FOLD proteins?

This is in direct comparison to Avr1 and Avr3. The disulfide in the C-domain of SIX6 is unique compared to Avr1 and Avr3. This has been made clear in text.

The structural similarity of FOLD proteins to other known structures have been discussed (lines 460ff), but it is not clear whether all structures and models identified in this work would yield cysteine inhibitor and tumor necrosis factors as best structural matches in the database or whether this is specific to a single FOLD protein. Please consider discussing recently published findings by others (Teulet et al. 2023, New Phytologist) on this aspect.

This analysis was performed for Avr1, we obtained relatively low similarity hits for Avr3/Six6. We have updated this text accordingly… “Unfortunately, the FOLD effectors share little overall structural similarity with known structures in the PDB outside of the similarity with each other. At a domain level, the N-domain of the FOLD effector Avr1 has some structural similarities with cystatin cysteine protease inhibitors (PDB code: 4N6V, PDB code: 5ZC1) [60, 61], and the C-domain with tumour necrosis factors (PDB code: 6X83) [62] and carbohydrate-binding lectins (PDB code: 2WQ4) [63]. Relatively weak hits were observed for Avr3/Six6.”

It might be useful to clearly point out that the ToxA fold and the C-terminus of the FOLD fold are different.

We have secondary structural topology maps of the FOLD and ToxA-like families in S8 Fig which highlight the differences in topology between these two families.

Please add information to Fig.S8 listing the approach to generate the secondary structure topology maps.

We have added this information in the figure caption.

Author Response

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

Reviewer #1 (Public Review):

The authors found that nifuroxazide has the potential to augment the efficacy of radiotherapy in HCC by reducing PD-L1 expression. This effect may be attributed to increased degradation of PD-L1 through the ubiquitination-proteasome pathway. The paper provides new ideas and insights to improve treatment effectiveness, however, there are additional points that could be addressed.

• The paper highlights that the combination of nifuroxazide increases tumor cell apoptosis. A discussion regarding the potential crosstalk or regulatory mechanisms between apoptotic pathways and PD-L1 expression would be valuable.

Response: Thank you very much for your suggestion. Research has shown that regulating the STAT3/PD-L1 pathway can effectively increase apoptosis in lung cancer cells (1). Our study confirmed that nifuroxazide can effectively inhibit the expression of p-STAT3 and PD-L1 in liver cancer cells, which may be the reason for the increased apoptosis of these cells. We have added relevant descriptions in the discussion.

• The benefits and advantages of nifuroxazide combination could be compared to the current clinical treatment options.

Response: Thank you greatly for your insightful feedback. The primary objective of this study is to explore whether nifuroxazide can effectively enhance the degradation of PD-L1, thereby increasing the radiosensitivity of HCC. Our research reveals that compared to radiation therapy alone, combination therapy involving nifuroxazide and radiation significantly inhibits tumor growth in mice and boosts the anti-tumor immune response. This finding could potentially provide a valuable strategy for patients who exhibit resistance to radiation therapy in clinical practice. Moreover, clinical trial investigations have demonstrated that nivolumab, a PD-1 monoclonal antibody, when combined with radiation therapy for HCC, exhibits promising safety and efficacy (2). This evidence supports the future application of nifuroxazide in the treatment of HCC. However, to reach this objective, we must continue to conduct extensive research, including comparing nifuroxazide with existing therapies in clinical practice. We believe that nifuroxazide not only significantly inhibits the expression of PD-L1 protein in HCC cells but also functions as a PD-L1 inhibitor. Furthermore, it effectively curbs the proliferation and migration of HCC cells, induces tumor cell apoptosis, and may exhibit enhanced anti-tumor effects, making it a promising candidate for clinical use. We have incorporated relevant discussion content in the article to address these points.

Reviewer #2 (Public Review):

Summary:

Zhao et al. aimed to explore an important question - how to overcome the resistance of hepatocellular carcinoma cells to radiotherapy? Given that the immune-suppressive microenvironment is a major mechanism underlying resistance to radiotherapy, they reasoned that a drug that blocks the PD-1/PD-L1 pathway could improve the efficacy of radiation therapy and chose to investigate the effect of Nifuroxazide, an inhibitor of stat3 activation, on radiotherapy efficacy in treating hepatocellular carcinoma cells. From in vitro experiments, they find combination treatment (Nifuroxazide+ radiotherapy) increases apoptosis and reduces proliferation and migration, in comparison to radiotherapy alone. From in vivo experiments, they demonstrate that combined treatment reduces the size and weight of tumors in vivo and enhances mice survival. These data indicate a better efficacy of combination therapy compared to radiotherapy alone. Moreover, they also determined the effect of combination therapy on tumor microenvironment as well as peripheral immune response. They find that combination therapy increases infiltration of CD4+ and CD8+ cells as well as M1 macrophages in the tumor microenvironment. Interestingly, they find that the ratio of Treg cells in spleen is increased by radiotherapy but decreased by Nifuroxazide. Considering the immune-suppressive role of Treg cells, this finding is consistent with reduced tumor growth by combination therapy. However, it is unclear whether the combined therapy affects the ratio of Treg cells in the tumors or not. The most intriguing part of the study is the determination of the effect of Nifuroxazide on PD-L1 expression in the context of radiotherapy. Considering Nifuroxazide is a stat3 activation inhibitor and stat3 inhibition leads to reduced expression of PD-L1, one would expect Nifuroxazide decreases PD-L1 expression through stat3. However, they found that the effect of Nifuroxazide on PD-L1 is dependent on GSK3 mediated Proteasome pathways and independent of stat3, in the given experimental context. To determine the relevance to human hepatocellular carcinoma, they also measured the PD-L1 expression in human tumor tissues of HCC patients pre- and post-radiotherapy. The increased PD-L1 expression level in HCC after radiotherapy is impressive. However, it is unclear whether the patients being selected in the study had resistant disease to radiotherapy or not.

Overall, the data are convincing and supportive to the conclusions.

Strengths:

1) Novel finding: Identified novel mechanism underlying the effect of Nifuroxazide on PD-L1 expression in hepatocellular carcinoma cells in the context of radiotherapy.

2) Comprehensive experimental approaches: using different approaches to prove the same finding. For example, in Fig 4, both IHC and WB were used. In Fig 5, both IF and WB were used.

3) Human disease relevance: Compared observations in mice with human tumor samples.

The question in the summary, “However, it is unclear whether the combined therapy affects the ratio of Treg cells in the tumors or not”.

Response: Thank you very much for your valuable feedback. We have included additional flow cytometry results regarding the expression of relevant Treg cells (CD4+CD25+Foxp3+ T lymphocytes) in tumor tissues (Supplementary Fig 2). Our findings indicate that the number of Treg cells in tumor tissues significantly decreased following combination therapy with nifuroxazide and radiotherapy.

The question in the summary, “However, it is unclear whether the patients being selected in the study had resistant disease to radiotherapy or not”.

Response: Thank you very much for your valuable feedback. All the HCC patients selected in this study experienced recurrence after radiation treatment.

Weaknesses:

1) It is hard to tell whether the observed phenotype and mechanism are generic or specific to the limited cell lines used in the study. The in vitro experiments were performed in one human cell line and the in vivo experiments were performed in one mouse cell line.

Response: Thank you very much for your feedback. We have included additional experimental data from another human cell line Huh7 (Supplementary Fig 3).

2) The study did not distinguish the effect of increased radiosensitivity by nifuroxazide from combined anti-tumor effects by two different treatments.

Response: Thank you greatly for your insightful feedback. In this study, we primarily compared the antitumor effects of nifuroxazide combined with radiotherapy versus either nifuroxazide or radiotherapy alone, and confirmed that the combined treatment demonstrated a more potent anti-hepatocellular carcinoma effect compared to single therapy. Furthermore, to achieve the goal of utilizing nifuroxazide for the treatment of clinical hepatocellular carcinoma, additional research is necessary, including comparisons with other clinically established therapies. We have also incorporated relevant discussions in our analysis.

Reviewer #3 (Public Review):

Summary:

In this study, the authors embarked on an exploration of how nifuroxazide could enhance the responsiveness to radiotherapy by employing both an in vitro cell culture system and an in vivo mouse tumor model.

Strengths:

The researchers conducted an array of experiments aimed at revealing the function of nifuroxazide in aiding the radiotherapy-induced reduction of proliferation, migration, and invasion of HepG2 cells.

Weaknesses:

The authors did not provide the molecular mechanism through which nifuroxazide collaborates with radiotherapy to effectively curtail the proliferation, migration, and invasion of HCC cells. Moreover, the evidence supporting the assertion that nifuroxazide contributes to the degradation of radiotherapy-induced upregulation of PD-L1 via the ubiquitin-proteasome pathway appears to be insufficient. Importantly, further validation of this discovery should involve the utilization of an additional syngeneic mouse HCC tumor model or an orthotopic HCC tumor model.

Response: Thank you very much for your insightful comments. Nifuroxazide has been demonstrated to inhibit the expression of p-STAT3, thereby suppressing tumor cell proliferation and migration (3, 4). In our study, we observed that after 48 hours of treatment with Nifuroxazide, the expression of p-STAT3 in irradiated cells was significantly inhibited. Furthermore, compared to radiation alone, combined Nifuroxazide and radiotherapy resulted in a more pronounced decrease in PCNA expression. Simultaneously, we performed additional detection of migration-related protein MMP2 expression (revised Fig 2B), confirming that combined Nifuroxazide and radiotherapy led to a more significant inhibition of MMP2 expression. These findings suggest that the combined treatment may be responsible for the synergistic suppression of HCC cell proliferation and migration. We have included relevant discussions in our manuscript.

Our initial results indicate that Nifuroxazide inhibits the expression of PD-L1 at the protein level, but does not affect its mRNA level. Interestingly, upon treatment with a proteasome inhibitor MG132, the inhibitory effect of Nifuroxazide on PD-L1 was eliminated, suggesting that Nifuroxazide may enhance the degradation of PD-L1 protein. Our experiments have demonstrated the inhibitory effect of Nifuroxazide on PD-L1 in both human and mouse cell lines. However, to translate these findings into clinical application for the treatment of hepatocellular carcinoma, additional research is necessary, including validation in genetically engineered mouse models of HCC. We have addressed these points in the discussion section of our manuscript.

Reviewer #1 (Recommendations For The Authors):

1) Please improve the quality of Figure 3E. It is hard to figure out the bar and details.

Response: Thank you for your valuable feedback. We have meticulously revised the figures to enhance their clarity and presentation (revised Fig 3E).

2) In Figure 7E, please elucidate the methods used for calculating the amount of PD-L1 mRNA level. Please adjust the picture angle and label the marker size on the left as well

Response: Thank you for your feedback. We have incorporated a method for calculating PD-L1 mRNA levels and revised the corresponding figures accordingly (revised Fig 7E).

Reviewer #2 (Recommendations For The Authors):

Questions:

1) What is the advantage of using a combination of nifuroxazide and radiotherapy in comparison to using a combination of anti-PD1/PDL1 and radiotherapy?

Response: Thank you very much for your insightful comments. We believe that the advantage of nifuroxazide over PD-1 or PD-L1 antibodies lies in its ability not only to effectively inhibit PD-L1 expression but also to suppress tumor cell proliferation, migration, and promote cell apoptosis (Supplementary Fig 1). We have also expanded on these aspects in the discussion section of the manuscript.

2) For the characterization of tumor microenvironment and immune cells in the spleen, were the same cell populations being investigated? What about NK and Treg cells in tumors? What about M1 macrophages in spleen?

Response: Thank you very much for your insightful suggestion. We have measured the infiltration of NK and Treg cells in tumor tissues (Supplementary Fig 2), as well as the abundance of M1 macrophages (revised Fig 6) in the spleen, and provided additional relevant data to strengthen our study.

Other comments:

1) The data in Fig 1 is solid. However, it is hard to distinguish the effect of increased radiosensitivity by nifuroxazide from combined anti-tumor effects by two different treatments. The anti-tumor role of Nifuroxazide has been reported in melanoma, colorectal carcinoma, and hepatocellular carcinoma previously (PMID: 26830149; 28055016, 26154152). Therefore, the increased apoptosis and decreased proliferation and migration could be caused by nifuroxazide and not related to the sensitivity of cells to radiation therapy.

Response: Thank you very much for your constructive feedback. As you suggested, the anti-tumor role of nifuroxazide has been reported. However, the innovation of our study does not lie in confirming its antitumor effects but rather in demonstrating how nifuroxazide can enhance radiotherapy's efficacy in treating hepatocellular carcinoma by inhibiting PD-L1 levels.

We compared the efficacy of combined therapy versus radiotherapy and found that compared to radiation alone, combined therapy more significantly inhibited hepatocellular carcinoma cell proliferation and migration. In our animal model, we compared the therapeutic effects of combined therapy, nifuroxazide, and radiotherapy on hepatocellular carcinoma-bearing mice. We observed that compared to individual treatment groups, combined therapy more profoundly suppressed tumor growth and enhanced the antitumor effects in the mice.

In response to your feedback, we have expanded the discussion on the impact of combined therapy versus nifuroxazide or radiotherapy on hepatocellular carcinoma cell proliferation, migration, and apoptosis (Supplementary Fig 1). The data show that compared to either individual therapy, combined therapy further inhibited cell proliferation and migration while promoting apoptosis.

2) There is no direct evidence to show the improved efficacy of radiation therapy by nifuroxazide through the degradation of PD-L1.

Response: Thank you very much for your valuable suggestions. In our cell experiments, we found that nifuroxazide inhibits the increased expression of PD-L1 in cells induced by radiation therapy, and this inhibitory effect is counteracted when using the proteasome inhibitor MG132. Therefore, we speculate that nifuroxazide may inhibit PD-L1 expression through a proteasome-dependent mechanism. To better reflect this, we have revised the title of our manuscript to "Nifuroxazide Suppresses PD-L1 Expression and Enhances the Efficacy of Radiotherapy in Hepatocellular Carcinoma."

3) "The oncogene Stat3.....was effectively inhibited by radiotherapy in cells" - this sentence may be rephrased to make the point clear. The authors might mean to say "activation of the oncogene stat3...."

"The results demonstrated that the combination therapy increased the expression of PARP," the authors might mean to say "expression of c-PARP"

Response: Thank you very much for your feedback. We have revised the relevant sentence descriptions to improve clarity and accuracy.

4) "histomorphology significantly improved after the treatment with nifuroxazide and radiation therapy (Fig 3E)." How to define "improved histomorphology"? The authors may want to provide more details to clarify "improved".

Response: Thank you very much for your feedback. We have revised the relevant sentence descriptions to improve clarity and accuracy.

5) In addition to normalizing protein expression by tubulin, the authors may consider normalizing p-stat3 expression level by stat3.

Response: Thank you very much for your feedback. We have conducted a quantitative analysis of the expression levels of p-STAT3 and STAT3 (revised Fig 2A).

6) Figure 3C and D, using a different color to represent each group might help the readers to better differentiate each group.

Response: Thank you very much for your feedback. Following your suggestion, we have revised the figures accordingly (revised Fig 3C and 3D).

Reviewer #3 (Recommendations For The Authors):

In this study, the authors revealed the pivotal role of nifuroxazide in augmenting the efficacy of radiotherapy. This was evidenced by its synergistic effect in suppressing the proliferation and migratory capabilities of HCC cells, alongside its capacity to induce apoptosis in these cells. Furthermore, their findings underscored the substantial synergy between nifuroxazide and radiotherapy in retarding tumor growth, thereby extending survival rates in a tumor-bearing murine model. Moreover, the authors observed that nifuroxazide combined with radiotherapy significantly increases the tumor-infiltrating CD4+ T cells, CD8+ T cells, and M1 macrophages. Finally, the authors found that nifuroxazide countered the radiotherapy-induced upregulation of PD-L1 through the ubiquitin-proteasome pathway. However, the evidence for supporting the main claims is only partially supported. The following are my concerns and suggestions.

1) In Figures 1 and 2, the authors convincingly demonstrate the synergistic impact of nifuroxazide and radiotherapy on curtailing the proliferation, colony formation, and migratory capabilities of HCC cells, while also instigating apoptosis in these cells. However, the underlying molecular mechanism remains elusive. A recent study highlighted nifuroxazide's potential to impede the proliferation of glioblastoma cells and induce apoptosis via the MAP3K1/JAK2/STAT3 pathway (Wang X., et al., Int Immunopharmacol. 2023 May;118:109987. doi: 10.1016/j.intimp.2023.109987). It would be valuable for the authors to investigate whether nifuroxazide employs a similar molecular mechanism to regulate proliferation and apoptosis in the context of HCC. This could offer deeper insights into the mechanisms at play in their observed effects.

Response: Thank you very much for your insightful comments. As you pointed out, previous studies have reported that nifuroxazide exerts antitumor effects by inhibiting the STAT3 pathway. However, in our experiments, we observed that radiation therapy significantly increased the expression of PD-L1, but showed a trend of decreased p-STAT3 expression. Therefore, we believe that nifuroxazide does not inhibit PD-L1 expression through the STAT3 pathway. Subsequently, our further research revealed that the inhibitory effect of nifuroxazide on PD-L1 can be counteracted by a proteasome inhibitor. Thus, we propose that nifuroxazide inhibits PD-L1 expression through a proteasome-dependent mechanism, thereby enhancing the efficacy of radiation therapy in hepatocellular carcinoma.

2) Figures 1 and 2 solely rely on the HepG2 cell line to establish their conclusions. To validate these findings robustly, it is recommended that another HCC cell line be included in the study. This additional cell line will contribute to the generalizability and reliability of the results, enhancing the overall credibility of the study's conclusions.

Response: Thank you very much for your suggestion. We have included additional experimental results with the relevant cell line Huh7 (supplementary Fig 3).

3) Figure 3 demonstrates the use of only one syngeneic mouse H22 tumor model. To ensure the robustness and validity of this finding, it would be advisable to incorporate at least one more syngeneic mouse HCC tumor model or even an orthotopic mouse tumor model. The inclusion of additional models would bolster the significance and reliability of the observed results, contributing to a more comprehensive understanding of the phenomenon under investigation.

Response: Thank you for your valuable suggestion. In the H22 mouse tumor model, we conducted relevant assessments of survival rate and tumor growth. The results confirm that the combination of nifuroxazide and radiation therapy exhibits a promising synergistic antitumor effect. However, to achieve the goal of applying nifuroxazide combined with radiation therapy for the treatment of clinical hepatocellular carcinoma, we still need to undertake extensive research, including validation on genetically identical mouse HCC tumor models. We have also included relevant discussions in our ongoing discussions.

4) In Figure 5, employing an alternative method, such as the flow cytometry assay, to analyze and corroborate the tumor-infiltrating immune cell profiling following various treatments would enhance the rigor of the study. This additional approach would provide a complementary perspective and validate the findings, strengthening the overall reliability and impact of the results presented.

Response: Thank you for your insightful suggestion. We have included additional experimental data to strengthen our study (supplementary Fig 2).

5) In Figure 7, the conclusion drawn regarding nifuroxazide's impact on PD-L1 expression through ubiquitination-proteasome mechanisms seems to lack the robust evidence needed to firmly establish nifuroxazide's role in regulating PD-L1 ubiquitination. To reinforce this aspect of the study, the authors may conduct comprehensive in vitro and in vivo ubiquitination assays. Performing these assays would offer direct insights into whether nifuroxazide genuinely influences PD-L1 ubiquitination, thus fortifying the credibility and importance of the reported findings.

Response: Thank you for your valuable feedback. Our initial findings suggest that nifuroxazide inhibits the expression of PD-L1 protein levels, but does not affect the mRNA levels. Moreover, upon treatment with the proteasome inhibitor MG132, the inhibitory effect of nifuroxazide on PD-L1 was found to be abolished. Concurrently, we observed that nifuroxazide significantly enhances GSK-3β expression in both cell and animal experiments. Consequently, we propose that nifuroxazide augments the degradation of PD-L1 protein.

6) Statistical methods should be included in the captions of all the figures with statistical graphs. The size of the scale should be supplemented with a description in the captions.

Response: Thank you for your valuable suggestion. We have made the appropriate modifications to our study based on your recommendations.

7) Considering the outcomes presented in the study, it appears that the title "Nifuroxazide enhances radiotherapy efficacy against hepatocellular carcinoma by upregulating PD-L1 degradation via the ubiquitin-proteasome pathway" may not accurately reflect the findings.

Response: Thank you for your insightful feedback. We have revised the title to read, "Inhibitory Effects of Nifuroxazide on PD-L1 Expression and Enhanced Radiotherapy Efficacy in Hepatocellular Carcinoma".

References

1) Xie C, Zhou X, Liang C, Li X, Ge M, Chen Y, et al. Apatinib triggers autophagic and apoptotic cell death via VEGFR2/STAT3/PD-L1 and ROS/Nrf2/p62 signaling in lung cancer. Journal of experimental & clinical cancer research : CR. 2021;40(1):266. doi: 10.1186/s13046-021-02069-4.

2) de la Torre-Alaez M, Matilla A, Varela M, Inarrairaegui M, Reig M, Lledo JL, et al. Nivolumab after selective internal radiation therapy for the treatment of hepatocellular carcinoma: a phase 2, single-arm study. Journal for immunotherapy of cancer. 2022;10(11). doi: 10.1136/jitc-2022-005457.

3) Yang F, Hu M, Lei Q, Xia Y, Zhu Y, Song X, et al. Nifuroxazide induces apoptosis and impairs pulmonary metastasis in breast cancer model. Cell Death Dis. 2015;6(3):e1701. doi: 10.1038/cddis.2015.63.

4) Nelson EA, Walker SR, Kepich A, Gashin LB, Hideshima T, Ikeda H, et al. Nifuroxazide inhibits survival of multiple myeloma cells by directly inhibiting STAT3. Blood. 2008;112(13):5095-102. doi: 10.1182/blood-2007-12-129718.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript aimed at elucidating the substrate specificity of two M23 endopeptidase Lysostaphin (LSS) and LytM in S. aureus. Endopeptidases are known to cleave the glycine-bridges of staphylococcal cell wall peptidoglycan (PG). To address this question, various glycine-bridge peptides were synthesized as substrates, the catalytic domain of LSS and LytM were recombinantly expressed and purified, and the reactions were analyzed using solution-state NMR. The major finding is that LytM is not only a Gly-Gly endopeptidase, but also cleaves D-Ala-Gly. Technically, the advantage of using real-time NMR was emphasized in the manuscript. The study explores an interesting aspect of cell wall hydrolases in terms of substrate-level regulation. It potentially identified new enzymatic activity of LytM. However, the biological significance and relevance of the conclusions remain clear, as the results are mostly from synthetic substrates.

Strengths:

The study explores an interesting aspect of cell wall hydrolases in terms of substrate-level regulation. It potentially identified new enzymatic activity of LytM.

Weaknesses:

1) Significance: while the current study provided a detailed analysis of various substrates, the conclusions are mainly based on synthesized peptides. One experiment used purified muropeptides (Fig. 3H); however, the results were unclear from this figure.

We acknowledge the Reviewer for comments and concerns regarding the potential weaknesses of this study.

Because peptidoglycan is insoluble, as such it is not amenable to solution-state NMR studies. However, soluble peptidoglycan (PG) fragments for NMR analyses can be obtained by digesting bacterial sacculi or via chemical synthesis. Whereas digestion results in mixtures of products, synthesis yields pure molecules. Analysis of NMR spectra of muropeptide-mimicking synthetic peptides before and after enzyme addition provides tools to identify peaks in the much more complex spectra of mutanolysin-treated sacculus.

We will improve data presentation in Figure 3H in the revised version of our manuscript and emphasize the similarity of product peaks in spectra acquired from experiments using either synthetic peptides or mutanolysin-digested sacculus.

The results from synthesized peptides may not necessarily correlate with their biological functions in vivo.

The Reviewer refers several times to the use of synthetic peptides in this study. While it is unclear to us whether the concern is about the synthetic nature of the molecules or because the peptides are devoid of PG disaccharide units, it is true that PG fragments lack the 3D architecture present in intact sacculus, and thus cannot perfectly mimic the in vivo milieu. The fragments, as well as purified sacculus, also lack all other components present in an intact bacterial cell wall. Our largest synthetic peptide (7), however, represents a crosslinked muropeptide (stem-pentaGly-stem) which according to the structural model recently presented by Razew et al. (2023) (Staphylococcus aureus sacculus mediates activities of M23 hydrolases. Nat Commun 14, 6706) is large enough to cover the peptidic interaction interface between substrate and enzyme.

Secondly, the study used only the catalytic domain of both proteins. It is known that the substrate specificity of these enzymes is regulated by their substrate-binding domains. There is no mention of other domains in the manuscript and no justification of why only the catalytic domain was studied. In short, the relevance of the results from the current study to the enzymes' actual physiological functions remains to be addressed, which attenuated the significance of the study.

Lysostaphin catalytic domain was used for experimental simplicity and to allow direct comparison with LytM catalytic domain. Because lysostaphin cell-wall targeting (SH3b) domain interacts with the substrate with variable affinities depending on the substrate structure (Tossavainen et al., Structural and functional insights into lysostaphin-substrate interaction, Front. Mol. Biosci. 5, 60 (2018) and Gonzalez-Delgado et al., Two-site recognition of Staphylococcus aureus peptidoglycan by lysostaphin SH3b, Nat. Chem. Biol. 16, 24-30 (2020)), we would have had skewed results on kinetics because of this interaction.

Catalytic domains were used also in the article by Razew et al. (Staphylococcus aureus sacculus mediates activities of M23 hydrolases. Nat Commun 14, 6706 (2023)). They showed that mature lysostaphin and lysostaphin catalytic domain hydrolysed the same Gly-Gly bonds.

Moreover, full-length LytM is catalytically inactive. This is because the linker between its N-terminal and catalytic domains occludes the catalytic site (Odintsov et al. Latent LytM at 1.3 Å resolution. J. Mol. Biol. 225, 775 (2004)). LytM catalytic domain without its N-terminal segment is active (Odintsov et al (2004) and Firczuk et al. Crystal structure of active LytM. J. Mol. Biol 354, 578 (2005)).

2) Impact and novelty:

(1) the current study provided evidence suggesting the novel function of LytM in cleaving D-Ala-Gly. The impact of this finding is unclear. The manuscript discussed Enterococcus faecalis EnpA. But how about other M23 endopeptidases? What is biological relevance?

EnpA was specifically mentioned because it has been reported to also cleave the D-Ala-Gly bond. Structural similarities between the enzymes could reveal the basis for this bond specificity. Moreover, the focus of the study was not to reveal the biological function of LytM but rather to understand which amino acid substitutions lead to differences in specificities in the two structurally very similar enzymes.

(2) A very similar study published recently showed that the activity of LSS and LytM is regulated by PG cross-linking: LSS cleaves more cross-linked PG and LytM cleaves less cross-linked PG (Razew, A., Laguri, C., Vallet, A., et al. Staphylococcus aureus sacculus mediates activities of M23 hydrolases. Nat Commun 14, 6706 (2023). The results of this paper are different from the current study whereby both LSS and LytM prefer cross-linked substrates (Fig, 2JKL). Moreover, no D-Ala-Gly cleavage was observed by LytM using purified PG substrate from Razew A et al. An explanation of inconsistent results is needed here. In my opinion, the knowledge generated from the current study has not been fully settled. If the results can be validated, the contribution to the field is incremental, but not substantial.

Another point raised by the Reviewer concerned the inconsistent results between our study and the recent paper by Razew et al. (2023) regarding LytM D-Ala--Gly cleavage. The explanation might lie in the type of NMR data acquired and its interpretation. We identified all hydrolysis products using 1H, 13C multiple bond correlation NMR spectra acquired from samples dissolved in deuterated buffers. Use of C-H signals is advantageous in that they are not prone to chemical exchange phenomena and enable unambiguous chemical shift assignment. Based on shown NMR spectra, Razew and co-workers identified cleaved muropeptide bonds by observing product glycine peaks in 1H, 15N correlation spectra, specifically amide peaks of product C-terminal glycines appearing in the 114-117 ppm 15N region of spectra of samples treated with LytM/LSS. D-Ala--Gly cleavage, however produces an N-terminal glycine, whose signal due to chemical exchange is not typically observed in regular N,H correlation spectra. Razew and co-workers validated their observations with UPLC-MS analysis. However, to our understanding, their data analysis was based on the assumption that LytM cleaves between Gly4-Gly5 (or Gly1-Gly2 using our numbering), and accordingly only masses corresponding to potential products containing 1 to 4 glycines anchored to the lysine side chain were considered.

(3) The authors emphasized a few times in the text that it is superior to use NMR technology. In my opinion, NMR has certain advantages, such as measuring the efficacy of cleavage, but it is not that superior. It should be complementary to other methods such as mass spectrometry. In addition, more relevant solid-state NMR using intact PG or bacterial cells was not discussed in the study. I am of the opinion that the corresponding text should be revised.

We value and agree with the Reviewer’s opinion that NMR spectroscopy is complementary to other methods e.g., mass spectrometry. However, in this particular case, NMR provided simultaneously information on reaction kinetics as well as scissile bonds in the substrates, which allowed us to compare rates of hydrolysis in different PG fragments and reshape the substrate specificities of LytM/LSS. We also agree that solid-state NMR is a wonderful technique. In our revised manuscript, we will edit the text accordingly.

3) The conclusions are not fully supported by the data

As mentioned above, the conclusions from synthesized peptide substrates may not necessarily reveal physiological functions. The conclusions need to be validated by more physiological substrates.

As pointed out above in our response to the potential weaknesses of this study, the aim of this work was not to reveal the physiological function of LytM but to glean information on its substrate specificity that echoes its functional role in a substrate level. Hitherto LytM has been shown to cleave amide bonds between glycines without providing detailed information about the specific scissile bonds in the established PG components in S. aureus cell wall. The same holds true for lysostaphin as well. This study provides concomitantly information on the rates of hydrolysis and scissile bonds of these two enzymes. We deduced that LytM, and especially lysostaphin substrate specificity is defined by D-Ala-Gly cross-linking, which is a structural property, whereas Razew et al. (2023) discuss about “more cross-linked” and “less cross-linked PG”, which is a supramolecular asset or density.

4) There are some issues with the presentation of the figures, text, and formatting.

We are grateful to the Reviewer for bringing up issues in figures and text. We will address these in the revised version of the manuscript.

Reviewer #2 (Public Review):

Summary:

This work investigates the enzymatic properties of lysostaphin (LSS) and LytM, two enzymes produced by Staphylococcus aureus and previously described as glycyl-glycyl endopeptidases. The authors use synthetic peptide substrates mimicking peptidoglycan fragments to determine the substrate specificity of both enzymes and identify the bonds they cleave.

Strengths:

• This work is addressing a real gap in our knowledge since very little information is available about the substrate specificity of peptidoglycan hydrolases.

• The experimental strategy and its implementation are robust and provide a thorough analysis of LSS and LytM enzymatic activities. The results are very convincing and demonstrate that the enzymatic properties of the model enzymes studied need to be revisited.

Weaknesses:

• The manuscript is difficult to read in places and some figures are not always presented in a way that is easy to follow. This being said, the authors have made a good effort to present their experiments in an engaging manner. Some recommendations have been made to improve the current manuscript but these remain minor issues.

We thank the Reviewer for providing positive feedback on our manuscript and for appreciating the systematic work behind this study which aims to unknot the substrate specificity of two S. aureus PG hydrolyzing enzymes. We are grateful for the comments aiming to improve the presentation of the current version of manuscript and we will take these into account while preparing the revised version of the manuscript.

Author response

eLife assessment

Using a genetically controlled experimental setting, the authors find that the lack of Polycomb-dependent epigenetic programming in the oocyte and early embryo influences the developmental trajectory through gestation in the mouse. By showing a two-phase outcome of early growth restriction followed by enhancement, the authors address previous inconsistencies in the field. However, the link with placenta function and gene misregulation is not yet fully supported.

We thank the Reviewers for their constructive comments. In response we have added significantly more data to the study and substantially rewritten the manuscript. New data include analyses of glucose, amino acid and metabolite levels in fetal and maternal blood samples, more highly resolved fetal growth analyses, a more detailed study of the hyperplastic placenta including IF analyses of labyrinth area, labyrinth to placenta and capillary to labyrinth ratios. We have also added analyses of placental DNA methylation state in offspring from oocytes lacking EED, which reveals a range of DNA methylation changes at imprinted and non-imprinted genes in HET-hom offspring compared to HET-het or WT-wt controls.

Reviewer #1 (Public Review):

Oberin, Petautschnig et. al investigated the developmental phenotypes that resulted from oocyte-specific loss of the EED (Embryonic Ectoderm Development) gene - a core component of the Polycomb repressive complex 2 (PRC2), which possess histone methyltransferase activity and catalyses trimethylation of histone H3 at lysine 27 (H3K27). The PRC2 complex plays essential roles in regulating chromatin structure, being an important regulator of cellular differentiation and development during embryogenesis. As novel findings, the authors find that PRC2-dependent programming in the oocyte, via loss of the core component EE2, causes placental hyperplasia and propose that the increase of placental transplacental flux of nutrients leads to fetal and postnatal overgrowth. At the mechanistic level, they show altered expression of genes previously implicated in placental hyperplasia phenotypes. They also establish interesting parallelism with the placental hyperplasia phenotype that is frequently observed in cloned mice.

Strengths:

The mouse breeding experiments are very well designed and are powerful to exclude potential confounding genetic effects on the developmental phenotypes that resulted from the loss of EED in oocytes. Another major strength is the developmental profiling across gestation, from pre-implantation to late gestation.

Weaknesses:

The evidence for 'oocyte' programming is restricted to phenotypic and gene expression analysis, without measurements of epigenetic dysregulation. It would be an added value if the authors could show evidence for altered H3K27me3 or DNA methylation in the placenta, for example.

In an earlier previous study we identified a large number of developmentally important genes that accumulated H3K27me3 in primary-secondary stage growing oocytes and were repressed by EED (Jarred et al., 2022 Clinical Epigenetics). However, H3K27me3 was removed from all from these genes during preimplantation development, indicating that maternal inheritance of H3K27me3 at a wide range of genes is unlikely (Jarred et al., 2022 Clinical Epigenetics). Consistent with this only a small number of genes, including Slc38a4 and C2MC, have been shown to be functionally important in H3K27me3-dependent imprinting (Matoba et al., 2022 Genes and Development). Moreover, a related study showed that deletion of Setd2 and consequent loss of H3K36me3 in oocytes led to spreading of H3K27me3 into regions that were otherwise marked by H3K36me3 and DNA methylation (Xu et al. 2019 Nature Genetics 51:844–56). Based on these studies, we proposed that loss of EED and H3K27me3 may result in the ectopic spreading of H3K36me3 and DNA methylation in oocytes and that altered DNA methylation may then be transmitted to offspring and affect developmental outcomes (Jarred et al., 2022 Clinical Epigenetics)

Given this hypothesis we analysed DNA methylation rather than H3K27me3 in the placenta of WT-wt, HET- het and HET-hom offspring. This revealed differentially methylated regions (DMRs) in HET-hom placentas at two H3K27me3 imprinted genes Sfmbt2 (C2MC) and Mbnl2, five classically imprinted genes and at 74 DMRs not associated with imprinted loci. Together, our data supports the hypothesis from Jarred et al., 2022 Clinical Epigenetics that loss of EED in oocytes results in altered DNA methylation patterning at both imprinted and non-imprinted genes in offspring and that this is likely to affect offspring growth and development. However, whether these changes result from direct alteration of DNA methylation in oocytes remains unclear.

These new data are now included in results (Lines 387-409), Figure 6I, Supplementary File H-J and Discussion Lines 569-581.

Reviewer Comment 1. The claim that placental hyperplasia drives offspring catch-up growth is not supported by current experimental data. The authors do not address if transplacental flux is increased in the hyperplastic placentae, measure amino acids and glucose in fetal/maternal plasma, or perform tetraploid rescue experiments to ascertain the contribution of the placenta to growth phenotypes. Furthermore, it is unclear, from the current data, if the surface area for nutrient transport is actually increased in the hyperplastic placenta and the extent to which other cell populations (i.e. spongiotrophoblasts) are affected in addition to glycogen cells. In addition, one of the supporting conclusions that the placenta is a key contributor to fetal overgrowth is based on a very crude measurement - placenta efficiency - which the authors claim is increased in the homozygous mutants compared to controls. After analysing the data carefully, I find evidence for decreased placental efficiency instead. I believe that the authors mistakenly present the data as placenta to fetal weight ratios, which led to the misinterpretation of the 'efficiency' concept.

We thank the reviewer for pointing out our error in the placental efficiency data and we have now corrected the placental efficiency graphs (fetal/placental weight ratios) and updated the text throughout the manuscript as required (Figure 3I-K). As requested and described below, we have also added significantly more data, which support the conclusion that placental function is not enhanced in HET-hom mice and is unlikely to support fetal growth recovery.

The new data and analyses we have added include:

1. Further analyses of glycogen-enriched and non-glycogen-enriched cell counts in the decidua and junctional zones (Figure 4F-J)

2. Total glycogen cell counts for male and female placentas (Figure 4 – figure supplement 1F)

3. New analyses of fetal blood glucose levels at E17.5 and E18.5 and matching data from the mothers of each litter (Figure 4M)

4. New analyses of the circulating amino acid levels and metabolites in fetal blood of E17.5 offspring and matching data from the mothers of each litter (Figure 8)

5. New IF analyses of CD31 (PECAM-1) and combined this with machine learning assisted quantitative analyses of labyrinth and capillary areas using HALO (Figure 5)

6. Separated male and female offspring and placental weights at E14.5 and E17.5 and total areas of the placenta, decidua, junctional zone and labyrinth (Figure 3 – figure supplement 1) which provide more insight into potential sex-specific differences in HET-hom offspring and placenta

We have significantly re-written the results and discussion to reflect our new data and interpretation.

While we did not assess transplacental flux, our new data revealed: 1. HET-hom fetuses had lower blood glucose levels at E18.5; 2. Circulating levels of amino acids and a wide range of metabolites did not differ between HET-hom and control offspring, or between the mothers of these offspring; 3. HET-hom placentas had lower total labyrinth area, labyrinth/placenta and capillary/labyrinth ratios based on analysis of total capillary and labyrinth areas, indicating that the surface area for nutrient transfer is not increased

Together these data strongly indicate that hyperplastic HET-hom placentas do not provide greater support to HET-hom fetuses than controls, and that increased placental function in HET-hom offspring is unlikely to explain the late gestation fetal growth recovery we observed in HET-hom offspring or how HET-hom offspring were able to attain normal weights by birth.

While we have not directly counted the spongiotrophoblast populations, we have now included analyses of both the glycogen-enriched and non-glycogen cell populations in the junctional zone and the decidua (Figure 4H-K). This revealed an increased area of both glycogen-enriched and non-glycogen cells in the junctional zone and in the decidua of HET-hom placentas, consistent with the greater junctional zone/placenta ratio observed in HET-hom placentas (Figure 4D). Together with data in Figure 4C-F and Supp. Fig. 3, our observations demonstrate that the overall decidua and junctional zone areas were increased in HET-hom offspring, but there was a disproportionate expansion of the junctional zone that was caused by increased areas of both glycogen and non-glycogen-enriched cells.

Tetraploid rescue experiments would require a very significant amount of time and investment and are technically very demanding. While creation of complementary tetraploid offspring would be informative, unfortunately these experiments are beyond the scope of this current study.

Reviewer Comment 1 cont. The authors do not mention alternative explanations for the observed fetal catch-up and postnatal overgrowth. Why would oocyte epigenetic programming effects be restricted to the placenta, and not include fetal organs?

Our intention was certainly not to convey a message that effects may be placenta specific. Indeed, our ongoing work beyond the scope of this study provides evidence for effects in other tissues (brain and bones) that will be published elsewhere. Our new data clearly show low placental efficiency, fetal blood glucose, low capillary/labyrinth ratio and no impact on circulating fetal amino acid or metabolite levels in HET-hom offspring. In light of these new data, we have reinterpreted the findings of this study and substantially updated the discussion.

Given our observations that fetal growth rate markedly increased during late gestation, but placental efficiency was reduced, our data strongly indicate that the effects of altered epigenetic oocyte programming due to loss of Eed affect both the placenta and the fetus. While our findings are significant, the precise mechanism underlying this growth response in HET-hom fetuses remains unknown. Understanding this mechanism will require substantially more work that will be the subject of future studies.

Reviewer #2 (Public Review):

Consistent fetal growth trajectories are vital for survival and later life health. The authors utilise an elegant and novel animal model to tease apart the role of Eed protein in the female germline from the role of somatic Eed. The authors were able to experimentally attribute placental overgrowth - particularly of the endocrine region of the placenta - to the function of Eed protein in the oocyte. Loss of Eed protein in the oocyte was also associated with dynamic changes in fetal growth and prolonged gestation. It was not determined whether the reported catch-up growth apparent on the day of birth was due to enhanced fetal growth very late in gestation, a longer gestational time ie the P0 pups are effectively one day "older" compared to the controls, or the pups catching up after birth when consuming maternal milk.

To understand if increased growth occurred in HET-hom fetuses prior to birth, we have now included analyses of offspring weight at E18.5 (Figure 2F), all pups collected with a verified E19.5 birth date (Figure 2J) and for pups from similar litter sizes (5-7 pups) at E19.5 (Figure 2K). Together with our existing data, these additional analyses provide average weights for fetuses at E14.5, E17.5, E18.5 and pups born on E19.5. This confirmed that HET-hom offspring undergo enhanced growth in the last few days of pregnancy, resulting in the progression of substantially growth and developmentally restricted HET-hom fetuses at E14.5, to pups with normal weight at birth within the 40% of pregnancies that were born on E19.5 in a normal gestational time.

However, in addition, gestational length was increased by one to two days in 60% of pregnancies from hom oocytes, but not in control pregnancies from het or wt oocytes. As average weights were significantly greater in all surviving HET-hom offspring at P0 (i.e. surviving pups born on E19.5-E21.5; Figure 2G), it appears that this additional gestational time contributed to the offspring overgrowth. This is logical, however it does not explain how growth and developmentally delayed fetuses at E14.5 attained normal weight and developmental stage by E19.5 (Figure 2J-K).

Together our data clearly show that HET-hom offspring undergo enhanced growth during the late stages of pregnancy, allowing them to resolve the developmental delay and growth insufficiency observed at E14.5 so that they were born at normal weight and stage at E19.5. In addition, increased gestational time contributes to weight of pups delivered on E20.5 or 21.5, partly explaining the overgrowth phenotype observed in this model.

The idea that increased milk consumption may explain the overgrowth of HET-hom offspring is interesting. It is possible that the increased growth rate of HET-hom offspring continues after birth and contributes to overgrowth. However, examining this outcome in a tightly controlled manner is complicated given that we cannot predict the day of birth of HET-hom litters, and that these litters are generally small and would need to be fostered on the day of birth alongside control litters. Given these challenges and that our primary observation is that HET-hom offspring underwent fetal growth recovery during pregnancies of normal length and via extension of gestational length, we have not examined the possibility of increased milk consumption after birth.

We have updated the results to reflect the new analyses and have provided relevant discussion to address these data. Our description of these data can be found in Results (lines 165-197) and in Figure 2.

Reviewer #3 (Public Review):

My understanding of the main claims of the paper, and how they are justified by the data are discussed below:

Overall, loss of PRC2 function in the developing oocyte and early embryo causes:

1) Growth restriction from at least the blastocyst stage with low cell counts and midgestational developmental delay.

Strengths:

• Live embryo imaging added an important dimension to this study. The authors were able to confirm an unquantified finding from a previous lab (reduced time to 2-cell stage in oocyte-deletion Eed offspring, Inoue 2018, PMID: 30463900) as well as identify developmental delay and mortality at the blastocyst- hatching transition.

• For the weight and morphological analysis the authors are careful to provide isogenic controls for most of the experiments presented. This means that any phenotypes can be attributed to the oocyte genotype rather than any confounding effects of maternal or paternal genotype.

• Overall, there is good evidence that oocyte deletion of Eed results in early embryonic growth restriction, consistent with previous observations (Inoue 2018, PMID: 30463900).

Reviewer 3, Comment 1: Weaknesses: Gaps in the reporting of specific features of the methodology make it difficult to interpret/understand some of the results.

While we are unsure exactly which methods Reviewer 3 would like expanded, we have updated parts that we thought required further detail and allow more informed interpretation of the results. These include methods for placental histology (Lines 650-669) and immuno- histochemistry (Lines 671-690), and new methods for CD31 immunofluorescence (Lines 692-714), glucose and metabolomics (Lines 752-769) and DNA methylation (RRBS; Lines 734-750) analyses.

To clarify the approach taken for histology, immunohistochemical and immunofluorescent staining, sections were cut in compound series from the centre of each placenta, ensuring that we collected representative data for each sample. QuPath was used to quantify the decidual and junctional zone areas in one complete, fully intact midline section for each placenta as close to the midline as possible. This provided data from 10 placentas for each genotype. In addition, glycogen-enriched and non-glycogen-enriched cells were identified and quantified using machine learning assisted QuPath analyses of the whole placenta, decidua and junctional zone regions. We have also added quantitative analyses of the labyrinth and labyrinth capillary network using immunofluorescent CD31 staining and machine learning assisted HALO software. This new analysis of placental morphology is included in the methods section.

Moreover, as there were no sex-specific differences in placental morphology or weight, we combined the samples from both sexes to provide greater numbers for analysis in each genotype. For example, as described for the analyses of labyrinth and capillaries using CD31 IF, 4 placentas of each sex were used for data collection. This provided data from a total of 8 placentas (4 male and 4 female) for each genotype from a total of 17 WT-wt (9 male and 8 female), 21 HET-het (9 male and 12 female) and 24 HET-hom (16 male and 8 female) sections (2-3 sections/placenta).

Reviewer 3, Comment 2: Placental hyperplasia with disproportionate overgrowth of the junctional trophoblast especially the glycogen trophoblast (GlyT) cells.

Strengths: • The authors provide a comprehensive description of how placental and embryo weight is affected by the oocyte-Eed deletion through mid-to-late gestation development. The case for placentomegaly is clear.

Weaknesses:

• The placental efficiency data presented in Figure 3G-I is incorrect. Placental efficiency is calculated as embryo mass/placental mass, and it increases over the late gestation period. For e14.5 for example (Fig3G), WT-wt embryo mass = ~0.3g, placenta mass = 0.11g (from Fig 3D) = placental efficiency 2.7; HET-hom = 0.25/0.12 = 2.1. The paper gives values: WT-wt 0.5, HET-hom 0.7. Have the authors perhaps divided placenta weight by embryo mass? This would explain why the E17.5 efficiencies are so low (WT-wt 0.11 rather than a more usual figure of 8.88. If this is the case then the authors' conclusion that placental efficiency is improved by oocyte deletion of Eed is wrong - in fact, placental efficiency is severely compromised.

The authors have performed cell type counting on histological sections obtained from placentas to discover which cells are contributing to the placentomegaly. This data is presented as %cell type area in the main figure, though the untransformed cross-sectional area for each cell type is shown in the supplementary data. This presentation of the data, as well as the description of it, is misleading because, while it emphasises the proportional increase in the endocrine compartment of the placenta it downplays the fact that the exchange area of the mutant placentas is vastly expanded. This is important for two reasons.

Firstly, the whole placenta is increased in size suggesting that the mechanism is not placental lineage- specific and instead acting on the whole organ. Secondly in relation to embryonic growth, generally speaking, genetic manipulations that modify labyrinthine volume tend to have a positive correlation with fetal mass whereas the relationship between junctional zone volume and embryonic mass is more complex (discussed in Watson PMID: 15888575, for example). The authors should reconsider how they present this data in light of the previous point.

We thank the reviewer for pointing out our error in the placental efficiency analysis and apologise for this error. We have corrected the presentation and interpretation of these data and have described this in detail in our response to Reviewer 1, Comment 1.

As discussed in our response to Reviewer 1, Comment 1, we have added a range of analyses to determine whether placental efficiency was enhanced in HET-hom offspring. These include measuring fetal and maternal circulating glucose levels (Figure 4K), individual amino acids and an extensive range of metabolites (Figure 8) and providing CD31 immunofluorescent analyses of labyrinth area, labyrinth/placental ratio and capillary/labyrinth ratio in HET-hom and control placentas (Figure 5).

We also added analyses of glycogen enriched and non-glycogen-enriched cell counts in the decidua and junctional zones. As suggested by Reviewer 3, both glycogen-enriched and non-enriched cell populations are significantly increased in HET-hom placentas.

Combined, these new analyses significantly expand the study and support the conclusion that placental efficiency in HET-hom offspring was either compromised or not different from controls, depending on the analysis. We find no evidence that placental efficiency was increased in HET-hom offspring and have reworked our results and discussion sections to reflect these new data and interpretation.

Reviewer 3, Comment 2 cont: Again, some of the methods are not clearly reported making interpretation difficult - especially how they have estimated their GlyT number.

As outlined in our response to Reviewer 3 Comment 1, in the methods section we have added further detail of how we counted glycogen-enriched and non-enriched cells in the decidua and junctional zone regions of sections for the middle of WT-wt, WT-het, HET-het and HET-hom placentas (Lines 650-669).

Reviewer 3, Comment 3: Perinatal embryonic/pup overgrowth.

Strengths:

• The overgrowth exhibited by the oocyte-Eed-deleted pups is striking and confirms the previous work by this group (Prokopuk, 2018). This is an important finding, especially in the context of understanding how PRC2-group gene mutations in humans cause overgrowth syndromes. It is also intriguing because it indicates that genetic/environmental insults in the mother that affect her gamete development can have long-term consequences on offspring physiology.

Weaknesses:

• Is the overgrowth intrauterine or is it caused by the increase in gestation length? The way the data is reported makes it impossible to work this out. The authors show that gestation time is consistently lengthened for mothers incubating oocyte-Eed-deleted pups by 1-2 days. In the supplementary material, the mutant embryos are not larger than WT at e19.5, the usual day of birth. Postnatal data is presented as day post-parturition. It would probably be clearer to present the embryonic and postnatal data as days post coitum. In this way, it will be obvious in which period the growth enhancement is taking place. This is information really important to determine whether the increased growth of the mutants is due to a direct effect of the intrauterine environment, or perhaps a more persistent hormonal change in the mother that can continue to promote growth beyond the gestation period.

We have used embryonic day (E) to denote embryo and fetal age throughout the study – this is the same as using DPC (i.e. E19.5 is equivalent to 19.5 DPC). As described in the Methods “Collection of post-implantation embryos, placenta and postnatal offspring”, mice were time mated for two-four nights, with females plug checked daily. Positive plugs were noted as day E0.5.

To make the data presentation clearer, we have shown the data for surviving HET-hom pups born on E19.5 (Figure 2J) separately from all HET-hom surviving pups born on E19.5-E21.5. (Figure 2G). As discussed in our response to Reviewer 2, we have also included growth data for pregnancies at E14.5, E17.5, E18.5 (Fig. 2C-F) and E19.5 (Figure 2J,K), as well as P0 (combined data for surviving pups born E19.5-E21.5), and P3 (combined data for surviving pups born E19.5-E21.5, Figure 2G,H).

These data clearly show that HET-hom fetuses are substantially growth and developmentally delayed at E14.5 (Figure 2D), but HET-hom pups born on E19.5 are the same weight as WT-wt, WT-het and HET-het control pups (Figure 2J). This demonstrates that weight of HET-hom fetuses is normalised in utero between E14.5 and day of birth on E19.5.

Importantly, as requested by Reviewer 3, we have separated average weight for all surviving pups with a day of birth of E19.5-21.5 (Figure 2G) from average weight of pups born on E19.5 only (Figure 2J). These analyses revealed that the average weight of surviving pups born between E19.5-21.5 was significantly higher than for controls (Figure 2G), but the average weight of pups born on E19.5 only was not. It is therefore clear that extended gestation also contributed to increased HET-hom pup birth weight. We have updated these additional analyses in Results (Lines 165-197) and Figure 2

As revealed in Figure 2H, it is also possible/likely that growth of HET-hom pups during the three days post- partum may have contributed to the offspring overgrowth we observed in this and our previous study (Prokopuk et al., 2018 Clinical Epigenetics). However, we cannot determine whether there is a contribution from a persistent maternal hormonal change that promotes post-natal offspring growth or whether there is an innate growth benefit in HET-hom pups. As this is very difficult to dissect, separating these possibilities is beyond the scope of our study.

Reviewer 3, Comment 4: "fetal growth restriction followed by placental hyperplasia, .. drives catch-up growth that ultimately results in perinatal offspring overgrowth".

Here the authors try to link their observations, suggesting that i) the increased perinatal growth rate is a consequence of placentomegaly, and ii) the placentomegaly/increased fetal growth is an adaptive consequence of the early growth restriction. This is an interesting idea and suggests that there is a degree of developmental plasticity that is operating to repair the early consequences of transient loss of Eed function.

Strengths:

• Discrepancies between earlier studies are reconciled. Here the authors show that in oocyte-Eed-deleted embryos growth is initially restricted and then the growth rate increases in late gestation with increased perinatal mass.

Weaknesses:

• Regarding the dependence of fetal growth increase on placental size increase, this link is far from clear since placental efficiency is in fact decreased in the mutants (see above).

• "Catch-up growth" suggests that a higher growth rate is driven by an earlier growth restriction in order to restore homeostasis. There is no direct evidence for such a mechanism here. The loss of Eed expression in the oocyte and early embryo could have an independent impact on more than one phase of development.

Firstly, there is growth restriction in the early phase of cell divisions. Potentially this could be due to depression of genes that restrain cell division on autosomes, or suppression of X-linked gene expression (as has been previously reported, Inoue, 2018 PMID: 30463900). The placentomegaly is explained by the misregulation of non-canonically imprinted genes, as the authors report (and in agreement with other studies, e.g. Inoue, 2020. PMID: 32358519).

• Explaining the perinatal phase of growth enhancement is more difficult. I think it is unlikely to be due to placentomegaly. Multiple studies have shown that placentomegaly following somatic cell nuclear transfer (SCNT) is caused by non-canonically imprinted genes, and can be rescued by reducing their expression dosage. However, SCNT causes placentomegaly with normal or reduced embryonic mass (for example -Xie 2022, PMID: 35196486), not growth enhancement. Moreover, since (to my knowledge) single loss of imprinting models of non-canonically imprinted genes do not exist, it is not possible to understand if their increased expression dosage can drive perinatal overgrowth, and if this is preceded by growth restriction and thus constitutes 'catch up growth'.

Reviewer 3 is correct in their assessment that placental efficiency was decreased in HET- hom offspring and we have corrected the placental efficiency analysis based on fetal/placental weight ratios (discussed in detail in our response to Reviewer 1 Comment 1). We have added substantially more data (glucose, amino acids, metabolites, labyrinth capillary area and density). These data support the conclusion that a placentally driven advantage for HET-hom fetal growth is unlikely, despite our observation that HET- hom fetuses are developmental delayed and underweight at E14.5, but are born at normal weight after a normal gestational length (19.5 days) (discussed in our responses to Reviewer 3, Comment 3 and Reviewer 2).

This demonstrates that HET-hom fetuses are able to attain normal birth weight despite being initially growth restricted state at E14.5, and that this occurs despite low placental function. Moreover, as we compared isogenic offspring with heterozygous loss of Eed (Het-het compared to HET-hom offspring) the outcomes we observed in HET-hom offspring originate from loss of EED in the growing oocyte or loss of maternal EED in the zygote strongly suggesting that a non-genetic mechanism is involved.

As pointed out by Reviewer 3, the initial developmental delay in HET-hom offspring may be due to increased expression of genes that regulate cell proliferation – this could clearly explain the lower number of cells we observed in the ICM and the growth delay at later stages of embryonic and fetal development. Another possibility is that maternal PRC2 provided by the oocyte promotes cell divisions in preimplantation embryos We have discussed these possibilities on Lines 467-476.

In addition, Matoba et al 2022 demonstrated that deletion of maternal Xist together with Eed was able to rescue male-biased lethality in offspring from oocytes lacking Eed, revealing a clear role for X-linked genes in this phenotype (Matoba et al 2022, Genes and Development). However, deletion of maternal Xist did not properly normalise survival offspring from Eed null oocytes (i.e. Eed/Xist double maternal null litters were smaller than litters derived from wild type oocytes) strongly suggesting other mechanisms provide the capacity for HET-hom offspring to attain normal weight at birth. We have added further discussion of the Matoba study in the context of our study on of the Discussion (Lines 544-555)

Finally, with respect to the outcomes for SCNT derived offspring, we extracted SCNT fetal growth and placental weight data from the supplementary data included in Matoba et al., 2018 Cell Stem Cell. 2018;23(3):343-54.e5 and compared it with data collected in our study (Figure 7). This analysis revealed that the weights of placentas and fetuses of offspring derived via SCNT were very similar to the HET-hom offpsring in our study and we have discussed the similarities and potential differences between HET-hom and SCNT offspring in the Discussion (Lines 478-500).

As pointed out by Reviewer 3, deletion of maternal non-canonically imprinted genes partially or fully rescued the placental hyperplasia phenotype in both SCNT derived and offspring from oocyte lacking EED. However, as we have discussed, the mechanisms underlying other aspects of the offspring phenotype, such as fetal growth recovery of HET-hom offspring observed in our study, remain unknown. Moreover, the comparison we provide in Figure 7 strongly indicates that HET-hom and SCNT fetuses are similarly delayed at E14.5 and undergo similar fetal growth recovery before birth, but the mechanism also remains unknown. Together, it appears that offspring derived from either Eed-null oocytes or by SCNT have an innate ability to remediate fetal growth restriction during the late stages of pregnancy without a requirement to correct maternally inherited impacts mediated by Xist or H3K27me3-dependent imprinting.

Author response

Reviewer #1 (Public Review):

The main contribution appears to be related to functional specialization. I suggest clarifying the major novelty of the present report and to focus the introduction on it.

We thank this reviewer for this suggestion. We have revised the introduction to emphasize the functional specialization question. The changes are extensive; we have included a tracked-changes version of the manuscript to make these edits easy to see.

There is a growing literature on fluctuating neural firing patterns that is not considered in this report. The scholarship appears a bit impoverished with only 19 references, many of which point to work from this group of collaborators. I suggest that the authors consider the present work in the context of the wider literature more scholarly, even if not all the relations of these different lines of work can be conclusively connected at this point. For a few examples, there is work by Kienitz and colleagues on fluctuating neural patterns in V4 evoked by competing grating stimuli. Also, the work by Engel, Moore, and colleagues on 'on' and 'off' states in the context of selective attention seems relevant, or the work by Fiebelkorn and Kastner on rhythmic perception and attention.

We agree completely with this suggestion! We have reworded the introduction to be more inclusive of other research in this area (especially Kienitz and colleagues – exciting work that we are pleased to have had brought to our attention) and we have added about 500 words in the Discussion to cover the work on on/off states (Engel et al.), rhythmic perception (Fiebelkorn & Kastner and others), and attention more generally (e.g., Triesman & Gelade’s work on serial sampling). We are particularly pleased to add these sections because these topics are very much on our minds – we have a commentary piece under review elsewhere in which we evaluate these synergistic lines of approach in a more complete fashion. In total, we’ve added about 15 additional references.

Reviewer #2 (Public Review):

The description of the results would benefit from a better explanation of how low spike counts may influence the outcome of the analysis. Due to a smoothing procedure used for visualization, the spike counts for the paired stimuli (AB, black lines) shown in Figure 3a-b and Figure 4a-d go below 0. However, the actual spike count on a trial can not go below 0. The symmetric smoothing procedure may hide an underlying skewed distribution of spike counts that can only be positive. The statistical analysis is not performed on the smoothed distribution but on the actual spike counts, and the validity of the result is therefore not in question. However, the paper would benefit from 1) visualization of the unsmoothed trial counts, and 2) an explanation of how assumptions of symmetric/skewed distributions may affect the outcome.

We thank the reviewers for noting this and making these suggestions. We now include unsmoothed raw spike counts in all the example figures (Figure 3a-b and Figure 4a-d). With regard to the symmetric/skewed distributions and the analysis methods, a Poisson distribution will be skewed at low rates and become more symmetric at higher rates, so this is already incorporated into the analysis. Indeed, the utility of Poisson distributions for fitting non-negative data is one of the reasons these distributions are so commonly used in neuroscience. We now make this point explicitly at the beginning of Methods/Data analysis: “Our method centers on modeling spike counts based on Poisson distributions, a common technique for handling non-negative count data in neuroscience and other fields.” With this edit as well as the revised example figures now making clear that no spike counts are below zero, we are optimistic that readers will better understand the analysis method and how the shape of response distributions are incorporated into it.

Author Response:

We take the liberty to thank all of you for your constructive and inspiring comments, which will help us substantially improve the final version of the paper. Before our final revision with details, I am writing this provisional letter to have a quick response to our reviewers’ comments.

I first give a quick and short summary for your public reviews, then respond point-by-point.

Editors:

1. More discussion is needed.

2. More discussion about eye fixation during adaptation. Discuss why increasing visual uncertainty by blurring the cursor in the present study produces the opposite findings of previous studies (Tsay et al., 2021; Makino et al., 2023).

3. Discuss the broad impact of the current model.

4. Share the codes and the metadata (instead of the current data format).

Response: This is a concise summary of the major concerns listed in the public review. Given these concerns are easy to address, we are giving a quick but point-to-point response for now. The elaborate version will be put into our formal revision.

**Reviewer 1: **

1) More credit should be given to the PReMo model: a) The PReMo model also proposes that perceptual error drives implicit adaptation, as in a new publication in Tsay et al., 2023, which was not public at the time of the current writing; and b) The PReMo model can account for some dataset, e.g. Fig 4A.

Response: We will add this new citation and point out that the new paper also uses the term perceptual error. We will also point out that the PReMo model has the potential to explain Fig 4A, though for now, it assumes an additional visual shift to explain the positive proprioceptive changes relative to the target. We would expand the discussion about the comparison between the two models.

2) The present study produced an opposite finding of a previous finding, i.e., upregulating visual uncertainty (by cursor blurring here) decreases adaptation for large perturbations but less so for small perturbations, while previous studies have shown the opposite (by using a cursor cloud; Tsay et al., 2021; Makino et al., 2023). This needs explanation.

Response: Using the cursor cloud (Tsay et al., 2021, Makino et al., 2023) to modulate visual uncertainty has inherent drawbacks that make it unsuitable for testing the sensory uncertainty effect for visuomotor rotation. For the error clamp paradigm, the error is defined as angular deviation. The cursor cloud consists of multiple cursors spanning over a range of angles, which affects both the sensory uncertainty (the intended outcome) AND the sensory estimate of angles (the error itself, the undesired outcome). In Bayesian terms, the cursor cloud aims to modulate the sigma of a distribution (sigma_v in our model), but it additionally affects the mean of the distribution (mu). This unnecessary confound is avoided by using cursor blurring, which is still a cursor with its center (mu) unchanged from an un-blurred cursor. Furthermore, as correctly pointed out in the original paper by Tsay et al., 2021, the cursor cloud often overlaps with the visual target. This “target hit” would affect adaptation, possibly via a reward learning mechanism (See Kim et al., 2019 eLife). This is a second confound that accompanies the cursor cloud. We will expand our discussion to explain the discrepancy between our findings and previous findings.

3) The estimation of visual uncertainty (our exp1) required people to fixate on the target, while this might not reflect the actual scenario during adaptation where people are free to look wherever they want.

Response: Our data shows otherwise: in a typical error-clamp setting, people fixate on the target for the majority of the time. For our Exp1, the fixation on the straight line between the starting position and the target is 86%-95% (as shown in Figure S1). We also collected eye-tracking data in our Exp4, which is a typical error-clamp experiment. More than 95% of gaze falls with +/- 50 pixels around the center of the screen, even slightly higher than Exp1. We will provide this part of the data in the revision. In fact, we designed our Exp1 to mimic the eye-tracking pattern as in typical error-clamp learning with carefully executed pilot experiments.

This high percentage of fixating on the target is not surprising: the error-clamp task requires participants to use their hands to move towards the target and to ignore the cursor. In fact, we would also like to point out that the high percentage of fixation on the aiming target is also true for conventional visuomotor rotation, which involves strategic re-aiming (shown in de Brouwer et al. 2018; Bromberg et al. 2019; we have an upcoming paper to show this). This is one reason that our new theory would also apply to other types of motor adaptation.

4) More methodology details are needed. E.g., a figure showing the visual blurring, a figure showing individual data, a table showing data from individual sessions, code sharing, and a possible new correlational analysis.

Response: All these additional methodological/analysis information will be provided. We were self-limited by writing a short paper, but the revision would be extended for all these details.

Reviewer 2:

1) More discussions are needed since the focus of this study is narrowly confined to visuomotor rotation. “A general computational principle, and its contributions to other motor learning paradigms remain to be explored”.

Response: This is a great suggestion since we also think our original Discussion has not elaborated on the possible broad impact of our theory. Our model is not limited to the error-clamp adaptation, where the participants were explicitly told to ignore the rotated cursor. The error-clamp paradigm is one rare example that implicit motor learning can be isolated in a nearly idealistic way. Our findings thus imply two key aspects of implicit adaptation: 1) localizing one’s effector is implicitly processed and continuously used to update the motor plan; 2) Bayesian cue combination is at the core of integrating multimodal feedback and motor-related cues (motor prediction cue in our model) when forming procedural knowledge for action control.

We will propose that the same two principles should be applied to various kinds of motor adaptation and motor skill learning, which constitutes motor learning in general. Most of our knowledge about motor adaptation is from visuomotor rotation, prism adaptation, force field adaptation, and saccadic adaptation. The first three types all involve localizing one’s effector under the influence of perturbed sensory feedback, and they also have implicit learning. We believe they can be modeled by variants of our model, or at least we should consider using the two principles above to think of their computational nature. For skill learning, especially for de novo learning, the area still lacks a fundamental computational model that accounts for the skill acquisition process on the level of relevant movement cues. Our model suggests a promising route, i.e., repetitive movements with a Bayesian cue combination of movement-related cues might underlie the implicit process of motor skills.

We will add more discussion on the possible broad implications of our model in the revision.

Reviewer 3:

1) Similar to Reviewer 1, raised the concern about whether people’s fixation in typical motor adaptation settings is similar to the fixation that we instructed in our Exp1.

Response: see above.

2) Similar to Reviewer 2, the concern was raised about whether our new theory is applicable to a broad context. Especially, error clamp appears to be a strange experimental manipulation that has no real-life appeal, “(i)Ignoring errors and suppressing adaptation would also be a disastrous strategy to use in the real world”.

Response: about the broad impact of our model, please see responses to Reviewer 2 above. We agree that ignoring errors (and thus “trying” to suppress adaptation) should not be a movement strategy for real-world intentional tasks. However, even in real life, we constantly attend to one thing and do the other thing; that’s when implicit motor processes are in charge. Furthermore, it is this exact “ignoring” instruction that elicits the implicit adaptation that we can work on. In this sense, the error-clamp paradigm is a great vehicle to isolate implicit adaptation and allows us to unpack its cognitive mechanism.

3) In Exp1, the 1s delay between the movement end and the presentation of the reference cursor might inflate the actual visual uncertainty.

Response: The 1s delay of the reference cursor would not inflate the estimate of visual uncertainty. Our Exp1 used a similar paradigm by visual science (e.g., White, Levi, and Aitsebaomo, Vision Research, 1992), which shows that delay does not lead to an obvious increase in visual uncertainty over a broad range of values (from 0.2s to >1s, see their Figure 5-6). We will add more methodology justifications in our revision.

4) Our Fig4A used Tsay et al., 2021 data, which, in the reviewer’s view, is not an appropriate measure of proprioceptive bias. The reason is that in this dataset, “participants actively move to a visual target, the reported hand positions do not reflect proprioception, but mostly the remembered position of the target participants were trying to move to.”

Response: We agree that Tsay et al., 2021 study used an unconventional way to measure the influence of implicit adaptation on proprioception. And, their observed “proprioceptive changes” should not be called “proprioceptive bias” which is conventionally a reserved term for measuring the difference between the estimated hand location relative to the actual hand location (and better to be a passively moved hand). However, we think their dataset is still subject to the same Bayesian cue combination principle and thus can be modeled. Our modeling of this dataset includes all relevant cues: the implicitly perceived hand position and the proprioceptive cue (given that the hand stays at the movement end). Both cues are in the extrinsic coordinates, which happened to set the target position as zero. But where to set the zero (whether it is the target or the actual hand location) does not matter for the model fitting. Note that our Exp4 is also based on PEA modeling of proprioceptive bias, and this time the data is presented relative to the actual location.

In the revision, we would keep the current Fig4A and start to call the data as proprioceptive change as opposed to proprioceptive bias to follow the convention.

Author Response

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

Reviewer #1:

In no particular order:

1. In Figs S3 and S4, can they also show gamma fit? (or rather corrected fit accounting for abundance conditioning?) The shapes look different, especially for the microbial mat.

Author response: We have added gamma distribution fits to the rescaled AFD plots (Figs. S3, S4).

1. Lines 170-176 seem like they should come before lines 164-166.

Author response: In lines 166-170 we discuss empirical patterns in the data that motivate the introduction of the SLM as a model in lines 170-175. We have clarified these points in the revision.

1. The wiggles in the gamma predictions in the occupancy-abundance plots are because occupancy depends not only on abundance but also on the shape parameter, right? Probably good to write a sentence or two explaining what's going on here.

Author response: We agree with the reviewer that the variation in the prediction could be in-part driven by variation in the shape parameter across community members. We now include this observation in our revision (lines 209-211).

1. In the predicted vs observed occupancy plots, it would be nice to add curves showing predicted standard deviation or similar to give a sense of how well the model is predicting the variability.

Author response: In the revised manuscript we now include predictions for the variance of occupancy using the gamma distribution under both taxonomic and phylogenetic coarse-graining (Fig. S9; S10; lines 211-214).

1. Covariance between sister groups: Figs S9 and S10 look very nice, but it's hard to see much because they're log-log plots over multiple decades, while even a several-fold difference from y = x would indicate a strong effect of correlations. It would be clearer if the y-axis showed the ratio of the coarsegrained variance to the sum of OTU variances and we were looking at how well it fit y = 1.

Author response: We have included these plots in the revision (Fig. S14, S15).

1. If the sum of gammas can be well-approximated by a gamma, does that mean that the gamma is just a fairly flexible distribution and we shouldn't take the quality of the gamma fits in general as a very specific indication of what's going on?

Author response: While the sum of random variables that are drawn from gamma distributions with different parameters is often well-approximated by another gamma, this does not tell us why the gamma distribution holds for microbial communities at the finest-grain level (i.e., OTUs/ASVs). At present, the best explanation is that the gamma is a stationary distribution for certain stochastic differential equations which have ecological interpretations (Grilli, 2020; Shoemaker et al., 2023). Furthermore, alternative two-parameter distributions have been tested alongside the gamma and have done a comparatively poor job capturing observed macroecological patterns (Grilli, 2020). These results suggest that the utility of the gamma distribution is not simply an outcome of its flexible nature, it succeeds because it has captured core ecological properties of microbial communities. In the case of the SLM, gamma-like distributions arise when a community member is subject to self-limiting growth and environmental noise. On the other hand, the stability of the gamma distribution might explain why it can be detected as shape of the AFD, as it does not fade out across coarse-graining level.

1. What's going on with the variance of diversity in Fig S12? Does this suggest that some of the problem in Figure 4 could be with the analytic approximation rather than the model? I had a hard time understanding the part of the Methods explaining the simulation details (lines 587-597). It would be worth expanding this. Is there some way to explain how the correlations were simulated in terms of the SLM, e.g., correlations in the noise term across OTUs?

Author response: We believe that deviations in the variance of diversity in Fig. S16g,h are driven by small deviations in our predictions of the second moment $$< (x*ln(x) | N_{m}, \bar{x}{i}, \beta{i}^{2} >$$ (Eq. S16). Alone these predictions are slight, but their effects become noticeable when summed over hundreds or thousands of taxa. We have included this observation in the revised manuscript (lines 268-271). However, this deviation pales in comparison with the magnitude of covariance in the empirical data, suggesting that our inability to predict the variance of richness and diversity is primarily driven by our assumption of statistical independence.

Regarding the source of the correlations, under the SLM correlations in abundances can be introduced either by adding deterministic interaction terms or through correlated environmental noise. Determining which of these two options drives empirical correlations is an active area of research (e.g., Camacho-Mateu et al., 2023). For the purpose of this study, we remain agnostic on the cause of the correlations, optioning to instead emphasize that that the inclusion of correlations is necessary to reproduce observed slopes of the fine vs. coarse-grained relationship for diversity.

1. In Figure 5ab, is the idea that the correlation in richness is primarily driven by the number of samples from the environment? Line 390 seems to say so, but it would be good to make this explicit and put it right in that section of the Results.

Author response: Our results suggest that sampling effort (# reads) plays a larger role in determining the correlations between fine and coarse-grained measures of richness. We now clarify this point in the revised manuscript (lines 429-435).

1. I don't totally understand the contrast in lines 369-372. If fine-scale diversity within one group begets coarse-grained diversity in another group, couldn't that show up as correlations in the AFDs? Or is the argument that only including within-group correlations in AFDs is enough to reproduce the pattern? I'm not sure I see how that could be.

Author response: The term “begets” implies both causation and direction. If we see a positive relationship between diversity estimates at two different scales of observation the causal mechanism cannot be determined solely from correlations between samples obtained once from different sites. So, mechanisms consistent with niche construction/"DBD" can produce correlations, though the existence of correlations do not necessarily imply DBD.

1. The discussion of niche construction on 429-431 doesn't match very well with 440-441. Basically, niche construction is a very broad concept, not a specific one, right?

Author response: In lines 472-576 (formerly 429-431) we discuss how the existence of correlations between fine and coarse-grained scales does not point to a single ecological mechanism. Alternatively stated, observing a non-zero slope does not mean that niche construction is driving the relationship.

In lines 476-487 (formerly 440-441) we discuss how the mechanism of cross-feeding has been shown to generate a positive relationship between fine and coarse-grained measures of diversity. This mechanism can be interpreted as a form of “niche construction”, so it is an instance of a tested ecological mechanism that aligns with the interpretation given in Madi et al. (2020).

1. Isn't (8) just the negative binomial distribution?

Author response: The convolution of the stationary solution of the SLM (i.e., a gamma distribution) and the Poisson limit of a multinomial sampling distribution returns a negative binomial distribution of read counts across hosts if samples have identical sampling depths. We now include this detail in the revision (line 593-595). Note however that if different samples have different sampling depths, the distribution of reads across samples is not a negative binomial.

1. Missing 1/M in (9).

Author response: We have fixed this omission in the revision.

1. Schematic figures illustrating what the different statistics are intuitively capturing would really help this work be understandable to a broader audience, but they'd also be a ton of work.

Author response: Richness and diversity are used in ecology to such an extent that we do not see the benefit of a conceptual diagram. Furthermore, we have included a conceptual diagram about our pipeline in our revision at the request of Reviewer 2 (Fig. S20).

Reviewer #2:

Major Recommendations

If I were reviewing this manuscript for a regular journal, I believe the following issues would be important to address prior to publication.

1. From my reading, the main points of this advance are that

a. SLM models AFDs well at all levels of coarse-graining.

b. This makes SLM a better null-model than UNTB for macroecological relationships.

c. Using SLM on the EMP data, the richness slopes are well explained by SLM but not the diversity slopes. Therefore, any theory that hopes to explain the diversity slopes must include interactions. Argument B appears to be one of the key points yet is missing from the abstract, and should be made clearer. If these aren't the main points the authors intended, then other main points need to be highlighted more.

Author response: In the revision we now explicitly mention argument b in the Abstract.

1. The title should be more specific, so as to better reflect the content. (E.g. "UNTB is not a good null model for macroecological patterns" would seem more appropriate.)

Author response: We would prefer to focus on the success of the SLM rather than the limitations of the UNTB in the title of this work. Therefore, we have modified our title as follows: “Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth”.

1. The manuscript would benefit from a clearer description of exactly what information the SLM retains about the data (perhaps even a cartoon panel in one of the figures). In particular, it is important to be explicit about the number of model parameters.

Author response: The number of model parameters for the gamma AFD are now explicitly stated in the revision (Lines 579-580).

1. The main point of Figures 2-4 seems to be that SLM is good at describing the data (and when it fails it is due to interactions) while UNTB fails to reproduce this behavior, in support of Argument B. This is not clear from the figure descriptions or titles, which focus on SLM's "predictive" power.

Author response: Fig. 2a demonstrates that the gamma distribution predicted by the SLM explains the empirical distribution of abundances. This result provides motivation to predict the fraction of sites harboring a given community member (i.e., occupancy, Fig. 2c) as well as general measures of community composition including mean richness (Fig. 3a,c) and mean diversity (Fig. 3b,d) using parameters estimated from the data (not free parameters).

This success led us to consider whether the gamma distribution could predict the variance of richness and diversity, which it could not because it does not capture covariance between community members (Fig. 4).

In the revision we have identified opportunities to make these points clear throughout the Results. Furthermore, we have added additional detail to the legends of Figs. 2-4.

1. The manuscript would benefit from clarifying the use of "prediction" related to the SLM. Since the gamma distributions predicted by SLM were fit to empirical data, it seems like the agreement between analytic means and empirical means (Fig. 3) is a statement on gamma distributions being a good fit for the AFD's more than SLM predicting richness and diversity. For example, from my reading, it seems like this analysis could be done numerically by shuffling species abundances across environments and seeing whether this changed the mean richness/diversity. I would not call this shuffling test a prediction, since it is more a statement on the relevance of interactions. SLM predicts gamma-distributed AFD's, but those distributions recovering the data they were trained on doesn't seem like a prediction.

Author response: In this manuscript we identified the gamma distribution as an appropriate probability distribution to describe the distribution of relative abundances across samples over a range of coarse-grained scales. Motivated by this result, we performed a separate analysis where at each scale we estimated the mean and variance of relative abundance across sites for each community member. We then used these parameters to obtain the expected value of a community-level measure using an equation we derived by assuming that the gamma distribution was appropriate (e.g., richness, Eq. 13). We then compared the expected value of richness to the mean value from empirical data and assessed the similarity between the two values.

The outcome of this procedure constitutes a prediction. While the mean and variance are parameters, estimating them from the empirical data has no connection with the operation of training a distribution on empirical data. We could have derived predictions such as Eq. 13 using any other probability distribution that can be parameterized using the mean and variance (e.g., Gaussian). Such a prediction would likely do a poor job even though it used the same means and variances used for our gamma predictions. This is because the choice of distribution would not have been a good descriptor of the distribution of abundances across hosts.

To better explain this last -- perhaps the most significant -- issue, I'd like to ask the authors if the following recasting would be an accurate reflection of their conclusions, or if something is missing.

1. "Focusing on the empirical relationship observed between diversity slopes by Madi 2020, we ask the question: does explaining these relationships require accounting for species-species correlations? Or could it be reproduced in a noninteracting model?" To address this question, one can perform a randomization test, shuffling abundances to preserve all single-OTU statistics but breaking any correlations. My reading of the authors' results is that (new result 1) the richness relationships would be preserved, while diversity relationships would not be preserved. [Note that this result 1 need not mention either SLM or UNTB.]

Author response: The question of whether correlations between species are necessary to explain the observed slope of the fine vs. coarse-grained relationship was only one component of our research goals. Our first question was whether the SLM would prove to be a more appropriate null for evaluating the novelty of observed slopes. We believe that our results support the conclusion that the SLM is an appropriate null for this question, as it was able to capture observed slopes of the fine vs. coarse-grained relationship for estimates of richness, determining that correlations and the interactions that are ultimately responsible are not necessary to explain this result.

We then find that the SLM as a null model fails to capture observed slopes of the fine vs. coarsegrained relationship for estimates of diversity and simulate the SLM with correlations to return reasonable estimates of the slope. However, here the question about correlations is a direct follow-up from our question about a null model that excludes interactions, so it is unclear how a randomization test would relate to this result.

1. Instead of doing a randomization test (resampling the empirical distribution), one might insist on instead fitting a model to the AFD distributions, and sampling from that distribution rather than the empirical one.

a. If doing it this way, one should of course ensure that the distribution being fit is a good description of the data.

b. UNTB is a bad fit. SLM is a better fit, and in fact (new result 2) continues to be a good empirical fit even at coarse-grained levels.

c. Can make statements on using SLM as a null model for these types of cross-scale relationships. Could try arguing that fitting an SLM model per-OTU (instead of resampling the empirical distribution) could offer some advantage if certain properties could be computed analytically from the fit parameters, instead of averaging over multiple computational rounds of resampling.

Do these two points accurately summarize the manuscript? If so, this presentation avoids the confusion with "prediction". If my summary is missing some important point, the presentation should be revised to clarify the points I appear to have missed.

Author response: In our manuscript we derive predictions from the gamma distribution, the stationary distribution of the SLM, that require parameters estimated from the data (i.e., mean and variance of relative abundance). These parameters are estimated from the data using normal procedures and then plugged into our predictions that assume the appropriateness of the gamma, returning values that are then compared to estimates from empirical data. Our estimation of the mean and variance does not assume that the empirical distribution following a gamma distribution, but the value returned by our function derived from the gamma distribution (e.g., Eq. 13) does make that assumption.

To address the reviewer’s broader comment, we believe that following points summarize our manuscript:

1. The gamma distribution as a stationary solution of the SLM captures macroecological patterns and predicts typical community-level properties (i.e., mean richness and diversity) across phylogenetic and taxonomic scales.

2. The gamma distribution fails to predict variation in community-level properties (i.e., variance of richness and diversity) across phylogenetic and taxonomic scales. This occurs because the SLM is a mean-field model that does not explicitly include interactions between community members.

3. Despite the inability to capture interactions, the gamma distribution succeeds at predicting the fine vs. coarse-grain slope for richness, a pattern that had previously been attributed to community member interactions. This result demonstrates that the novelty of a macroecological pattern hinges on one’s choice of null model.

4. However, the gamma cannot capture the same relationship for diversity. Simulations of the gamma distribution that incorporate correlations between community members are capable of generating reasonable estimates of the slope.

To address the reviewer’s comments regarding the appropriateness fitted gamma distributions, in our revision we have added fitted gamma distributions to plots of AFDs so that the reader can visually assess the ability of the gamma to describe empirical patterns (Fig. S3, S4).

We have also obtained predictions for the slope of the fine vs. coarse-grained relationship for community richness using the same form of UNTB used by Madi et al (2020). In our revised manuscript we establish a procedure to infer the single parameter of this model, generate predictions of richness at fine and coarse-grained scales, and then evaluate whether the UNTB is capable of predicting the slope of the fine vs. coarse-grained relationship for richness (Supplementary Information; Figs. S18, 24-28; lines 277-278; 370-380).

Other/minor comments

1. The manuscript would be improved with more consistent terminology ("fine vs. coarse-grained relationship"/"the relationship" vs. "diversity slope"). Also, many readers may be used to OTUs referring to the rather fine level of description, as opposed to any chosen level; and could interpret indexing over groups as being in contrast with indexing over OTU's (coarse vs fine). The authors' use is perfectly correct, but keeping a consistent terminology would help.)

Author response: We have revised our manuscript to specify the “slope” as the “slope of the fine vs. coarse-grained relationship” (e.g., Line 318). We also specify in the Results and in the Methods that we use “fine” and “coarse” as relative terms, keeping with the sliding-scale approach used in Madi et al (2020).

1. While I appreciate this "slope" is something borrowed from other work, the clarity of the paper might benefit from a cartoon of how one goes from the raw data to the slopes at a particular coarse-graining level. (Optional).

Author response: We had added a conceptual diagram to the revision (Fig. S20).

1. The text often colloquially references "the gamma," "predictions of the gamma," etc. This phrasing comes across as sloppy, and the manuscript would be improved by being more specific.

Author response: We now specify “gamma” as the “gamma distribution” throughout the manuscript.

1. Equation 6 appears to be missing some subscripts on the x terms (included on the left of the equation).

Author response: We thank the reviewer for noticing this error and we have corrected it in the revision.

1. In "Simulating communities of correlated...AFDs", the acronym SAD is not defined.

Author response: We thank the reviewer for noticing this error and we have corrected it in the revision.

1. In Figure 2:

a. Invariant is probably the wrong word for the title, since all the AFD's were rescaled by mean and variance before being compared. Data does support that the gamma distributions are good at describing the AFD's, but as stated in the description it's the general shape that is preserved, not the distribution itself.

Author response: When we mention the invariance of the AFD we now specify that we mean that the shape of the distribution remained qualitatively invariant.

b. I'd recommend changing the color coding to something with more contrast, since currently it's impossible to assess the claim that the shape of the distribution collapses.

Author response: Our coarse-graining procedure is a sequential operation that has no intuitive point that would suggest the use of a contrasting colormap (e.g., if our scale ranged from -1 to 1 then there would be a natural point of contrast at zero).

c. The legend is missing relevant technical details: How many OTU's were used to make plot a? How many samples?

Author response: The number of samples was listed in the Materials and Methods (line 523). In the revision we now include a table with the average and total number of OTUs as well as the average number of reads for each environment (Table S1, S2).

d. In plot b, is the mean relative abundance referring to "mean abundance when observed" or "mean across all samples"?

Author response: The mean relative abundance is the mean abundance across all sites (line 204) and in the legend of Fig. 2.

e. Since one argument here is that SLM fits these distributions better than UNTB, if possible it would be nice to see UNTB's failed fits here.

Author response: A major feature of the UNTB is that the demographic parameters of community members are indistinguishable. Under the SLM, the variation in the mean relative abundance we observe suggests that the carrying capacities of community members vary over multiple orders of magnitude, a result that is incompatible with most forms of the UNTB (x-axis of Fig. 2b). We now mention this point in the revised manuscript (lines 110; 229; 455-471).

1. In Figure 3:

a. It is not clear how coarse-graining is included in model fitting. The "Deriving biodiversity measure predictions" section would benefit from including how coarse-graining is incorporated.

Author response: We predict measures of biodiversity separately at each coarse-grained scale. We now clarify this detail in the revised manuscript (Lines 624-627).

b. Reference Shannon Diversity in Methods.

Author response: We now cite Shannon’s diversity.

c. What is the blue/white color coding in plots a & c? It doesn't have any color key.

Author response: Figs. 3-6 use a uniform light-to-dark scale for all environments, with each environment having its own color. For example, Fig. 3a contains data from the human gut microbiome. Human gut data were assigned the color aquamarine, so the shade of aquamarine for a given datapoint in Fig. 3a indicates the phylogenetic scale.

In the revision we now clarify the colorscale in the legend of Fig. 3 and specify that the same scale is used in all subsequent figure legends.

d. Re: earlier comments, why is richness considered a prediction? (Am I correct in my interpretation that panel b is almost a tautology - counting the number of zeros in the matrix either by rows or by columns - whereas panel d is nontrivial?)

Author response: Mean richness as a measure of biodiversity depends on the fraction of sites where a given community member is present (i.e., occupancy). The mean relative abundance of a community member and its variation across sites (beta) is clearly related to occupancy, but those two statistics do not give you a prediction of occupancy. Obtaining a prediction of occupancy and, subsequently, richness, requires 1) a probability distribution of abundances (i.e., the gamma) and 2) a probability distribution of sampling (i.e., the Poisson). Using these two pieces of information, we derived a prediction for mean richness (Eq. 13). We then compare the value of richness obtained by plugging in the mean relative abundances, betas, and known number of reads to the observed mean richness obtained from the data.

e. The lettering of subplots in Figure 3 is not consistent with Figure 4. Figure 3 subplots are also cited incorrectly in paragraph two on page six (lines 251-254).

Author response: We thank the reviewer for noticing the error and we have corrected it in the revision.

f. Again, if possible show UNTB predictions in plots a & c.

Author response: In our revised manuscript we provide extensive descriptions and predictions of mean richness and the slope of the fine vs. coarse-grained relationship for richness using the form of the UNTB used in Madi et al. (2020; Figs. S18, S24 - S29; lines 277-282; 370-380). We then compare the error of these slope predictions to those obtained from the SLM, finding that the SLM generally outperforms UNTB (Figs. S27-S29).

1. In Figure 4:

a. What are the color codings in plots a & b?

Author response: The color scale used in Fig. 4 is identical to the color scale used in Fig. 3. This detail is now specified in the legend of Fig. 4.

b. What are the two lines of empirical data in plots a & b, and why is one of them dashed?

Author response: We now specify what the two lines mean in the key within the figure.

c. Same comment as earlier on predictions and richness.

Author response: We now specify what the two lines mean in the key within the figure.

1. In Figure 5:

a. It wasn't clear to me in the manuscript how the authors generated these plots from the raw data. The manuscript would benefit from a clear cartoon/description of the data pipeline, from raw data to empirical (and analytic) slopes.

Author response: We have added a conceptual diagram to the revised manuscript (Fig. S20).

b. Make the figure title more descriptive to better connect it to the figure's objective (the richness slopes relationship is not novel, but the diversity slopes relationship is).

Author response: We have revised the figure title.

References

Camacho-Mateu, J., Lampo, A., Sireci, M., Muñoz, M. Á., & Cuesta, J. A. (2023). Species interactions reproduce abundance correlations patterns in microbial communities (arXiv:2305.19154). arXiv. https://doi.org/10.48550/arXiv.2305.19154

Grilli, J. (2020). Macroecological laws describe variation and diversity in microbial communities. Nature Communications, 11(1), 4743. https://doi.org/10.1038/s41467-020- 18529-y

Madi, N., Vos, M., Murall, C. L., Legendre, P., & Shapiro, B. J. (2020). Does diversity beget diversity in microbiomes? eLife, 9, e58999. https://doi.org/10.7554/eLife.58999

Shoemaker, W. R., Sánchez, Á., & Grilli, J. (2023). Macroecological laws in experimental microbial systems (p. 2023.07.24.550281). bioRxiv. https://doi.org/10.1101/2023.07.24.550281

Author Response

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

We thank the reviewers for their thorough assessment of our study, their overall enthusiasm, and the helpful suggestions for clarifying the methods and results, additional analyses, and discussion points. We have made earnest efforts to address the weaknesses raised in the public review and other recommendations made by the reviewers.

Public Reviews:

Reviewer #1 (Public Review):

Herein, Blaeser et al. explored the impact of migraine-related cortical spreading depression (CSD) on the calcium dynamics of meningeal afferents that are considered the putative source of migraine-related pain. Critically previous studies have identified widespread activation of these meningeal afferents following CSD; however, most studies of this kind have been performed in anesthetized rodents. By conducting a series of technically challenging calcium imaging experiments in conscious head fixed mice they find in contrast that a much smaller proportion of meningeal afferents are persistently activated following CSD. Instead, they identify that post-CSD responses are differentially altered across a wide array of afferents, including increased and decreased responses to mechanical meningeal deformations and activation of previously non-responsive afferents following CSD. Given that migraine is characterized by worsening head pain in response to movement, the findings offer a potential mechanism that may explain this clinical phenomenon.

Strengths:

Using head fixed conscious mice overcomes the limitations of anesthetized preps and the potential impact of anaesthesia on meningeal afferent function which facilitated novel results when compared to previous anesthetized studies. Further, the authors used a closed cranial window preparation to maximize normal physiological states during recording, although the introduction of a needle prick to induce CSD will have generated a small opening in the cranial preparation, rendering it not fully closed as suggested.

Weaknesses:

Although this is a well conducted technically challenging study that has added valuable knowledge on the response of meningeal afferents the study would have benefited from the inclusion of more female mice. Migraine is a female dominant condition and an attempt to compare potential sex-differences in afferent responses would undoubtedly have improved the outcome.

Our study included only two females, largely reflecting the much higher success rate of AAV-mediated meningeal afferent GCaMP expression in males than in females. The reason for the lower yield in female mice is unclear to us at present but may involve, at least partly, sex-specific differences in the mechanisms responsible for efficient transduction with this AAV vector observed in peripheral tissues (Davidoff et al. 2003). While our study did not address sex differences, a recent study (Melo-Carrillo et al. 2017) reported CSD equally activating and sensitizing second-order dorsal horn neurons that receive input from meningeal afferents in male and female rats.

The authors imply that the current method shows clear differences when compared to older anaesthetized studies; however, many of these were conducted in rats and relied on recording from the trigeminal ganglion. Inclusion of a subgroup of anesthetized mice in the current preparation may have helped to answer these outstanding questions, being is this species dependent or as a result of the different technical approaches.

We have tried to address the anesthesia issue by conducting imaging sessions in several isoflurane-anesthetized mice. However, during these experiments, we observed a substantial decrease in the GCaMP fluorescence signal with a much lower signal-to-noise ratio that made the analyses of the afferents’ calcium signal unreliable. Reduced GCaMP signal in meningeal axons during anesthesia may be related to the development of respiratory acidosis, since lower pH leads to decreased GCaMP signal, as also mentioned by Reviewer #3. Of note, urethane anesthesia, which was used in all previous rat experiments, also produces respiratory acidosis.

The authors discuss meningeal deformations as a result of locomotion; however, despite referring to their previous work (Blaeser et al., 2022), the exact method of how these deformations were measured could be clearer. It is challenging to imaging that simple locomotion would induce such deformations and the one reference in the introduction refers to straining, such as cough that may induce intracranial hypertension, which is likely a more powerful stimulus than locomotion.

As part of the revision, we now provide a better description of the methodology (“Image processing and calcium signal extraction” section) used to determine meningeal deformations, including scaling, shearing, and Z-shift. In our previous paper (Blaeser et al. 2023), we provided an extensive description of the types of meningeal deformations occurring in locomoting mice. It should also be noted that locomotion drives cerebral vasodilation and intracranial pressure increases (Gao and Drew, 2016), which likely mediate, at least in part, the movement of the meninges towards the skull (positive Z-shift) and potentially other meningeal deformation parameters. We also agree with the reviewer that sudden maneuvers such as coughing and sneezing that lead to a larger increase in intracranial pressure are likely to be even more powerful drivers of endogenous intracranial mechanical stimulation than locomotion. Thus, our finding of increased responsiveness to locomotion-related meningeal deformation post-CSD may underestimate the increased afferent responsivity post-CSD during other behaviors such as coughing. We added this point to the discussion.

More recently, several groups have used optogenetic triggering of CSD to avoid opening of the cranium for needle prick. Given the authors robustly highlight the benefit of the closed cranium approach, would such an approach not have been more appropriate.

We agree with the reviewer that optogenetic methods used for CSD induction in non-craniotomized animals will further ensure accurate pressurization and, thus, will be an even better approach that avoids the burr hole used for pinprick. It should be noted, however, that the burr hole used for the pinprick likely had a minimal effect on intracranial pressure, as we minimized depressurization by plugging the burr hole throughout the experiments with a silicone elastomer. We have added this information to the revised Methods section.

It is also worth noting that the optogenetic methodology used by others to provoke CSD was optimized only recently and relies on transgenic mice with a strong expression of YFP (Thy1.ChR2-YFP mice) within the superficial cortex that is not compatible with the afferent GCaMP imaging of meningeal afferents. Modifications using red-shifted opsins may allow the use of this strategy in the future.

It was not clear how deformations predictors increased independent of locomotion (Figure 4D) as locomotion is essentially causing the deformations as noted in the study. This point was not so clear to this reviewer.

As noted in our previous paper (Blaeser et al., 2023), deformation variables often exhibit different time courses than locomotion, even when a deformation is initially induced by the onset of locomotion. Most notably, the scaling-related deformation ramps up slowly and often persists for tens of seconds after the onset and termination of locomotion, which may be related to the recovery dynamics of the meningeal vascular response to locomotion. Overall, while locomotion serves as a predictor of meningeal deformation, we observed previously (Blaeser et al. 2023) many afferents whose responses were more closely associated with the moment-to-moment deformations than with the state of locomotion per se, suggesting that a unique set of stimuli is responsible for the activation of this deformation-sensitive afferent population. The increased sensitivity to deformation signals we observed following CSD suggests that the afferent population sensitive to deformation has unique properties that render it most susceptible to becoming sensitized following CSD. We now discuss this possibility.

Reviewer #2 (Public Review):

This is an interesting study examining the question of whether CSD sensitizes meningeal afferent sensory neurons leading to spontaneous activity or whether CSD sensitizes these neurons to mechanical stimulation related to locomotion. Using two-photon in vivo calcium imaging based on viral expression of GCaMP6 in the TG, awake mice on a running wheel were imaged following CSD induction by cortical pinprick. The CSD wave evoked a rise in intracellular calcium in many sensory neurons during the propagation of the wave but several patterns of afferent activity developed after the CSD. The minority of recorded neurons (10%) showed spontaneous activity while slightly larger numbers (20%) showed depression of activity, the latter pattern developed earlier than the former. The vast majority of neurons (70%) were unaffected by the CSD. CSD decreased the time spent running and the numbers of bouts per minute but each bout was unaffected by CSD. There also was no influence of CSD on the parameters referred to as meningeal deformation including scale, shear, and Z-shift. Using GLM, the authors then determine that there there is an increase in locomotion/deformation-related afferent activity in 51% of neurons, a decrease in 12% of neurons, and no change in 37%. GLM coefficients were increased for deformation related activity but not locomotion related activity after CSD. There also was an increase in afferents responsive to locomotion/deformation following CSD that were previously silent. This study shows that unlike prior reports, CSD does not lead to spontaneous activity in the majority of sensory neurons but that it increases sensitivity to mechanical deformation of the meninges. This has important implications for headache disorders like migraine where CSD is thought to contribute to the pathology in unclear ways with this new study suggesting that it may lead to increased mechanical sensitivity characteristic of migraine attacks.

1) It would be helpful to know what is meant by "post-CSD" in many of the figures where a time course is not shown. The methods indicate that 4, 30 min runs were collected after CSD but this would span 2 hours and the data do not indicate whether there are differences across time following CSD nor whether data from all 4 runs are averaged.

While we monitored time course changes in ongoing activity (see Figure 2), it was challenging to evaluate post-CSD changes in locomotion-related deformation responses at a fine temporal scale, as running bouts resumed at different time points post-CSD and occurred intermittently throughout the post-CSD analysis period. Our experiments were also not sufficiently powered to break out analyses at multiple different epochs post-CSD, partly because there wasn’t much locomotion. To allow comparisons using a sufficient number of bouts, we conducted our GLM analyses using all data collected during running bouts in the 2-hour post-CSD period (termed “post-CSD) versus in the 1-hour pre-CSD period. We have now clarified this further in the main text and figure legends.

2) Why is only the Z-shift data shown in Figures 4A-C? Each of the deformation values seems to contribute to the activity of neurons after CSD but only the Z-shift values are shown.

In many afferents, only one deformation variable best predicted the activity at both the pre- and post-CSD epochs. However, at the population level, all deformation variables were equally predictive. In the examples provided, the afferent developed augmented sensitivity that could only be predicted by the Z-shift variable, and the other deformation variables were not included to keep the figure legible. This is now clarified in the figure legend.

3) How much does the animal moving its skull against the head mount contribute to deformations of the meninges if the skull is potentially flexing during these movements? Even if mice are not locomoting, they can still attempt to move their heads thus creating pressure changes on the skull and underlying meninges. The authors mention in the methods that the strong cement used to bind the skull plates and headpost together minimize this, but how do they know it is minimized?

We did not measure skull flexing during locomotion and its potential effect on meningeal deformation. However, we would like to point out several considerations. It is evident from numerous imaging studies across various brain regions in freely moving animals, utilizing brain motion registration, that brain motion of the same scale (a few microns), as that observed in our studies, also occurs in the absence of head fixation (e.g., Glas et al, 2019; Zong et al 2021). In our system, the head-fixed mouse is locomoting on a cantilevered (spring-like) running wheel (see also Ramesh et al., 2018), which dissipates most, albeit not all, upward and forward forces applied to the skull during locomotion. Furthermore, the position of the headpost, anterior to where the mouse's paws touch the wheel, makes it hard for the mouse to push straight up and apply forces to the skull. We have updated the text in the methods section (Running wheel habituation) to address this. In our previous work (See Figure 2B in Blaeser et al. 2023), we found a substantial subset of afferents showing an increase in calcium activity that began after each bout of locomotion had terminated, and that lasted for many seconds, suggesting that skull flexing during locomotion may not play a leading role. Finally, we proposed in that study that meningeal deformations play a major role in the afferent response, given our findings of (i) sigmoidal stimulus-response curves between afferent activity and meningeal deformation and (ii) of different afferents that track scaling deformations along different axes. It is unlikely that all of these are related to any residual forces generated from skull deformations.

4) What is the mechanism by which afferents initiate the calcium wave during the CSD itself? Is this mechanical pressure due to swelling of the cortex during the wave? If so, why does the CSD have no impact on the deformation parameters? It seems that this cortical swelling would have some influence on these values unless the measurements of these values are taken well after cortical swelling subsides. Related to point 1 above, it is not clear when these measurements are taken post-CSD.

We provide, for the first time, evidence that CSD evokes local calcium elevation in meningeal afferent fibers in a manner that is incongruent with action potential propagation, as the activity gradually advances along individual afferents across many seconds during the wave. As indicated in Figure 1H, we measured these changes during the first 2 minutes post-CSD. Based on the reviewer’s question, we have now addressed whether mechanical changes occurring in the cortex in the wake of CSD might be responsible for the acute afferent activation we observed. We now include new data (Results, “Acute afferent activation is not related to CSD-evoked meningeal deformation” and Figure S2) showing an acute phase of meningeal deformation (as expected given the changes in extracellular fluid volume) lasting 40-80 seconds following the induction of CSD. Our data suggests, however, that these meningeal deformations are unlikely to be the main driver of the acute afferent calcium response. We propose that, based on the speed of the afferent calcium wave propagation and the distinct dynamics of calcium activity as compared to the dynamics of the deformations, the acute afferent response is more likely to be mediated by the spread of algesic mediators (e.g., glutamate, K+ ATP) and their diffusion into the overlying meninges.

Because the peri-CSD meningeal deformations return to baseline soon after the cessation of the CSD wave, they are unlikely to affect our analyses of post-CSD changes in afferent sensitivity in the following 2 hours. This is also supported by our data (see Figure 3F-H) showing similar locomotion-related deformations pre- and post-CSD, which were measured after the deformations related to the CSD itself had subsided.

5) How does CSD cause suppression of afferent activity? This is not discussed. It is probably a good idea in this discussion to reinforce that suppression in this case is suppression of the calcium response and not necessarily suppression of all neuronal activity.

The mechanism underlying the suppression of afferent activity remains unclear. We now discuss the following points:

First, the pattern of afferent responses resembles the rapid loss of cortical activity in the wake of a CSD, but its faster recovery points to a mechanism distinct from the pre-and post-synaptic changes responsible for the silencing of cortical activity (Sawant-Pokam et al., 2017; Kucharz and Lauritzen, 2018). Whether CSD drives the local release of mediators capable of reducing afferent excitability and spiking dynamics will require further studies.

Second, the reviewer proposes that the suppressed calcium activity we observed in ~20% of the afferents immediately following CSD may reflect a decreased calcium response independent of afferent spiking activity. Such a process could theoretically involve factors influencing the GCaMP fluorescence (see also our response to Reviewer #3) and/or factors modifying the afferents’ spiking-to-calcium coupling. We note that if a CSD-related factor could modify the calcium response independent of afferent spiking, one would expect a more consistent effect across axons, reflected as a reduced signal in a larger proportion of the afferents, which we did not observe.

6) How do the authors interpret the influence of CSD on locomotor activity? There was a decrease in bouts but the bouts themselves showed similar patterns after CSD. Is CSD merely inhibiting the initiation of bouts? Is this consistent with what CSD is known to do to motor activity? And again related to point 1, how long after CSD were these measurements taken? Were there changes in locomotor activity during the actual CSD compared to post-CSD?

To the best of our knowledge, there is very little data on the effect of CSD on motor activity, making it challenging to engage in further speculation regarding the mechanisms underlying the preservation of running bouts patterns post-CSD. Houben et al. (2017) described a similar reduction in locomotion in mice, corresponding to decreased motor cortex (M1) activity, and preservation of intermittent locomotion bouts. In the revised Results section, we now provide information about the cessation of locomotor activity during the CSD wave and have added information regarding the measurement of locomotion following CSD.

7) The authors mention the caveats of prior work where the skull is open and is thus depressurized. Is this not also the case here given there is a hole in the skull needed to induce CSD?

Unlike previous electrophysiological studies, which involved several large openings (~2x2 mm), including at the site of the afferents’ receptive field, our study involved only a small burr hole located remotely (1.5 mm) from the frontal edge of our imaging window. As noted in our response to Reviewer #1, this burr hole (~0.5 mm diameter) was unlikely to produce inflammation at the imaging site or cause depressurization as it was sealed with a silicone plug throughout the experiment.

8) The authors should check the %'s and the numbers in the pie chart for Figure 4. Line 224 says 53 is 22% but it does not look this way from the chart.

The 22% reported is the percentage of afferents that developed sensitivity post-CSD among all the non-sensitive ones pre-CSD. The pie chart illustrates only afferents that were deemed sensitive before and/or after the CSD. We removed the % to clarify.

9) Line 319 mentions that CSD causes "powerful calcium transients" in sensory neurons but it is not clear what is meant by powerful if there are no downstream effects of these transients being measured. The speculation is that these calcium transients could cause transmitter release, which would be an important observation in the absence of AP firing, but there are no data evaluating whether this is the case.

We changed the term to “robust”

Reviewer #3 (Public Review):

Summary:

Blaeser et al. set out to explore the link between CSD and headache pain. How does an electrochemical wave in the brain parenchyma, which lacks nociceptors, result in pain and allodynia in the V1-3 distribution? Prior work had established that CSD increased the firing rate of trigeminal neurons, measured electrophysiologically at the level of the peripheral ganglion. Here, Blaeser et al. focus on the fine afferent processes of the trigeminal neurons, resolving Ca2+ activity of individual fibers within the meninges. To accomplish these experiments, the authors injected AAV encoding the Ca2+ sensitive fluorophore GCamp6s into the trigeminal ganglion, and 8 weeks later imaged fluorescence signals from the afferent terminals within the meninges through a closed cranial window. They captured activity patterns at rest, with locomotion, and in response to CSD. They found that mechanical forces due to meningeal deformations during locomotion (shearing, scaling, and Z-shifts) drove non-spreading Ca2+ signals throughout the imaging field, whereas CSD caused propagating Ca2+ signals in the trigeminal afferent fibers, moving at the expected speed of CSD (3.8 mm/min). Following CSD, there were variable changes in basal GCamp6s signals: these signals decreased in the majority of fibers, signals increased (after a 25 min delay) in other fibers, and signals remained unchanged in the remainder of fibers. Bouts of locomotion were less frequent following CSD, but when they did occur, they elicited more robust GCamp6s signals than pre-CSD. These findings advance the field, suggesting that headache pain following CSD can be explained on the basis of peripheral cranial nerve activity, without invoking central sensitization at the brain stem/thalamic level. This insight could open new pathways for targeting the parenchymal-meningeal interface to develop novel abortive or preventive migraine treatments.

Strengths:

The manuscript is well-written. The studies are broadly relevant to neuroscientists and physiologists, as well as neurologists, pain clinicians, and patients with migraine with aura and acephalgic migraine. The studies are well-conceived and appear to be technically well-executed.

Weaknesses:

1) Lack of anatomic confirmation that the dura were intact in these studies: it is notoriously challenging to create a cranial window in mouse skull without disrupting or even removing the dura. It was unclear which meningeal layers were captured in the imaging plane. Did the visualized trigeminal afferents terminate in the dura, subarachnoid space, or pia (as suggested by Supplemental Fig 1, capturing a pial artery in the imaging plane)? Were z-stacks obtained, to maintain the imaging plane, or to follow visualized afferents when they migrated out of the imaging plane during meningeal deformations?

We agree that avoiding disruption of the dura is challenging. Indeed, it took many months of practice before conducting the experiments in this manuscript to master methods for a craniotomy that spared the dura.

We addressed the issue of meningeal irritation due to cranial window surgery in our previous work (Blaeser et al., 2023). In brief, we conducted vascular imaging using the same cranial window approach and showed no leakage of macromolecules from dural or pial vessels anywhere within the imaging window at 2-6 weeks after the surgery (Figure S1D in Blaeser et al. 2022). This data suggested no ongoing meningeal inflammation below the window. The very low level of ongoing activity we observed at baseline also suggests a lack of an inflammatory response that could lead to afferent sensitization before CSD. This is now mentioned in the Discussion.

We conducted volumetric imaging for three main reasons: 1) To capture the activity of afferents throughout the meningeal volume. In our volumetric imaging approach, including in this work, we observed afferent calcium signals throughout the meningeal thickness (see Figure 5 in Blaeser et al. 2022). However, the majority of afferents were localized to the most superficial 20 microns (Figure S1E in Blaeser et al. 2022), suggesting that we mostly recorded the activity of dural afferents; 2) to enable simultaneous quantification of three-dimensional deformation and the activity of afferents throughout the thickness of the meninges. This allowed us to determine whether changes in mechanosensitivity could involve augmented activity to intracranial mechanical forces that produced meningeal deformation along the Z-axis of the meninges (e.g., increased intracranial pressure); 3) to provide a direct means to confirm that the afferent GCaMP fluorescent changes we observed were not due to artifacts related to meningeal motion along the Z-axis. We have now added this information to the “Two-photon imaging” section of the Methods.

2) Findings here, from mice with chronic closed cranial windows, failed to fully replicate prior findings from rats with acute open cranial windows. While the species, differing levels of inflammation and intracranial pressure in these two preparations may contribute, as the authors suggested, the modality of measuring neuronal activity could also contribute to the discrepancy. In the present study, conclusions are based entirely on fluorescence signals from GCamp6s, whereas prior rat studies relied upon multiunit recordings/local field potentials from tungsten electrodes inserted in the trigeminal ganglion.

As a family, GCamp6 fluorophores are strongly pH dependent, with decreased signal at acidic pH values (at matched Ca2+ concentration). CSD induces an impressive acidosis transient, at least in the brain parenchyma, so one wonders whether the suppression of activity reported in the wake of CSD (Figure 2) in fact reflects decreased sensitivity of the GCamp6 reporter, rather than decreased activity in the fibers. If intracellular pH in trigeminal afferent fibers acidifies in the wake of CSD, GCamp6s fluorescence may underestimate the actual neuronal activity.

Previous in vivo rodent studies observed a tissue acidosis transient that peaks during the DC shift corresponding to the wavefront of the spreading depolarization, and lasting for ~ 10 min. (Mutch and Hansen, 1984). Since we observed a massive increase in afferent calcium activity with a propagation pattern resembling the cortical wave, it is unlikely that the cortical acidosis during the CSD wave strongly affected the GCaMP signal in the overlying meninges. Furthermore, if cortical acidosis non-discriminately affects the GCaMP signal, one would expect a more consistent effect across axons, reflected as a reduced calcium signal in a larger proportion of the afferents, which we did not observe. Finally, the finding that in affected afferents, decreased calcium activity lasted for > 20 min – a time point when cortical acidosis has fully recovered - points to a distinct underlying mechanism. We also note that any residual acidosis would not confound our main finding of increased calcium responses to meningeal deformation at later periods post-CSD, as acidosis should, if anything, decrease calcium-related fluorescence.

The authors might consider injecting an AAV encoding a pHi sensor to the trigeminal ganglion, and evaluating pHi during and after CSD, to assess how much this might be an issue for the interpretation of GCamp6s signals. Alternatively, experiments assessing trigeminal fiber (or nerve/ganglion) activity by electrophysiology or some other orthologous method would strengthen the conclusions.

Please see our comment above regarding the short duration of the pH changes post-CSD.

N's are generally reported as # of afferents, obscuring the number of technical/biological replicates (# of imaging sessions, # of locomotion bouts, # of CSDs induced, # of animals).

We now report the number of replicates (# of afferent, # of CSD events, and # of mice).

Fig 1F trace over the heatmap is not explained in the figure legend. Is this the speed of the running wheel? Is it the apparent propagation rate of the GCamp6s transient through the imaging field?

We have added to the legend of Figure 1 that the trace in panel F depicts locomotion speed.

Author Response

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

eLife assessment

This valuable paper examines gene expression differences between male and female individuals over the course of flower development in the dioecious angiosperm Trichosantes pilosa. Male-biased genes evolve faster than female-biased and unbiased genes, which is frequently observed in animals, but this is the first report of such a pattern in plants. In spite of the limited sample size, the evidence is mostly solid and the methods appropriate for a non-model organism. The resources produced will be used by researchers working in the Cucurbitaceae, and the results obtained advance our understanding of the mechanisms of plant sexual reproduction and its evolutionary implications: as such they will broadly appeal to evolutionary biologists and plant biologists.

Public Reviews:

Reviewer #1 (Public Review):

The evolution of dioecy in angiosperms has significant implications for plant reproductive efficiency, adaptation, evolutionary potential, and resilience to environmental changes. Dioecy allows for the specialization and division of labor between male and female plants, where each sex can focus on specific aspects of reproduction and allocate resources accordingly. This division of labor creates an opportunity for sexual selection to act and can drive the evolution of sexual dimorphism.

In the present study, the authors investigate sex-biased gene expression patterns in juvenile and mature dioecious flowers to gain insights into the molecular basis of sexual dimorphism. They find that a large proportion of the plant transcriptome is differentially regulated between males and females with the number of sex-biased genes in floral buds being approximately 15 times higher than in mature flowers. The functional analysis of sex-biased genes reveals that chemical defense pathways against herbivores are up-regulated in the female buds along with genes involved in the acquisition of resources such as carbon for fruit and seed production, whereas male buds are enriched in genes related to signaling, inflorescence development and senescence of male flowers. Furthermore, the authors implement sophisticated maximum likelihood methods to understand the forces driving the evolution of sex-biased genes. They highlight the influence of positive and relaxed purifying selection on the evolution of male-biased genes, which show significantly higher rates of non-synonymous to synonymous substitutions than female or unbiased genes. This is the first report (to my knowledge) highlighting the occurrence of this pattern in plants. Overall, this study provides important insights into the genetic basis of sexual dimorphism and the evolution of reproductive genes in Cucurbitaceae.

Reviewer #2 (Public Review):

Summary:

This study uses transcriptome sequence from a dioecious plant to compare evolutionary rates between genes with male- and female-biased expression and distinguish between relaxed selection and positive selection as causes for more rapid evolution. These questions have been explored in animals and algae, but few studies have investigated this in dioecious angiosperms, and none have so far identified faster rates of evolution in male-biased genes (though see Hough et al. 2014 https://doi.org/10.1073/pnas.1319227111).

Strengths:

The methods are appropriate to the questions asked. Both the sample size and the depth of sequencing are sufficient, and the methods used to estimate evolutionary rates and the strength of selection are appropriate. The data presented are consistent with faster evolution of genes with male-biased expression, due to both positive and relaxed selection.

This is a useful contribution to understanding the effect of sex-biased expression in genetic evolution in plants. It demonstrates the range of variation in evolutionary rates and selective mechanisms, and provides further context to connect these patterns to potential explanatory factors in plant diversity such as the age of sex chromosomes and the developmental trajectories of male and female flowers.

Weaknesses:

The presence of sex chromosomes is a potential confounding factor, since there are different evolutionary expectations for X-linked, Y-linked, and autosomal genes. Attempting to distinguish transcripts on the sex chromosomes from autosomal transcripts could provide additional insight into the relative contributions of positive and relaxed selection.

Reviewer #3 (Public Review):

The potential for sexual selection and the extent of sexual dimorphism in gene expression have been studied in great detail in animals, but hardly examined in plants so far. In this context, the study by Zhao, Zhou et al. al represents a welcome addition to the literature.

Relative to the previous studies in Angiosperms, the dataset is interesting in that it focuses on reproductive rather than somatic tissues (which makes sense to investigate sexual selection), and includes more than a single developmental stage (buds + mature flowers).

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

I have reviewed this new version and find that it now addresses some of the shortcomings of the previous manuscript. However, several important limitations still remain:

1) The conclusion that sex-linked genes contribute relatively little to the patterns described is important and would be worth including in the manuscript briefly (not just the response letter), focusing for instance on the overall comparable proportions of sex-linked genes among male-biased (3/343=0.087%), female-biased (19/1145=1.66%) and unbiased genes (36/2378=1.51%).

Authors’ response: Thank you for your advice. We have added these sentences in “Discussion” section (Lines 492-499).

2) The new sentence included in the results "we also found that most of them were members of different gene families generated by gene duplication" is too vague. The motivation of this analysis is not explained, leaving the intended message unclear.

Authors’ response: In the previous revision, as stressed by reviewer #1 “(2) Paragraph (407-416) describes the analysis of duplicated genes under relaxed selection but there is no mention of this in the results”, we added the sentence “we also found that most of them were members of different gene families generated by gene duplication” in “Relaxed selection” paragraph of the results. Accordingly, in “Discussion” section, we discussed the associations between gene duplication and relaxed selection (Lines 461-473).

Following your suggestion, we revised the results (Lines 304-307) to “Using the RELAX model, we detected that 18 out of 343 OGs (5.23%) showed significant evidence of relaxed selection (K = 0.0184–0.6497) (Tables S9). Most of the 18 OGs are members of different gene families generated by gene duplication (Table S13)”. This makes it more coherent with the discussion.

3) The sentences "given that dN/dS values of sex-biased genes were higher due to codon usage bias..." are very confusing. I do not understand the argument being made here. I do not see why "lower dS rates would be expected in sex-biased genes ..."

Authors’ response: We respectfully argue that codon usage bias was positively related to synonymous substitution rates. That is, stronger codon usage bias may be related to higher synonymous substitution rates (Parvathy et al., 2022). Lower ENC values represent stronger codon usage bias. So, if ω (dN/dS) values of sex-biased genes are higher due to codon usage bias, we expect lower dS rates (That is, higher ENC values). Please refer to the relevant papers (e. g. Darolti et al., 2018; Catalan et al., 2018; Schrader et al., 2021, cited in the references of the paper).

4) The manuscript now reports the proportion of unitigs annotated by similarity with a number of species. While this is an interesting observation, the reviewer was actually asking for a comparison between the number of unitigs (59,051) and the number of genes annotated in a typical cucurbitaceae genome. This would give an indication of the level of redundancy of the de novo assembled transcriptome.

Authors’ response: We admit that in the final assembly, transcripts may be overestimated. We respectfully suggest that it may be inappropriate to assess the redundancy of the de novo assembled transcriptome by comparing the transcriptome sequences with the genomic sequences. An appropriate approach is to compare transcriptome sequences and transcriptome sequences among different species. For example, Hu et al., 2020 (reference cited in the paper) obtained 145,975 non-redundant unigenes from flower buds of female and male plants in Trichosanthes kirilowii. Mohanty et al. (2017) obtained 71,823 non-redundant unigenes from flower buds of female and male plants in Coccinia grandis.

Reference:

Mohanty JN, Nayak S, Jha S, Joshi RK. 2017. Transcriptome profiling of the floral buds and discovery of genes related to sex-differentiation in the dioecious cucurbit Coccinia grandis (L.) Voigt. Gene. 626: 395-406.

5) From reading the text I could not understand the extent to which the permutation test actually agreed with the Wilcoxon rank sum test. The text says that the results were "almost consistent", which is too vague. This paragraph should be clarified.

Authors’ response: We performed permutation test for sex-biased genes in floral buds and flowers at anthesis. However, only in floral buds, the results of both tests (permutation test and Wilcoxon rank sum test) are significant. Taking your suggestions in consideration, we have revised them as “Additionally, we found that only in floral buds, there were significant differences in ω values in the results of ‘free-ratio’ model (female-biased versus male-biased genes, P = 0.04282 and male-biased versus unbiased genes, P = 0.01114) and ‘two-ratio’ model (female-biased versus male-biased genes, P = 0.01992 and male-biased versus unbiased genes, P = 0.02127, respectively) by permutation t test, which is consistent with the results of Wilcoxon rank sum test.(Lines 273-280)”.

6) The paragraph on the link between codon usage and dN/dS is very unclear and quite unnecessary. I would suggest to simply remove lines 312-323.

Authors’ response: We respectfully argue that codon usage bias is one of the most important factors for higher rates of sequence evolution. Please refer to Darolti et al. (2018), Catalan et al. (2018) and Schrader et al. (2021) (cited in the references of the paper). We retain these lines here.

7) The discussion contains many unnecessary repeats from the introduction and results section. I suggest shortening drastically at several places, including:

• remove lines 367-369

Authors’ response: Thank you for your suggestion. We revised these lines to “In this study, we compared the expression profiles of sex-biased genes between sexes and two tissue types, investigated whether sex-biased genes exhibited evidence of rapid evolutionary rates of protein sequences and identified the evolutionary forces responsible for the observed patterns in the dioecious Trichosanthes pilosa (Lines 369-373)”.

We removed the sentence “We compared the expression profiles of sex-biased genes between sexes and two tissue types and examined the signatures of rapid sequence evolution for sex-biased genes, as well as the contributions of potential evolutionary forces. (Lines 374-376)”.

• remove lines 395-410

Authors’ response: Here we mainly discussed the possible associations between sex-biased genes, adaptation and sexual dimorphic traits. We retain them here for clarity.

• remove lines 449-483, as they are almost entirely repetitions of elements already made clear in the results section.

Authors’ response: In these paragraphs, we discussed reasons that lead to relaxed purifying selection for sex-biased genes. They are coherent with the results section. We retain them to make it clearer.

Minor comments:

• line 146: remove "However"

Authors’ response: We have revised it.

• line 187: "female flower buds tend to masculinize": the meaning is obscure

Authors’ response: We revised them as “Using hierarchical clustering analysis, we evaluated different levels of gene expression across sexes and tissues (Fig. 2C). Gene expression for female floral buds clustered most distantly from expression in female flowers at anthesis. However, expression in male floral buds clustered with expression in female flowers at anthesis, suggesting that male floral buds maybe tend to feminization in the early stages of floral development.”.

• line 226: "we sequenced transcriptomes of T. pilosa": rather say "we used the transcriptomes described above for T. pilosa"

Authors’ response: We have revised it.

• line 279: the meaning of "branch-site model A and branch site model null" is still not made clear.

Authors’ response: We have revised it.

• line 324: change to: "we also analysed whether female-biased and unbiased genes underwent... "

Authors’ response: We have revised it.

Author Response

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

Reviewer #1 (Public Review):

The apicoplast, a non-photosynthetic vestigial chloroplast, is a key metabolic organelle for the synthesis of certain lipids in apicomplexan parasites. Although it is clear metabolite exchange between the parasite cytosol and the apicoplast must occur, very few transporters associated with the apicoplast have been identified. The current study combines data from previous studies with new data from biotin proximity labeling to identify new apicoplast resident proteins including two putative monocarboxylate transporters termed MCT1 and MCT2. The authors conduct a thorough molecular phylogenetic analysis of the newly identified apicoplast proteins and they provide compelling evidence that MCT1 and MCT2 are necessary for normal growth and plaque formation in vitro along with maintenance of the apicoplast itself. They also provide indirect evidence for a possible need for these transporters in isoprenoid biosynthesis and fatty acid biosynthesis within the apicoplast. Finally, mouse infection experiments suggest that MCT1 and MCT2 are required for normal virulence, with MCT2 completely lacking at the administered dose. Overall, this study is generally of high quality, includes extensive quantitative data, and significantly advances the field by identifying several novel apicoplast proteins together with establishing a critical role for two putative transporters in the parasite. The study, however, could be further strengthened by addressing the following aspects:

Response: We thank very much the reviewer for his/her positive evaluation of our work. To address the detailed function of the transporters, in the past three months, we have re-constructed plasmids (with codon-optimized DNA sequences of the genes) for expression of the transporters in a regular expression E. coli strain (BL21DE3) and in a pyruvate import knockout E. coli strain (a gift from Prof. Kirsten Jung), to examine the transport capability in vitro. And, we have also re-constructed a new plasmid containing a new leading peptide for targeting the pyruvate sensor PyronicSF to the apicoplast in the parasite, to probe the possible substrate pyruvate. However, we did not successfully observe expression of the transporters in the above E. coli strains, and we were unable to target the sensor to the correct localization (the apicoplast) in the parasite. As a result, all efforts have led the study to the current version of manuscript on the functional identification of transporters. We will keep working on this aspect, attempting to dissect out the exact transport function of the transporters in the future. In the current manuscript, we have discussed the limitations of our study in the last part of the manuscript.

Main comments

1) The conclusion that condition depletion of AMT1 and/or AMT2 affects apicoplast synthesis of IPP is only supported by indirect measurements (effects on host GFP uptake or trafficking, possibly due to effects on IPP dependent proteins such as rabs, and mitochondrial membrane potential, possibly due to effects on IPP dependent ubiquinone). This conclusion would be more strongly supported by directly measuring levels of IPP. If there are technical limitations that prevent direct measurement of IPP then the author should note such limitations and acknowledge in the discussion that the conclusion is based on indirect evidence.

Response: We thank the reviewer very much for the suggestions. We have tried to establish the measurement of IPP using a commercial company in recent months, yet we have not been successful in making the assay work. Considering the problem of indirect evidence, we have discussed this limitation in the discussion.

2) The conclusion that condition depletion of AMT1 and/or AMT2 affects apicoplast synthesis of fatty acids is also poorly supported by the data. The authors do not distinguish between the lower fatty acid levels being due to reduced synthesis of fatty acids, reduced salvage of host fatty acids, or both. Indeed, the authors provide evidence that parasite endocytosis of GFP is dependent on AMT1 and AMT2. Host GFP likely enters the parasite within a membrane bound vesicle derived from the PVM. The PVM is known to harbor host-derived lipids. Hence, it is possible that some of the decrease in fatty acid levels could be due to reduced lipid salvage from the host. Experiments should be conducted to measure the synthesis and salvage of fatty acids (e.g., by metabolic flux analysis), or the authors should acknowledge that both could be affected.

Response: We thank the reviewer very much for comments and suggestions. We partially agree with the comments that the depletion of transporters could affect lipids scavenged from the host cells, as endocytic vesicles are indeed derived from the parasite plasma membrane at the micropore and potentially from the host cell endo-membrane system, as demonstrated with the micropore endocytosis in our previous study (pmid: 36813769). Our latest study has addressed this by showing that the endocytic trafficking of GFP vesicles is regulated by prenylation of proteins (e.g. Rab1B and YKT6.1), depletion of which resulted in diffusion of GFP vesicles, but not disappearance of GFP vesicles in the parasites (pmid: 37548452), indicating that the vesicles (containing lipids) enter the parasites. In the current manuscript, the percentage of parasites containing GFP foci was significantly reduced in AMT1/AMT2-depleted parasites, and instead, parasites containing GFP diffusion appeared and the percentage was almost equal to the reduced level of parasites with GFP foci. These results suggested that endocytic vesicles (e.g. GFP vesicles) were continuously generated by the micropore in the parasites depleted with AMT1/AMT2, and that the vesicle trafficking was regulated by proteins modified by IPP derivatives that were derived from the apicoplast. Based on these observations, we considered that lipids in endocytic vesicles should not contribute to the reduced level of fatty acids and other lipids in parasites depleted with AMT1/AMT2. We have added in a short discussion concerning the fatty acids and lipids reduced in the parasites.

Reviewer #2 (Public Review):

In this study Hui Dong et al. identified and characterized two transporters of the monocarboxylate family, which they called Apcimplexan monocarboxylate 1 and 2 (AMC1/2) that the authors suggest are involved in the trafficking of metabolites in the non-photosynthetic plastid (apicoplast) of Toxoplasma gondii (the parasitic agent of human toxoplasmosis) to maintain parasite survival. To do so they first identified novel apicoplast transporters by conducting proximity-dependent protein labeling (TurboID), using the sole known apicoplast transporter (TgAPT) as a bait. They chose two out of the three MFS transporters identified by their screen based and protein sequence similarity and confirmed apicoplast localisation. They generated inducible knock down parasite strains for both AMC1 and AMC2, and confirmed that both transporters are essential for parasite intracellular survival, replication, and for the proper activity of key apicoplast pathways requiring pyruvate as carbon sources (FASII and MEP/DOXP). Then they show that deletion of each protein induces a loss of the apicoplast, more marked for AMC2 and affects its morphology both at its four surrounding membranes level and accumulation of material in the apicoplast stroma. This study is very timely, as the apicoplast holds several important metabolic functions (FASII, IPP, LPA, Heme, Fe-S clusters...), which have been revealed and studied in depth but no further respective transporter have been identified thus far. hence, new studies that could reveal how the apicoplast can acquire and deliver all the key metabolites it deals with, will have strong impact for the parasitology community as well as for the plastid evolution communities. The current study is well initiated with appropriate approaches to identify two new putatively important apicoplast transporters, and showing how essential those are for parasite intracellular development and survival. However, in its current state, this is all the study provides at this point (i.e. essential apicoplast transporters disrupting apicoplast integrity, and indirectly its major functions, FASII and IPP, as any essential apicoplast protein disruption does). The study fails to deliver further message or function regarding AMC1 and 2, and thus validate their study. Currently, the manuscript just describes how AMC1/2 deletion impacts parasite survival without answering the key question about them: what do they transport? The authors yet have to perform key experiments that would reveal their metabolic function. I would thus recommend the authors work further and determine the function of AMC1 and 2.

Response: We thank very much the reviewer for his/her positive evaluation of our work. To address the detailed function of the transporters, in the past three months, we have re-constructed plasmids (with codon-optimized DNA sequences of the genes) for expression of the transporters in a regular expression E. coli strain (BL21DE3) and in a pyruvate import knockout E. coli strain (a gift from Prof. Kirsten Jung), to examine the transport capability in vitro. And, we have re-constructed a new plasmid containing a new leading peptide for targeting the pyruvate sensor PyronicSF to the apicoplast in the parasite, to probe the possible substrate pyruvate. However, we were unable to successfully observe expression of the transporters in the above E. coli strains, and we were unable to target the sensor to the correct localization (the apicoplast) in the parasite. As a result, all these efforts have led the study to the current version of manuscript on the functional identification of transporters. We will keep working on this aspect, attempting to dissect out the exact transport function of the transporters in the near future. In this current manuscript, we have discussed the limitations of our study in the last part of the manuscript.

Reviewer #1 (Recommendations For The Authors):

Minor comments

Line 35: ...appears to have evolved...

Line 67: remove first comma

Line 105: thereafter or therefore?

Line 130: define ACP

Line 131: define TMD

Response: We thank very much the reviewer for the suggestions, and we have revised the points in the current manuscript.

Figure 1: more information on APT1 would be helpful for readers to interpret the results from turboID e.g., consider showing an illustration showing, according to Karnataki et al 2007 that APT1 likely occupies all 4 membranes of the apicoplast. Also, according to DeRocher et al 2012, APT1 N-term and C-term are both cytosolically exposed, at least in the outermost membrane. The orientation in the other membranes is not known.

Response: We thank very much the reviewer for the suggestions. We analyzed the localization information of APT1 in T. gondii, based on the studies as the reviewer proposed (Karnataki, et al., 2007; DeRocher et al., 2012). The HA tag at the C-terminus of APT1 was distributed at the four membranes of the apicoplast, indicating that the topology of APT1 might be difficult to be defined at the membranes. Considering this information, we felt hesitant to clearly describe the topology in a schematic diagram about the protein APT1. Nevertheless, the TurboID tagging at the C-terminus of APT1 was an excellent model for identification of potential transporters localized at membranes of the apicoplast. We have put more information about the topology of APT1 in the manuscript, thus providing a better understanding of the proteomic results.

Figure 2: add a space between "T." and "gondii"

Figure 2: remove period between "Fitness" and "scores"

Figure 2: different fonts are used within the figure. Consider using only one font such as arial. Same for Figure 4.

Figure 2: "Fitness scores" is not bold in panel A but is bold in panel B.

Response: We thank very much the reviewer for the suggestions. We have revised the points in the current version of the manuscript.

Line 187: superscript -7

Line 249: Caution should be used in interpreting two bands as being a precursor and mature product without additional experiments to establish such a relationship. Consider using the term "might" rather than "appear to". The presence of multiple bands could be due to phenomena other than proteolytic processing e.g., alternative splicing, alternative initiator codons, etc.

Response: We thank very much the reviewer for the suggestions. We have revised the sentences in the current version of manuscript.

Line 291: define IPP

Figure 3E. The data points for KD strains appear to be positioned above the zero value on the y-axis. Is this correct?

Response: We thank very much the reviewer for the suggestions. We have rechecked the figure and replaced it with the correct one.

Figure 3 G/H legend. Please describe what a single data point represents e.g., the average of one field of view, the average of a certain number of fields of view, or something else? Are the data combined from three experiments or from a representative experiment?

Response: We thank very much the reviewer for the suggestions. Three independent experiments were performed with at least three replicates. At least 150 vacuoles were scored in each replicate, thus resulting in at least 9 data points in total. The data points were shown with the results from each replicate.

Line 325: define MEP and explain how it is connected to IPP

Response: We thank very much the reviewer for the suggestions. We have provided the information in the current version of the manuscript.

Lines 351-355: The authors refer to Figure 4D to support this statement, but presumably they mean 4E. Also, the authors use the terms C14, C16, and C18. They should more precisely use the terms myristic acid, palmitoleic acid, and trans_oleic acid if this is what they are referring to. Finally, the authors should determine if there is a statistically significant difference between levels of these fatty acids between AMT1 KD and AMT2 KD. If not, they should suggest there is an overall trend toward lower levels of these fatty acids in AMT2 KD parasites compared to AMT1 KD parasites.

Response: We thank very much the reviewer for the suggestions. We have revised the information in the current version of the manuscript.

Lines 363-364: The basis of this comment is unclear. Please clarify.

Lines 369-370: the authors have not shown that the observed lower levels of fatty acids are due to synthesis, as noted above

Response: We thank very much the reviewer for the suggestions. We have accordingly revised the information in the current version of the manuscript.

Line 383: Should be Figure S6D

Line 386: An entire section of the results is used to describe data that are entirely in a supplemental figure. Consider moving this data to a main figure.

Response: We thank very much the reviewer for the suggestions. We have transferred the data to the main figure in the current version of the manuscript.

Line 391: Consider using the term virulence instead of growth since now experiments were performed to specifically assess parasite growth in the infected mice.

Response: We thank very much the reviewer for the suggestions. We have revised the terms in the Results section.

Line 427: Perhaps the authors mean "...strong growth defect..." or ...strong growth impairment..."

Line 460-461: This statement is unclear. Please explain how strong backgrounds in proteomics have made it difficult to identify apicoplast transporters. Because they are low abundance? Because they are membrane proteins?

Response: We thank very much the reviewer for the suggestions. We have revised the corresponding sentences in the current version. The strong backgrounds in the proteomics resulted from the high activity and nonspecific labeling of biotin ligase fused with the apicoplast proteins.

518-521: It would be helpful for non-specialists if the authors explained how pyruvate is connected to IPP biosynthesis.

523: delete period after "Escherichia"

548-549: "We observed similar decreases in level of the MEP biosynthesis activity upon depletion of AMT1 and AMT2..." Reword this since no experiments were done to measure MEP biosynthesis activity.

Response: We thank very much the reviewer for the suggestions. We have accordingly revised the relevant sentences in the manuscript.

Reviewer #2 (Recommendations For The Authors):

Major points:

• The metabolomic data on fatty acid synthesis and isoprenoid levels is relevant but cannot inform about the function of the transporter, since any protein causing loss of the apicoplast would behave in such a manner, i.e. block the apicoplast pathways.

Response: We thank very much the reviewer for the comment. We agree with this comment. We have thus discussed these points in a subsection in the Discussion, pointing out some of the limitations in the study.

• Currently, the manuscript fails to directly prove what AMC1 and AMC2 transports, potentially pyruvate as suggested to putatively fuel FASII and MEP/DOXP. Further experimental approaches using exogenous complementation and/or metabolomic analyses using stable isotope labelling (for example) should potentially bring light to the putative functions of AMC1/2.

Response: We thank very much the reviewer for the comments. As described above, we attempted several approaches to find out the substrates that the AMT1 and AMT2 transports. However, we could not successfully express the proteins in E. coli strains, and we did not generate a T. gondii strain that a pyruvate sensor was properly targeted to the apicoplast. At the end of the Discussion, we have a subsection that discusses the limitations of this study. We hope that our future approaches will be able to tackle these difficulties on the substrate identification.

Furthermore, the authors have not considered other pathways of interest, like heme or lysophosphatidic acid (LPA)n synthesis, which are two other key pathway, which may be related to AMC1/2 function. Those proposed experiments represent an important body of work, required to bring light to their metabolic functions.

Response: We thank very much the reviewer for the comments. We thought about that, but we finally decided to mainly discuss two of the pathways that the transporters might participate in, since the transporters contain specific domains on the proteins sequences that potentially are associated with pyruvate.

Further, the authors might have partially missed some referencing and data about the apicoplast in their introduction (and potentially to address other facets of the apicoplast metabolic functions/capacities in regards to AMC1/2 function): the introduction referencing and explanations are somehow not fully exact/precise for the part of the apicoplast and its pathway: references about the apicoplast, discovery and origin are not citing the original work (that should be Wilson et al. 1996, McFadden et al. 1996, Kohler et al. 1997,), same for the discovery of FASII and MEP./DOXP (Waller 1998, Jomaa et al...). The introduction (and the study?) lacks information about other key functions of the apicoplast: heme synthesis, lysophosphatidic acid synthesis (using FASII products). The explanations about the roles of FASII/DOXP are partial and not fully citing important references: Krishnan et al. 2020, and Amiar et al. 2020 are also key to understanding how the role of FASII is metabolically flexible depending on nutrient content. A whole part on the fact that FASII is not only dispensible but can also become essential under metabolic adaptations conditions, are missing (Botté et al. 2013, Amiar et al. 2020, Primo et al. 2021). These novel important facets of parasite biology should be mentioned as well as directly linked to the author's topic. This is more minor but could bring new ideas to the authors.

Response: We thank very much the reviewer for the suggestions. We have revised the relevant part in the introduction.

We are grateful for the suggestions to improve the manuscript.

Author Response

Reviewer #1 (Public Review):

Summary:

The evolution of transporter specificity is currently unclear. Did solute carrier systems evolve independently in response to a cellular need to transport a specific metabolite in combination with a specific ion or counter metabolite, or did they evolve specificity from an ancestral protein that could transport and counter-transport most metabolites? The present study addresses this question by applying selective pressure to Saccharomyces cerevisiae and studying the mutational landscape of two well-characterised amino acid transporters. The data suggest that AA transporters likely evolved from an ancestral transporter and then specific sub-families evolved specificity depending on specific evolutionary pressure.

Strengths:

The work is based on sound logic and the experimental methodology is well thought through. The data appear accurate, and where ambiguity is observed (as in the case of citruline uptake by AGP1), in vitro transport assays are carried out to verify transport function.

Weaknesses:

Although the data and findings are well described, the study lacked additional contextual information that would support a clear take-home message.

We appreciate the reviewer’s positive assessment of the work, and the helpful comment to summarize the findings into a short take-home message. We chose not to discuss protein evolution theories in detail to keep the text as concise as possible. However, we do acknowledge the fact that the reader might want to see our results embedded in more context. In a revised version, we will integrate our findings more with the pertinent literature, which will show how our results align with theoretical models for protein evolution towards novel functions. We will also discuss in more detail how our laboratory results could be translated into a “natural” setting of evolution.

Reviewer #2 (Public Review):

Summary:

This paper describes evolution experiments performed on yeast amino acid transporters aiming at the enlargement of the substrate range of these proteins. Yeast cells lacking 10 endogenous amino acid transporters and thus being strongly impaired to feed on amino acids were again complemented with amino acid transporters from yeast and grown on media with amino acids as the sole nitrogen source.

In the first set of experiments, complementation was done with seven different yeast amino acid transporters, followed by measuring growth rates. Despite most of them have been described before in other experimental contexts, the authors could show that many of them have a broader substrate range than initially thought.

Moving to the evolution experiments, the authors used the OrthoRep system to perform random mutagenesis of the transporter gene while it is actively expressed in yeast. The evolution experiments were conducted such that the medium would allow for poor/slow growth of cells expressing the wt transporters, but much better/faster growth if the amino acid transporter would mutate to efficiently take up a poorly transported (as in the case of citrulline and AGP1) or non-transported (as in case of Asp/Glu and PUT4) amino acid.

This way and using Sanger sequencing of plasmids isolated from faster-growing clones, the authors identified a number of mutations that were repeatedly present in biological replicates. When these mutations were re-introduced into the transporter using site-directed mutagenesis, faster growth on the said amino acids was confirmed. Growth phenotype data were attempted to be confirmed by uptake experiments using radioactive amino acids; however, the radioactive uptake data and growth-dependent analyses do not fully match, hinting at the existence of further parameters than only amino acid uptake alone to impact the growth rates.

When mapped to Alphafold prediction models on the transporters, the mutations mapped to the substrate permeation site, which suggests that the changes allow for more favourable molecular interactions with the newly transported amino acids.

Finally, the authors compared the growth rates of the evolved transporter variants with those of the wt transporter and found that some variants exhibit a somewhat diminished capacity to transport its original range of amino acids, while other variants were as fit as the wt transporter in terms of uptake of its original range of amino acids.

Based on these findings, the authors conclude that transporters can evolve novel substrates through generalist intermediates, either by increasing a weak activity or by establishing a new one.

Strengths:

The study provides evidence in favour of an evolutionary model, wherein a transporter can "learn" to translocate novel substrates without "forgetting" what it used to transport before. This evolutionary concept has been proposed for enzymes before, and this study shows that it also can be applied to transporters. The concept behind the study is easy to understand, i.e. improving growth by uptake of more amino acids as nitrogen source. In addition, the study contains a large and extensive characterization of the transporter variants, including growth assays and radioactive uptake measurements.

Weaknesses:

The authors took a genetic gain-of-function approach based on random mutagenesis of the transporter. While this has worked out for two transporters/substrate combinations, I wonder how comprehensive and general the insights are. In such approaches, it is difficult to know which mutation space is finally covered/tested. And information that can be gained from loss-of-function analyses is missed. The entire conclusions are grounded on a handful of variants analyzed. Accordingly, the outcome is somewhat anecdotal; in some cases, the fitness of the variants was changed and in others not. Highlighting the amino acid changes in the context of the structural models is interesting, but does not fully explain why the variants exhibit changed substrate ranges. Two important technical elements have not been studied in detail by the authors, but may well play a certain role in the interpretation of the results. Firstly, the authors did not quantify the amount of transporter being present on the cell surface; altered surface expression can impact uptake rates and thus growth rates. Secondly, the authors have not assessed whether overexpressing wt versus variant transporters has an impact on the growth rate per se. Overexpressing transporters from plasmids is quite a burden for the cells and often impacts growth rates. Variants may be more or less of a burden, an effect that may (or may also not) go hand in hand with increased/decreased surface production levels.

And finally, I was somewhat missing an evolutionary analysis of these transporters to gain insights into whether the identified substitutions also occurred during natural evolution under real-life conditions.

First of all, we thank the reviewer for the attention to detail with which they have read the manuscript, and the very helpful comments on how to improve it. We will indeed take on some of the suggestions in a revised version of the text:

Regarding the match of growth rate and uptake rate measurements, we plan to plot their correlation in a graph.

Regarding the amount of transporter on the plasma membrane, we acknowledge that the visual representation of the fluorescence micrographs already in the text might not be enough. We therefore will quantify expression levels from said micrographs and include the information in the manuscript.

On a similar note, we had already measured the growth rates of all transporter variant cultures in the absence of selection for amino acid uptake (i.e., in medium with ammonium as the nitrogen source; Figure 4 - Supplement figure 1). We will include the measured growth rates in the text to give an indication of what the impact of transporter overexpression is on the growth rate per se.

Regarding the proposed analysis of natural transporter sequences, we do see the possible value in such an analysis. However, it is currently out of scope for the present study. The reasons are 1) that preliminary analyses show that the sequence similarity of functionally verified/annotated transporters is too low to reliably pinpoint a phenotype to a single residue, and 2) that we do not envision that the variants that we discovered are necessarily beneficial in a natural setting, where fine-grained regulation of amino acid transport may be more important than a broad substrate range. Regarding the generality of the insights, we do agree on the reviewer’s comment that we “only” analyzed a relatively small number of variants. However, the target of the study was not to generate high-throughput data on a large set of variants (e.g., by NGS of the whole culture) but to provide in-depth data for characterized and verified variants in a clean genetic background (i.e., verified phenotype and fitness measurements on all native and novel substrates).

As to the mutation space, we will include an estimate in a revised version of the text. We estimate that a majority of all possible single mutants is covered in the first and second passages of the selection experiment, which is corroborated by the fact that we repeatedly find the same mutants in biological replicates.

Regarding the mentioned loss-of-function analyses, we are unsure about what the reviewer intends with this statement at this point. To briefly summarize, we feel that our results are a good indication that transporters can evolve new functions analogously to enzymes. We explicitly do not imply that this is the only way to evolve novelty.

Reviewer #3 (Public Review):

The goal of the current manuscript is to investigate how changes in transporter substrate specificity emerge through experimental evolution. The authors investigate the APC family of amino acid transporters, a large family with many related transporters that together cover the spectrum of amino acid uptake in yeast.

The authors use a clever approach for their experimental evolutions. By deleting 10 amino acid uptake transporters in yeast, they develop a strain that relies on amino acid import by introducing APC transporters under nitrogen-limiting conditions. They can thus evolve transporters towards the transport of new substrates if no other nitrogen source is available. The main takeaway from the paper is that it is relatively easy for the spectrum of substrates in a particular transporter of this family to shift, as a number of single mutants are identified that modulate substrate specificity. In general, transporters evolved towards gain-of-function mutations (better or new activities) and also confer transport promiscuity, expanding the range of amino acids transported.

The data in the paper support the conclusions, in general, and the outcomes (evolution towards promiscuity) agree with the literature available for soluble enzymes. However, it is also a possibility that the design of these experiments selects for promiscuity among amino acids. The selections were designed such that yeast had access to amino acids that were already transported, with a greater abundance of the amino acid that was the target of selection. Under these conditions, it seems probable that the fittest variants will provide the yeast access to all amino acid substrates in the media, and unlikely that a specificity swap would occur, limiting the yeast to only the new amino acid.

The authors also examine the fitness costs of mutants, but only in the narrow context of growth on a single (original) amino acid under conditions of nitrogen limitation. Amino acid uptake is typically tightly controlled because some amino acids (or their carbon degradation products) are toxic in excess. This paper does not address or discuss whether there might be a fitness cost to promiscuous mutants in conditions where nitrogen is not limiting.

We are grateful for the reviewer’s insightful comments on the paper.

Regarding the design of our experiments, we followed the concept of directed evolution as described by pioneers of the field, in which the starting point for evolving a protein is to have a basic level of that activity. In the case of AGP1, the promiscuous activity is Cit uptake. We recognize that elimination of all the already transported amino acids from the evolution media could also yield very insightful results. However, we aimed to simulate the effect of the evolutionary pressure acting in a “natural” environment, where the uptake of the specific amino acid is not initially crucial for its survival. In the case of PUT4, the experimental design was chosen to ensure the initial survival of the culture (since neither Glu nor Asp support the growth of the strain) by providing a low level of already transported amino acids. In the revised manuscript, we will state this more clearly.

Regarding the second point, we agree that a short discussion about the potentially detrimental effects of promiscuous transporters would be beneficial for the reader. We will touch on this aspect in the revised version of the text. Indeed, our system is intentionally simplified, as we try to take regulation of transport out of the equation (e.g., by using the constitutive ADH1 promoter as opposed to a nitrogen-regulated one). In a natural setting, microorganisms encounter fluctuations of nutrient availability, necessitating tight control of nutrient transport. This is probably a major reason why microorganisms typically encode transporters with redundant specificities (i.e., promiscuous and specific ones). Otherwise, one very broad-range nutrient transporter would suffice. In our system, we artificially select for broad-range transport, which is reflected in the observed phenotypes of the evolved transporters. We expect that in a natural setting, a broad-range transporter would be a stepping stone to evolve a narrow-range transporter with a new specificity (which is actually what we see in the double-mutant AGP1-NV, with lowered fitness in original substrates and increased fitness in Cit).

Author Response

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

eLife assessment

This important study advances our understanding of the ways in which different types of communication signals differentially affect mouse behaviors and amygdala cholinergic/dopaminergic neuromodulation. Researchers interested in the complex interaction between prior experience, sex, behavior, hormonal status, and neuromodulation should benefit from this study. Nevertheless, the data analysis is incomplete at this stage, requiring additional analysis and description, justification, and - potentially - power to support the conclusions fully. With the analytical part strengthened, this paper will be of interest to neuroscientists and ethologists.

GENERAL COMMENTS ON REVIEWS AND REVISIONS

Experimental design

Here we address questions from several reviewers regarding our periods of neuromodulator and behavioral analysis. First, we recognize that the text would benefit from an overview of the experimental structure different from the narrative we provide in the first paragraphs of the Results. We now include this near the beginning for the Materials and Methods (page 17). We further articulate that the 10-minute time periods were dictated by the sampling duration required to perform accurate neurochemical analyses (and to reserve half of the sample in the event of a catastrophic failure of batch-processing samples). Since neurochemical release may display multiple temporal components (e.g., ACh: Aitta-aho et al., 2018) during playback stimulation, and since these could differ across neurochemicals of interest, we decided to collect, analyze, and report in two stimulus periods as well as one Pre-Stim control. We now clarify this in additional text in the Material and Methods (p. 24, lines 20-22; p. 26, lines 17-19). We decided not to include analyses of the post-stimulus period because this is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback.

We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13) to clarify these periods.

For behavioral analyses, observation periods were much shorter than 10 mins, but the main purpose of behavioral analyses in this report is to relate to the neurochemical data. As a result, we matched the temporal features of the behavioral and neurochemical analyses (p. 22, lines 17-22). We plan a separate report, focused exclusively on a broader set of behavioral responses to playback, that may examine behaviors at a more granular level.

Data and statistical analyses

Reviewers 1 and 3 expressed concerns about our normalization of neurochemical data, suggesting that it diminishes statistical power or is not transparent. We note that normalization is a very common form of data transformation that does not diminish statistical power. It is particularly useful for data forms in which the absolute value of the measurement across experiments may be uninformative. Normalization is routine in microdialysis studies, because data can be affected by probe placement and factors affecting neurochemical recovery and processing. Recent examples include:

Li, Chaoqun, Tianping Sun, Yimu Zhang, Yan Gao, Zhou Sun, Wei Li, Heping Cheng, Yu Gu, and Nashat Abumaria. "A neural circuit for regulating a behavioral switch in response to prolonged uncontrollability in mice." Neuron (2023).

Gálvez-Márquez, Donovan K., Mildred Salgado-Ménez, Perla Moreno-Castilla, Luis Rodríguez-Durán, Martha L. Escobar, Fatuel Tecuapetla, and Federico Bermudez-Rattoni. "Spatial contextual recognition memory updating is modulated by dopamine release in the dorsal hippocampus from the locus coeruleus." Proceedings of the National Academy of Sciences 119, no. 49 (2022): e2208254119.

Holly, Elizabeth N., Christopher O. Boyson, Sandra Montagud-Romero, Dirson J. Stein, Kyle L. Gobrogge, Joseph F. DeBold, and Klaus A. Miczek. "Episodic social stress-escalated cocaine self-administration: role of phasic and tonic corticotropin releasing factor in the anterior and posterior ventral tegmental area." Journal of Neuroscience 36, no. 14 (2016): 4093-4105.

Bagley, Elena E., Jennifer Hacker, Vladimir I. Chefer, Christophe Mallet, Gavan P. McNally, Billy CH Chieng, Julie Perroud, Toni S. Shippenberg, and MacDonald J. Christie. "Drug-induced GABA transporter currents enhance GABA release to induce opioid withdrawal behaviors." Nature neuroscience 14, no. 12 (2011): 1548-1554.

However, since all reviewers requested raw values of neurochemicals, we provide these in supplementary tables 1-3. The manuscript references these table early in the Results (p. 6, lines 18-19) and in the Material and Methods (p. 27, lines 3-4)

All reviewers commented on correlation analyses that we presented, with different perspectives. Reviewer 2 questioned the validity of such analyses, performed across experimental groups, while Reviewer 1 pointed out that the analyses were redundant with the GLM. We agree with these criticisms, and note the challenges associated with correlations involving behaviors for which there is a “floor” in the number of observations. As a result, we have removed most correlation analyses from the manuscript. The text and figures have been modified accordingly. Due these changes, we have to decline requests of Reviewer 3 to include many more such analyses. While correlation analyses could still be performed between neurochemicals and behaviors for each group, the relatively small size of each experimental group, the large number of groups, and the even larger numbers of pairings between neurochemicals and behavior, the statistical power is very low. The only correlations we utilize in the manuscript concern the interpretation of our increased acetylcholine levels.

As part of this revision, we re-ran our statistical analyses on neuromodulators because of a calculation error in 3 animals (regarding baseline values). In a few instances, a significance level changed, but none of these changed a conclusion regarding neuromodulator changes under our experimental conditions.

Other revisions

INTRODUCTION: We modified the Introduction to provide both a more general framework and specific gaps in our understanding relating neuromodulators with vocal communication.

DISCUSSION: We have added material in the first two pages of the Discussion to provide more framework to our conclusions, to address the issues of the temporal aspects of neurochemical release and behavioral observations, and to identify limitations that should be addressed in future studies.

FIGURES: All figures are now in the main part of the manuscript. We modified most figures in response to reviewer comments. We removed neuromodulator – behavior correlations from several figures. We modified all box plots to ensure that all data points are visible. The visible data points match the numbers reported in figure captions. We brought 5-HIAA data into the main figures reporting on neuromodulator results.

Public Reviews:

Reviewer #1 (Public Review):

The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain states and neurochemistry. In addition, the manuscript highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized. The authors are thoughtful about their quantitative approaches and interpretations of the data.

That being said, the authors need to work on justifying some of their analytical approaches (e.g., normalization of neurochemical data, dividing the experimental period into two periods (as opposed to just analyzing the entire experimental period as a whole)) and should provide a greater discussion of how their data also demonstrate dissociations between neurochemical release in the basolateral amygdala and behavior (e.g., neurochemical differences during both of the experimental periods but behavioral differences only during the first half of the experimental period). The normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and could be problematic; by normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power.

Please see our general responses to structure of observation periods and normalization of neuromodulator data. Normalization is a common and appropriate procedure in microdialysis studies that does not alter statistical power.

We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback (p. 12, lines 3-17). We note where the linkage is particularly strong (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

The Introduction could benefit from a priori predictions about the differential release of specific neuromodulators based on previous literature.

We added some material to the Introduction to provide additional rationale for the study. However, we did not attempt to develop predictions for the range of neuromodulators that we sought to test. The literature can lead to opposite predictions for a given neuromodulator. For example, acetylcholine could be associated with both positive and negative valence. Instead, we note in the Introduction the association of both DA and ACh with vocalizations.

The manuscript would also benefit from a description of space use and locomotion in response to different valence vocalizations.

We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report a few correlations based on these data in the Results to demonstrate that increased ACh in Restraint males and Mating estrus females was not related to the amount of locomotion (p. 9, lines 8-14).

Nevertheless, the current manuscript seems to provide some compelling support for how positive and negative valence vocalizations differentially affect behavior and the release of acetylcholine and dopamine in the basolateral amygdala. The research is relevant to broad fields of neuroscience and has implications for the neural circuits underlying social behavior.

Reviewer #2 (Public Review):

Ghasemahmad et al. report findings on the influence of salient vocalization playback, sex, and previous experience, on mice behaviors, and on cholinergic and dopaminergic neuromodulation within the basolateral amygdala (BLA). Specifically, the authors played back mice vocalizations recorded during two behaviors of opposite valence (mating and restraint) and measured the behaviors and release of acetylcholine (ACh), dopamine (DA), and serotonin in the BLA triggered in response to those sounds.

Strength: The authors identified that mating and restraint sounds have a differential impact on cholinergic and dopaminergic release. In male mice, these two distinct vocalizations exert an opposite effect on the release of ACh and DA. Mating sounds elicited a decrease of Ach release and an increase of DA release. Conversely, restraint sounds induced an increase in ACh release and a trend to decrease in DA. These neurotransmission changes were different in estrus females for whom the mating vocalization resulted in an increase of both DA and ACh release.

Weaknesses: The behavioral analysis and results remain elusive, and although addressing interesting questions, the study contains major flaws, and the interpretations are overstating the findings.

Although Reviewer 2 raises several valid issues that we have addressed in our response and revision, we believe that none represent “major flaws” in the study that challenge the validity of our central conclusions. In brief, we will:

--provide enhanced description of behaviors (pp. 22-23 and Table 1)

--clarify / modify box-plot representations of data (p 28. Lines 3-9)

--point to our methods that describe corrections for multiple comparisons (p. 27; lines 15-16)

--revise figures to clarify sample size (Figs. 3-6)

Reviewer #3 (Public Review):

Ghasemahmad et al. examined behavioral and neurochemical responses of male and female mice to vocalizations associated with mating and restraint. The authors made two significant and exciting discoveries. They revealed that the affective content of vocalizations modulated both behavioral responses and the release of acetylcholine (ACh) and dopamine (DA) but not serotonin (5-HIAA) in the basolateral amygdala (BLA) of male and female mice. Moreover, the results show sex-based differences in behavioral responses to vocalizations associated with mating. The authors conclude that behavior and neurochemical responses in male and female mice are experience-dependent and are altered by vocalizations associated with restraint and mating. The findings suggest that ACh and DA release may shape behavioral responses to context-dependent vocalizations. The study has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the BLA while an animal listens to social vocalizations; however, multiple concerns must be addressed to substantiate their conclusions.

Major concerns:

1) The authors normalized all neurochemical data to the background level obtained from a single pre-stimulus sample immediately preceding playback. The percentage change from the background level was calculated based on a formula, and the underlying concentrations were not reported. The authors should report the sample and background concentrations to make the results and analyses more transparent. The authors stated that NE and 5-HT had low recovery from the mouse brain and hence could not be tracked in the experiment. The authors could be more specific here by relating the concentrations to ACh, DA, and 5-HIAA included in the analyses.

Please see our general statement regarding normalization of neurochemical data. We have added supplemental tables that shows concentrations of dopamine, acetylcholine, 5-HIAA. We do not report serotonin or noradrenalin since these were below the detection threshold.

2) For the EXP group, the authors stated that each animal underwent 90-min sessions on two consecutive days that provided mating and restraint experiences. Did the authors record mating or copulation during these experiments? If yes, what was the frequency of copulation? What other behaviors were recorded during these experiences? Did the experiment encompass other courtship behaviors along with mating experiences? Was the female mouse in estrus during the experience sessions?

In the mating experience, mounting or attempted mounting was required for the animal to be included in subsequent testing. Since the session lasted 90 minutes, more general courtship behavior was likely. However, we did not record detailed behaviors or track estrous stage for the mating experience. See p. 21, line 20-22.

3) For the mating playback, the authors stated that the mating stimulus blocks contained five exemplars of vocal sequences emitted during mating interactions. The authors should clarify whether the vocal sequences were emitted while animals were mating/copulating or when the male and female mice were inside the test box. If the latter was the case, it might be better to call the playback "courtship playback" instead of "mating playback".

We have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

4) Since most differences that the authors reported in Figure 3 were observed in Stim 1 and not in Stim 2, it might be better to perform a temporal analysis - looking at behaviors and neurochemicals over time instead of dividing them into two 10-minute bins. The temporal analysis will provide a more accurate representation of changes in behavior and neurochemicals over time.

Please see our general response to the structuring of experimental periods. The 10-min periods are the minimum for the neurochemical analyses, and we adopted the same periods for behavioral analyses to match the two types of observations. Our repeated measures analysis is a form of temporal analysis, since it compares values in three observation periods.

5) In Figures 2 and 3, the authors show the correlation between Flinching behavior and ACh concentration. The authors should report correlations between concentrations of all neurochemicals (not just ACh) and all behaviors recorded (not just Flinching), even if they are insignificant. The analyses performed for the stim 1 data should also be performed on the stim 2 data. Reporting these findings would benefit the field.

Please see general comments regarding correlation analyses. We removed almost all such analyses and references to them from the manuscript based on concerns of the other reviewers.

6) The mice used in the study were between p90 - p180. The mice were old, and the range of ages was considerable. Are the findings correlated with age? The authors should also discuss how age might affect the experiment's results.

Our p90-p180 mice are not “old”. CBA/CaJ mice display normal hearing for at least 1 year (Ohlemiller, Dahl, and Gagnon, JARO 11: 605-623, 2010) and adult sexual and social behavior throughout our observation period. They are sexually mature adults, appropriate for this study. We decline to perform correlation analyses with age, both because this was not a question for this study and because the very large number of correlations, for each experimental group (as requested by reviewer #2), render this approach statistically problematic.

7) The authors reported neurochemical levels estimated as the animals listened to the sounds played back. What about the sustained effects of changes in neurochemicals? Are there any potential long-term effects of social vocalizations on behavior and neurochemical levels? The authors might consider discussing long-term effects.

We have not included discussion of long term effects of neuromodulatory release, both because our data analysis doesn’t address it (see response to Comment #10) and because we desired to keep the Discussion focused on topics more closely related to the results.

8) Histology from a single recording was shown in supplementary figure 1. It would benefit the readers if additional histology was shown for all the animals, not just the colored schematics summarizing the recording probe locations. Further explanation of the track location is also needed to help the readers. Make it clear for the readers which dextran-fluorescein labeling image is associated with which track in the schematic.

Based on the recent publications cited in our overall response to reviewer comments about statistical methods, our reporting of histological location of microdialysis exceeds the standard. We believe that the inclusion of all histology is unnecessary and not particularly helpful. Raw photomicrographs do not always illustrate boundaries, so interpretation is required. However, we added a second photomicrograph example and we identified which tracks correspond to these photomicrographs (see Figure 2; now in main body of manuscript).

9) The authors did not control for the sounds being played back with a speaker. This control may be necessary since the effects are more pronounced in Stim 1 than in Stim 2. Playing white noise rather than restraint or courtship vocalizations would be an excellent control. However, the authors could perform a permutation analysis and computationally break the relationship between what sound is playing and the neurochemical data. This control would allow the authors to show that the actual neurochemical levels are above or below chance.

We considered a potential “control” stimulus in our experimental design. We concluded, based on our previous work (e.g., Grimsley et al., 2013; Gadziola et al., 2016), that white noise is not or not necessarily a neutral stimulus and therefore the results would not clarify the responses to the two vocal stimuli. Instead, we opted to use experience as a type of control. This control shows very clearly that temporal patterns and across-group differences in neurochemical response to playback disappear in the absence of experience with the associated behavior.

10) The authors indicated that each animal's post-vocalization session was also recorded. No data in the manuscript related to the post-vocalization playback period was included. This omission was a missed opportunity to show that the neurochemical levels returned to baseline, and the results were not dependent on the normalization process described in major concern #1. The data should be included in the manuscript and analyzed. It would add further support for the model described in Figure 6.

We decided not to include analyses of the post-stimulus period because this period is subject to wider individual and neuromodulator-specific effects and because it weakens statistical power in addressing the core question—the change in neuromodulator release DURING vocal playback. We agree that the general question is of interest to the field, but we don’t think our study is best designed to answer that question.

11) The authors could use a predictive model, such as a binary classifier trained on the CSF sampling data, to predict the type of vocalizations played back. The predictive model could support the conclusions and provide additional support for the model in Figure 6.

We recognize that a binary classifier could provide an interesting approach to support conclusions. However, we do not believe that the sample size per group is sufficient to both create and test the classifier.

Reviewer #1 (Recommendations For The Authors):

Major comments:

• Introduction: It would be useful to set up an experimental framework before delving into the results. What are the predictions about specific neuromodulators based on previous literature?

Because this narrative is laid out in the first two paragraphs of the Results, which immediately follow the Introduction, we believe that additional text in the Introduction on the experimental framework is redundant. As stated above, detailing predictions for a range of neuromodulators would make for a long and not particularly illuminating Introduction. We instead have related our findings to more general understanding of DA and ACh in the Discussion.

• There really isn't a major difference in stimuli during the "Stim 1" and "Stim 2" phases, and it's not clear why the authors divided the experimental period into two phases. Therefore, the authors need to justify their experimental approach. For example, the authors could first anecdotally mention that behavioral responses to playbacks seem to be larger in the first half of the playbacks than during the second half, therefore they individually analyzed each half of the experimental period. Or adopt a different approach to justify their design. Overall, the analytical approach is reasonable but it is currently not justified.

See general comment for analysis periods. As noted, we clarified these issues in several locations with Materials and Methods (pp. 24, lines 20-22; p. 26, lines 17-19). We also sought to clarify the meaning of the periods “Stim 1” and “Stim 2”; they are two data collection periods, using the same examplar sequences in the same order. We have added statements in the Material and Methods (p. 18, lines 4-7; Fig. caption, p. 39, lines 11-13).

• The normalization of neurochemical data seems problematic and unnecessary. By normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) and this has implications for statistical power. Because the analysis is a within-subjects analysis, this normalization is not necessary for the analysis itself. It can be useful to normalize data for visualization purposes, but raw data should be analyzed. Indeed, behavioral data are qualitatively similar to the neurochemical data, and those data are not normalized to baseline values.

Please see our general comment on this issue. We believe normalization does not affect statistical power and is both the standard way and an appropriate way to analyze microdialysis results. We include concentrations of ACh, DA, and 5-HIAA in supplementary tables?

• The authors should include a discussion (in the Discussion section) of how behavior and neurochemical release are associated during the first half of the experimental session but not in the second half (e.g., differences in Ach and DA release between mating and restraint groups during stim 1 and 2, but behavioral differences only during stim 1).

We have included a section in the Discussion concerning the temporal relationship between behavioral responses and neurochemical changes in response to vocal playback. We note that the linkage is particularly strong in some cases (e.g., ACh release and flinching). This points to a need to examine these phenomena with finer temporal resolution, but also with the recognition that the brain circuits driving a behavioral response may extend beyond the BLA.

Minor comments:

• Keywords: add "serotonin" (even though there are no significant differences on 5-HIAA, people interested in serotonin would find this interesting).

Added to keywords list.

• Do the authors collect data on the vocalizations of mice in response to these playbacks?

We monitored vocalizations during playback, noting that vocalizations–especially “Noisy” vocalization–were common. However, we did not record vocalizations and are therefore unable quantify our observations.

• First line of page 7: readers do not know about "stim 1" and "stim 2". Therefore, the authors need to describe their approach to analyzing behavior and neurochemical release.

We first introduce these terms earlier, citing Figure 1D,E. We have added some additional wording for further clarification. page 7, lines 4-5.

• Make sure citations are uniformly formatted (e.g., Inconsistencies in: "As male and female mice emit different vocalizations during mating (Finton et al., 2017; J. M. S. Grimsley et al., 2013; Neunuebel et al., 2015; Sales (née Sewell), 1972)").

We have reviewed and corrected citations throughout the manuscript.

• Last paragraph of page 7: "attending behavior" has not been defined yet.

Table 1 contains our description of the behaviors analyzed in this study. We have now inserted a reference to Table 1 earlier in the Results (p. 6, line 12).

• Figure 2E and 3G: I find these correlations to be redundant with the GLMs. This is because the significant relationship is likely to be driven by group differences in behavior and in neurochemical release.

Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

• Page 2, 2nd paragraph, 2nd sentence: this paragraph seems to be rooted in comparing and contrasting experienced and inexperienced mice, so there should be explicit comparisons in each sentence. For example, the 2nd sentence should read: "Whereas EXP estrus females demonstrated increased flinching behaviors in response to mating vocalizations, INEXP ....". This paragraph overall could use some refining.

We believe this refers to page 9. We have revised the paragraph to clarify our findings (Beginning p. 9, line 23).

• Page 9: "Further, there were no significant differences across groups during Stim 1 or Stim 2 periods. These results contrast sharply with those from all EXP groups, in which both ACh and DA release changed significantly during playback (Figs. 2C, 2D, 3E, 3F)." While I understand their perspective, this is misleading because changes were only observed during the Stim 1 period.

We have slightly revised the wording in this paragraph, because the restraint males did not show significant ACh decreases. However, we do not believe our statements mislead readers just because some changes are observed in only one of the stimulation periods (p 10, lines 13-16).

• Last paragraph of page 14: it would be useful to mention the increase in flinching in experienced females in response to mating vocalizations.

We have added a sentence in this paragraph relating flinching in estrus females to increased ACh (p. 15, lines 18-20).

• Was there a full analysis of locomotion in response to playbacks? I see that locomotion was correlated with neurochemical release but was it different in response to different stimuli? Were there changes to the part of the arena that mice occupied in response to restraint vs. mating vocalizations? Given their methods section, it would be useful for the authors to mention the results of the analyses of these aspects of movement.

We have provided additional descriptions of space use and video tracking data in Material and Methods (p. 23, lines 1-6). We now report additional results associated with these analyses (p. 8, lines 13-15; p. 9, lines 8-14).

• I believe that each experimental mouse only heard one of the stimuli (given the analytical approach). Because it is plausible to measure neurochemical release in response to both types of stimuli, I encourage the authors to be more explicit about this aspect of the experimental design (e.g., mention in Results section).

Sentence modified to read: “Each mouse received playback of either the mating or restraint stimuli, but not both: same-day presentation of both stimuli would require excessively long playback sessions, the condition of the same probe would likely change on subsequent days, and quality of a second implanted probe on a subsequent day was uncertain.” (p. 7, lines 5-9).

• Figure 1A and 1B: add labels to the panels so readers don't have to read the legend to know what spectrogram is associated with what context.

We added these labels to Figure 1.

• Table 1: in the definition of "still and alert", should this mention "abrupt attending" instead of "abrupt freezing"? The latter isn't described.

Yes, we intended “abrupt attending”, and now indicated that in Table 1

Reviewer #2 (Recommendations For The Authors):

Major comments:

• The authors report they performed manual behavioral analysis, and provide a table defining the different behaviors. However, it remains unclear how some of these behaviors were detected (such as still-and-alert events). A thorough description of the criteria used to define these events needs to be provided.

We have modified some descriptions of manually analyzed behaviors in Table 1, and have added additional description of how we developed this set of behaviors for analysis in the study (pp. 22-23).

• The box plots do not appear to represent the "minimum, first quartile, median, third quartile, and maximum values." as specified on page 24 (Methods). Indeed, the individual data points sometimes do not reach the max or min of the bar plot, and sometimes are way beyond them.

We used the “inclusive median” function in Excel to generate final boxplots. These boxplots will sometimes result in a data point being placed outside of the whiskers. SPSS considers these to be “outliers”, but our GLM analysis includes these values. We describe this in Data Analysis section of Materials and Methods (p. 28, lines 3-9)

• Some of the data are replicated in different Figures: Figure 2A and Figure 3C. While this is acceptable, the authors did not correct for multiple comparisons (dividing the p value by the number of comparisons).

Our analysis included corrections for multiple comparisons, as we have indicated on p. 27, lines 15-16.

• Overall, the sample sizes are too small (for example in Figure 3, non-estrus females are at n=3), and are different in experiments where they should be equal (Figure 2B: mating stim 1 is at n=5 and mating stim 2 is at n=3).

We apologize that sample sizes were not properly displayed in figures. Please note that sample sizes are identified in the figure captions. For neuromodulator data, all sample sizes are at least 7. For behavioral data, the minimum sample size is 5. We have revised Figures 3-6 to ensure that all data points are visible.

• It remains unclear why the impact of mating vocalizations has been tested only in males.

We assume the reviewer meant that only males were tested in restraint. We now indicate that our preliminary evidence indicated no difference in behavioral responses to restraint vocalization between males and females, so we opted to perform the neurochemical analysis for restraint only in males (page 22 lines 4-5). If there were no limitations to time and cost, we would have preferred to test responses to restraint in females as well. We note that such inclusion would have added up to 4 experimental groups (estrus and non-estrus groups in both EXP and INEXP groups).

• The correlation between the number of flinching and ACh release changes (Figure 2E) visually appears to be opposite between mating and restraint playbacks. The authors should perform independent correlations for these 2 playbacks.

Please see general comments regarding correlation analyses. We removed such analyses and references to them from the manuscript.

• The authors state that their findings "indicate that behavioral responses to salient vocalizations result from interactions between sex of the listener or context of vocal stimuli with the previous behavioral experience associated with these vocalizations.". However, in male mice, they do not report any difference in previous experience on flinching for both restraint and mating sounds, as well as no difference in rearing for the restrain sounds (Figure 4A-B). Thus, the discussion of these results should be completely revisited.

We revised the paragraph in question (p. 9, line 22 through p. 10, line 9). For instance, we note that significant differences between EXP male-mating and male-restraint flinching do not exist between the INEXP groups. We believe that the last sentence correctly summarizes findings described in this paragraph.

• For serotonin experiments in Figure S2 there are strong outliers (150% increase in 5HIAA release). Did the authors correlate these levels with the behavior of the animals?

Outliers are identified by the Excel function that generated the boxplots, but we have no reason to consider these as outliers and exclude them. As noted above, we have clarified that these “outliers” are the result of the Excel function in the Materials and Methods (p. 28, lines 3-9) and we have revised the plotting of data points

Minor comments:

• Mating vocalization playback is mainly emitted by males, thus, instead of a positive valence signal, this could also be interpreted as a competitive signal to other males.

There is support in the literature for viewing our mating stimulus as having positive valence. Gaub et al., 2016 describe the emission of stepped calls, lower frequency harmonics, and increased sound level as indicators of “positive emotion”. We have shown (Grimsley et al, 2013) that the female LFH vocalization can be highly attractive to male mice, under the right conditions, indicating something like “sex is happening”. The inclusion of both the male and female vocalizations in our stimuli was a key piece of our experimental design, based on our understanding of the contributions of both vocalizations to the meaning of the overall acoustic experience.

• Figure 1 should include panel titles.

No change. This information is available in the Figure caption.

• n=31 should be indicated in the EXP group.

We’re not sure where the reviewer is referring to this value.

• The color legend of Figure 1E is absent, making the Figure not understandable.

We added text in the Figure 1 caption to indicate that each color represents a different exemplar. We don’t think a legend provides additional useful information.

• The point of making two blocks (stim 1 and stim2) should be stated more clearly.

Please see general statement regarding experimental blocks. We have modified our description of these in an Experimental overview section in the Material and Methods.

• Including raw data of micro-dialysis in the supplementary figures would allow assessment of the variability and quality of the measurements.

We have added concentrations of neurochemicals in supplemental tables 1-3.

• Baseline (prestimulus) number of flinch and rearing should systematically be indicated (missing in Figure 4).

The focus in this figure is on the differences that occur in Stim 1 values. There are no differences between EXP and INEXP animals of any group during the Pre-Stim period. We now state that in the Figure 4 caption.

• Discussion: "increase in AMPA/NMDA currents". We believe the authors are referring to the ratio of AMPA to NMDA currents. This sentence should be reformulated.

These are modified to refer to “… the AMPA/NMDA current ratio…” in two locations in the Discussion (p. 14, lines 8-9; p. 15, line 4)

• Overall the discussion is very speculative and should rely more on the data.

We believe that the Discussion provides appropriate speculation that is based on our experimental data and previous literature. We have added a paragraph to identify limitations of our findings and recommendations of future experiments to resolve some issues (p. 12, lines 3-17)

Reviewer #3 (Recommendations For The Authors):

Minor concerns:

1) The authors stated that USVs are most likely to be emitted by males, and LFH are likely to be emitted by females. However, Oliveira-Stahl et al. 2023, Matsumoto et al. 2022, Warren et al. 2018, Heckman et al. 2017, Neunuebel et al., 2015 showed that females also emit USVs. The authors should mention that USVs are emitted by both males and females and discuss how the sex of the vocalizing animal (both males and females) can influence neuromodulator release.

The reviewer slightly mis-stated the wording of our text, changing the meaning significantly. Our wording is “These sequences included ultrasonic vocalizations (USVs) with harmonics, steps, and complex structure, mostly emitted by males, and low frequency harmonic calls (LFHs) emitted by females (Fig. 1A,C)…” This phrasing is correct and carefully chosen. The Discussion in Oliveira-Stahl et al 2023 (p. 10-11) supports our statement: “The exact fraction of USVs emitted by females as concluded in all previous studies on dyadic courtship has varied, ranging from 18%, 17.5%, and 16% to 10.5% in the present study…”.

2) The authors should explain why ECF from BLA was collected unilaterally from the left hemisphere.

p. 23, lines 9-11: We inserted a sentence to explain why we targeted the BLA unilaterally. “Since both left and right amygdala are responsive to vocal stimuli in human and experimental animal studies (Wenstrup et al., 2020), we implanted microdialysis probes into the left amygdala to maintain consistency with other studies in our laboratory..” Beyond that, the choice was arbitrary.

3) The authors said each animal recovered in its home cage for four days before the playback experiment. A 4-day period may not be sufficient for every animal to recover from surgery, so the authors should describe how a mouse's recovery was assessed.

p. 23, lines 20-23: We provide more description about the recovery and how it was assessed. Except for a few animals that were not included in the experiments, all animals recovered within 4 days.

4) The authors stated that each animal was exposed to 90-min sessions with mating and restraint behaviors in a counterbalanced design. This description for Figure 1D should also include the duration of the mating and restraint experience.

The Results that immediately precede citation to this figure include this information.

5) The authors stated, "Data are reported only from mice with more than 75% of the microdialysis probe implanted within the BLA". What are the implications of having 25% of the probe outside the BLA? The authors should shed more light on this by discussing this issue as it relates to the findings and commenting on where the other 25% of the probe was located.

We inserted a sentence to explain the rationale for this inclusion criterion. “We verified placement of microdialysis probes to minimize variability that could arise because regions surrounding BLA receive neurochemical inputs from different sources (e.g., cholinergic inputs to putamen and central amygdala).” (p. 25, lines 21-23).

All brain regions that surround BLA, dorsal, medial, ventral, or lateral, could have been sampled by the “other” 25%. Some of these, e.g., the central amygdala or caudate-putamen, have different sources of cholinergic input that may not have the same release pattern. We do not think it is worthy of further speculation in the Discussion. Due to the high cost of the neurochemical analysis, we often did not process the neurochemistry data if histology indicated that a probe missed the BLA target.

6) The authors confirmed that the estrus stage did not change during the experiment day by evaluating and comparing estrus prior to and after data collection. This strategy was a fantastic experimental approach, but the authors should have discussed the results. How did the results the authors included change when the females were in estrus before but not after data collection? What percentage of females started in estrus but ended in metestrus? Assuming that some females changed estrus state, were these animals excluded from the analyses?

All animals were in the same estrus state at the beginning and end of the playback session.

7). Authors cite Neunuebel et al., 2015 for the sentence "As male and female mice emit different vocalizations during mating". However, Neunuebel et al., 2015 showed vocalizations emitted during chasing--not mating. If mating is a general term for courtship, then this reference is appropriate, but see major concern #3.

In the Results (p. 8, line 5), we changed the phrasing to “courtship and mating” to include the Neunubel et al study.

As we indicate in our response to Public Comment #3, we have modified the Results (p. 5, lines 18-20) and Materials and Methods (p. 21, lines 8-15) to clarify our meaning. We continue to use the term “mating” because this refers to a specific set of behaviors associated with mounting and copulation, rather than the more general term “courtship”. We also indicate that we based these behaviors on previous work (e.g., Gaub et al., 2016).

8) Authors interpret Figure 3F as DA release showed a "consistent" increase during mating playback across all three experimental groups. However, the increase in the estrus female group is inconsistent, as seen in the graph. This verbiage should be reworded to describe the data more accurately.

p. 8, line 23 “consistent” was deleted.

9) In all the box plots, multiple data points overlay each other. A more transparent way of showing the data would be adding some jitter to the x value to make each data point visible. The mean (X's) in Figure 3D (pre-stim mating and mating estrus) are difficult to see, as are all the data points in mating non-estrus. Adding all the symbols to the figure legend or a key in the figure instead of the method section would aid the reader and make the plots easier to interpret

We have revised the boxplots to ensure that all data points are visible.

10) Some verbiage used in the discussion should be toned down. For example, "intense" experiences and "emotionally charged" vocalizations should be removed.

We have not changed these terms, which we believe are appropriate to describe these experiences and vocalizations.

11) The authors include "Emotional Vocalizations" in the title. It would be beneficial if the authors included more detail and references in the introduction to help set up the emotional content of vocalizations. It may benefit a broader readership as typically targeted by eLife.

We now cite Darwin and some more recent publications that articulate the general understanding that social vocalizations carry emotional content.

Author Response

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

Reviewer #2 (Recommendations For The Authors):

While the details are mostly well-explained, I think that the authors could better bring forth the goals and potential usages of hippocampome.org overall.

I think that this is a great and helpful tool that can leverage various and detailed cellular experimental studies that are out there in the literature to garner potential insights, direct future experimental studies, observe/classify experimental 'differences' (e.g., the deep and superficial pyramidal studies they mention) and so on. Say that one gets some mechanistic insight from more abstract theoretical models, hippocampome can be used to determine whether the experimental data where available is supportive of the theory. They also describe CA3 model and grid cells. While I am not suggesting that the authors completely re-organize the manuscript, I did feel that the last section 'potential applications...' could have perhaps been brought forth earlier (in a summarized form) for the reader/user to better appreciate hippocampome - indeed it is line 288 that should be near the beginning of the paper I thought.

We thank the Reviewer for the suggestion. We have now included a summary of the simulation readiness of Hippocampome.org in the Introduction.

I thought the 'application' paragraph (starting line 288) needed expansion to appreciate - I did not have a chance to look at the cited papers in that section - but maybe 2 paragraphs, one on CA3 and the other on grid cells, with a few more sentences of goal/context and tool usage details could be provided?

We thank the Reviewer for the suggestion. We have added expanded paragraphs describing the simulation work on CA3 and grid cells.

The authors start their Discussion by mentioning other resources (e.g. blue brain) in comparison. I thought that this was not too helpful without a bit more expansion about these other resources and what in particular is comparable. For example, the blue brain project is different in that it does not mine the literature per se (I think)? But then I am not sure of the extent of the comparison that the authors intend with blue brain and the other mentioned resources.

Thank you for the helpful suggestion. We have now expanded upon the paragraph to draw more explicit parallels and contrasts among the various projects, in particular between the Blue Brain Project and Hippocampome.org.

Minor comments

• Fig 3D caption missing

Thank you for pointing this out. We have now amended the figure caption.

• Fig 5A line 211-12 refers to v2.0 but Fig 5 caption says v1.0?

We apologize for the confusion. We have now added text clarifying the V1.X relevant descriptions around Figure 5.

• Fig 6A confusing with thin and thick arrows and direction?

We apologize for the confusion. We have re-colored the thick arrows orange to emphasize the fact that they are feeding directly into the spiking neural simulations.

• Line 260 - not sure what this means - how is importance defined?

We apologize for the confusion. We have now added text clarifying that “importance” refers to the role the neuron type plays in the functioning circuitry of the hippocampal formation.

• CARLsim vs Brian/NEST in choosing - maybe a sentence or two for rationale

Thank you for the suggestion. We have now added a sentence explaining the selection of CARLsim. CARLsim was selected due to its ability to run on collections of GPUs. CARLsim was the only simulator with this capability at the time the simulation work was being planned, and the power of a GPU supercomputer was needed to simulate the millions of neurons that comprise a full simulation of the complete hippocampal formation.

• Fig 9 mv should be mV, and the voltage values specified there refer to which dash?

Thank you for pointing these situations out. We have amended the millivolts label and have made changes to the figure to help clarify which specific tick marks are being labeled.

Reviewer #3 (Recommendations For The Authors):

Compliments to the authors on this nicely organized and structured presentation of V 2.0 of hippocampome.org. The paper is well prepared giving a useful short summary of the history of hippocampome for the newcomers and refreshing the memory of users, switching to highlighting the new data additions, why these are relevant and how these complement the existing database, and opening up to new applications. The added potential is well illustrated and in addition, the authors provide numerical information on the usage of this amazing resource. I enjoyed roaming around in the new version, which was made available for reviewers, and although it has been a while since I worked with the system, the new version is easy to work with. I have not had the time to use it extensively so cannot comment in detail but based on the long experience of the authors and their support team, I trust that version 2 will be almost not completely flawless; however that will for sure become clear when it is released.

One could always wish for more, disagree, or even criticize choices made to cluster neurons, divide areas, and so forth, though in my view that does not contribute to what the resource has to offer. Having said this, the authors might consider addressing briefly issues about differences in the nomenclature used in original descriptions and how they handled the translation into their nomenclature. To mention one that is constantly being debated: how does one define the border between SMo and SMi.

Thank you for the suggestion. We have added text to the Introduction that addresses the nomenclature issue, as presented in Hamilton et al. (2017), and provide a definition for SMo and SMi.

Another confusing issue is presented by layers in the entorhinal cortex or its subdivisions (how many and how are these defined). So, some remarks for newcomers in the field who might use the database without spending too much energy to read the original data, might be useful.

Thank you for the suggestion to clarify this situation pertaining to the entorhinal cortex. Often, we have assumed the authors’ own definitions of the layers and subdivisions (medial and lateral), when naming neuron types. When our name is a hybrid of two published names that include both medial and lateral neurons, our name is prefixed by a simple EC, rather than by MEC or LEC.

As noted, the authors present version 2 nicely and comprehensibly and I have only a few additional comments, meant to further improve the already high quality of the paper.

1) The figures, nice as they are, are incredibly information-dense, so they require serious study to get the details; the legends do help, but the many abbreviations coming from totally different fields make it challenging to keep track of them while reading. This is a pity since there is a lot of new information in this version of the dataset, compared to previous versions and the authors overall succeed in emphasizing what is new and why this might be of use/importance.

So a few suggestions: i) add relevant/most important abbreviations to the legends of the individual figures; ii) introduce all abbreviations upon first use and do not simply refer to the table in the methods. Interestingly, even the authors lose track in the introduction where they use BICCN in line 43 and refer to the abbreviation list, though the full name is given two lines below.

We apologize for the confusion. We have amended the main text to clarify abbreviations. We have added the abbreviation definitions to the captions of the figures, and in some instances, removed the abbreviations from the figures altogether where space allowed.

2) Figure 3 and even more so figure 5 depend strongly on the color differences red/green; please change since generally red/green is no longer used for obvious reasons.

Thank you for pointing this out. We have switched the fonts in Figure 3 to black (excitatory) and gray (inhibitory) to match our previous publication. We have also changed the color schemes in Figure 5 to avoid red and green.

Reviewer #3 commented on the complexity of our figures and how the figures are information dense. To partially address this, we have decided to remove panel A2 of Figure 3. It was originally meant to emphasize where the information came from to add new axonal projections to two v1.0 neuron types; however, it is not necessary to make the point in the illustration. Thus, we have removed the panel and amended the caption for Figure 3A to include the cited reference.

Author Response

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

Reviewer #1 (Recommendations for The Authors):

1) While the specificity of the observed muscle phenotypes seems clear, the subsequent molecular analysis of Numb protein interactors does not seem to consider the potential involvement of Numb-like. The authors should demonstrate the relative expression levels of Numb and Numb-like in the models used, and establish the specificity of the antibodies used in IP, western and staining experiments.

Response: Perhaps the most convincing evidence that the anti-Numb antibody did not pull down Numb-like is that this protein was not detected among immunoprecipitated protein complexes pulled down by the anti-Numb antibody used. The antibody used in the immunoprecipitation was validated by the supplier and was previously reported to immunoprecipitate Numb [1, 2]. We previously demonstrated that a morpholino against Numb mRNA almost completely eliminated the band detected by this antibody and that this band was at the expected molecular weight [ref]. In our hands, mRNA levels for Numb-like in skeletal muscle are 5-10-fold lower than those for Numb [3]. We have been unable to detect Numb-like protein in healthy adult skeletal muscle by immunoblotting or immunofluorescence staining. Taking all of these findings together, it seems unlikely that the antibodies used for immunoprecipitating Numb-protein complexes pulls down Numb-like.

2) The authors use PCR to investigate Numb isoform expression and conclude that p65 is likely the dominant protein isoform expressed. While this agrees with the single band observed in Supp Figure 4A, a positive control for exon 9 excluded and included isoforms in the PCR reactions would strengthen this conclusion.

Response: The amplicons shown in Supplemental 4 were sequenced. The clones corresponded to the isoforms with the exon 3 present or removed. No amplicons containing exon 9 were detected. The following sentence was added to the Analysis of Splice Variants section of Methods to address this point: “PCR products were cloned using the TOPO TA cloning system (ThermoFisher) and multiple resulting clones were sequenced to confirm that the expected products were generated.”

3) PCR analysis of total Numb and Numb-like expression levels are not shown. This is important given the specificity of the Numb antibodies used for AP-MS experiments are not described and some Numb antibodies are well known to also recognize Numb-like. Two different Numb antibodies were used for Western and immunoprecipitation but the specificity for Numb and Numb-like is not described. In particular, does the antibody used in the AP-MS experiment recognize both Numb and Numb-like? Supplementary Table 1 does not list Numb or Numb-like, but presumably peptides were identified?

Response: As noted above, the specificity of anti-Numb antibodies was confirmed in previous studies [3]. Importantly, Numb-like mRNA levels are 5-10-fold lower than Numb mRNA, and NumbL protein is undetectable in healthy adult skeletal muscle by Western. The physiology data reported in this manuscript supports the conclusion that a single KO of Numb is sufficient to recapitulate the physiological phenotype of Numb/Numb-like KO . We therefore reason that the majority, if not all, of the physiological contribution of these proteins to muscle contractility due to Numb (Fig. 1).

4) The validation experiment used the same Numb antibody for immunoprecipitation, immunoblotted with Septin 7. A reciprocal IP of Septin 7 and blotted with Numb should be performed. In addition, a Numb-like IP or immunoblot would also be useful to demonstrate the specificity of the interaction. Efforts to map the interaction between Numb and Septin 7 would be useful to demonstrate specificity of the interaction and strategies to establish the biological relevance of the interaction.

Response: We agree with the reviewer and attempted several IPs with anti-Septin7 antibodies. These were unsuccessful. In a new collaboration, Dr. Italo Cavini (University of Sao Paulo) has used machine-learning-based approaches to model binding between Numb and several septins, including Septin 7. The analysis suggests that binding of Numb with septins involves a domain of Numb that has not yet been ascribed a function in protein-protein interactions. These computational predictions require experimental validation but provide rational starting point for experiments to define the domains responsible for these interactions. Such experiments were included in our recent NIH R01 renewal application. We hope to be able to report on results of confirmatory experiments of these computational models in the future.

5) Other septins were identified in the AP-MS experiment and might have been anticipated to also be disrupted by Numb/Numb-like deletion. Are these septins known to interact in a complex?

Response: This is an excellent question. Septins have conserved motifs providing a clear reason to imagine that many different mammalian septins could directly interact with Numb. Septins form heterooligomers consisting of complexes formed by 3, 6 or 8 septins [4]. It is likely that when Numb binds to one septin, antibodies against Numb pull down other septins present in the septin oligomer to which Numb is bound. The following paragraph was added to the discussion: “Our findings suggest that Numb may also interact with other septins such as septins 2, 9 and 10, which were also identified with a high level of confidence as Numb interacting proteins by our LC/MS/MS analysis. Our data to not allow us to determine if Numb binds directly to these septins. Septins contain highly conserved regions, and, consequently, if one such region of septin 7 interacts with Numb, then many septins would be expected to directly bind Numb through the same domain. However, because septins self-oligomerize, is possible that when Numb binds to one septin, antibodies against Numb could also pull down other septins present in the septin oligomer to which Numb is bound regardless of whether or not they are also bound by Numb. “

6) The text for Figure 5 describes analysis of Septin localization in inducible Numb/Numb-like cKO muscle, but the figure indicates only Numb is knocked out. Please clarify.

Response: We apologize for this oversight on our part. The Legend to Figure 5 has been corrected.

7) Supplementary Figure 2 seems to show that TAM treatment increases Numb expression. Please clarify. Also, please correct reference 9.

Response: The figure was incorrectly labeled. We apologize for this oversight and have corrected the figure in the revised manuscript.

Reviewer #2 (Recommendations for The Authors):

Overall, the manuscript is well written. I do have a few minor issues/concerns, which are detailed below.

Abstract: Please be a little more specific regarding which where the tissue came from (i.e. humans, mice, cell) when referring to your previous studies.

Response: The abstract has been revised as requested.

Introduction: Please be more specific regarding the technique used for detecting ultrastructural changes. I assume it was done with TEM, but the reference is listed as an "invalid citation" in your reference list.

Response: The introduction was revised as requested and the citation was updated to reference a valid citation.

Methods / Numb Co-Immunoprecipitation: Please indicated the level of confluency of the C2C12 cells as this will alter gene expression.

Response: As indicated in the updated Methods section, confluent C2C12 cells were switched to differentiation media (low serum) for seven days. When harvested, the cells had differentiated and fused into myotubes.

Methods / Immunohistochemical Staining: The first sentence needs to be edited regarding plurality and grammar.

Response: Thank you for this comment. The text was revised accordingly.

Results / GWAS and WGS Identify...: Please spell out phosphodiesterase (I assume) for PDE4D

Response: This change was incorporated in the text.

Author Response

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

eLife assessment

This important study reports jAspSnFR3, a biosensor that enables high spatiotemporal resolution of aspartate levels in living cells. To develop this sensor, the authors used a structurally guided amino acid substitution in a glutamate/aspartate periplasmic binding protein to switch its specificity towards aspartate. The in vitro and in cellulo functional characterization of the biosensor is convincing, but evidence of the sensor's effectiveness in detecting small perturbations of aspartate levels and information on its behavior in response to acute aspartate elevations in the cytosol are still lacking.

We thank the reviewers and editors for the detailed assessment of our work and for their constructive feedback. Most comments have now been experimentally addressed in the revised manuscript, which we feel is substantially improved from the initial draft.

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript, Davidsen and coworkers describe the development of a novel aspartate biosensor jAspSNFR3. This collaborative work supports and complements what was reported in a recent preprint by Hellweg et al., (bioRxiv; doi: 10.1101/2023.05.04.537313). In both studies, the newly engineered aspartate sensor was developed from the same glutamate biosensor previously developed by the authors of this manuscript. This coincidence is not casual but is the result of the need to find tools capable of measuring aspartate levels in vivo. Therefore, it is undoubtedly a relevant and timely work carried out by groups experienced in aspartate metabolism and in the generation of metabolite biosensors.

Reviewer #2 (Public Review):

In this work the IGluSnFR3 sensor, recently developed by Marvin et al (2023) is mutated position S72, which was previously reported to switch the specificity from Glu to Asp. They made 3 mutations at this position, selected a S72P mutant, then made a second mutation at S27 to generate an Asp-specific version of the sensor. This was then characterized thoroughly and used on some test experiments, where it was shown to detect and allow visualization of aspartate concentration changes over time. It is an incremental advance on the iGluSnFR3 study, where 2 predictable mutations are used to generate a sensor that works on a close analog of Glu, Asp. It is shown to have utility and will be useful in the field of Asp-mediated biological effects.

Reviewer #3 (Public Review):

In this manuscript, Davidsen and collaborators introduce jAspSnFR3, a new version of aspartate biosensor derived from iGluSnFR3, that allows monitoring in real-time aspartate levels in cultured cells. A selective amino acids substitution was applied in a key region of the template to switch its specificity from glutamate to aspartate. The jAspSnFR3 does not respond to other tested metabolites and performs well, is not toxic for cultured cells, and is not affected by temperature ensuring the possibility of using this tool in tissues physiologically more relevant. The high affinity for aspartate (KD=50 uM) allowed the authors to measure fluctuations of this amino acid in the physiological range. Different strategies were used to bring aspartate to the minimal level. Finally, the authors used jAspSnFR3 to estimate the intracellular aspartate concentration. One of the highlights of the manuscript was a treatment with asparagine during glutamine starvation. Although didn't corroborate the essentiality of asparagine in glutamine depletion, the measurement of aspartate during this supplementation is a glimpse of how useful this sensor can be.

Reviewer #1 (Recommendations For The Authors):

The authors should evaluate the effectiveness of the sensor in detecting small perturbations of aspartate levels and its behavior in response to acute aspartate elevations in the cytosol. In vivo aspartate determinations were performed exclusively in conditions that cause aspartate depletion. By means the use of mitochondrial respiratory inhibitors or aspartate withdrawal, it was determined the reliability of the sensor performing readings during relatively long periods, until reaching a steady-state of aspartate-depletion 12-60 hours later. Although in Hellweg and coworkers, it has been demonstrated that a related aspartate sensor could detect increases in aspartate in cell overexpressing the aspartate-glutamate GLAST transporter, the differences reported here between both sensors advise testing whether this aspect is also improved, or not, using jAspSNFR3.

Similarly, Davidsen et al. did not test if the sensor can be able to detect transient variations in cytosolic aspartate levels. In proliferative cells aspartate synthesis is linked to NAD+ regeneration by ETC (Sullivan et al., 2015, Cell), indeed the authors deplete aspartate using CI or CIII inhibitors but do not analyze if those are recovered, and increased, after its removal. Furthermore, the sequential addition of oligomycin and uncouplers could generate measurable fluctuations of aspartate in the cytosol.

We agree with the reviewer that only including situations of aspartate depletion in our cell culture experiments provided an incomplete evaluation of the utility of this biosensor. In the revised manuscript we provide three additional experiments using secondary treatments that restore aspartate synthesis to conditions that initially caused aspartate depletion. First, we conducted experiments where cells expressing jAspSnFR3/NucRFP were changed into media without glutamine, inducing aspartate depletion, with glutamine being replenished at various time points to observe if GFP/RFP measurements recover. As expected, glutamine withdrawal caused a decay in the GFP/RFP signal and we found that restoring glutamine caused a subsequent restoration of the GFP/RFP signal at all time points, with each fully recovering the GFP/RFP signal over time (Revised Manuscript Figure 2E). Next, we conducted the experiment suggested by the reviewer, testing whether the published finding, that oligomycin induced aspartate limitation can be remedied by co-treatment with electron transport chain uncouplers, could be visualized using jAspSnFR3 measurements of GFP/RFP. Indeed, after 24 hours of oligomycin induced aspartate depletion, treatment with the ETC uncoupler BAM15 dose dependently restored GFP/RFP signal (Revised Manuscript Figure 2G). Finally, we also measured whether the ability of pyruvate to mitigate the decrease in aspartate upon co-treated with rotenone (Figure 2B) could also be detected in a sequential treatment protocol after aspartate depletion. Indeed, after 24 hours of aspartate depletion by rotenone treatment, the GFP/RFP signal was rapidly restored by additional treatment with pyruvate (Revised Manuscript Figure 2, figure supplement 1C). Collectively, these results provide support for the utility of jAspSnFR3 to measure transient changes in aspartate levels in diverse metabolic situations, including conditions that restore aspartate to cells that had been experiencing aspartate depletion.

Reviewer #2 (Recommendations For The Authors):

Weaknesses: Sensor basically identical to iGluSnFR3, but nevertheless useful and specific. The results support the conclusions, and the paper is very straightforward. I think the work will be useful to people working on the effects of free aspartate in biology and given it is basically iGluSnFR3, which is widely used, should be very reproducible and reliable.

We appreciate the reviewer’s comment that sensor is useful for specific detection of aspartate. We agree that the advance of the paper is primarily in demonstrating its utility to measure aspartate, rather than any fundamental innovation on the biosensor approach. We hope the fact that jAspSnFR3 derives from a well validated biosensor (iGluSnFR3) will support its adoption.

Reviewer #3 (Recommendations For The Authors):

Although this is a well-performed study, I have some comments for the authors to address:

1) A red tag version of the sensor (jAspSnFR3-mRuby3) was generated for normalization purposes, with this the authors plan to correct GFP signal from expression and movement artifacts. I naturally interpret "movement artifacts" as those generated by variations in cell volume and focal plane during time-lapse experiments. However, it was mentioned that jAspSnFR3-mRuby3 included a histidine tag that may induce a non-specific effect (responses to the treatment with some amino acids). This suggests that a version without the tag needs to be generated and that an alternative design needs to be set for normalization purposes. A nuclear-localized RFP was expressed in a second attempt to incorporate RFP as a normalization signal. Here the cell lines that express both signals (sensor and RFP) were generated by independent lentiviral transductions (insertions). Unless the number of insertions for each construct is known, this approach will not ensure an equimolar expression of both proteins (sensor and RFP). In this scenario is not clear how the nuclear expression of RFP will help the correction by expression or monitor changes in cell volume. The authors may be interested in attempting a bicistronic system to express both the sensor and RFP.

The reviewer noted several potential issues concerning the use of RFP for normalization, which will be separated into sections below:

Movement artifacts:

We are glad the reviewer raised this issue since we see how it was confusingly worded. We have deleted the text “and movement artefacts” from the sentence.

His-tag and non-specific responses to some amino acids:

We also found it concerning that non-specific responses to amino acids could potentially contribute to our RFP normalization signal, and so we conducted additional experiments to address whether this was likely to be an issue in intracellular measurements. We first tested whether the non-specific signal was related to the histidine tag, or was intrinsic to the mRuby3 protein itself, by comparing the fluorescence response to a titration of histidine (which showed the largest effect of red fluorescence), aspartate, and GABA (structurally related to glutamate and aspartate, but lacking a carboxylate group) across a group of mRuby containing variants, with or without histidine tags. We replicated the non-specific signal originally observed in jAspSnFR3-mRuby3-His and found that another biosensor with a histidine tagged on the C terminus of mRuby3 had a similar response (iGlucoSnFR2.mRuby3-His), as did mRuby3-His alone, indicating that the aspect of being fused with jAspSnFR3 or another binding protein was not required for this effect. Additionally, we also compared the fluorescence response of lysates expressing mRuby2 and mRuby3 without histidine tags and found that the non-specific signal was essentially absent (Revised Manuscript Figure 1, figure supplement 4B-D). Collectively. These data support our original hypothesis that the histidine tag was responsible for the non-specific signal, alleviating concerns about more substantial protein design issues or with using nuc-RFP for normalization. Since we also found that measuring aspartate signal using GFP/RFP ratios from cells with linked the jAspSnFR3-Ruby3-His agreed with measurements from cells separately expressing jAspSnFR3 and nucRFP (without a His tag), and the amino acid concentrations needed to significantly alter His tagged Ruby3 signal are above those typically found in cells, we conclude that this is unlikely to be a significant factor in cells. Nonetheless, we have added all the relevant data to the manuscript to allow readers to make their own decision about which construct would be best for their purposes.

Original text:

"Surprisingly, the mRuby3 component responds to some amino acids at high millimolar concentrations, indicating a non-specific effect, potentially interactions with the C-terminal histidine tag (Figure 1—figure Supplement 2, panel B). Notably, this increase in fluorescence is still an order of magnitude lower than the green fluorescence response and it occurs at amino acid concentrations that are unlikely to be achieved in most cell types."

Revised text:

"Surprisingly, the mRuby3 fluorescence of affinity-purified jAspSnFR3.mRuby3 responds to some amino acids at high millimolar concentrations, indicating a non-specific effect (Figure 1—figure Supplement 4, panel A). This was determined to be due to an unexpected interaction with the C-terminal histidine tag and could be reproduced with other proteins containing mRuby3 and purified via the same C-terminal histidine tag (Figure 1—figure Supplement 4, panel B and C). Interestingly, a structurally related, non-amino acid compound, GABA, does not elicit a change in red fluorescence; indicating, that only amino acids are interacting with the histidine tag (Figure 1—figure Supplement 4, panel D). Nevertheless, most of our cell culture experiments were performed with nuclear localized mRuby2, which lacks a C-terminal histidine tag, and these measurements correlated with those using the histidine tagged jAspSnFR3-mRuby3 construct (Figure 1—figure Supplement 1 panel D)."

Lentiviral transductions

We agree that splitting the two fluorescent proteins across two expression constructs and infections effectively guarantees that there will not be equimolar expression of jAspSnFR3 and RFP, however we do not think equimolar expression is necessary in this context. The primary goal of RFP measurements in these experiments (and in experiments using the jAspSnFR3-mRuby3 fused construct) is to control for global alterations in protein expression that might confound the interpretation that a change in GFP fluorescence corresponds to a change in aspartate levels. While a bicistronic system is arguably a better approach to improve the similarity of expression of jAspSnFR3 and nuc-RFP in a cell, we only require that the cells have consistent expression of both proteins across all cells in the population, not that the expression of one necessarily be a similar molarity to the other. We accomplish consistent expression of proteins by single cell cloning after expression of jAspSnFR3 and nucRFP (or jAspSnFR3-mRuby3), and screening for clones that have high enough expression of both proteins such that they are well detected by standard Incucyte conditions. Given that our data do not identify an obvious downside to separate expression of jASPSnFR3 and nuc-RFP compared to the fused jAspSnFR3-mRuby3 construct (where the fluorescent proteins are truly equimolar) (Figure 2, Figure Supplement 1C), we elected to prioritize the separate jAspSnFR3 and nuc-RFP combination, which provides additional opportunities to measure cell number in the same experiment (see below).

2) The authors were interested in establishing the temporal dynamics of aspartate depletion by genetics and pharmaceutical means. For the inhibition of mitochondrial complex I rotenone and metformin were used. Although the assays are clearly showing aspartate depletion the report of cell viability is missing. Considering that glutamine deprivation induces arrest in cell proliferation, I think will be important to know the conditions of the cell cultures after 60 hours of treatment with such inhibitors.

We agree that ensuring that cells are still viable in conditions where aspartate is depleted, as determined by GFP/RFP in jAspSnFR3 expressing cells, is an important goal. To this end, we added a new experiment investigating the restoration of glutamine on the GFP/RFP signal at different time points after glutamine depletion (Revised Manuscript Figure 2E, see response to reviewer 1). One advantage of using the nuclear RFP as a normalization marker is that it also enables measurements of nuclei counts, a surrogate measurement for cell number. In the same glutamine depletion experiment we therefore measured cell counts using nuclear RFP incidences and confluency as measurements of cell proliferation/growth. In both cases, the arrest in cell proliferation upon glutamine withdrawal was obvious, as was the restoration of cell proliferation following glutamine replenishment, with the amount of growth delay corresponding to the length of glutamine withdrawal (Revised Manuscript Figure 2, Figure Supplement 2A-B). Nonetheless, there was no obvious lasting defects in restarting cell proliferation even after 12 hours of glutamine withdrawal, indicating that cell viability is preserved. In the case of mitochondrial inhibitors, we also observe even that after 24 hours of treatment with oligomycin or rotenone, restoration of aspartate synthesis from BAM15 or pyruvate, respectively, can also restore GFP/RFP signal, supporting the conclusion that cellular metabolism is still active in these conditions (Revised Manuscript Figure 2G; Revised Manuscript Figure 2, figure supplement 1C).

3) The pH sensitivity was checked in vitro with jAspSnFR3-mRuby3 and the sensor reported suitable for measurements at physiological pH. It would be an opportunity to revisit the analysis for pH sensitivity in cultured cells using an untagged version of jAspSnFR3 coupled, for example, to a sensor for pH.

We thank the reviewer for the suggestion and agree that pH effects on sensor signal could be a confounding factor in some conditions. Unfortunately, measuring intracellular pH is not trivial and using multiple fluorescent sensors that change simultaneously would be complex to interpret, particularly in the absence of controls to unambiguously control intracellular pH and aspartate concentrations. Thus, we believe that proper investigation of the variable of pH is beyond the scope of this study. Nonetheless, we agree that measuring the contribution of pH to sensor signal is an important goal for future work, particularly if deploying it in conditions likely to cause substantial pH differences, such as comparing compartmentalized signal of jAspSnFR3 in the cytosol and mitochondria. We have added the following italicized text to the conclusions section to underscore this point:

“Another potential use for this sensor would be to dissect compartmentalized metabolism, with mitochondria being a critical target, although incorporating the influence of pH on sensor fluorescence will be an important consideration in this context.”

4) While the authors take an interesting approach to measuring intracellular aspartate concentration, it will be highly desirable if a calibration protocol can be designed for this sensor. Clearly, glutamine depletion grants a minimal ("zero") aspartate concentration. However, having a more dynamic way for calibration will facilitate the introduction of this tool for metabolism studies. This may be achieved by incorporating a cultured cell that already expresses the transporter or by ectopic expression in the cells that have already been used.

We appreciate the suggestion and would similarly desire a calibration protocol to serve as a quantitative readout of aspartate levels from fluorescence signal, if possible. While we do calibrate jAspSnFR3 fluorescence in purified settings, conducting an analogous experiment intracellularly is currently difficult, if not impossible. While we have several methods to constrain the production rate of aspartate (glutamine withdrawal, mitochondrial inhibitors, and genetic knockouts of GOT1 and GOT2), we cannot prevent cells from decreasing aspartate consumption and so cannot get a true intracellular zero to aid in calibration. Additionally, the impermeability of aspartate to cell membranes makes it challenging to specifically control intracellular concentrations using environmental aspartate, and the best-known aspartate transporter (SLC1A3) is concentrative and so has the reciprocal problem. Considering these issues, we are wary of implying to readers that any specific fluorescence measurement can be used to directly interpret aspartate concentration given the many variables that can impact its signal, both related to the biosensor system itself (expression of jAspSnFR3, expression of Nuc-RFP, sensitivity and settings of the fluorescence detector) and based on cell intrinsic variability (differences in basal ASP levels, different sensitivity to treatments, influence of pH, etc.). We maintain that jAspSnFR3 has utility to measure relative changes in aspartate within a cell line across treatment conditions and over time, but absolute quantitation of aspartate still will require complementary approaches, like mass spectrometry, enzymatic assays, or NMR.

5) jAspSnFR3 seems to have the potential to be incorporated easily for several research groups as a main tool. In general, a minor correction to replace F/F with ΔF/F in the text.

Thank you for catching this error, the text has been edited accordingly.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors provide evidence to show that an increase in Kv7 channels in hilar mossy cells of Fmr1 knock out mice results in a marked decrease in their excitability. The reduction in excitatory drive onto local hilar interneurons produces an increased excitation/inhibition ratio in granule cells. Inhibiting Kv7 channels can help normalize the excitatory drive in this circuit, suggesting that they may represent a viable target for targeted therapeutics for fragile-x syndrome.

Strengths:

The work is supported by a compelling and thorough set of electrophysiological studies. The authors do an excellent job of analysing their data and present a very complete data set.

We thank the Reviewer for the positive comments.

Weaknesses:

There are no significant weaknesses in the experimental work, however the complexity of the data presentation and the lack of a schematic showing the organizational framework of this circuit make the data less accessible to non-experts in the field. I highly encourage a graphical abstract and network diagram to help individuals understand the implications of this work.

We thank the Reviewer for the suggestion, and added a schematic of the dentate network organization (Figure 1A).

The work is important as it identifies a unique regional and cell-specific abnormality in Fmr1 KO mice, showing how the loss of one gene can result in region-specific changes in brain circuits.

Reviewer #2 (Public Review):

Summary:

Deng et al. investigate, for the first time to my knowledge, the role that hippocampal dentate gyrus mossy cells play in Fragile X Syndrome. They provide strong evidence that, in slice preparations from Fmr1 knockout mice, mossy cells are hypoactive due to increased Kv7 function whereas granule cells are hyperactive compared to slices from wild-type mice. They provide indirect evidence that the weakness of mossy cell-interneuron connections contributes to granule cell hyperexcitability, despite converse adaptations to mossy cell inputs. The authors show that application of the Kv7 inhibitor XE991 is able to rescue granule cell hyperexcitability back to wild-type baseline, supporting the overall conclusion that inhibition of Kv7 in the dentate may be a potential therapeutic approach for Fragile X Syndrome. However, any claims regarding specific circuit-based intervention or analysis are limited by the exclusively pharmacological approach of the manipulations.

Strengths:

Thorough electrophysiological characterization of mossy cells in Fmr1 knockout mice, a novel finding.

Their electrophysiological approach is quite rigorous: patched different neuron types (GC, MC, INs) one at a time within the dentate gyrus in FMR1 KO and WT, with and without 'circuit blockade' by pharmacologically inhibiting neurotransmission. This allows the most detailed characterization possible of passive membrane/intrinsic cell differences in the dentate gyrus of Fmr1 knockout mice.

Provide several examples showing the use of Kv7 inhibitor XE991 is able to rescue excitability of granule cell circuit in Fmr1 knockout mice (AP firing in the intact circuit, postsynaptic current recordings, theta-gamma coupling stimulation).

We thank the Reviewer for the positive comments.

Weaknesses:

The implications for these findings and the applicability of the potential treatment for the disorder in a whole animal are limited due to the fact that all experiments were done in slices.

We appreciate the Reviewer’s point and agree. To address this concern, we have revised the Discussion to state that “the applicability of a circuit-wide approach as a potential treatment in vivo will require extensive future behavioral analyses, which are beyond the scope of the current study”. We also now emphasize in Discussion that “these findings provide a proof-of-principle demonstration that a circuit-based intervention can normalize dynamic E/I balance and restore dentate circuit output in vitro”.

The authors' interpretation of the word 'circuit-based' is problematic - there are no truly circuit-specific manipulations in this study due to the reliance on pharmacology for their manipulations. While the application of the Kv7 inhibitor may have a predominant effect on the circuit through changes to mossy cell excitability, this manipulation would affect many other cells within the dentate and adjacent brain regions that connect to the dentate that express Kv7 as well.

We appreciate the reviewer’s point but would like to clarify that by using a term “circuit-based” we did not intend to imply that it is a “’circuit-specific” intervention. Our intended interpretation of the term ‘circuit-based’ stems from the following reasoning: the dentate circuit has two types of excitatory neurons which show opposite excitability defects in FXS mice, thus presenting an irreconcilable conflict to correct pharmacologically for each cell type individually. Instead, we sought an approach to correct the overall dentate circuit output, rather than to restore excitability defects of individual cell types. Notably, when we pharmacologically isolated granule cells from the circuit, inhibition of Kv7 failed to restore their excitability, suggesting that normalization of the dentate output depends on the circuit activity. Since we focused on correcting dentate output using such a circuit-dependent approach, we used the term ‘circuit-based intervention’ to emphasize this notion.

Reviewer #3 (Public Review):

The paper by Deng, Kumar, Cavalli, Klyachko describes that, unlike in other cell types, loss of Fmr1 decreases the excitability of hippocampal mossy cells due to up-regulation of Kv7 currents. They also show evidence that while muting mossy cells appears to be a compensatory mechanism, it contributes to the higher activity of the dentate gyrus, because the removal of mossy cell output alleviates the inhibition of dentate principal cells. This may be important for the patho-mechanism in Fragile X syndrome caused by the loss of Fmr1.

These experiments were carefully designed, and the results are presented ‎in a very logical, insightful, and self-explanatory way. Therefore, this paper represents strong evidence for the claims of the authors. In the current state of the manuscript, there are only a few points that need additional explanation.

We thank the Reviewer for the positive comments.

One of the results, which is shown in the supplementary dataset, does not fit the main conclusions. Changes in the mEPSC frequency suggest that in addition to the proposed network effects, there are additional changes in the synaptic machinery or synapse number that are independent of the actual activity of the neurons. Since the differences of the mEPSC and sEPSC frequencies are similar and because only the latter can signal network effects, while the former is typically interpreted as a presynaptic change, it cannot be claimed that sEPSC frequency changes are due to the hypo-excitability of mossy cells.

We thank the Reviewer for this important point and agree. To address this concern, we now state in Results that “We note that changes in the excitatory drive onto interneurons include both mEPSC and sEPSC frequencies, which reflect not only potential deficits in excitability of their input cells, such as MCs, but also changes in synaptic connectivity/function, that may arise from homeostatic circuit reorganization/compensation (see Discussion)”.

We also now emphasize this point in Discussion by stating that “alterations in excitatory drives, including both mEPSC and sEPSC frequencies onto interneurons, suggest changes in the excitatory synapse number and/or function. Together with alterations in inhibitory drives these changes may reflect compensatory circuit reorganization of both excitatory and inhibitory connections, including mossy cell synapses”.

We also note in Discussion that “Such circuit reorganization can explain the balanced E/I drive onto granule cells in Fmr1 KO mice we observed in the basal state, which can result from reorganization of excitatory and inhibitory axonal terminals”.

Notably, our findings that Kv7 blocker acting by increasing MC excitability is sufficient to correct dentate output, supports the notion that hypo-excitability of mossy cells is a major factor contributing to dentate circuit E/I imbalance. This does not exclude the presence of additional mechanisms contributing to E/I imbalance, such as changes of synaptic connectivity or release machinery. To reflect this point, we revised the Results to temper the initial claim that “this analysis supports the notion that the hypo-excitability of MCs in Fmr1 KO mice caused (now replaced with “is a major factor contributing to”) the reduction of excitatory drive onto hilar interneurons, which ultimately results in reduced local inhibition”.

An apparent technical issue may imply a second weak point in the interpretation of the results. Because the IPSCs in the PP stimulation experiments (Fig 8) start within a few milliseconds, it is unlikely that its first ‎components originate from the PP-GC-MC-IN feedforward inhibitory circuit. The involvement of this circuit and MCs in the Kv7-dependent excitability changes is the main implication of the results of this paper. But this feedforward inhibition requires three consecutive synaptic steps and EPSP-AP couplings, each of them lasting for at least 1ms + 2-5ms. Therefore, the inhibition via the PP-GC-MC-IN circuit can be only seen from 10-20ms after PP stimulation. The earlier components of the cPSCs should originate from other circuit elements that are not related to the rest of the paper. Therefore, more isolated measurements on the cPSC recordings are needed ‎which consider only the later phase of the IPSCs. This can be either a measurement of the decay phase or a pharmacological manipulation that selectively enhances/inhibits a specific component of the proposed circuit.

We appreciate the Reviewer’s point. As we mentioned in Results: “The EPSP measured in granule cells in response to the PP stimulation integrates both excitatory and inhibitory synaptic inputs onto granule cells, including the direct synaptic input from the PP and all the PP stimulation-associated feedforward and feedback synaptic inputs. In other words, the EPSP in granule cells integrates all dentate circuit ‘operations’.” As the Reviewer pointed out, this is also the case in the measurements of cPSCs, which comprise all of PP stimulation-associated feedforward and feedback inhibition. We thank the Reviewer for the suggestion to isolate specific components of IPSC. However, we did not attempt to do it in this study for three reasons. First, activity of all of these circuit components likely overlaps extensively in time and it is difficult to identify the specific time point that can separate contributions from earlier canonical feed-forward and feed-back components from the contribution of the later MC-dependent PP-GC-MC-IN feed-forward component. Notably the tri-synapse PP-GC-MC-IN component differs temporarily from the canonical di-synaptic (PP-GC-IN) feed-back inhibition only by a single synaptic activation step, resulting in only a few milliseconds difference. Moreover, the temporal differences in the contributions of these components vary widely among different recordings making a uniform analysis very difficult. Second, we used three different metrics to assess E/I changes in cPSC measurements, which capture a wide range of temporal processes and their integration, including peak-to-peak measurements, the charge transfer, and the excitation window metrics. Third, the principal readout in our study was the overall dentate output (i.e., granule cell firing), which reflects the integration of all dentate circuit ‘operations’ thus making the overall cPSC measurements appropriate, in our view, for this readout.

I suggest refraining from the conclusions saying "‎MCs provide at least ~51% of the excitatory drive onto interneurons in WT and ~41% in KO mice", because too many factors (eg. IN cell types, slice condition, synaptic reliability) are not accounted for in these actual numbers, and these values are not necessary for the general observation of the paper.

We thank the reviewer for this suggestion, and have revised the manuscript accordingly.

There are additional minor issues about the presentation of the results.

We have carefully checked and corrected the minor errors that reviewer pointed out.

Recommendations for the authors:

Revisions that are considered essential for improved assessment regarding the strengths of support of the claims:

• Temper claims regarding circuit-based effects

• Temper claims regarding very specific quantitative assessments of synaptic drives

• Differentiate between monosynaptic inputs and inputs arriving through multiple synaptic contacts with proper analytical techniques.

We appreciate these suggestions and have revised the manuscript to address the concerns raised by the reviewers.

Reviewer #1 (Recommendations For The Authors):

The authors do an outstanding job of reviewing and presenting all of their data. This is a paper I will recommend all of my trainees read, as it is an excellent example of a complete research project. While I am impressed with the effort involved, I also wondered if the complexity and thoroughness of their presentations could make the story less accessible to non-expert readers. My comments are simply intended to help them present a more coherent and succinct story to a wider audience, though I am not sure I really provide any meaningful changes. This is simply a very thorough and complete body of work that the authors should be commended for. After reading it I felt they had gone above and beyond what most authors would provide in terms of data to support their story, and thus I had no doubt that a change in Kv7 plays a role in changing the excitability of the network.

We thank the Reviewer for the positive comments and great suggestions. We have made numerous changes to present our work in a more coherent and succinct way, in part by re-plotting some of the figures, as well as by adding a schematic of the dentate circuit in Figure 1.

Figure 1. A visual of mossy cells and the local circuit they are studying would be a useful addition to Figure. 1. I also feel this is important for conveying the story of how hypo-excitability can impact the E/I of the network. I think it has to be more of a cell structure/circuit-based figure than is presented in Supplementary Figure 8.

We thank the reviewer for this suggestion. We have added a schematic of the dentate circuit with all major cell types involved in Figure 1A.

Figure 1. A, B, and C tell a coherent story and are easy to understand. The interpretation of the phase plot in D is harder to access. Perhaps having this as a separate figure and providing a clearer presentation of the way the phaseplot was created (see Figure 3 Bove et al., 2019, Neuroscience 418; DOI: 10.1016/j.neuroscience.2019.08.048)

We appreciate the Reviewer’s point and agree. In order to keep Figure 1 more concise and readable, we removed the phase plot in the revised version. This change did not negatively impact the result presentation because the primary aim of this plot was to visualize changes in voltage threshold in an alternative way, but it was already clearly shown by the ramp-evoked AP traces (revised Figure 1D, insert), and thus was not essential to show.

Figure 1 E-N might be better situated in a supplementary graph as the characteristics of the AP aren't changing.

We understand the Reviewer’s point, but we feel it would be better to keep all action potential metrics together in one figure, to show that only a specific subset of parameters was affected in Fmr1 KO mice.

Figure 2: (A-D) I am not sure having so many figures is required given the focus is on having a small change in Ir at one membrane potential. I do worry that the significance appears to be due to 2 cells with an IR of over 100 in the WT group and 2 with an IR of around 62 in the KO group. All other cells are between 75-100 in both groups. I also worry a bit bc in the literature IRs between 55 and 125 seem to be commonly reported by groups that do this work normally (Buzsacki, Westbrook, etc.). I would be cautious about making too much out of this result.

We thank the Reviewer for these comments. We have performed additional analyses of these data, as also suggested by Reviewer 3 (Point #1), and improved presentation of the data in Figure 2D-F by showing the effect of XE991 on increasing input resistance in WT vs KO. We also plotted other panels in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991, which makes the results easy to follow. For more details, please also see the response to Reviewer 3, Point 1.

Figure 2D-E: As in the text, this result is really pointing towards there being a Kv7 issue. Worries about the data in D aside, I think these two figures alone tell a clearer story. Figure 3 on the other hand tells a story of the effects of blocking Kv7 on membrane potential. Is this central to the story the others are trying to tell?

We thank the reviewer for this point. We believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

Figure 3. This is an interesting finding that shows how detailed their analysis was. Showing that the change in holding current in KO animals is greater than in WT is the first solid piece of evidence that there is a change in Kv7 in these cells that affects their excitability.

We appreciate the reviewer’s comment. As mentioned above, we believe that Figure 2, Figure 3 and Figure 4—figure supplement 1 together provide strong and multifaceted evidence to support changes in Kv7 function in Fmr1 KO mossy cells.

Figures 4 and 5 provide additional detail to support the idea that Kv& changes by showing how the E/I ratio and spontaneous minis are shifted in KO animals.

We thank the Reviewer for the comments.

Figures 6-8 build a compelling story for the reduction in excitatory drive in mossy cells affecting the network dynamics in excitatory/inhibitory interactions in DG cells.

We appreciate the Reviewer’s comment.

Reviewer #2 (Recommendations For The Authors):

1) Other than location and characteristic morphology, the other parameters that were used to identify mossy cells and granule cells were also parameters used to find differences in cellular properties between wild-type and Fmr1 KO mice (RMP, sEPSC frequency, etc.), which would confound the results shown. The use of available transgenic mouse lines would provide for a more unbiased screen of these cells. Afterhyperpolarization was also used as a parameter while screening cells, yet none of the data on this measurement is shown.

We thank the reviewer for this point and agree that transgenic mouse lines provide a more unbiased way to identify various types of neurons. However, since the present study involves analyses of at least three different types of neurons, establishing multiple transgenic lines labeling different types of dentate neurons in the Fmr1 KO mouse model would be very time consuming and beyond the current resources of the lab. We would also like to clarify that the three types of dentate neurons are easily distinguished according to the large differences in location, morphology and basal electrophysiological properties, none of which were essential in defining differences between genotypes. Specifically, granule cells are located in the granule cell layer, have a small cell body (<10 m), RMP around -80mV, capacitance ~20 pF, and infrequent sEPSCs (<20 events/min); mossy cells are located in the hilus, have a large cell body (>15 m), RMP around -65 mV, capacitance >100 pF, and fast afterhyperpolarization less than -10 mV (WT –5.1 ± 0.7 mV, KO -5.8 ± 0.5 mV); interneurons are located in the hilus or border of granule cell layer, have a relative smaller cell body (10-15 m), RMP around -55 mV, capacitance <60 pF, and afterhyperpolarization larger than -15 mV (WT -20.4 ± 1.3 mV, KO -19.8 ±1.4 mV). We note that the cells that could not be definitively classified into the three categories were not included in analyses, and we have now clarified this further in the Methods. To address the reviewer’s second concern regarding AHP, we now provided the corresponding values in the Methods.

2) A definitive way to test the cell-autonomous nature of the Kv7 changes would be to use female mice, who will have a mosaic of cells affected by the fragile X chromosome, and the Fmr1 KO cells could be engineered to express GFP to help identify them from wild-type cells.

We agree and appreciate this suggestion. This could be an interesting follow up study to further verify the cell-autonomous nature of Kv7 changes.

3) The authors heavily rely on XE991 as a selective Kv7 blocker. Is it blocking all Kv7 channels at the concentration used? If so, given the significant expression of Kv7 in the dentate as shown by Western blot, is it surprising that there is no effect of this inhibitor on wild-type slices in most cases?

We thank the reviewer for this important point. We used 10x of IC50 concentration in the present study, suggesting that more than 80% of Kv7 should be blocked. Notably, we observed several effects of XE991 in WT mice: it significantly increased input resistance (new Figure 2D-F), and strongly enhanced AP firing evoked by step depolarization (Figure 7E-H), although we did not observe effect of XE991 in WT in the analyses of spiking evoked by theta-gamma stimulation in Figure 8. However, this is not surprising. If a parameter we measured is predominately cell-autonomous (for example, input resistance), the effects of XE991 are easy to observe. However, if a parameter reflects integration of all dentate circuit operations (for example, AP probability in response to theta-gamma stimulation), it is difficult to detect the effect of XE991 in WT mice because the dentate circuit of WT mice has larger capability to maintain E/I balance in response to XE991.

4) E/I ratio is a helpful concept, and it is heavily relied upon in the results text, but statistically shaky, especially for sEPSC:sIPSCs since you are combining uncertainty in the sEPSC and sIPSC to make one very uncertain ratio that doesn't undergo any subsequent statistical confirmation (such as in Fig 4I).

We appreciate the reviewer’s point and apologize for the confusion in presentation of Fig 4I (and 5I), due to lack of detailed explanation. The E/I ratio shown in Figs. 4I (and 5I) is a single data-point estimate calculated from the mean values of independent sEPSC and sIPSC measurements (Figs. 4G-H and 5G-H, respectively). This ratio was used only as an estimate/illustration of the changes, rather than a precise determination of the shift in E/I balance. Because there is only one data-point for this ratio, statistical analysis is not possible. For this reason we performed extensive additional analyses in Figures 7 and 8, in which the EPSC and IPSC were measured from the same cells and at the same time to define the actual E/I ratio with the corresponding statistical analyses (i.e., a real matched and dynamic E/I ratio).

5) Is this mGlur2/CB1 specificity to PP/granule and MC axons, respectively, true in the Fmr1 KO mice? It is possible that mGluR2 and CB1 expression patterns are altered in FMR1 KO, thus the assumption used to isolate these distinct inputs may not hold true.

This is a very good point. We do assume that the specificity of Group II mGluR and CB1 is similar between Fmr1 KO and WT mice, but this is an assumption that we have not directly verified. However, our results in Figures 7 and 8 strongly support this assumption, because if it were not true, then our intervention would be unlikely to correct the excessive dentate output.

6) XE991 only normalized GC firing when other cells were not pharmacologically blocked. The authors suggest this means blockage of MC Kv7 reduces GC excitability back to normal...presumably by increasing MC --> IN --> GC firing. This is a conclusion from many indirect comparisons (comparing XE991 effect on GC with/without GABA and glutamate blockers; comparing MC firing rates with/without XE991, and using CB1 agonist versus mGluR2 agonist to say it is mossy cells that are mostly controlling INs) - a clincher experiment would be to acutely knockdown Kv7 in mossy cells specifically and measure GC and IN firing.

Thank you, this is a great suggestion. Indeed, as an expansion of this project, in the future studies we are planning to manipulate excitability of mossy cells through manipulating Kv7, or using chemogenetic or optogenetic approaches.

7) The reasoning behind the FMRP-Kv7 connection is quite weak, citing the paper Darnell 2011 as "translational target", but FMRP has myriad translational targets.

We agree, and attempted to define the mechanism of increased Kv7 function using co-immunoprecipitation approach, as well as immunostaining to look at cell-type specific expression changes. However, both of these approaches were difficult to interpret due to technical limitations of the available antibodies. We also note that “We did not further investigate the precise mechanisms underlying enhancement of Kv7 function in the absence of FMRP, since the present study primarily focuses on the functional consequences of abnormal cellular and circuit excitability”. To address this concern, we extensively discussed the potential mechanisms of FMRP-Kv7 connection, acknowledged in Discussion that “further studies will be needed to elucidate the precise mechanism responsible for the increased Kv7 function in Fmr1 KO mice”, and will continue to investigate it in the future studies.

8) The authors attempt to look for changes in Kv7 expression with Western blot, but since they hypothesize that Kv7 changes are mainly in the mossy cells, it is perhaps not surprising that they would not be able to see any changes when they look at dentate as a whole. Staining for Kv7 subunits to look at expression on a cellular level would be beneficial.

We appreciate the reviewer’s suggestion. We attempted to perform the suggested experiments using immunostaining for KCNQ2, KCNQ3 and KCNQ5 in different subtypes of dentate neurons. However, these experiments failed to produce interpretable results due to technical limitations of the available antibodies.

9) Is Kv7 localization or splice/composition different in FMR1 KO mice?

This is a very good point. As we mentioned in Point 8 above, we were not able to perform these experiments and do not have the answer at this point.

10) Regarding the 3 subtypes of interneurons in the dentate, the authors are pooling data based on similar intrinsic properties, but this conclusion may be affected by the low number of recorded neurons for the regular-spiking type. In addition, it is unclear whether these different interneuron types have differential circuit connectivity (most likely) which would make it imperative to keep circuit analysis for interneurons segregated into these cell types.

We appreciate the reviewer’s point. Indeed, these different interneuron types may have distinct circuit connectivity and contributions to circuit activity. However, identification of these 3 types of interneurons and determination of their respective functions is in itself a very extensive set of experiments which is beyond the scope of the current manuscript. We also note that the functional readout of circuit activity in our measurements was the AP firing and EPSPs evoked in granule cells by PP stimulation, which integrate all dentate circuit operations, including all of the feedforward and feedback loops which are mediated by all of these different types of interneurons. For simplicity, we thus pooled all interneuron data for the purposes of this study. But we fully agree that extensive future work is required to elucidate interneuron-type specific changes in Fmr1 KO mice and their contributions to the dentate circuit dysfunction.

11) To do statistics treating each cell individually, and therefore assuming each cell is independent of one another, is not correct. Two cells from the same mouse will be more similar than two cells from different mice, therefore they are not independent data points. Nested statistical methods (n cells from o slices from p mice) will be important in future work, as discussed by (Aarts et al., Nat. Neurosci. 2014).

We agree with the Reviewer’s point and appreciate this suggestion. In the present study, the cells tested in electrophysiological experiments were from at least 3 different mice for each condition, which help minimize this kind of errors.

Reviewer #3 (Recommendations For The Authors):

Is there a difference in the Rin at -45mV of the control cell after the application of XE991? This is important to appreciate whether the XE991-sensitive conductances contribute to the basal excitability of MCs. Furthermore, the statistical comparison of the Rin at -45mV of the FXS animals in the control solution and in the presence of XE991 would be also important‎. Actually, the most accurate measurement would be to show a difference in the acute Kv7-blockade between control and FXS animals, if that is possible with this blocker. Additionally, it would be also informative if the bar graphs in Fig.2 D & E were merged for this purpose, similarly as in the later figures.

We thank the Reviewer for this suggestion and agree. Following this suggestion, we have re-plotted the data in Figure 2 accordingly. Specifically, we now show that XE991 significantly increased input resistance in both WT and KO mossy cells, and the effect of XE991 on increasing input resistance was markedly larger in KO than WT mossy cells. For other figures, we have plotted data in a similar way to show the comparisons between WT and KO, as well as comparisons within genotype +/- XE991.

Because of the cell-to-cell variability of the voltage responses, it would be more informative and representative if the average of traces from all cells were shown in Fig.2 D & E.

We agree with the Reviewer’s point. For clarity of presentation, we presented the cell-to-cell variability of the data as scatter points of input resistance values in the bar graph (Figure 2E), together with the representative traces (Figure 2D). Plotting the average traces from all cells would result in a total of 30 traces for all the WT and KO mice, which is difficult to visually assess clearly.

On page 7, please clarify the recorded cell type in this sentence: "In ‎contrast, WIN markedly reduced the number of sEPSCs in both WT and KO mice...".

We thank the Reviewer for pointing out this omission and have clarified it in the revised version.

In Figures 6 C, F, and I, the title of the Y-axis should be normalized frequency. Please also correct the figure legend accordingly because the current sentence can be also interpreted as the absolute or total number of events that were compared, irrespective of the duration of the recordings.

We thank the Reviewer for this point and have corrected the revised version accordingly.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

I highly appreciate this study and found the paper to be very well-written and easy to follow. However, a more extensive discussion of what I summarized under "weakness" would strengthen the paper. This may include a broader discussion of the canopy effect itself and the most relevant literature on its extent in rainforest settings in general and primate foods in particular, as well as more details on the dietary behavior of modern orangutans (stratigraphy of orangutan foods) and how seasonal their diet is. The extreme seasonality in orangutan plant food availability should be discussed. Now there are only 2 sentences in the discussion (lines 304-312) and I find the word "plant' only twice overall, though variation in plant food d18O is what drives variation in orangutan dental d18O values.

We very much appreciate the support of this reviewer, and their feedback about the clarity of the paper. As noted in the provisional reply to reviewers, we are happy to add additional context about the issue of isotopic enrichment within forest canopies, and have expanded the original paragraph in the discussion devoted to this subject. We made reference to the fact that orangutan diets vary by season and site in the original submission, and have now acknowledged that seasonal diet variation may also contribute to variation in enamel isotope values.

Also, I'd like to note that there has been only one recent study so far that made some level of an attempt to find a breastfeeding effect in orangutans using fecal isotope data. Tsutaya et al. 2022 (AJBA) report some seasonality in adult orangutan fecal isotope values, which could be relevant here as well. But also they reported some data from 2 to 7-year-old orangutan offspring and did not see any breastfeeding pattern in isotope values here either. Probably not too surprising at this older age, but still worth noting in the context of this study.

There is a 2019 study that sampled fecal isotopes in 43 mother-infant orangutan pairs and found a different pattern than Tsutaya et al. (2022), although these data have not been published in full (Knott et al. (2019) AJBA 168, S68, 128-129). Given these contradictions, the fact that neither study serially sampled the first two years of life, and caveats to fecal isotope sampling of wild primates reviewed in Bădescu et al. (2023: American Journal of Primatology 2023;e235), introducing these nitrogen isotope studies does not aide in the interpretation of oxygen isotope data during intensive nursing, and thus is beyond the scope of this paper. The seasonality Tsutaya et al. (2022) reported in adult fecal samples was for carbon isotopes rather than nitrogen isotopes, and its relevance to the current study is unclear given that the orangutan plant foods measured did not show seasonal variation in carbon isotopes. As requested above, we have noted orangutans’ dietary seasonality might influence the variation of oxygen isotope values.

Reviewer #2 (Recommendations For The Authors):

First, the manuscript offers upfront flashy numbers with respect to the number of samples, but what the reader really needs to know upfront is the number of individuals and the number of teeth per individual. These facts are buried and make the reader work too hard to keep track. While the specimen ID numbers are valuable in the table, perhaps a different ID could be used in the text, such as individuals modern Borneo A and B, fossil Sumatra A and B, etc.? Similarly, it would be helpful to remind readers of each locality - Borneo or Sumatra, modern or fossil.

Tables 1 and 2 and the first sentence of the results and the materials and methods stated that we measured 18 teeth in this study. It is likely that the placement of the tables at the very end of the manuscript in the submitted version made the sample sizes and specimen information less evident to the reviewer. In response to this critique we have now added the number of teeth to the abstract, and trust that when the tables are placed within the text as indicated it will be easier to follow textual references to particular individuals. Museum identification codes have been provided in two previous publications of these teeth, and we retain them here for consistency.

Second, the manuscript mentions some climate change in Sumatra, but what about Borneo?

The results on the Bornean fossil teeth stated: “The range of values from these two fossil molars (14.2–24.8 ‰) markedly exceeds the range of modern Bornean orangutans (12.7–20.0 ‰) (Figure 4), with the mean δ18O value at least 2‰ heavier, suggesting possibly drier conditions with greater seasonality during their formation.” In the final section of the discussion, we devoted two paragraphs to discussing evidence for climate change at Niah Cave in Borneo - more than we devote to discussing such data from Sumatra.

The most valuable figure in the manuscript is Figure 3 showing the serial sampling of modern teeth. It would be incredibly useful to see a similar graph for the fossils and a graph of the modern and fossils together for each island. The violin plots demonstrate a range of values but fail to provide the important seasonality signals. The manuscript is promising but as written is difficult to follow, and the results and conclusions with regard to climate change need more demonstration. On a minor note, I found myself wanting to know about the dates of fossils before knowing the isotopic values. You might wish to move the dating section to precede the isotopes.

As requested, we have added an additional Supplemental figure making the comparisons of seasonality between fossil and modern individual more evident.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This study addressed an alternative hypothesis to temporal binding phenomena. In temporal binding, two events that are separated in time are "pulled" towards one another, such that they appear more coincidental. Previous research has shown evidence of temporal binding events in the context of actions and multisensory events. In this context, the author revisits the well-known Libet clock paradigm, in which subjects view a moving clock face, press a button at a time of their choosing to stop the clock, a tone is played (after some delay), and then subjects move the clock dial to the point where the one occurred (or when the action occurred). Classically, the reported clock time is a combination of the action and sound times. The author here suggests that attention can explain this by a mechanism in which the clock dial leads to a roving window of spatiotemporal attention (that is, it extends in both space and time around the dial). To test this, the author conducted a number of experiments where subjects performed the Libet clock experiment, but with a variety of different stimulus combinations. Crucially, a visual detection task was introduced by flashing a disc at different positions along the clock face. The results showed that detection performance was also "pulled" towards the action event or sensory event, depending on the condition. A model of roving spatiotemporal attention replicated these effects, providing further evidence of the attentional window.

Strengths:

The study provides a novel explanation for temporal binding phenomena, with clear and cleverly designed experiments. The results provide a nice fit to the proposed model, and the model itself is able to recapitulate the observed effects.

Weaknesses:

Despite the above, the paper could be clearer on why these effects are occurring. In particular, the control experiment introduced in Experiment 3 is not well justified. Why should a tactile stimulus not lead to a similar effect? There are possibilities here, but the author could do well to lay them out. Further, from a perspective related to the attentional explanation, other alternatives are not explored. The author cites and considers work suggesting that temporal binding relies on a Bayesian cue combination mechanism, in which the estimate is pulled towards the stimulus with the lowest variance, but this is not discussed. None of this necessarily detracts from the findings, but otherwise makes the case for attention less clear.

I would like to thank the reviewer for the helpful comments and recommendations. Regarding Experiment 3, the rationale is this. We showed in Experiments 1 and 2 that, for outcome binding, there were two types of difference between Action Sound condition and Sound Only condition: the reported time of sound onset (i.e. the reported clock hand location at the sound onset) and the attention distribution. To experimentally test the relevance of the attention difference to the difference of reported time, we created a situation where the attention difference could be minimised and then checked the difference of reported time. We found that when the attention difference was controlled for between the two conditions, the difference of reported time was also gone, thus providing further evidence for a close link between attention and time report in the current testing paradigm. Therefore, Experiment 3 was primarily targeting the experimental evidence for the claim of the current study. What we needed in Experiment 3 was a condition that could have a smaller attention difference with the Action Sound condition than the attention difference between Sound Only and Action Sound conditions in Experiments 1 and 2. We expected that a tactile stimulus before the sound onset could work, without a clear prediction of the strength of the tactile stimulus in shifting attention, which was also not necessary. This experimental manipulation was a nice fit for the purpose of experiment 3, as we could empirically measur the effectiveness of the tactile stimulus on attention shift and then relate it to the changes in outcome binding.

As the reviewer correctly suggested, the Bayesian framework has been applied in several studies to explain the time judgement distortion in sensorimotor situations (e.g. the temporal binding effect studied here). However, the current study asked what temporal binding is really about when it is measured with the Libet clock method. Is it really about a distortion in time perception (which the Bayesian account tries to explain)? Or is it also about attention? The results showed that the spatiotemporal attention distribution is at least a confound in measuring the perceived time of an event using the Libet clock method. Therefore, the Bayesian account raised in previous studies is relevant when explaining the distortion in time perception, given that it really exists. We here asked if the distortion really exists, and to what extent.

Reviewer #2 (Public Review):

Summary:

Temporal binding, generally considered a timing illusion, results from actions triggering outcomes after a brief delay, distorting perceived timing. The present study investigates the relationship between attention and the perception of timing by employing a series of tasks involving auditory and visual stimuli. The results highlight the role of attention in event timing and the functional relevance of attention in outcome binding.

Strengths:

• Experimental Design: The manuscript details a well-structured sequence of experiments investigating the attention effect in outcome binding. Thoughtful variations in manipulation conditions and stimuli contribute to a thorough and meaningful investigation of the phenomenon.

• Statistical Analysis: The manuscript employs a diverse set of statistical tests, demonstrating careful selection and execution. This statistical approach enhances the reliability of the reported findings.

• Narrative Clarity: Both in-text descriptions and figures provide clear insights into the experiments and their results, facilitating readers in following the logic of the study.

Weaknesses:

• Conceptual Clarity: The manuscript aims to integrate key concepts in human cognitive functions, including attention, timing perception, and sensorimotor processes. However, before introducing experiments, there's a need for clearer definitions and explanations of these concepts and their known and unknown interrelationships. Given the complexity of attention, a more detailed discussion, including specific types and properties, would enhance reader comprehension.

• Computational Modeling: The manuscript lacks clarity in explaining the model architecture and setup, and it's unclear if control comparisons were conducted. These details are critical for readers to properly interpret attention-related findings in the modeling section. Providing a clearer overview of these aspects will improve the overall understanding of the computational models used.

I would like to thank the reviewer for the helpful comments and recommendations. The attention in the current study, which has been made clearer in the revised manuscript, refers specifically to visuospatial attention. It is presented as a key factor shaping the results of timing report obtained with the clock method, thereby contributing to the explanation of temporal binding. Indeed, attention has been mentioned previously in a similar context, but was treated vaguely as a kind of general cognitive resources. The current study specifically tested and verified that the visuospatial attention paid to the clock face influenced the timing reports. This point has been discussed in a dedicated paragraph in the discussion section of the revised manuscript.

The modelling of the timing report using the attention data was based on a very simple idea: The clock hand location receiving more attention should be given more weight when participants made the timing report (i.e. reporting the clock hand position). The weight for each location was calculated using the detection rate at each location. The relevant methods section has been extensively revised to provide a step-by-step implementation of the modelling, with rationales and pitfalls in the interpretation of the modelling results given (also in the discussion section).

Author Response

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

We thank the reviewers and the editors for their constructive and critical comments/ suggestions regarding our paper. We have since extensively revised the manuscript accordingly, including the addition of new experimental data. Hope the readers, reviewers, and editors are now satisfied with the quality and significance of the revised paper.

Our responses to the eLife assessment and the reviewers’ comment as well as the details of the revisions are described below.

Wang et al present a useful manuscript that builds modestly on the group's previous publication on KLF1 (EKLF) K47R mice focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo (Shyu et al., Adv Sci (Weinh). 2022). The data demonstrates that Eklf (K74R) imparts these advantages in a background, age, and gender independent manner, not the consequence of the specific amino acid substitution, and transferable by BMT. However, the authors overstate the meaning of these results and the strength of evidence is incomplete, since only a melanoma model of cancer is used, it is unclear why only homozygous mutation is needed when only a small fraction of cells during BMT confer benefit, they do not show EKLF expression in any cells analyzed, and the PD-1 and PDL-1 experiments are not conclusive. The definitive mechanism relative to the prior publication from this group on this topic remains unclear.

The issues in the assessment by the editor on our paper were also brought up by the reviewers. We have taken care of them by carrying out new experiments as well as rewriting of the paper to highlight the rationales and novel aspects of the current study, as described below in our responses to the three reviewers.

Public Reviews:

Reviewer #1 (Public Review):

The authors Wang et al. present a study of a mouse model K74R that they claim can extend the life span of mice, and also has some anti-cancer properties. Importantly, this mechanism seems to be mediated by the hematopoietic system, and protective effects can be transferred with bone marrow transplantation.

The authors need to be more specific in the title and abstract as to what is actually novel in this manuscript (a single tumor model), and what relies on previously published data (lifespan). Because many of these claims derive from previously published data, and the current manuscript is an extension of previously published work. The authors need to be more specific as to the actual data they present (they only use the B16 melanoma model) and the actual novelty of this manuscript.

Especially experiments on life span are published and not sufficiently addressed in this actual paper, as the title would suggest.

Indeed important to point out the novelty of this paper in comparison to the previous paper. First, we have modified the title, the abstract, and the text so to emphasize that the extended lifespan as well as tumor resistance could be transferred by from Eklf(K74R) mice to WT mice by a single transplantation of the Eklf(K74R) bone marrow mononuclear cells (BMT) to the WT mice at their young age (2 months).

We now also provide several new experimental data including the one demonstrating that Eklf(K74R) mice are resistant to tumorigenesis of hepatocellular carcinoma as well (new Fig. 1E). These points are elaborated in more details below in my responses to the reviewers’ comments/ suggestions.

Reviewer #2 (Public Review):

The manuscript by Wang et al. follows up on the group's previous publication on KLF1 (EKLF) K47R mice and reduced susceptibility to tumorigenesis and increased life span (Shyu et al., Adv Sci (Weinh). Sep 2022;9(25):e2201409. doi:10.1002/ advs.202201409). In the current manuscript, the authors have described the dependence of these phenotypes on age, gender, genetic background, and hematopoietic translation of bone marrow mononuclear cells. Considering the current study is centered on the phenotypes described in the previous study, the novelty is diminished. Further, there are significant conceptual concerns in the study that make the inferences in the manuscript far less convincing. Major concerns are listed below:

1) The authors mention more than once in the manuscript that KLF1 is expressed in range of blood cells including hematopoietic stem cells, megakaryocytes, T cells and NK cells. In the case of megakaryocytes, studies from multiple labs have shown that while EKLF is expressed megakaryocyte-erythroid progenitors, EKLF is important for the bipotential lineage decision of these progenitors, and its high expression promotes erythropoiesis, while its expression is antagonized during megakaryopoiesis. In the case of HSCs, the authors reference to their previous publication for KLF1's expression in these cells- however, in this study nor in the current study, there is no western blot documented to convincingly show that KLF1 protein is expressed at detectable levels in these cells. For T cells, the authors have referenced a study which is based on ectopic expression of KLF1. For NK cells, the authors reference bioGPS: however, upon inspection, this is also questionable.

2) The current study rests on the premise that KLF1 is expressed in HSCs, NK cells and leukocytes, and the references cited are not sufficient to make this assumption, for the reasons mentioned in the first point. Therefore, the authors will have to show both KLF1 mRNA and protein levels in these cells, and also compare them to the expression levels seen in KLF1 wild type erythroid cells along with knockout erythroid cells as controls, for context and specificity.

Regarding the novelties of the current story. Besides demonstration of the independence of the healthy longevity characteristics on age, gender, and genetic background, as exemplified by the tumor resistance, another novelty of the current study is that the healthy longevity characteristics, in particular the tumor resistance and extended lifespan, could be transferred by one-time long-term transplantation of the Eklf(K74R) bone marrow mononuclear cells from young Eklf(K74R) mice to young WT mice. Also, since submission of the last version of the paper, we have carried out new experiments, including the characterization of the anti-cancer capability of NK cells (new Fig. 6) as well as assay of the tumor-resistance of Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E), etc.

We have also modified the title, Abstract, and different parts of the text to highlight the novelties of the current study.

As to the expression of EKLF in different hematopoietic blood cell types, we have now added a paragraph in Result (p.6 and p.7) describing what have been known in literature in relation to our data presented in the paper. Importantly, following the reviewer’s comments, we have since carried out Western blot analysis of EKLF expression in NK, T, and B cells (p. 6, p.7 and new Fig. S4B). Also noted is that the level of EKLF in B cells is very low and only could be detected by RT-qPCR (Fig. S4C) and RNA-Seq (Bio-GPS database)

3) To get to the mechanism driving the reduced susceptibility to tumorigenesis and increased life span phenotypes in EKLF K74R mice, the authors report some observations- However, how these observations are connected to the phenotypes is unclear.

a. For example, in Figure S3, they report that the frequency of NK1.1+ cells is higher in the mutant mice. The significance of this in relation to EKLF expression in these cells and the tumorigenesis and life span related phenotypes are not described. Again, as mentioned in the second point, KLF1 protein levels are not shown in these cells.

b. In Figure 4, the authors show mRNA levels of immune check point genes, PD-1 and PD-l1 are lower in EKLF K74R mice in PB, CD3+ T cells and B220+ B cells. Again, the questions remain on how these genes are regulated by EKLF, and whether and at what levels EKLF protein is expressed in T cells and B cells relative to erythroid cells. Further, while the study they reference for EKLF's role in T cells is based on ectopic expression of EKLF in CD4+ T cells, in the current study, CD3+ T cells are used. Also, there are no references for the status of EKLF in B cells. These details are not discussed in the manuscript.

Regarding this part of the questions and comments by the reviewer.

First, we have since assayed the effect of the K74R substitution of EKLF on the in vitro cancer cell-killing ability of NK cells (termed NK1.1 cells in the previous version). The data showed that NK(K74R) cells have higher ability than the WT NK cells (new Fig. 6). This property together with the higher expression level of NK(K74R) cells in 24 month-old Eklf (K74R) mice than NK cells in 24 month-old WT mice would contribute to the higher tumor-resistance of the Eklf (K74R) mice. This point is also addressed on p. 8 andp.9.

Second, as stated in previous sections, we have since carried out comparative Western blot analysis of the expression of EKLF protein in NK, CD3 T, and B cells of the WT and Eklf(K74R) mice, respectively (please see the new Fig. S4B). Also, description regarding what are known in literature in relation to our data on the expression of EKLF protein/ Eklf mRNA in different types of hematopoietic blood cells is now included in the Result (please see p.6 and p.7). Notably though, the level of EKLF protein in B cells was too low to be detected by WB (Fig. S4B).

4) The authors perform comparative proteomics in the leukocytes of EKLF K74R and WT mice as shown in Figure S5. What is the status of EKLF levels in the mutant lysate vs wild type lysates based on this analysis? More clarity needs to be provided on what cells were used for this analysis and how they were isolated since leukocytes is a very broad term.

The leukocytes used by us were isolated from the peripheral blood after removal of red blood cells, as described in the Materials and Methods.

Also, the Western blot analysis of EKLF expression in the lysates of leukocytes/ white blood cells (WBC) has been shown previously, now presented in the new Figure S4A.

5) In the discussion the authors make broad inferences that go beyond the data shown in the manuscript. They mention that the tumorigenesis resistance and long lifespan is most likely due to changes in transcription regulatory properties and changes in global gene expression profile of the mutant protein relative to WT leukocytes. And based on reduced mRNA levels of Pd-1 Pd-l1 genes in the CD3+ T cells and B220+ B cells from mutant mice, they "assert" that EKLF is an upstream regulator of these genes and regulates the transcriptomes of a diverse range of hematopoietic cells. The lack of a ChIP assay to show binding of WT EKLF on genes in these cells and whether this binding is reduced or abolished in the mutant cells, make the above statements unsubstantiated.

We have since carried out ChIP-PCR analysis of EKLF-binding in the Pd-1 promoter (new Fig. S5). The data showed that EKLF was bound on the CACCC box at -103 of the promoter in WT CD3+T as well as in CD3+T(K74R) cells. This result is discussed on p.7.

6) Where westerns are shown, the authors need to show the molecular weight ladder, and where qPCR data are shown for EKLF, it will be helpful to show the absolute levels and compare these levels to those in erythroid cells, along the corresponding EKLF knock out cells as controls.

We have since included the molecular weight markers by the side of Western blots in Fig. S4. Also, we have added a new figure (Fig.S4C) showing the comparison of the expression levels of Eklf mRNA in B cells and CD3+ T cells to the mouse erythroleukemia (MEL) cells, as analyzed by RT-qPCR.

Also, as indicated now in the Material and Methods section, the specificity of the primers used for RT-qPCR quantitation of mouse Eklf mRNA has been validated before by comparative analysis of wild type and EKLF-knockout mouse erythroid cells (Hung et al., IJMS, 2020).

7) Figure S1D does not have a figure legend. Therefore, it is unclear what the blot in this figure is showing. In the text of the manuscript where they reference this figure, they mention that the levels of the mutant EKLF vs WT EKLF does not change in peripheral blood, while in the figure they have labeled WBCs for the blot, and the mRNA levels shown do seem to decrease in the mutant compared to WT peripheral blood.

We apologize for this ignorance on our side. The data shown in the original Fig. SID (new Fig. S4A) are from Western blot analysis of EKLF protein and RT-qPCR analysis of Eklf mRNA in leukocytes/ white blood cells (WBC) isolated from the peripheral blood samples. We have now added back the figure legend and also rewritten the corresponding description in the text on p.6.

Reviewer #3 (Public Review):

Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated. The major strengths of the manuscript include the clarity of the effect and the appropriate controls. For instance, the authors query whether Eklf (K74R) imparts these advantages in a background, age, and gender dependent manner, demonstrating that the findings are independent. In addition, the authors demonstrate that the effect is not the consequence of the specific amino acid substitution, with a similar effect on anticancer activity. Furthermore, the authors provide some evidence that PD-1 and PDL-1 are altered in Eklf (K74R) mice.

Here we thank the encouraging comments by this reviewer.

Finally, they demonstrate that the effects are transferrable with BMT. Several weaknesses are also evidence. For instance, only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach.

We have now included new data showing that the Eklf(K74R) mice also carry a higher anti-cancer ability against hepatocellular carcinoma than the WT mice (new Fig. 1E).

It is also unclear why a homozygous mutation is needed when only a small fraction of cells during BMT can confer benefit. It is also difficult to explain how transplanted donor Eklf (K74R) HSCs confer anti-melanoma effect 7 and 14 days after BMT.

First, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). These points are now clarified in Discussion (p.9 and p.10).

Second, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT. This point is now touched upon on p.8 and p.9.

Furthermore, it would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

Finally, the data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation without changing EKLF expression impacts immune cells. The work is impactful as it provides evidence that healthspan and lifespan may be modulated by specific hematological mutation but the mechanism by which this occurs is not completely elucidated by this work.

As described in a previous section, we have since also carried out ChIP-qPCR analysis of the binding of WT EKLF and EKLF (K74R) on the Pd-1 promoter (new Fig. S5).

Reviewer #1 (Recommendations For The Authors):

The authors present interesting melanoma model data but need to tone down their claim of multiple effects of their model system. It needs to be clear what is new and what is previously known.

As respond in the Public Reviews, we have since added new data on the tumor resistance of the Eklf(K74R) mice to hepatocellular carcinoma (new Fig. 1E). We have also modified the title as well as highlighted the novel points in the Abstract and text of the revised draft.

Reviewer #2 (Recommendations For The Authors):

In addition to the major concerns listed in the public review, the minor concerns that the authors could address are listed below:

1) Will be helpful to describe why was the pulmonary melanoma focus assay chosen for metastasis assay?

We now describe on p. 4 the rationale behind the initial choice of this assay for analysis of the anti-cancer capability of the Eklf(K74R) mice. Also, we have since included data from experiment using the subcutaneous cancer cell inoculation assay for comparative analysis of the anti-hepatocellular carcinoma capability of Eklf(K74R) and WT mice (Fig. 1E and p.5).

2) Reference #61 for B16-F10-luc cells cited in the methods does not have details on the generation of these cells. What these cells are and why this model was chosen needs to be described.

Sorry about not providing this information before. We now describe the generation of B16F10-luc cells in the Material and Methods section (p.13). The rationale of choosing the B16-F10 cells for the pulmonary lung foci assay is also added on p.4.

3) The DNA binding consensus site for EKLF needs to be expanded in the introduction.

This part has been taken care of now on p.13.

Reviewer #3 (Recommendations For The Authors):

Hung et al provide a well-written manuscript focused on understanding how Eklf mutation confers anticancer and longevity advantages in vivo. The work is fundamental and the data is convincing although several details remain incompletely elucidated.

1) Only melanoma is tested as a model of cancer such that a broad claim of "anti-cancer activity" may be somewhat of an overreach. The authors, therefore, need to provide evidence of a second type of malignancy to which Eklf mutation confers anticancer and longevity advantages or temper the claims in the discussion that the effect still needs to be tested in non-melanoma cancer models to determine the broad anti-cancer effect.

As responded in the Public Reviews, we have since shown that Eklf(K74R) mice also exhibited a higher resistance to the carcinogenesis of hepatocellular carcinoma (new Fig. 1E).

2) Why is a homozygous mutation needed when only a small fraction of cells during BMT can confer benefit of Eklf mutation? Is there evidence that the cellular effect is binary but only a few such cells are needed? This is confusing and requires further clarification.

As responded in the Public Reviews, these two observations not necessarily conflict with each other. It is likely that homozygosity, but not heterozygosity, of the K74R substitution in EKLF allows one or more types of hematopoietic blood cells to gain new functions, e.g. the higher cancer cell- killing capability of NK(K74R) cells (new Fig. 6), that help the mice to live long and healthy. Also, the data in Fig. 2D indicated that as low as 20% of the blood cells carrying homozygous Eklf(K74R) alleles in the recipient mice upon BMT could be sufficient to confer the mice a higher anti-cancer capability, likely in part due to cells such as NK(K74R). This point is now clarified in Discussion (p.9).

3) BMT typically requires at least 3-4 weeks to reconstitute the marrow compartment but the authors are able to see effects of Eklf mutation as early as 7 days following BMT. This is surprising and brings into question the mechanism of effect.

As responded in the Public Reviews, we think the NK(K74R) cells contributed a significant part to the anti-cancer capability of the transplanted Eklf(K74R) blood in the recipient WT mice. As documented in some literature, e.g. Ferreira et al., Journal of Molecular Medicine (2019), the hematopoietic lineage of the NK cells would be fully reconstituted as early as 2 weeks after BMT. Of course, there could be other still unknown factors/ cells that also contribute to the tumor-resistance of the recipient mice at 7 day following BMT (please see discussion of this point on p. 9).

4) It would be useful to see whether there are virulence marker alterations in the melanoma loci in WT vs Eklf (K74R) mice.

As responded in the Public Reviews, we will analyze this in future together with other types of tumors in a separate study.

5) The data in Fig 4c is difficult to interpret as decreased PD-1 and PDL-1 after knockdown of EKLF in vitro is not a useful experiment to corroborate how mutation WITHOUT changing EKLF expression impacts immune cells.

Indeed, the RNAi knockdown experiment only demonstrated a positive regulatory role of EKLF in Pd1/Pd-l1 gene expression. We have followed the reviewer’s suggestion and carried out ChIP-qPCR analysis and shown that the factor is bound on the Pd-1 promoter in both WT CD3+T cells and CD3+T(K74R) cells (new Fig. S5). We briefly discuss these data on p.7 in relation to the possible effect of K74R substitution of EKLF on Pd-1 expression.

We have now further clarified this point on p. 7.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

Congratulations on the very nice structure! In my opinion, which you can feel free to take or leave, this would work better as a short report focused on the improvement of the structure relative to the current published model. To my mind, while the functional and dimerization studies are supportive of the cryo-EM studies (specifically, the purified protein is functional, and does tend to dimerize in various membrane mimetics), these experiments don't provide a lot of new mechanistic insight on their own. The dimerization, in particular, could be developed further.

Response: Thank you for the comments. We have chosen to stick with the current article format. That the protein is dimeric is exciting in our view and we are working to further define the functional significance of this formation.

Reviewer #2 (Recommendations For The Authors):

Ln 48. Abstract. "highlighting feature of the complex interface" sounds a bit vague. I was wondering if the authors considered including more specific findings here.

Response: This sentence has been removed.

Ln 149 and elsewhere. The authors refer to the previously published structure of HiSiaQM as "low resolution". It may just be me and likely not the intention of the authors, but this comes across as an attempt to diminish the validity of this previous work from another group, which is not necessary. I would recommend rewording these parts slightly, even if it is just to say "lower resolution" instead of "low resolution".

Response: It was not our intention to diminish the excellent work published by another group, we have changed “low resolution” to “lower resolution” throughout.

Ln 160. The authors state that the inward-open conformation is likely "the resting state of the transporter". I think this statement should be modified slightly to acknowledge that this is only true under these conditions, i.e. in the absence of the bilayer, membrane potential and chemical gradients.

Response: We have edited this as follows “That we observe the inward-open conformation without either a bound P-subunit or fiducial marker, suggests that this is the resting state of the transporter under experimental conditions (in the absence of a membrane bilayer, membrane potential and chemical gradients).”

Ln 202. I'm not convinced that the use of the word "probable" is appropriate here; "possible" would likely fit better in the absence of compelling evidence that this dimer forms in a bacterial cell membrane with physiological levels of HiSiaQM expression.

Response: We have changed “probable” to “possible”.

The authors show an SEC trace for DDM solubilised protein, which is a single peak, whereas the LMNG extracted protein has 2 distinctly different elution profiles depending on the LMNG concentration. Was the same phenomenon observed when varying the DDM concentration?

Response: We observed significantly more aggregation with DDM than L-MNG, so it was infrequently used and considerably less well characterised. In one purification, moderately higher DDM shifted the elution peak to be slightly later but retained a similar profile. Overall, we did not observe the same phenomenon of distinctly different elution profiles with DDM, but we have limited data.

Ln 245. The two positions cited as important for the elevator-type mechanism are the fusion helix and the dimer interface. However, there is no evidence that the dimer interface observed in this work has any relevance to the transport mechanism. To make this statement, the interface would need to be disrupted and the effects on transport evaluated.

Response: This has been edited as follows. “Evident in our cryo-EM maps are well-defined phospholipid densities associated with areas of HiSiaQM that may be important for the function of an elevator-type mechanism (Figure 4), but require further testing.”

Ln 257. The authors state that the lipids form "specific and strong interactions" with the protein, but without knowing the identity of the lipids present, it is difficult to say anything about the specificity of this interaction. I think the authors could consider rewording this. Response: We have edited this by removing the term “specific” and describing the lipid interactions only as strong interactions.

Ln 270. The authors identify a lipid-binding site and residues that likely interact with the headgroup. It would be interesting if the authors could speculate on the purpose of this lipid binding site and how it could affect transport. The residues are not conserved, which the authors suggest reflects the variety of lipid compositions in different bacteria. Are the authors suggesting that this lipid binding site is a general feature for all fused TRAP transporters and that the identity of the lipid changes depending on the species?

Response: Yes, we speculate that the lipid binding site may be a general feature for fused TRAP transporters. We have added speculation about this binding site, specifically that “the fusion helix and concomitant lipid molecule may provide a more structurally rigid scaffold than a Q-M heterodimer, i.e., PpSiaQM, although how this impacts the elevator transition requires further testing” at Line 283.

Though we believe that a binding pocket is likely found in a number of fused TRAPs (based on sequence and Alphafold predictions, e.g., FnSiaQM and AaSiaQM), we have now acknowledged that some fusions may not necessarily bind a lipid molecule here, by stating “While this binding pocket is likely found in a number of fused TRAPs (based on sequence predictions, e.g., FnSiaQM and AaSiaQM in Supplementary Figure 8), it is not clear whether they also bind lipids here without experimental data” at Line 290.

Ln 306. The authors state that the HiSiaPQM has a 10-fold higher transport activity than PpSiaPQM. Unless the transport assays were performed in parallel (to mitigate small changes in experimental set-up) and the reconstitution efficiency for each proteoliposome preparation was carefully analysed, it is very difficult for this to be a meaningful comparison. Even if the amount of protein incorporated into the proteoliposomes is quantified (e.g. by evaluating protein band intensity when the proteoliposomes are analysed using SDS-PAGE), this does not account for an inactive protein that was incorporated, nor the proportion of the protein that was incorporated in the inside-out orientation, which would be functionally silent in these assays. I'm not suggesting these assays actually need to be performed, but I think the text should be modified to reflect what can actually be compared.

Response: We agree with the reviewer that a meaningful comparison is difficult to make without a careful analysis of the reconstitution efficiency and have modified the text to reflect this. We have altered the paragraph beginning at Line 319 to the following: “The fused HiSiaPQM system appears to have a higher transport activity than the non-fused PpSiaPQM system. With the same experimental setup used for PpSiaPQM (5 M Neu5Ac, 50 M SiaP) (33), the accumulation of [3H]-Neu5Ac by the fused HiSiaPQM is ~10-fold greater. Although this difference may reflect the reconstitution efficiency of each proteoliposome preparation, it is possible that it has evolved as a result of the origins of each transporter system—P. profundum is a deep-sea bacterium and as such the transporter is required to be functional at low temperatures and high pressures… ”

Ln 335. "S298A did not show an effect on growth when mutated to alanine previously." Suggest changing "S298A" here to "S298".

Response: This has been changed.

Ln 340. In addition to PpSiaQM, the large cavity was also presumably observed in the lower resolution structure of HiSiaQM?

Response: The cavity is detectable in the lower resolution structure (7qe5), though very poorly defined by the density. Furthermore, the AlphaFold model fitted to this density has positioned sidechains inside the cavity, which we consider very likely to be an error (in comparison to our structures, VcINDY and our estimates of the volume required to house sialic acid). The cavity is generally much better defined by the structures we have referenced.

Ln 345. Reference missing after "previously reported"? Response: This has been added. Measuring the affinity for the P-to-QM interaction is very useful, but it would have enhanced the study if some of the residues identified as important for this interaction (detailed on p.13) had been tested for their contributions to binding using this approach.

Response: We do aim to perform this assay with these mutants in the future, but are also developing parallel assays to further test this interaction in different membrane mimetics.

Ln 436. As stated previously, it is more accurate to say that "this is the most stable conformation" under these conditions.

Response: We have edited this to say “The ‘elevator down’ (inward-facing) conformation is preferred in experimental conditions”. We have also changed the last sentence of this paragraph to say “However, the dimeric structures we have presented have no other proteins bound, yet exist stably in the elevator down state, suggesting this is the most stable conformation in experimental conditions, where there is no membrane bilayer, membrane potential, or chemical gradient present.”

Ln 438. "Lipids associated with HiSiaQM are structurally and mechanistically important." This conclusion is not supported by the data presented; there is no evidence that the bound lipids influence the mechanism at all. The lipids observed are certainly interestingly placed and one could speculate about their relevance, but this statement of fact is not supported. Therefore, their importance to the mechanism needs to be tested or this conclusion needs to be substantially softened.

Response: We have softened this statement by changing it to “Lipids have strong interactions with HiSiaQM and are likely to be important for the transport mechanism.”

Reviewer #3 (Recommendations For The Authors):

The fact that HiSiaQM samples consist of a mixture of compact monomer and dimer is clear, from Fig. S5 and S6. However, the analysis displayed in Fig 3 and Fig S4 would require more explanation. To my understanding, it requires the values of the sedimentation and diffusion coefficients. It could be good to provide the experimental values of D, and explain a little more about the method in the material and method section.

Response: Yes, the analysis requires the experimental diffusion coefficients. These have been added to the Figure 3 and S4 legends and more detail has been added to the method section.

In addition, I am puzzled when reading, in the legend of Fig 3, considerations that peak 2 could not correspond to a monomer or trimer: do these sentences correspond to other mathematical solutions, or is a given frictional ratio considered, or do they refer to Fig. S5 analysis?

We can see where this confusion could arise from. These sentences do not correspond to a given frictional ratio or the Fig. S5 analysis (this is a separate, complementary analysis). For peak 2 not existing as a monomer is strictly a physical justification – with pure protein and an observed peak smaller than peak 2, a monomer is not possible for peak 2. For peak 2 not existing as a trimer is a mathematical solution using the s and D coefficients. The solutions identify that an unreasonably low amount of detergent would be bound to a trimer (32 molecules for L-MNG or 0 for DDM) to exist at those s and D values so we have ruled the trimer out. Reassuringly, the complementary analysis in Fig. S5/S6 agrees with the monomer-dimer outputs from the s and D analysis. We have adjusted the text in the legends of Fig. 3 and S4 to better convey these points.

Author Response

eLife assessment

This useful study uses a mouse model of pancreatic cancer to examine mitochondrial mass and structure in atrophying muscle along with aspects of mitochondrial metabolism in the same tissue. Most relevant are the solid transcriptomics and proteomics approaches to map out related changes in gene expression networks in muscle during cancer cachexia.

Response: We very much appreciate the positive feedback from the editors on our article and are delighted to have it published in eLife. Our sincere thanks to the Reviewers for their positive feedback on our work, and for their insightful and constructive comments.

Reviewer #1 (Public Review):

Summary:

This important study provides a comprehensive evaluation of skeletal muscle mitochondrial function and remodeling in a genetically engineered mouse model of pancreatic cancer cachexia. The study builds upon and extends previous findings that implicate mitochondrial defects in the pathophysiology of cancer cachexia. The authors demonstrate that while the total quantity of mitochondria from skeletal muscles of mice with pancreatic cancer cachexia is similar to controls, mitochondria were elongated with disorganized cristae, and had reduced oxidative capacity. The mitochondrial dysfunction was not associated with exercise-induced metabolic stress (insufficient ATP production), suggesting compensation by glycolysis or other metabolic pathways. However, mitochondrial dysfunction can lead to increased production of ROS/oxidative stress and would be expected to interfere with carbohydrate and lipid metabolism, events that are linked to cancer-induced muscle loss. The data are convincing and were collected and analyzed using state-of-the-art techniques, with unbiased proteomics and transcriptomics analyses supporting most of their conclusions.

Additional Strengths:

The authors utilize a genetically engineered mouse model of pancreatic cancer which recapitulates key aspects of human PDAC including the development of cachexia, making the model highly appropriate and translational.

The authors perform transcriptomic and proteomics analyses on the same tissue, providing a comprehensive analysis of the transcriptional networks and protein networks changed in the context of PDAC cachexia.

Weaknesses:

The authors refer to skeletal muscle wasting induced by PDAC as sarcopenia. However, the term sarcopenia is typically reserved for the loss of skeletal muscle mass associated with aging.

Response: We agree that the term sarcopenia initially refers to aged muscle, but its use has spread to other fields, including oncology (for example, in this article, which we quote: Mintziras I et al. Sarcopenia and sarcopenic obesity are significantly associated with poorer overall survival in patients with pancreatic cancer: Systematic review and meta-analysis. Int J Surg 2018;59:19-26). Actually, the term sarcopenia is now widely used in the literature and in the clinic to describe the loss of muscle mass and strength in cancer patients (see for example, this recent review: Papadopetraki A. et al. The Role of Exercise in Cancer-Related Sarcopenia and Sarcopenic Obesity. Cancers 2023;15;5856).

In Figure 2, the MuRF1 IHC staining appears localized to the extracellular space surrounding blood vessels and myofibers-which causes concern as to the specificity of the antibody staining. MuRF1, as a muscle-specific E3 ubiquitin ligase that degrades myofibrillar proteins, would be expected to be expressed in the cytosol of muscle fibers.

Response: We agree that MuRF1 IHC staining was also observed in the extracellular space, which was a surprise, for which we have no explanation to date.

Disruptions to skeletal muscle metabolism in PDAC mice are predicted based on mitochondrial dysfunction and the transcriptomic and proteomics data. The manuscript could therefore be strengthened by additional measures looking at skeletal muscle metabolites, or linking the findings to previous work that has looked at the skeletal muscle metabolome in related models of PDAC cachexia (Neyroud et al., 2023).

Response: We agree that our omics data could be strengthened by additional measures looking at skeletal muscle metabolites. It's an excellent suggestion to parallel the transcriptomic and proteomic data we obtained on the gastrocnemius muscle with the metabolomic data obtained by Neyroud et al. on the same muscle. These authors used another mouse model of PDAC than our KIC GEMM model, namely the allograft model implanting KPC cells (derived from the pancreatic tumor of KPC mice, another PDAC GEMM model) into syngeneic recipient mice. They carried out a proteomic study on the tibialis anterior muscle and a metabolomic study on the gastrocnemius muscle. Proteomics data identified in particular a KPC-induced reduction in the relative abundance of proteins annotating to oxidative phosphorylation, consistently with our data showing reduced mitochondrial activity pathways. Metabolomic data showed reduced abundance of many amino acids as expected, and of intermediates of the mitochondrial TCA cycle (malate and fumarate) in KPC-atrophied muscle consistently with reduced mitochondrial metabolic pathways that we illustrated. In contrast, metabolites that were increased in abundance included those related to oxidative stress and redox homeostasis, which is not surprising regarding the profound oxidative stress affecting atrophied muscle. Finally, we noted in Neyroud's metabolomic data the dysregulation of certain lipids and nucleotides in atrophied muscle, which is very interesting to relate to our study describing alterations in lipid and nucleotide metabolic pathways.

Reviewer #2 (Public Review):

The present work analyzed the mitochondrial function and bioenergetics in the context of cancer cachexia induced by pancreatic cancer (PDAC). The authors used the KIC transgenic mice that spontaneously develop PDAC within 9-11 weeks of age. They deeply characterize bioenergetics in living mice by magnetic resonance (MR) and mitochondrial function/morphology mainly by oxygraphy and imaging on ex vivo muscles. By MR they found that phosphocreatine resynthesis and maximal oxidative capacity were reduced in the gastrocnemius muscle of tumor-bearing mice during the recovery phase after 6 minutes of 1 Hz electrical stimulation while pH was reduced in muscle during the stimulation time. By oxygraphy, the authors showed a decrease in basal respiration, proton leak, and maximal respiration in tumor-bearing mice that was associated with the decrease of complex I, II, and IV activity, a reduction of OXPHOS proteins, mitochondrial mass, mtDNA, and to several morphological alterations of mitochondrial shape. The authors performed transcriptomic and proteomic analyses to get insights into mitochondrial defects in the muscles of PDAC mice. By IPA analyses on transcriptomics, they found an increase in the signature of protein degradation, atrophy, and glycolysis and a downregulation of muscle function. Focusing on mitochondria they showed a downregulation mainly in OXPHOS, TCA cycle, and mitochondrial dynamics genes and upregulation of glycolysis, ROS defense, mitophagy, and amino acid metabolism. IPA analysis on proteomics revealed major changes in muscle contraction and metabolic pathways related to lipids, protein, nucleotide, and DNA metabolism. Focusing on mitochondria, the protein changes mainly were related to OXPHOS, TCA cycle, translation, and amino acid metabolism.

The major strength of the paper is the bioenergetics and mitochondrial characterization associated with the transcriptomic and proteomic analyses in PDAC mice that confirmed some published data of mitochondrial dysfunction but underlined some novel metabolic insights such as nucleotide metabolism.

There are minor weaknesses related to some analyses on mitochondrial proteins and to the fact that proteomic and transcriptomic comparison may be problematic in catabolic conditions because some gene expression is required to maintain or re-establish enzymes/proteins that are destroyed by the proteolytic systems (including the autophagy proteins and ubiquitin ligases). The authors should consider the following points.

Point 1. The authors used the name sarcopenia as synonymous with muscle atrophy. However, sarcopenia clearly defines the disease state (disease code: ICD-10-CM (M62.84)) of excessive muscle loss and force drop during ageing (Ref: Anker SD et al. J Cachexia Sarcopenia Muscle 2016 Dec;7(5):512-514.). Therefore, the word sarcopenia must be used only when pathological age-related muscle loss is the subject of study. Sarcopenia can be present in cancer patients who also experience cachexia, however since the age of tumor-bearing mice in this study is 7-9 weeks old, the authors should refrain from using sarcopenia and instead replace it with the words muscle atrophy/ muscle wasting/muscle loss.

Response: This issue has also been raised by the Reviewer #1. We agree that the term sarcopenia historically refers to aged muscle, but it is also used in oncology (for example, in this article, which we quote: Mintziras I et al. Sarcopenia and sarcopenic obesity are significantly associated with poorer overall survival in patients with pancreatic cancer: Systematic review and meta-analysis. Int J Surg 2018;59:19-26). Actually, the term sarcopenia is now widely used in the literature and in the clinic to describe the loss of muscle mass and strength in cancer patients (see for example, this recent review: Papadopetraki A. et al. The Role of Exercise in Cancer-Related Sarcopenia and Sarcopenic Obesity. Cancers 2023;15;5856).

Point 2. Most of the analyses of mitochondrial function are appropriate. However, the methodological approach to determining mitochondrial fusion and fission machinery shown in Fig. 5F is wrong. The correct way is to normalize the OPA1, MFn1/2 on mitochondrial proteins such as VDAC/porin. In fact, by loading the same amount of total protein (see actin in panel 5F) the difference between a normal and a muscle with enhanced protein breakdown is lost. In fact, we should expect a decrease in actin level in tumor-bearing mice with muscle atrophy while the blots clearly show the same level due to the normalization of protein content. Moreover, by loading the same amount of proteins in the gel, the atrophying muscle lysates become enriched in the proteins/organelles that are less affected by the proteolysis resulting in an artefactual increase. The correct way should be to lyse the whole muscle of control and tumor-bearing mice in an identical volume and to load in western blot the same volume between control cachectic muscles. Alternatively, the relative abundance of mitochondrial shaping proteins related to mitochondrial transmembrane or matrix proteins (mito mass) should compensate for the loading normalization. Because the authors showed elongated mitochondria despite mitophagy genes being up, fragmentation may be altered. Moreover, DNM1l gene is suppressed and therefore DRP1 protein must be analyzed. Finally, OPA 1 protein has different isoforms due to the action of proteases like OMA1, and YME1L that elicit different functions being the long one pro-fusion while the short ones do not. The authors must quantify the long and short isoforms of OPA1.

Response: We acknowledge that our analysis of a minor set of proteins involved in mitochondrial dynamics by Western blotting (Figure 5F) is basic and could have been improved. We thank the Reviewer for all the suggestions, which will be very useful in future projects studying the subject in greater depth and according to the molecular characteristics of each player in mitochondrial fusion, fission, mitophagy and biogenesis.

Point 3. The comparison of proteomic and transcriptomic profiles to identify concordance or not is problematic when atrophy programs are induced. In fact, most of the transcriptional-dependent upregulation is to preserve/maintain/reestablish enzymes that are consumed during enhanced protein breakdown. For instance, the ubiquitin ligases when activated undergo autoubiquitination and proteasome degradation. The same happens for several autophagy-related genes belonging to the conjugation system (LC3, Gabarap), the cargo recognition pathways (e.g. Ubiquitin, p62/SQSTM1) and the selective autophagy system (e.g. BNIP3, PINK/PARKIN) and metabolic enzymes (e.g. GAPDH, lipin). Finally, in case identical amounts of proteins have been loaded in mass spec the issues rise in point 2 of selective enrichment should be considered. Therefore, when comparing proteomic and transcriptomic these issues should be considered in discussion.

Response: We fully agree with the Reviewer that seeking concordance between transcriptomic and proteomic data in the case of an organ affected by a high level of proteolysis is a difficult business. Another major difficulty we discussed in the Discussion section of the article is the fact that there is no concordance between RNA and protein level for a good proportion of proteins, for multiple reasons, so each level of omics has to be interpreted independently to give information on the pathophysiology of the organ studied.

Author Response

We thank the editors and reviewers for taking the time to provide a critical assessment of our manuscript. We are delighted our work was found to have merit, and will revise the manuscript based on their valuable input.

Author Response

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

Reviewer #1 (Recommendations for The Authors):

Major comments:

1) The immunolabeling data in Figure S4 shows no change in puncta number but reduced puncta size in Kit KO. sIPSC data show reduced frequency but little change in amplitude. These data would seem contradictory in that one suggests reduced synaptic strength, but not number, and the other suggests reduced synapse number, but not strength. How do the authors reconcile these results?

Regarding the synaptic puncta, In Kit KO (or KL KO), we have not detected an overt reduction in the average VGAT/Gephyrin/Calbindin positive puncta density or puncta size per animal. With respect to puncta size, only in the Kit KO condition, and only when individual puncta are assessed does this modest (~10%) difference in size become statistically significant. In the revision, we eliminate this figure and focus on the per animal averages.

We interpret that the reduction in sIPSC and mIPSC frequency likely stems from a decreased proportion of functional synapse sites. The number of MLIs, their action potential generation, the density of synaptic puncta, and the ability of direct stimulation to evoke release and equivalent postsynaptic currents, are all similar in Control vs Kit KO. It is therefore feasible that a reduced frequency of postsynaptic inhibitory events is due to a reduced ability of MLI action potentials to invade the axon terminal, and/or an impaired ability for depolarization to drive (e.g. coordinated calcium flux) transmitter release. That is, while the number of MLIs and their synapses appear similar, the reduced mIPSC frequency suggests that there is a reduced proportion of, or probability that, Kit KO synapse sites that function properly.

2) Related to point 1, it would be helpful to see immunolabeling data from Kit ligand KO mice? Do these show the same pattern of reduced puncta size but no change in number?

Although we have not added a figure, we have now added experiments and a corresponding analysis in the manuscript. As we had previously for Kit KO, we now for KL KO conducted IHC for VGAT, Gephyrin, and Calbindin, and we analyzed triple-positive synaptic puncta in the molecular layer of Pcp2 Cre KL KO mice and Control (Pcp2 Cre negative, KL floxed homozygous) mice. We did not find a gross reduction in the average synaptic puncta size or density, or in the PSD-95 pinceau size. From this initial analysis, it appears that the presynaptic hypotrophy is more notable in the receptor than in the ligand knockout. We speculate that this is perhaps because the Kit receptor may have basal activity in the absence of Kit ligand, that Kit may serve a presynaptic scaffolding role that is lost in the receptor (but not the ligand) knockout, or simply that the embryonic timing of the Pax2 Cre vs Pcp2 Cre recombination events is more relevant to pinceaux development, especially as basket cells are born primarily prenatally.

3) The data using KL overexpression in PC (figure 4E,F) are intriguing, but puzzling. The reduction in sIPSC frequency and amplitude in the control PC is much greater than seen in the Kit or KL KO. The interpretation of these data, "Thus, KL-Kit levels may not set the number of MLI:PC release sites, but may instead influence the proportion of synapses that are functional for neurotransmission (Figure 4G)" is not clear and the reasoning here should be explained in more detail, perhaps in the discussion.

We have attempted to clarify this portion of the manuscript by eliminating the cartoon of the proposed model, and by revising and adding to the discussion. Either MLI Kit KO or PC KL KO seems to preserve the absolute number of MLI:PC anatomical synapse sites (IHC) but to reduce the proportion of those synapse that are contributing to neurotransmission (mIPSC). We speculate that sparse PC KL overexpression (OX) may either 1) weaken inhibition to surrounding control PCs by either diminishing KL OX PC to KL Control PC inhibition, and/or 2) act retrogradely through MLI Kit to potentiate MLI:MLI inhibition, reducing the MLI:PC inhibition at neighboring Control PCs.

Minor comments:

1) In the first sentence of the results, should "Figure 1A, B" be "Figure C, D"?

Yes, corrected.

2) The top of page 6 states "the mean mIPSC amplitude was ~10% greater in PC KL KO than in control", this does not appear to be the case in Figure 3E. control and KL KO look very similar here.

In this portion of the text citing the modest 10% increase in mIPSC amplitude, we are referring to the average amplitude of all individual mIPSC events in the PC KL KO condition; in the figure referred to by the reviewer (3E), we are instead referring to the average of all mIPSC event amplitudes per KL KO PC. Because of the dramatic difference in sample size for individual events vs cells, this modest difference rises to statistical, if not biological, significance. We include this individual event analysis only to suggest that, since we in fact saw a slightly higher event amplitude in the KL KO condition, it is unlikely that a reduced amplitude would have been a technical reason that we detected a lower event frequency.

3) Figure 3 D, duration, y-axis should be labelled "ms"

Event duration is no longer graphed or referenced. This has been replaced with total inhibitory charge.

Reviewer #2 (Recommendations For The Authors):

Methods:

• Pax2-Cre line: embryonal Cre lines sometimes suffer from germline recombination. Was this evaluated, and if yes, how?

The global loss of Kit signaling is incompatible with life, as seen from perinatal lethality in other Kit Ligand or Kit mutant mouse lines or other conditional approaches. Furthermore, a loss of Kit signaling in germ cells impedes fertility. Thus, while not explicitly ruled out, since conditional Pax2 Cre mediated Kit KO animals were born, survived, and produced offspring in normal ratios, we do not suspect that germline recombination was a major issue in this specific study.

• Include rationale for using different virus types in different studies (AAV vs. Lenti).

This rationale is now included and reflects the intention to achieve infection sparsity in the smaller and less dense tissue of perinatal mouse brains.

• How, if at all, was blinding performed for histological and electrophysiological experiments?

It was not possible for electrophysiology to be conducted blinded for the Kit KO experiments, owing to the subjects’ hypopigmentation. However, whenever feasible, resultant microscopy images or electrophysiological data sets were analyzed by Transnetyx Animal ID, and the genotypes unmasked after analysis.

• Provide justification for limiting electrophysiology recordings to lobule IV/V and why MLIs in the middle third of the molecular layer were prioritized when inhibition of PCs is dominated by large IPSCs from basket cells. Why were 2 different internals used for recording IPSCs and EPSCs in PCs and MLIs? While that choice is justified for action potential recordings, it provides poor voltage control in PC voltage clamp. Both IPSCs and EPSCs could have been isolated pharmacologically using a CsCl internal.

The rationale for regional focus has been added to the text. For MLI action potential recordings, we opted to sample the middle third of the molecular layer so that we would not be completely biased to either classic distal stellate vs proximal basket subtypes. It is our hope, in future optogenetic interrogations, to simultaneously record the dynamics of all MLI subtypes in a more unbiased way. With respect to internal solutions, we initially utilized a cesium chloride internal to maximize our ability to resolve differences in GABAA mediated currents, which was the hypothesis-driven focus of our study. While we agree that utilizing a single internal and changing the voltage clamp to arrive at per-cell analysis of Excitatory/Inhibitory input would have been most informative, our decision to utilize pharmacological methods was driven by our experience that achieving adequate voltage clamp across large Purkinje cells was often problematic, particularly in adult animals.

Introduction:

In the introduction, the authors state that inactivating Kit contributes to neurological dysfunction - their examples highlight neurological, psychiatric, and neurodevelopmental conditions.

The language has been changed.

General:

Using violin plots illustrates the data distribution better than bar graphs/SEM.

We have included violin plots throughout, and we have changed p values to numeric values, both in the interest of presenting the totality of the data more clearly.

Synapses 'onto' PCs sounds more common than 'upon' PCs.

We have changed the wording throughout.

Figure 1:

1F - there seems to be an antero-posterior gradient of Kit expression.

Though not explicitly pursued in the manuscript, it is possible that such a gradient may reflect differences in the timing of the genesis and maturation of the cerebellum along the AP axis. Regional variability is however now briefly addressed as a motivator for focused studies within lobules IV/V.

E doesn't show male/female ratios but only hypopigmentation.

This language has been corrected.

Figure 2 and associated supplementary figures:

2A/B: The frequency of sIPSCs is very high in PCs, making the detection of single events challenging. How was this accomplished? Please add strategy to the methods.

We have added methodological detail for electrophysiology analysis.

How were multi-peak events detected and analyzed? 'Duration' is not specified - do the authors refer to kinetics? If so, report rise and decay. It is likely impossible to show individual aligned sIPSCs with averages superimposed, given that sIPSCs strongly overlap. Alternatively, since no clear baseline can be determined in between events, and therefore frequency, amplitude, and kinetics quantification is near-impossible, consider plotting inhibitory charge.

Given the heterogeneity of events, we now do not refer to individual event kinetics. As suggested, we have now included an analysis of the total inhibitory charge transferred by all events during the recording epoch.

S2: Specify how density, distribution, and ML thickness were determined in methods. How many animals/cells/lobules?

For consistency with viral injections and electrophysiology, the immunohistochemical analysis was restricted to lobule IV/V. This is clearer in the revision and detail is added in the methods.

S3:

S3B: the labels of Capacitance and Input resistance are switched.

This has been corrected.

How were these parameters determined? Add to methods.

Added

In the previous figure the authors refer to 'frequency', in this figure to 'rate' - make consistent

This has been corrected.

D: example does not seem representative. Add amplitude of current pulse underneath traces.

We added new traces from nearer the group means and we now include the current trace.

F/G example traces (aligned individual events + average) are necessary.

We added example traces near the relevant group means for each condition.

Statement based on evoked IPCSs that 'synapses function normally' is a bit sweeping and can only be fully justified with paired recordings. Closer to the data would be the release probability of individual synapses is similar between control and Kit KO.

Paired recordings in both Kit Ligand and Kit receptor conditional knockout conditions is indeed an informative aim of future studies should support permit. For now, we have clarified the language to be more in line with the reviewer’s welcome suggestion.

S4:

Histological strategy cannot unambiguously distinguish MLI-PC and PC-PC synapses. Consider adding this confound to the text.

We have added this confound to the discussion.

The observation that the pinceau is decreased in size could have important implications for ephaptic coupling of MLI and PC and could be mentioned.

We agree and have added this notion to the discussion.

Y-label is missing in B.

Corrected.

Figure 3 and associated supplementary figures:

In the text, change PC-Cre to L7-Cre or Pcp2-Cre.

Changed

How do the authors explain a reduction in frequency, amplitude, and duration of sIPSCs in the KL KO but not in the Kit KO? Add to the discussion

We now address this apparent discordance in the discussion. Pax2 Cre mediates recombination weeks ahead of Pcp2 Cre. We therefore suspect that postnatal PC KL KO may be more phenotypic than embryonic MLI Kit KO because there is less time for developmental compensation. A future evaluation of the impact of postnatal Kit KO would be informative to this end.

As in Figure 2, plotting the charge might be more accurate.

We now plot total charge transfer.

Are the intrinsic properties in KL KO PCs altered? (Spontaneous firing, capacitance, input resistance).

We have added to the text that we found no difference in capacitance or input resistance between Purkinje cells from KL floxed homozygous Control animals versus those from KL floxed homozygous, PCP2 Cre positive KL KO animals. We plan to characterize both basal and MLI modulated PC firing in a future manuscript, especially since Pcp2 Cre mediated KL KO seems more phenotypic than Pax2 Cre mediated Kit KO, we agree that this seems a better testbed for investigating differences in both the basal, and the MLI-mediated modulations in, PC firing.

3D-F - Example traces would be desirable (see above, analogous to Fig. 2).

More example traces have been added.

Figure 4: 'In vivo mixtures' sounds unusual. Consider revision (e.g., 'to sparsely delete KL').

Changed

The observation that control PC sIPSC frequency is lower in KL OX PCs than in sham is interesting. This observation would be consistent with overall inhibitory synapse density being preserved. This could be evaluated with immunohistochemistry. For how far away from the injection area does this observation hold true?

Because we have now analyzed and failed to find an overt (per animal average) change in synaptic puncta size or density in the whole animal Control vs PCP2 Cre mediated KL KO conditions, we do not have confidence that it is feasible to pursue this IHC strategy in the sparse viral-mediated KL KO or OX conditions. To the reviewer’s valid point however, we intend to probe the spatial extent/specificity of the sparse phenomenon when we are resourced to complement the KL/Kit manipulations with transgenic methods for evaluating MLI-PC synapses specifically, potentially by GRASP or related methods that would not be confounded by PC-PC synapses. Transgenic MLI access would also facilitate determining the spatial extent to which opto-genetically activated MLIs evoke equivalent responses in Control vs KL manipulated PCs.

Y-legend in D clipped.

Corrected

Existing literature suggests that MLI inhibition regulates the regularity of PC firing - this could be tested in Kit and KL mutants.

For now, based upon transgenic animal availability, we have now included an evaluation of PC firing in the (Pax2 Cre mediated) Kit KO condition. PC average firing frequency, mean ISI, and ISI CV2 were not significantly different across genotypes. A KS test of individual ISI durations for Control vs Kit KO did reveal a difference (p<0.0001). We have added a supplementary figure (S6) with this data. It is possible that in the more phenotypic PC KL KO condition that we may find a difference in these PC spiking patterns of PC firing, however, we are also eager to test in future studies whether postnatal KL or Kit KO impairs the ability of MLI activation to produce pauses or other alterations in PC firing or in PF-PC mediated plasticity.

Reviewer #3 (Recommendations For The Authors):

Reference to Figure 1A in the Results section is slightly inaccurate. Kit gene modifications are illustrated in Figures 1A, B. Where Figure 1A shows Kit distribution. Please rephrase. Relatedly, the reference to Figs 1B - D are shifted in the results section, and 1E is skipped.

We have changed the text.

Please show cumulative histograms for frequency too for consistency with amplitude (e.g. Fig 2).

We have instead, for reasons outlined by other reviewers, documented total charge transfer for both Kit KO and KL KO experiments where sIPSC events were analyzed.

Fig S3: include example traces of PPR.

This is now included.

Include quantifications of GABAergic synapse density in Fig S4.

This is now included.

Include inset examples of KO in Fig S4A.

This is now included.

Add average puncta size graphs along Figure S4B. The effect apparent in the histogram of S4B is small and statistics using individual puncta as n values (in the 20,000s) therefore misleading.

Per animal analysis is now instead included in the figure and text.

Figure S4B y axis label blocked.

Corrected

Include quantification referenced in "As PSD95 immunoreactivity faithfully follows multiple markers of pinceaux size 40, we quantified PSD95 immunoreactive pinceau area and determined that pinceaux area was decreased by ~50% in Kit KO (n 26 Control vs 43 Kit KO, p<0.0001, two-tailed t-test)."

We added a graph of per animal averages, instead of in text individual pinceau areas.

Include antibody dilutions in the methods.

Added.

It's unclear from the text where the Mirow lab code comes from.

Detail has now been added in text.

Typo in methods "The Kit tm1c alle was bred...".

Corrected

Typo in Figure S4 legend "POSD-95 immuno-reactivity".

Corrected

Author Response

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

First of all, we'd like to thank the three reviewers for their meticulous work that enable us to present now an improved manuscript and substantial changes were made to the article following reviewers' and editors' recommendations. We read all their comments and suggestions very carefully. Apart from a few misunderstandings, all comments were very pertinent. We responded positively to almost all the comments and suggestions, and as a result, we have made extensive changes to the document and the figures. This manuscript now contains 16 principal figures and 15 figure supplements.

The number of principal figures is now 16 (1 new figure), and additional panels have been added to certain figures. On the other hand, we have added 7 additional figures (supplement figures) to answer the reviewers' questions and/or comments.

Main figures

▪ Figures 1, 4, 5, 10, 11, 12, 13, 14: unchanged ▪ Figure 7 and 8 were switched.

▪ Figure 2: we added panel F in response to reviewer 3's and request for sperm defect statistics

▪ Figure 3: the contrast in panel B has been taken over to homogenize colors

▪ Figure 6: This figure was recomposed. The WB on testicular extract was suppressed and we present a new WB allowing to compare the presence of CCDC146 in the flagella fraction. Using an anti-HA Ab, we demonstrate that the protein is localized in the flagella in epididymal sperm. Request of the 3 reviewers.

▪ Figure 7 (old 8): to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. Moreover, the WB was removed and is now presented in figure 6 (improved as requested).

▪ Figure 8. Was old figure 7

▪ Figure 9: figure 9 was recomposed and improved for increased clarity as suggested by reviewer 2 and 3.

▪ Figure 16 was before appendix 11

Figure supplements and supplementary files

▪ Figure 1-Figure supplement 1 New. Sperm parameters of the 2 patients. requested by editor (remark #1) by the reviewer 1 (Note #3)

▪ Figure 2-Figure supplement 1 new. Sperm parameters of the line 2 (KO animals) requested by the reviewer 1 (Note #5)

▪ Figure 4-Figure supplement 1 New. Experiment to evaluate the specificity of the human CCDC146 antibody. Minimal revision request and reviewer 1 note #8

▪ Figure 6-Figure supplement 1 New. Figure recomposed; Asked by reviewer 2 note #4 and reviewer 3

▪ Figure 8-Figure supplement 1 New. We now provide new images to show the non-specific staining of the midpiece of human sperm by secondary Abs in ExM experiments; Asked by reviewer 2

▪ Figure 10-Figure supplement 1 New. We added new images to show the non-specific staining of the midpiece of mouse sperm by secondary Abs in IF (panel B). Rewiever 1 note #9 and reviewer 2 note #5

▪ Figure 12-Figure supplement 1 New. Control requested by reviewer 3 Note #23

▪ Figure 13-Figure supplement 1 New. We provide a graph and a statistical analysis demonstrating the increase of the length of the manchette in the Ccdc146 KO. Requested by editor and reviewer 3 Note 24

▪ Figure 15-Figure supplement 1 New. Control requested by reviewer 2. Minor comments

▪ Figure supplementary 1 New. Answer to question requested by reviewer 2 note #1

All the reviewers' and editors’ comments have been answered (see our point to point response) and we resubmit what we believe to be a significantly improved manuscript. We strongly hope that we meet all your expectations and that our manuscript will be suitable for publication in "eLife". We look forward to your feedback,

Point by point answer

Please note that there has been active discussion of the manuscript and the summarize points below is the minimal revision request that the reviewers think the authors should address even under this new review model system. It was the reviewers' consensus that the manuscript is prepared with a lot of oversights - please see all the minor points to improve your manuscript.

All minimal revision requests have been addressed

Minimal revision request

1) Clinical report/evaluation of the two patients should be given as it was not described even in their previous study as well as full description of CCDC146.

We provide now a new Figure 1-figure supplement 1 describing the patients sperm parameters

2) Antibody specificity should be provided, especially given two of the reviewers were not convinced that the mid piece signal is non-specific as the authors claim. As both KO and KI model in their hands, this should be straightforward.

To validate the specificity of the Antibody, we transfected HEK cells with a human DDK-tagged CCDC146 plasmid and performed a double immunostaining with a DDK antibody and the CCDC146 antibody. We show that both staining are superimposable, strongly suggesting that the CCDC146 Ab specifically target CCDC146. This experiment is now presented in Figure 4-Figure supplement 1. Next, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

3) The authors should improve statistical analysis to support their experimental results for the reader can make fair assessment. Combined with clear demonstration of ab specificity, this lack of statistical analysis with very few sample number is a major driver of dampening enthusiasm towards the current study.

Several statistical analyses were carried out and are now included:

1) distribution of the HA signal in mouse sperm cells (see point 2 Figure 7 panel B)

2) quantification and statistical analyses of the defect observed in Ccdc146 KO sperm (figure 2 panel E)

3) Quantification and statistical analyses of the length of the manchette in spermatids 13-15 steps (Figure 13-Figure supplement 1 new)

4) The authors need to clarify (peri-centriolar vs. centriole)

In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein in somatic cells. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein, and this is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described, and the best example is maybe gamma-tubulin (PMID: 8749391).

or tone down (CCDC146 to be a MIP) of their claim/description.

Concerning its localization in sperm, we agree with the reviewer that our demonstration that CCDC146 is MIP would deserve more results. Because of that, we have toned down the MIP hypothesis throughout the manuscript. See lines 491495

Testis-specific expression of CCDC146 as it is not consistent with their data.

We have also modified our claim concerning the testis-expression of CCDC146. Line 176

Reviewer #1 (Recommendations For The Authors):

Major comments

1) As described in general comments, this study limits how the CCDC146 deficiency impairs abnormal centriole and manchette formation. The authors should explain their relationship in developing germ cells.

In fact, there are limited information about the relationship between the manchette and the centriole. However, few articles have highlighted that both organelles share molecular components. For instance, WDR62 is required for centriole duplication in spermatogenesis and manchette removal in spermiogenesis (Commun Biol. 2021; 4: 645. doi: 10.1038/s42003-021-02171-5). Another study demonstrates that CCDC42 localizes to the manchette, the connecting piece and the tail (Front. Cell Dev. Biol. 2019 https://doi.org/10.3389/fcell.2019.00151). These articles underline that centrosomal proteins are involved in manchette formation and removal during spermiogenesis and support our results showing the impact of CCDC146 lack on centriole and manchette biogenesis. This information is now discussed. See lines 596-603

2) The authors generated knock-in mouse model. If then, are the transgene can rescue the MMAF phenotype in CCDC146-null mice? This reviewer strongly suggest to test this part to clearly support the pathogenicity by CCDC146.

We indeed wrote that we created a “transgenic mice”, which was misleading. We actually created a CCDC16 knock-in expressing a tagged-protein. The strain was actually made by CRISPR-Cas9 and a sequence coding for the HA-tag was inserted just before the first amino acid in exon 2, leading to the translation of an endogenous HA-tagged CCDC146 protein. We have removed the word transgenic from the text and made changes accordingly (see lines 250-253). We can therefore not use this strain to rescue the MMAF phenotype as suggested by the reviewer.

3) Although the authors cite the previous study (Coutton et al., 2019), the study does not describe any information for CCDC146 and clinical information for the patients. The authors must show the results for clinical analysis to clarify the attended patients are MMAF patients without other phenotypic defects.

We have now inserted a table, indicating all sperm parameters for the patients harboring a mutation in the CCDC146 gene (Figure 1-Figure supplement 1) and is now indicated lines 159-160

4) The authors describe CCDC146 expression is dominant in testes, However, the level in testis is only moderate in human (Supp Figure 1). Thus, this description is not suitable.

In Figure 1-figure supplement 2 (old FigS1), the median of expression in testis is around 12 in human, a value considered as high expression by the analysis software from Genevestigator. However, for mouse, it is true that the level of expression is medium. We assumed that reviewer’s comment concerned testis expression in mouse. To take into account this remark, we changed the text accordingly. See line 176.

5) Although the authors mentioned that two mice lines are generated, only one line information is provided. Authors must include information for another line and provide basic characterization results to support the shared phenotype within the lines.

We now provide a revised Figure 2-figure supplement 1CD, presenting the second line and the corresponding text in the main text is found lines 178-183.

6) In somatic cells, the CCDC146 localizes at both peri-centriole and microtubule but its intracellular localization in sperm is distinguished. The authors should explain this discrepancy.

The multi-localization of a centriolar protein is already discussed in detail in discussion lines 520-526. We have written:

“Despite its broad cellular distribution, the association of CCDC146 with tubulin-dependent structures is remarkable. However, centrosomal and axonemal localizations in somatic and germ cells, respectively, have also been reported for CFAP58 [37, 55], thus the re-use of centrosomal proteins in the sperm flagellar axoneme is not unheard of. In addition, 80% of all proteins identified as centrosomal are found in multiple localizations (https://www.proteinatlas.org/humanproteome/subcellular/centrosome). The ability of a protein to home to several locations depending on its cellular environment has been widely described, in particular for MAP. The different localizations are linked to the presence of distinct binding sites on the protein…. “

7) Authors mention CCDC146 is a centriolar protein in the title and results subtitle. However, the description in results part depicts CCDC146 is a peri-centriolar protein, which makes confusion. Do the authors claim CCDC146 is centrosomal protein?

In figure 4A, we have clearly shown that the protein colocalizes with centrin, a centriolar core protein. This colocalization strongly suggests that CCDC146 is therefore a centriolar protein in somatic cells, and is now clearly indicated lines 211-212. However, its localization is not restricted to the centrioles and a clear staining was also observed in the pericentriolar material (PCM). The presence of a protein in PCM and centriole was already described and the best example is maybe gamma-tubulin (PMID: 8749391).

8) Verification of the antibody against CCDC146 must be performed and shown to support the observed signal are correct. 2nd antibody only signal is not proper negative control.

It is a very important remark. The commercial antibody raised against human CCDC146 was validated in HEK293-cells expressing a DDK-tagged CCDC146 protein. Cells were co-marked with anti-DDK and anti-CCDC146 antibodies. We have a perfect colocalization of the staining. This experiment is now presented in Figure 4-figure supplement 1 and presented in the text (lines 206-208).

9) In human sperm, conventional immunostaining reveals CCDC146 is detected from acrosome head and midpiece. However, in ExM, the signal at acrosome is not detected. How is this discrepancy explained? The major concern for the ExM could be physical (dimension) and biochemical (properties) distortion of the sample. Without clear positive and negative control, current conclusion is not clearly understood. Furthermore, it is unclear why the authors conclude the midpiece signal is non-specific. The authors must provide experimental evidence.

Staining on acrosome should always be taken with caution in sperm. Indeed, numerous glycosylated proteins are present at the surface of the plasma membrane regarding the outer acrosomal membrane for sperm attachment and are responsible for numerous nonspecific staining. Moreover, this acrosomal staining was not observed in mouse sperm, strongly suggesting that it is not specific.

Concerning the staining in the midpiece observed in both conventional and Expansion microscopy, it also seems to be nonspecific and associated with secondary Abs.

For IF, we now provide new images showing clearly the nonspecific staining of the midpiece when secondary Ab were used alone (see Figure 10-figure supplement 1B).

For ExM, we provide new images in Figure 8-figure supplement 1B (POC5 staining) showing a staining of the midpiece (likely mitochondria), although POC5 was never described to be present in the midpiece. Both experiments (CCDC146 and POC5 staining by ExM) shared the same secondary Ab and the midpiece signal was likely due to it.

Moreover, we now provide new images (figure 7C) in ExM on mouse sperm showing no staining in the midpiece and demonstrating that the punctuated signal is present all along the flagellum. Finally, we would like to underline that we now provide new IF results, using an anti-HA conjugated with alexafluor 488 and confirming the ExM results.

These points are now discussed lines 498-502 for acrosome and lines 503-511 for midpiece staining.

10) For intracellular localization of the CCDC146 in mouse sperm, the authors should provide clear negative control using WT sperm which do not carry the transgene.

This experiment was performed.

To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum.

11) Current imaging data do not clearly support the intracellular localization of the CCDC146. Although western blot imaging reveal that CCDC146 is detected from sperm flagella, this is crude approach. Thus, this reviewer highly recommends the authors provide more clear experimental evidence, such as immuno EM.

We provide now a WB comparing the presence of the protein in the flagellum and in the head fractions; see new figure 6. We show that CCDC146 is only present in the flagellum fraction; The detection of the band appeared very quickly at visualization and became very strong after few minutes, demonstrating that the protein is abundant in the flagella. It is important to note that epididymal sperm do not have centrioles and therefore this signal is not a centriolar signal. We also now provide new statistical analyses showing that the immuno-staining observed in the principal piece is very specific (Figure 7B). Altogether, these results demonstrate unequivocally the intracellular localization of CCDC146 in the flagellum. This point is now discussed lines 480-489

12) Although sarkosyl is known to dissociate tubulin, it is not well understood and accepted that the enhanced detection of CCDC146 by the detergent indicates its microtubule inner space. Sperm axoneme to carry microtubule is also wrapped peri-axonemal components with structural proteins, which are even not well solubilized by high concentration of the ionic detergent like SDS.

We agree with the reviewer that the solubilization of the protein by sarkozyl is not a proof of the presence of the protein inside microtubule. Taking into account this point, the MIP hypothesis was toned down and we now discuss alternative hypothesis concerning these results; See discussion lines 490-497

13) SEM image is not suitable to explain internal structure (line 317-323).

We agree with the reviewers and changes were made accordingly. See lines 354-357

Minor comments

1) In main text, supplementary figures are cited "Supp Figure". And the corresponding legends are written in "Appendix - Figure". Please unify them.

Done Labelled now “Figure X-figure supplement Y”

2) Line 159, "exon 9/19" is not clear.

We have written now exons 9 and indicated earlier that the gene contains 19 exons

3) Line 188, "positive cells" are vague.

Positive was changed by “fluorescent”

4) Representative TUNEL assay image for knockout testes were not shown in Supp Figure 3B.

It was a mistake now Figure 2-figure supplement 2C

5) Please provide full description for "IF" and "AB" when described first.

Done

6) Line 262, It is unclear what is "main piece".

Changed to principal piece

7) Line 340, Although the "stage" information might be applicable, this is information for "seminiferous tubule" rather than "spermatid". This reviewer suggests to provide step information rather than stage information.

We agree with the reviewer that there was a confusion between “stage” and “step”. We change to step spermatids

8) Line 342, Step 1 is not correct in here.

OK corrected. now steps 13-15 spermatids

9) Line 803, "C." is duplicated.

Removed

10) Figure 3A, it will be good to mark the defective nuclei which are described in figure legends.

These cells are now indicated by white arrow heads

11) Figure 5, Please provide what MT stands for.

Now explained in the legend of figure 5

12) Figure 6. Author requires clear blot images for C. In addition, Panel B information is not correct. If the blot was performed using HA antibody, then how "WT" lane shows bands rather than "HA" bands?

The reviewer is correct. It was a mistake; The figure was recomposed and improved.

Reviewer #2 (Recommendations For The Authors):

Overall, editing oversights are present throughout the manuscript, which has made the review process quite difficult. Some repetitive figures can be removed to streamline to grasp the overall story easier. Some claims are not fully supported by evidence that need to tone down. Some figures not referenced in the main text need to be mentioned at least once.

All figures are now referenced in the text

Major comments:

1) 163-164 - Please clarify the claim that there is going to be an absence of the protein or nonfunctional protein, especially for the patient with a deletion that could generate a truncated protein at two third size of the full-length protein. Similarly, 35% of the protein level is present for the patient with a nonsense mutation. Some in silico structural analysis or analysis of conserved domains would be beneficial to support these claims.

Both mutations are predicted to produce a premature stop codons: p.Arg362Ter and p.Arg704serfsTer7, leading either to the complete absence of the protein in case of non-sense mediated mRNA decay or to the production of a truncated protein missing almost two third or one fourth of the protein respectively. CCDC146 is very well conserved throughout evolution (Figure supplementary 1), including the 3’ end of the protein which contains a large coil-coil domain (Figure 1B). In view of the very high degree of conservation, it is most likely that the 3’ end of the protein, absent in both subjects, is critical for the CCDC146 function and hence that both mutations are deleterious. This explanation is now added to the discussion. see lines 439-448

2) 173, 423 - Please clearly state a rationale of your mouse model design (i.e., why a mouse model that recapitulate human mutation is not generated) as the truncations identified in human patients are located further towards the C-terminus, and it is not clear whether truncated proteins are present, and if so, they could still be functional. Basically, the current mouse model supports the causality of the human mutations.

This is an important question, which goes beyond the scope of this article, and raises the question of how to confirm the pathogenicity of mutations identified by high-throughput sequencing. The production of KO or KI animals is an important tool to help confirm one’ suspicions but the first element to take into consideration is the nature of the genetic data.

Here we had two patients with homozygous truncating variants. In human, it is well established that the presence of premature stop codons usually induces non-sense mediated mRNA decay (NMD), inducing the complete absence of the protein or a strong reduction in protein production. In the unlikely absence of NMD in our two patients, the identified variants would induce the production of proteins missing 60% and 30% of their C terminal part. Often (and it is particularly true for structural proteins) the production of abnormal proteins is more deleterious than the complete absence of the protein (and it is most likely the purpose of NMD, to limit the production of abnormal “toxic” proteins). For these reasons, to try to recapitulate the most likely consequences of the human variants, without risking obtaining an even more severe effect, we decided to introduce a stop codon in the first exon in order to remove the totality of the protein in the KO mice.

The second element is to interpret the phenotype of the KO animals. Here, the human sperm phenotype is perfectly recapitulated in the KO mice.

Overall, we have strong genetic arguments in human and the reproduction of the phenotype in KO mice confirming the pathogenicity of the variants identified in men.

This point is now discussed see lines 433-438

3) Figure 6A - the labelling is misleading as it seems to suggest that the specific cells were isolated from the testes for RT-PCR.

We have modified the labelling to avoid any confusion.

Figure 6B -Signal of HA-tag is shown in WT, not in transgenic. Please check the order of the labels. Figure 6C - This blot is NOT a publication-quality figure. The bands are very difficult to observe, especially in lane D18. Because it is one of the important data of this study, replacing this figure is a must.

The figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed.

4) Supplementary fig 6 is also not a publication-level figure, and the top part seems largely unnecessary (already in the figure legend).

The figure has been completely remade as well (now Figure 6-Figure Supplement 1).

5) 261/267- The conclusion that mitochondrial staining in the flagellum (in both mice and humans) is non-specific is not convincing. Supplementary fig 8 shows that the signal from secondary only IF possibly extends beyond the midpiece - but it is hard to determine as no mitochondrial-specific staining is present. Either need to tone down the conclusion or provide supporting experimental evidence.

First, to avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the flagellum. These experiments are now described lines 271-279

Second, we provide new images of the signal obtained with secondary Abs only that shows more clearly that the secondary Ab gave a non-specific staining (Figure 10-Figure supplement 1B). This point is discussed lines 503-511

6) Figure 9 A - Please relate the white line to Fig. 9B label in X-axis. The information from Fig 9A+D and 9E+F are redundant. The main text nor the figure legends indicate why these specific two sperm were chosen for quantification and demonstrating the outcomes. One of them could be moved to supplementary information or removed, or the two could be combined.

As suggested by the reviewer, we have combined the two sperm to demonstrate that CCDC146 staining is mostly located on microtubule doublets. Moreover, the figure was recomposed to make it clearer.

Minor comments:

All of the supplementary figures are referred to as Supp Fig X in the text, however, they are actually titled Appendix - Figure X. This needs to be consistent.

The figures are now referred as figure supplement x in both text and figures

Line 125 - edit spacing.

We think this issue (long internet link) will be curated later and more efficiently by the journal, during the step of formatting necessary for publication.

144 - With which to study  with which we studied?

We made the change as suggested.

151 - Supp Fig 1 - the text says that the gene is highly transcribed in human and mouse testes, but the information in the figure states that the level in mouse tissues is "medium"

We have corrected this mistake in the text; See line 176

165 - The two mutations are most likely deleterious. Please specifically mention what analyses done to predict the deleterious nature to support these claims.

Both variants, c.1084C>T and c.2112del, are extremely rare in the general population with a reported allele frequency of 6.5x10-5 and 6.5x10-06 respectively in gnomAD v3. Moreover, these variants are annotated with a high impact on the protein structure (MoBiDiC prioritization algorithm (MPA) score = 10, DOI: 10.1016/j.jmoldx.2018.03.009) and predicted to induce each a premature termination codon, p.(Arg362Ter) and p.(Arg704SerfsTer7) respectively, leading to the production of a truncated protein. This information is now given line 164-169

196-200/Figure 4 - As serum starved cells/basal body (B) are not mentioned in the main text, as is, Fig 4A would be sufficient/is relevant to the text. Please make the text reflect the contents of the whole figure, or re/move to supplement.

We agree with the reviewer that the full description of the figure should be in the text. We added two sentences to describe figure 4B see lines 217-218.

224 - spermatozoa (plural) fits better here, not spermatozoon

OK changed accordingly

236 - According to the figure legend, 6B is only showing data from the epididymal sperm, not postnatal time points; should be referencing 6C. Alignment of Marker label

As indicated above, the figure has been completely remade, including new results. See new figure 6. Figure 6C was suppressed. The corresponding text was changed accordingly see lines 249-266

255-256 - Referenced figure 7B3, however, 7B3 only shows tubulin staining, so no CCDC146 can be observed. Did authors mean to reference fig 7B as a whole?

Sorry for this mistake. We agree and the text is now figure 8B6 (figure 7 and 8 were switched)

305 - "of tubules" - I presume it is meant to be microtubules?

Yes; The text was changed as suggested

317-321 - a diagram of HTCA would be useful here

We have added a reference where HTCA diagram is available see line 363. Moreover, a TEM view of HTCA is presented figure 12A

322/Fig 11A - an arrow denoting the damage might be useful, as A1 and A3 look similar. The size of the marker bar is missing. Please update the information on figure legend.

Concerning, the comparison between A1 and A3, the take home message is that there is a great variability in the morphological damages. This point is now underlined in the corresponding text. We updated the size of the marker bar as suggested (200 nm). See line 365-367

323 - Please mark where capitulum is in the figure

Capitulum was changed for nucleus

Since Fig 11B2 is not referenced in the main text, it does not seem to add anything to the data, and could be removed/moved to supplement.

We added a sentence to describe figure 11B2 line 370

342-343 - manchette in step I is not seen clearly - the figure needs to be annotated better. However, DPY19L2 is absent in step I in the KO, but the main text does not reflect that - why is that?

We do not understand the remark of the reviewer “manchette in step I is not seen clearly”. The figure shows clearly the manchette (red signal) in both WT and KO (Figure 13 D1/D2).

For steps 13-15 WT spermatids, the size of the manchette decreases and become undetectable. In KO spermatids, the shrinkage of the manchette is hampered and in contrast continue to expand (Figure 13D2). We also provide a new Figure 13-figure supplement 1 for other illustrations of very long manchettes and a statistical analysis. In the meantime, the acrosome is strongly remodeled, as shown in figure 16-new, with detached acrosome (panel H). This morphological defect may induce a loss of the DPY19L2 staining (Figure 13 D2 stage I-III). This explanation is now inserted in the text line 396399

Figure 15B and 15C only show KO, corresponding images from the WT should be present for comparison.

WT images are now provided in Figure 1-figure supplement 1 new

Figure 12 - Figure 12 - JM?.

JM was removed. It does not mean anything

Figure 12C and Supplementary Fig 10 - structures need to be labelled, as it is unclear what is where

Done

338 - text mentions step III, but only sperm from step VII are shown in Figure 13

As suggested by reviewer 3, we changed stage by step. The text was modified to take into account this remark see lines 388-396

360 - This is likely supposed to say Supp Figure 11E-G, not 13??

Yes, it is a mistake. Corrected

388 Typo "in a in a".

Yes, it is a mistake. Corrected

820 - Fig 3 legend - in KO spermatid nuclei were elongated - could this be labelled by arrows? I am not convinced this phenotype is that different from the WT.

In fact, the nuclei of elongating KO spermatids are elongated and also very thin, a shape not observed in the WT; We have added arrow heads and modified the text to indicate this point line 200.

836 - Figure 5 legend says that in yellow is centrin, but that is not true for 5A, where the figure shows labelling for y-tubulin (presumably, according to the figure itself).

We have modified the text of the legend to take into account the remark

837- 5A supposedly corresponds to synchronized HEK293T cells, but the reasoning behind using synchronized cells is not mentioned at all in the main text; furthermore, how this synchronization is achieved is not explained in materials and methods (serum starvation? Thymidine block?).

Yes, figure 5A was obtained with synchronized cells. We have added one paragraph in the MM section. For cell synchronization experiments, cells underwent S-phase blockade with thymidine (5 mM, SigmaAldrich) for 17 h followed by incubation in a control culture medium for 5 h, then a second blockade at the G2-M transition with nocodazole (200 nM, Sigma-Aldrich) for 12 h. Cells were then fixed with cold methanol at different times for IF labelling. See line 224 for changes made in the result section and lines 700-704 for changes made in the MM section.

845- figure legend says that the RT-PCR was done on CCDC146-HA tagged mice, but the main text does not reflect that.

We made changes and the description of the KI is now presented before (line 240) the RT-PCR experiment (line 257).

949 - it is likely supposed to say A2, not B1 (B1 does not exist in Fig 15)

Yes, it is a mistake. Corrected

971 - Appendix Fig 3 legend - I believe that the description for B and C are swapped.

Yes, it is a mistake. Corrected

Furthermore, some questions to address in A would be: Which cross sections were from which animal/points? How many per animal? Were they always in the same location?

Yes, we have a protocol for arranging and orienting all testes in the same way during the paraffin embedding phase. The cross-sections are therefore not taken at random, and we can compare sections from the same part of the testis. The number of animals was already indicated in the figure legend (see line 1128)

Reviewer #3 (Recommendations For The Authors):

1) There are a number of grammatical and orthographical errors in the text. Careful proofreading should be performed.

We have sent the manuscript to a professional proofreader

2) The author should also check for redundancies between the introduction and the discussion.

The discussion has modified to take into account reviewers’ remarks. Nevertheless, we did our best to avoid redundancies between introduction and discussion.

3) Can the authors provide a rationale why they have chosen to tag their gene with an HA tag for localisation? One would rather think of fluorescent proteins or a Halo tag.

Because the functional domains of the protein are unknown, adding a fluorescent protein of 24 KDa may interfere with both the localization and the function of CCDC146. For this reason, we choose a small tag of only 1.1 KDa, to limit as such as possible the risk of interfering with the structure of the protein. This rational is now indicated in the manuscript lines 251-254. It is worth to note, that the tagged-strain shows no sperm defect, demonstrating that the HA-tag does not interfere with CCDC146 function.

4) In the abstract, line 53, "provide evidence" is not the right term for something that is just suggestive. The term "suggests" would be more appropriate.

The text was modified to take into account this remark

5) Line 74: "genetic deficiency" sounds strange here, do the authors mean simply "mutation"?

Infertility may be due to several genetic deficiency such as chromosomal defects (XXY (Klinefelter syndrome)), microdeletion of the Y chromosome or mutations in a single gene. Therefore, mutation is too restrictive. Nevertheless, we modified the sentence which is now “…or a genetic disorder including chromosomal or single gene deficiencies”

6) Lines 163-164: the authors describe the mutations (premature stop mutations) and say that they could either lead to complete absence of the gene product, or the expression of a truncated protein. Did they test this, for example, with some immuno blot analyses?

As stated above, unfortunately, we were unable to verify the presence of RNA-decay in these patients for lack of biological material.

7) Line 184 and Fig 2E: the sperm head morphologies should be quantitatively assessed.

We provide now a full statistical analysis of the observed defects: see new panel in Figure 2 F

8) Fig 3: The annotation should be more precise - KO certainly means CDCC146-KO. The colours of the IH panels is different, which attracts attention but is clearly a colour-adjustment artefact. Colours should be adjusted for the panels to look comparable. It would be also helpful to add arrowheads into the figure to point at the phenotypes that are highlighted in the text.

We have added Ccdc146 KO in all figures. We have added arrow heads to point out the spermatids showing a thin and elongated nucleus. Concerning adjustment of colors, we attempted to make images of panel B comparable. See new figure 3.

9) Fig 6A: the authors use RT PCR to determine expression dynamics of their gene of interested, and use actin (apparently) as control. However, actin and CDCC146 expression levels follow the same trend. How is the interpreted?

The reviewer did not understand the figure. The orange bars do not correspond to actin expression and the grey bars to Ccdc146 expression but both bars represent the mRNA expression levels of Ccdc146 relative to Actb (orange) and Hprt (grey) expression in CCDC146-HA mouse pups’ testes. We tested two housekeeping genes as reference to be sure that our results were not distorted by an unstable expression of a housekeeping gene. We did not see significant difference between both house keeping genes. Actin was not used.

10) In line 235, the authors suggest posttranslational modifications of their protein as potential cause for a slightly different migration in SDS PAGE as predicted from the theoretical molecular weight. This is not necessarily the case, some proteins do migrate just differently as predicted.

We have changed the text accordingly and now provide alternative explanation for the slightly different migration. See lines 258-259

11) The annotation of Fig 6 panels is problematic. First, why do the authors write "Laemmli" as description of the gel? It would be more helpful to write what is loaded on the gel, such as "sperm". Second, in panels B and C it would be helpful to add the antibodies used. It is not clear why there is a signal in the WT lane of panel B, but not in the HA lane (supposing an anti-HA antibody is used: why has WT a specific HA band?). In panel C, it is not clear why the blot that has so beautifully shown a single band in panel B suddenly gives such a bad labelling. Can the authors explain this? Also, they cut off the blot, likely because to too much background, but this is bad practice as full blots should be shown. In the current state, the panel C does not allow any clear conclusion. To make it conclusive, it must be repeated.

Several mistakes were present in this figure. This figure was recomposed. The WB on testicular extract was suppressed and we now present a new WB allowing to compare the presence of CCDC146 in the flagella and head fractions from WT and HA-CCDC146 sperm. Using an anti-HA Ab, we demonstrate that in epididymal sperm the protein is localized in the flagella only. See new figure 6. The corresponding text was changed accordingly.

12) The authors have raised an HA-knockin mouse for CDCC146, which they explained by the unavailability of specific antibodies. However, in Fig 7, they use a CDCC146 antibody. Can they clarify?

The commercial Ab work for HUMAN CCDC146 but not for MOUSE CCDC146. We have added few words to make the situation clearer, we have added the following information “the commercial Ab works for human CCDC146 only”. See line 240

13) In Fig 7A (line 258), the authors hypothesise that they stain mitochondria - why not test this directly by co-staining with mitochondria markers?

We chose another solution to resolve this question:

To avoid the issue of the non-specificity of secondary antibodies, we performed a new set of IF experiments using an HA Tag Alexa Fluor® 488-conjugated Antibody (anti-HA-AF488-C Ab) on WT and HA-CCDC146 sperm. These results are now presented in figure 7 panel A (new). The specificity of the signal obtained with the anti-HA-AF488-C Ab on mouse spermatozoa was evaluated by performing a statistical study of the density of dots in the principal piece of the flagellum from HA-CCDC146 and WT sperm. These results are now presented in figure 7 panel B (new). This study was carried out by analyzing 58 WT spermatozoa and 65 CCDC146 spermatozoa coming from 3 WT and 3 KI males. We found a highly significant difference, with a p-value <0.0001, showing that the signal obtained on spermatozoa expressing the tagged protein is highly specific. We have added a paragraph in the MM section to describe the process of image analysis. We finally present new images obtained by ExM showing no staining in the midpiece (figure 7C new). Altogether, these results demonstrate unequivocally the presence of the protein in the whole flagellum.

14) It seems that in both, Fig 7 and 8, the authors use expansion microscopy to localise CDCC146 in sperm tails. However, the staining differs substantially between the two figures. How is this explained?

In figure 8 we used the commercial Ab in human sperm, whereas in figure 7 we used the anti-HA Abs in mouse sperm. Because the antibodies do not target the same part of the CCDC146 protein (the tag is placed at the N-terminus of the protein, and the HPA020082 Ab targets the last 130 amino acids of the Cter), their accessibility to the antigenic site could be different. However, it is important to note that both antibodies target the flagellum. This explanation is now inserted see lines 304-312

15) Fig 8D and line 274: the authors do a fractionation, but only show the flagella fraction. Why?

Showing all fractions of their experiment would have underpinned the specific enrichment of CDCC146 in the flagella fraction, which is what they aim to show. Actually, given the absence of control proteins, the fact that the band in the flagellar fraction appears to be weaker than in total sperm, one could even conclude that there is more CDCC146 in another (not analysed) fraction of this experiment. Thus, the experiment as it stands is incomplete and does not, as the authors claim, confirm the flagellar localisation of the protein.

We agree with the reviewer’s remark. We provide now new results showing both flagella and nuclei fractions in new figure 6A. This experiment is presented lines 253-256

16) Line 283, Fig 9D,F: The description of the microtubules in this experiment is not easy to understand. Do the authors mean to say that the labelling shows that the protein is associated with doublet microtubules, but not with the two central microtubules? They should try to find a clearer way to explain their result.

As suggested by reviewer 2, we have changed the figure to make it clearer. The text was changed accordingly. See new figure 9 and new corresponding legend lines 1006.

17) Fig 9G - how often could the authors observe this? Why is the axoneme frayed? Does this happen randomly, or did the authors apply a specific treatment?

Yes, it happens randomly during the fixation process.

18) Line 300 and Fig 10A - the authors talk about the 90-kDa band, but do say anything about what they think this band is representing.

We have now added the following sentence lines 340-342: “This band may correspond to proteolytic fragment of CCDC146, the solubilization of microtubules by sarkosyl may have made CCDC146 more accessible to endogenous proteases.”

19) Fig 11A, lines 321-322: the authors write that the connecting piece is severely damaged. This is not obvious for somebody who does not work in sperm. Perhaps the authors could add some arrow heads to point out the defects, and briefly describe them in the text.

We realized from your remark that our message was not clear. In fact, there is a great variability in the morphological damages of the HTCA. For instance, the HTCA of Ccdc146 KO sperm presented in figure 10A2 is quite normal, whereas that in figure 10A4 is completely distorted. This point is now underlined in the corresponding text. See lines 367-369

We also added the size of the marker bar (200 nm), which were missing in the figure’s legend.

20) Line 323: it will be important to name which tubulin antibody has been used to identify centrioles, as they are heavily posttranslationally modified.

The different types of anti-tubulin Abs are described in the corresponding figure’s legend

21) Fig 11B - phenotypes must be quantified to make these observations meaningful.

We agree that a quantification would improve the message. However, testicular sperm are obtained by enzymatic separation of spermatogenic cells and the number of testicular sperm are very low. Moreover, not all sperm are stained. Taking these two points into account, it seems to us that quantification could be difficult to analyze. For this reason, the quantification was not done; however, it is important to note that these defects were not observed in WT sperm, demonstrating that these defects are cased by the lack of CCDC146. We have added a sentence to underline this point; See lines 374-375

22) Line 329: Figure 12AB - is this a typo - should it read Figure 12B?

We have split the panel A in A1 and A2 and changed the text accordingly. See line 378

23) Why are there not wildtype controls in Fig 12B, C?

We provide now as Figure 12-figure supplement 1, a control image for fig 12B. For figure 12C, the emergence of the flagellum from the distal centriole in WT is already shown in Fig 12A1

24) Fig 13: the authors write that the manchette is "clearly longer and wider than in WT cells" (lines 342-343). How can they claim this without quantitative data?

We now provide a statistical analysis of the length of the manchette. See figure 13-figure supplement 1A. We also provide a new a new image illustrating the length of the manchette in Ccdc146 KO spermatids; See Figure 13-figure supplement 1B.

Author Response

We appreciate the insightful and constructive feedback from the reviewers regarding our manuscript, "Gain neuromodulation mediates perceptual switches: evidence from pupillometry, fMRI, and RNN Modelling." The comments have provided us with a number of valuable perspectives that will undoubtedly strengthen the impact and clarity of our work.

We recognize the need for a more detailed and comparative analysis of the perceptual tasks used in our pupil and fMRI experiments. To address these points directly: the jittered intertrial intervals (ITIs) in the fMRI work were deemed necessary to effectively deconvolve the BOLD response (see Stottinger et al., 2018). In our fMRI work, each image was randomly preceded and followed by varying ITIs (2, 4, 6, and 8 seconds), ensuring an equitable distribution across sets and subjects. Importantly, our analysis of both fMRI and behavioral studies, including eye tracking data, indicates that perceptual switch behavior – the point at which switches occur – is consistent across modalities. If more predictive or preparatory activity were present in the fMRI version of the task, we would expect earlier switches or choices and altered reaction time distributions – neither of these signatures was observed in the original study (Stottinger et al., 2018). Importantly, this suggests that the additional time available in the fMRI experiments did not significantly alter behavioral outcomes. Thus, our findings suggest that despite the differences in timing and task structure, the behavioural responses remain consistent across both experimental setups. We will clarify this in the revised manuscript.

In response to the reviewer's comments on our computational model, particularly regarding the modelling of noradrenaline (NA) effects in the RNN, we agree that modelling gain as stationary is a substantial approximation. However, given the slow ramping of pupil diameter, which served as our proxy for gain, it is an approximation that we believe is justified: in the revised manuscript, we will run additional simulations to ensure the validity of this approximation. In addition, whilst we agree that the model is more complicated than is needed for the task, we opted for RNN modelling, in lieu of a simpler modelling approach, because we wanted to use RNN modelling as a method for both hypothesis testing and generation. To build the RNN, the only key elements of model structure we had to specify in advance were the inputs and the target outputs of the network. The solution the RNN arrived at, although involving many more parameters than a simpler model, was entirely determined by optimisation (i.e., not our a priori hypotheses). We feel that this strengthens the result considerably. Importantly, this approach also allowed us to be surprised by the results of the model – for instance, we did not anticipate that the effect of gain on the energy landscape to be primarily mediated by inhibitory gain. In the revised manuscript, we will integrate this line of thinking into the paper. We are also sensitive to the fact that this result is both counterintuitive and difficult to study in high-dimensional dynamical systems like RNNs. In revisions, we will provide further analysis of the RNN and build a 2D approximation to the RNN that can be studied on the phase plane to better conceptually illuminate the mechanisms at play.

Furthermore, we agree with the suggestion to consider alternative mechanisms that might contribute to perceptual switches, such as attention and top-down processing. While our study primarily focuses on LC-mediated gain modulation, we acknowledge the complexity of neural processes involved in perception and will expand our discussion to include these potential mechanisms. Furthermore, noting the importance of moderating the causal language used in our manuscript. We will revise our wording to more accurately reflect the correlational nature of our findings and ensure that our conclusions are firmly grounded in the data presented.

In conclusion, we are enthusiastic about the opportunity to refine our manuscript based on these valuable comments. In an updated version, we will address the overall points by providing clearer explanations of our methods, refining our figures for better readability, and ensuring that our conclusions are supported by robust analysis. We believe that these revisions will not only address the concerns raised but also significantly enhance the overall quality of our research. We thank the reviewers for their thorough and thoughtful critiques and look forward to submitting our revised manuscript.

Author Response

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

Reviewer #1 (Public Review):

In this manuscript, the authors explore the effects of DNA methylation on the strength of regulatory activity using massively parallel reporter assays in cell lines on a genome-wide level. This is a follow-up of their first paper from 2018 that describes this method for the first time. In addition to adding more indepth information on sequences that are explored by many researchers using two main methods, reduced bisulfite sequencing and sites represented on the Illumina EPIC array, they now show also that DNA methylation can influence changes in regulatory activity following a specific stimulation, even in absence of baseline effects of DNA methylation on activity. In this manuscript, the authors explore the effects of DNA methylation on the response to Interferon alpha (INFA) and a glucocorticoid receptor agonist (dexamethasone). The authors validate their baseline findings using additional datasets, including RNAseq data, and show convergences across two cell lines. The authors then map the methylation x environmental challenge (IFNA and dex) sequences identified in vitro to explore whether their methylation status is also predictive of regulatory activity in vivo. This is very convincingly shown for INFA response sequences, where baseline methylation is predictive of the transcriptional response to flu infection in human macrophages, an infection that triggers the INF pathways.

Thank you for your strong assessment of our work!

The extension of the functional validity of the dex-response altering sequences is less convincing.

We agree. We note that genes close to dex-specific mSTARR-seq enhancers tend to be more strongly upregulated after dex stimulation than those near shared enhancers, which parallels our results for IFNA (lines 341-344). However, there is unfortunately no comparable data set to the human flu data set (i.e., with population-based whole genome-bisulfite sequencing data before and after dex challenge), so we could not perform a parallel in vivo validation step. We have added this caveat to the revised manuscript (lines 555-557).

Sequences altering the response to glucocorticoids, however, were not enriched in DNA methylation sites associated with exposure to early adversity. The authors interpret that "they are not links on the causal pathway between early life disadvantage and later life health outcomes, but rather passive biomarkers". However, this approach does not seem an optimal model to explore this relationship in vivo. This is because exposure to early adversity and its consequences is not directly correlated with glucocorticoid release and changes in DNA methylation levels following early adversity could be related to many physiological mechanisms, and overall, large datasets and meta-analyses do not show robust associations of exposure to early adversity and DNA methylation changes. Here, other datasets, such as from Cushing patients may be of more interest.

Thank you for making these important points. We have expanded the set of caveats regarding the lack of enrichment of early adversity-reported sites in the mSTARR-data set (lines 527-533). Specifically, we note that the relationship between early adversity and glucocorticoid physiology is complex (e.g., Eisenberger and Cole, 2012; Koss and Gunnar, 2018) and that dex challenge models one aspect of glucocorticoid signaling but not others (e.g., glucocorticoid resistance). Nevertheless, we also see little evidence for enrichment of early adversity-associated sites in the mSTARR data set at baseline, independently of the dex challenge experiment (lines 483-485; Figure 4).

We also agree that large data sets (e.g., Houtepen et al., 2018; Marzi et al., 2018) and reviews (e.g., Cecil et al., 2020) of early adversity and DNA methylation in humans show limited evidence of associations between early adversity and DNA methylation levels. However, the idea that early adversity impacts downstream outcomes remains pervasive in the literature and popular science (see Dubois et al., 2019), which we believe makes tests like ours important to pursue. We also hope that our data set (and others generated through these methods) will be useful in interpreting other settings in which differential methylation is of interest as well—in line with your comment below. We have clarified both of these points in the revised manuscript (lines 520-522; 536-539).

Overall, the authors provide a great resource of DNA methylation-sensitive enhancers that can now be used for functional interpretation of large-scale datasets (that are widely generated in the research community), given the focus on sites included in RBSS and the Illumina EPIC array. In addition, their data lends support that differences in DNA methylation can alter responses to environmental stimuli and thus of the possibility that environmental exposures that alter DNS methylation can also alter the subsequent response to this exposure, in line with the theory of epigenetic embedding of prior stimuli/experiences. The conclusions related to the early adversity data should be reconsidered in light of the comments above.

Thank you! And yes, we have revised our discussion of early life adversity effects as discussed above.

Reviewer #1 (Recommendations For The Authors):

While the paper has a lot of strengths and provides new insight into the epigenomic regulation of enhancers as well as being a great resource, there are some aspects that would benefit from clarification.

a. It would be great to have a clearer description of how many sequences are actually passing QC in the different datasets and what the respective overlaps are in bps or 600bp windows. Now often only % are given. Maybe a table/Venn diagram for overview of the experiments and assessed sequences would help here. This concern the different experiments in the K652, A549, and Hep2G cell lines, including stimulations.

We now provide a supplementary figure and supplementary table providing, for each dataset, the number of 600 bp windows passing each filter (Figure 2-figure supplement 1; Supplementary File 9), as well as a supplementary figure providing an upset plot to show the number of assessed sequences shared across the experiments (Figure 2-figure supplement 2).

b. It would also be helpful to have a brief description of the main differences in assessed sequences and their coverage of the old (2018) and new libraries in the main text to be able better interpret the validation experiments.

We now provide information on the following characteristics for the 2018 data set versus the data set presented for the first time here: mean (± SD) number of CpGs per fragment; mean (± SD) DNA sequencing depth; and mean (± SD) RNA sequencing depth (lines 169-170 provide values for the new data set; in line 194, we reference Supplementary File 5, which provides the same values for the old data set). Notably, the coverage characteristics of analyzed windows in both data sets are quite high (mean DNA-seq read coverage = 94x and mean RNA-seq read coverage = 165x in the new data set at baseline; mean DNA-seq read coverage = 22x and mean RNA-seq read coverage = 54x in Lea et al. 2018).

c. Statements of genome-wide analyses in the abstract and discussion should be a bit tempered, as quite a number of tested sites do not pass QC and do not enter the analysis. From the results it seems like from over 4.5 million sequences, only 200,000 are entering the analysis.

The reason why many of the windows are not taken forward into our formal modeling analysis is that they fail our filter for RNA reads because they are never (or almost never) transcribed—not because there was no opportunity for transcription (i.e., the region was indeed assessed in our DNA library, and did not show output transcription, as now shown in Figure 2-figure supplement 1). We have added a rarefaction analysis (lines 715-722 in Materials and Methods) of the DNA fragment reads to the revised manuscript which supports this point. Specifically, it shows that we are saturated for representation of unique genomic windows (i.e., we are above the stage in the curve where the proportion of active windows would increase with more sequencing: Figure 1figure supplement 4). Similarly, a parallel rarefaction curve for the mSTARR-seq RNA-seq data (Figure 1-figure supplement 4) shows that we would gain minimal additional evidence for regulatory activity with more sequencing depth. We now reference these analyses in revised lines 179-184 and point to the supporting figure in line 182.

In other words, our analysis is truly genome-wide, based on the input sequences we tested. Most of the genome just doesn’t have regulatory activity in this assay, despite the potential for it to be detected given that the relevant sequences were successfully transfected into the cells.

d. Could the authors comment on the validity of the analysis if only one copy is present (cut-off for QC)?

We think this question reflects a misunderstanding of our filtering criteria due to lack of clarity on our part, which we have modified in the revision. We now specify that the mean DNA-seq sequencing depth per sample for the windows we subjected to formal modeling was quite high:

93.91 ± 10.09 SD (range = 74.5 – 113.5x) (see revised lines 169-170). In other words, we never analyze windows in which there is scant evidence that plasmids containing the relevant sequence were successfully transfected (lines 170-172).

Our minimal RNA-seq criteria require non-zero counts in at least 3 replicate samples within either the methylated condition or the unmethylated condition, or both (lines 166-168). Because we know that multiple plasmids containing the corresponding sequence are present for all of these windows—even those that just cross the minimal RNA-seq filtering threshold—we believe our results provide valid evidence that all analyzed windows present the opportunity to detect enhancer activity, but many do not act as enhancers (i.e., do not result in transcribed RNA). Notably, we observe a negligible correlation between DNA sequencing depth for a fragment, among analyzed windows, and mSTARR-seq enhancer activity (R2 = 0.029; now reported in lines 183-184). We also now report reproducibility between replicates, in which all replicate pairs have r > 0.89, on par with previously published STARR-seq datasets (e.g., Klein et al., 2020; Figure 1-figure supplement 6, pointed to in line 193).

e. While the authors state that almost all of the control sequences contain CpGs sites, could the authors also give information on the total number of CpG sites in the different subsets? Was the number of CpGs in a 600 bp window related to the effects of DNA methylation on enhancer activity?

We now provide the number of CpG sites per window in the different subsets in lines 282-284. As expected, they are higher for EPIC array sites and for RRBS sites because the EPIC array is biased towards CpG-rich promoter regions, and the enzyme typically used in the starting step of RRBS digests DNA at CpG motifs (but control sequences still contain an average of ~13 CpG sites per fragment). We also now model the magnitude of the effects of DNA methylation on regulatory activity as a function of number of CpG sites within the 600 bp windows. Consistent with our previous work in Lea et al., 2018, we find that mSTARR-seq enhancers with more CpGs tend to be repressed by DNA methylation (now reported in lines 216-219 and Figure 1figure supplement 11).

f. In the discussion, a statement on the underrepresented regions, likely regulatory elements with lower CG content, that nonetheless can be highly relevant for gene regulation would be important to put the data in perspective.

Thanks for this suggestion. We agree that regulatory regions, independent of CpG methylation, can be highly relevant, and now clarify in the main text that the “unmethylated” condition of mSTARR-seq is essentially akin to a conventional STARR-seq experiment, in that it assesses regulatory activity regardless of CpG content or methylation status (lines 128-130).

Consequently, our study is well-designed to detect enhancer-like activity, even in windows with low GC content. We now show with additional analyses that we generated adequate DNA-seq coverage on the transfected plasmids to analyze 90.2% of the human genome, including target regions with no or low CpG content (lines 148-149; 153-156; Supplementary file 2). As noted above, we also now clarify that regions dropped out of our formal analysis because we had little to no evidence that any transcription was occurring at those loci, not because sequences for those regions were not successfully transfected into cells (see responses above and new Figure 1-figure supplement 4 and Figure 2-figure supplement 1).

g. To control for differences in methylation of the two libraries, the authors sequence a single CpGs in the vector. Could the authors look at DNA methylation of the 600 bp windows at the end of the experiment, could DNA methylation of these windows be differently affected according to sequence? 48 hours could be enough for de-methylation or re-methylation.

We agree that variation in demethylation or remethylation depending on fragment sequence is possible. We now state this caveat in the main text (lines 158-159), and specify that genomic coverage of our bisulfite sequencing data across replicates are (unfortunately) too variable to perform reliable site-by-site analysis of DNA methylation levels before and after the 48 hour experiment (lines 1182-1185). Instead, we focus on a CpG site contained in the adapter sequence (and thus included in all plasmids) to generate a global estimate of per replicate methylation levels. We also now note that any de-methylation or re-methylation would reduce our power to detect methylation-dependent activity, rather than leading to false positives (lines 163-165).

h. The section on the method for correction for multiple testing should be more detailed as it is very difficult to follow. Why were only 100 permutations used, the empirical p-value could then only be <0.01? The description of a subsample of the N windows with positive Betas is unclear, should the permutation not include the actual values and thus all windows - or were the no negative Betas? Was FDR accounting for all elements and pairs?

We have now expanded the text in the Materials and Methods section to clarify the FDR calculation (lines 691, 695-699, 702, 706). We clarify that the 100 permutations were used to generate a null distribution of p-values for the data set (e.g., 100 x 17,461 p-values for the baseline data set), which we used to derive a false discovery rate. Because we base our evidence on FDRs, we therefore compare the distribution of observed p-values to the distribution of pvalues obtained via permutation; we do not calculate individual p-values by comparing an observed test statistic against the test statistics for permuted data for that individual window.

We compare the data to permutations with only positive betas because in the observed data, we observe many negative betas. These correspond to windows which have no regulatory activity (i.e., they have many more input DNA reads than RNA-seq reads) and thus have very small pvalues in a model testing for DNA-RNA abundance differences. However, we are interested in controlling the false discovery rate of windows that do have regulatory activity (positive betas). In the permuted data, by contrast and because of the randomization we impose, test statistics are centered around 0 and essentially symmetrical (approximately equally likely to be positive or negative). Retaining all p-values to construct the null therefore leads to highly miscalibrated false discovery rates because the distribution of observed values is skewed towards smaller values— because of windows with “significantly” no regulatory activity—compared to the permuted data. We address that problem by using only positive betas from the permutations.

i. The interpretation of the overlap of Dex-response windows with CpGs sites associated with early adversity should be revisited according to the points also mentioned in the public review and the authors may want to consider exploring additional datasets with other challenges.

Thank you, see our responses to the public review above and our revisions in lines (lines 555559). We agree that comparisons with more data sets and generation of more mSTARR-seq data in other challenge conditions would be of interest. While beyond the scope of this manuscript, we hope the resource we have developed and our methods set the stage for just such analyses.

Reviewer #2 (Public Review):

This work presents a remarkably extensive set of experiments, assaying the interaction between methylation and expression across most CpG positions in the genome in two cell types. To this end, the authors use mSTARR-seq, a high-throughput method, which they have previously developed, where sequences are tested for their regulatory activity in two conditions (methylated and unmethylated) using a reporter gene. The authors use these data to study two aspects of DNA methylation:

1) Its effect on expression, and 2. Its interaction with the environment. Overall, they identify a small number of 600 bp windows that show regulatory potential, and a relatively large fraction of these show an effect of methylation on expression. In addition, the authors find regions exhibiting methylation-dependent responses to two environmental stimuli (interferon alpha and glucocorticoid dexamethasone).

The questions the authors address represent some of the most central in functional genomics, and the method utilized is currently the best method to do so. The scope of this study is very impressive and I am certain that these data will become an important resource for the community. The authors are also able to report several important findings, including that pre-existing DNA methylation patterns can influence the response to subsequent environmental exposures.

Thank you for this generous summary!

The main weaknesses of the study are: 1. The large number of regions tested seems to have come at the expense of the depth of coverage per region (1 DNA read per region per replicate). I have not been convinced that the study has sufficient statistical power to detect regulatory activity, and differential regulatory activity to the extent needed. This is likely reflected in the extremely low number of regions showing significant activity.

We apologize for our lack of clarity in the previous version of the manuscript. Nonzero coverage for half the plasmid-derived DNA-seq replicates is a minimum criterion, but for the baseline dataset, the mean depth of DNA coverage per replicate for windows passing the DNA filter is quite high: 12.723 ± 41.696 s.d. overall, and 93.907 ± 10.091 s.d. in the windows we subjected to full analysis (i.e., windows that also passed the RNA read filter). We now provide these summary statistics in lines 148-149 and 169-170 and Supplementary file 5 (see also our responses to Reviewer 1 above). We also now show, using a rarefaction analysis, that our data set saturates the ability to detect regulatory windows based on DNA and RNA sequencing depth (new Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Due to the position of the tested sequence at the 3' end of the construct, the mSTARR-seq approach cannot detect the effect of methylation on promoter activity, which is perhaps the most central role of methylation in gene regulation, and where the link between methylation and expression is the strongest. This limitation is evident in Fig. 1C and Figure 1-figure supplement 5C, where even active promoters have activity lower than 1. Considering these two points, I suspect that most effects of methylation on expression have been missed.

Thank you for pointing this out. We agree that we have not exhaustively detected methylationdependent activity in all promoter regions, given that not all promoter regions are active in STARR-seq. However, there is good evidence that some promoter regions can function like enhancers and thus be detected in STARR-seq-type assays (Klein et al., 2020). This important point is now noted in lines 187-189; an example promoter showing methylation-dependent regulatory activity in our dataset is shown in Figure 3E.

We also now clarify that Figure 1C shows significant enrichment of regulatory activity in windows that overlap promoter sequence (line 239). The y-axis is not a measure of activity, but rather the log-transformed odds ratio, with positive values corresponding to overrepresentation of promoter sequences in regions of mSTARR-seq regulatory activity. Active promoters are 1.640 times more likely to be detected with regulatory activity than expected by chance (p = 1.560 x 10-18), which we now report in a table that presents enrichment statistics for all ENCODE elements shown in Figure 1C for clarity (Supplementary file 4). Moreover, 74.1% of active promoters that show regulatory activity have methylation-dependent activity, also now reported in Supplementary file 4.

Overall, the combination of an extensive resource addressing key questions in functional genomics, together with the findings regarding the relationship between methylation and environmental stimuli makes this a key study in the field of DNA methylation.

Thank you again for the positive assessment!

Reviewer #2 (Recommendations For The Authors):

I suggest the authors conduct several tests to estimate and/or increase the power of the study:

1) To estimate the potential contribution of additional sequencing depth, I suggest the authors conduct a downsampling analysis. If the results are not saturated (e.g., the number of active windows is not saturated or the number of differentially active windows is not saturated), then additional sequencing is called for.

We appreciate the suggestion. We have now performed a downsampling/rarefaction curve analysis in which we downsampled the number of DNA reads, and separately, the number of RNA reads. We show that for both DNA-seq depth and RNA-seq depth, we are within the range of sequencing depth in which additional sequencing would add minimal new analysis windows in the dataset (Figure 1-figure supplement 4; lines 179-184; 715-722).

2) Correlation between replicates should be reported and displayed in a figure because low correlations might also point to too few reads. The authors mention: "This difference likely stems from lower variance between replicates in the present study, which increases power", but I couldn't find the data.

We now report the correlations between RNA and DNA replicates within the current dataset and within the Lea et al., 2018 dataset (Figure 1-figure supplement 6). The between-replicate correlations in both our RNA libraries and DNA libraries are consistently high (r ≥ 0.89).

3) The correlation between the previous and current K562 datasets is surprisingly low. Given that these datasets were generated in the same cell type, in the same lab, and using the same protocol, I expected a higher correlation, as seen in other massively parallel reporter assays. The fact that the correlations are almost identical for a comparison of the same cell and a comparison of very different cell types is also suspicious.

Thanks for raising this point. We think it is in reference to our original Figure 1-Figure supplement 6, for which we now provide Pearson correlations in addition to R2 values (now Figure 1-Figure supplement 8). We note that this is not a correlation in raw data, but rather the correlation in estimated effect sizes from a statistical model for methylation-dependent activity. We now provide Pearson correlations for the raw data between replicates within each dataset (Figure 1-Figure supplement 6), which for the baseline dataset are all r > 0.89 for RNA replicates and r > 0.98 for DNA replicates, showing that replicate reproducibility in this study is on par with other published studies (e.g., Klein et al., 2020 report r > 0.89 for RNA replicates and r > 0.91 for DNA replicates).

We do not know of any comparable reports in other MPRAs for effect size correlations between two separately constructed libraries, so it’s unclear to us what the expectation should be. However, we note that all effect sizes are estimated with uncertainty, so it would be surprising to us to observe a very high correlation for effect sizes in two experiments, with two independently constructed libraries (i.e., with different DNA fragments), run several years apart—especially given the importance of winner’s curse effects and other phenomena that affect point estimates of effect sizes. Nevertheless, we find that regions we identify as regulatory elements in this study are 74-fold more likely to have been identified as regulatory elements in Lea et al., 2018 (p < 1 x10-300).

4) The authors cite Johnson et al. 2018 to support their finding that merely 0.073% of the human genome shows activity (1.7% of 4.3%), but:

a. the percent cited is incorrect: this study found that 27,498 out of 560 million regions (0.005%) were active, and not 0.165% as the authors report.

We have modified the text to clarify the numerator and denominator used for the 0.165% estimate from Johnson et al 2018 (lines 175-176). The numerator is their union set of all basepairs showing regulatory activity in unstimulated cells, which is 5,547,090 basepairs. The denominator is the total length of the hg38 human genome, which is 3,298,912,062 basepairs.

Notably, the denominator (the total human genome) is not 560 million—while Johnson et al (2018) tested 560 million unique ~400 basepair fragments, these fragments were overlapping, such that the 560 million fragments covered the human genome 59 times (i.e., 59x coverage).

b. other studies that used massively parallel reporter assays report substantially higher percentages, suggesting that the current study is possibly underpowered. Indeed, the previous mSTARR-seq found a substantially larger percentage of regions showing regulatory activity (8%). The current study should be compared against other studies (preferably those that did not filter for putatively active sequences, or at least to the random genomic sequences used in these studies).

We appreciate this point and have double checked comparisons to Johnson et al., 2018 and Lea et al., 2018. Our numbers are not unusual relative to Johnson et al., 2018 (0.165%), which surveyed the whole genome. Also, in comparing to the data from Lea et al., 2018, when processed in an identical manner (our criteria are more stringent here), our values of the percent of the tested genome showing significant regulatory activity are also similar: 0.108% in the Lea et al., 2018 dataset versus 0.082% in the baseline dataset. Finally, our rarefaction analyses (see our responses above) indicate that we are not underpowered based on sequencing depth for RNA or DNA samples. We also note that there are several differences in our analysis pipeline from other studies: we use more technical replicates than is typical (compare to 2-5 replicates in Arnold et al., 2013; Johnson et al., 2018; Muerdter et al., 2018), we measure DNA library composition based on DNA extracted from each replicate post-transfection (as opposed to basing it on the pre-transfection library: [Johnson et al., 2018], and we use linear mixed models to identify regulatory activity as opposed to binomial tests [Johnson et al., 2018; Arnold et al., 2013; Muerdter et al., 2018].

I find it confusing that the four sets of CpG positions used: EPIC, RRBS, NR3C1, and random control loci, add up together to 27.3M CpG positions. Do the 600 bp windows around each of these positions sufficient to result in whole-genome coverage? If so, a clear explanation of how this is achieved should be added.

Thanks for this comment. Although our sequencing data are enriched for reads that cover these targeted sites, the original capture to create the input library included some off target reads (as is typical of most capture experiments, which are rarely 100% efficient). We then sequenced at such high depth that we ultimately obtained sequencing coverage that encompassed nearly the whole genome. We now clarify in the main text that our protocol assesses 27.3 million CpG sites by assessing 600 bp windows encompassing 93.5% of all genomic CpG sites (line 89), which includes off-target sites (line 149).

scatter plot showing the RNA to DNA ratios of the methylated (x-axis) vs unmethylated (y-axis) library would be informative. I expect to see a shift up from the x=y diagonal in the unmethylated values.

We have added a supplementary figure showing this information, which shows the expected shift upwards (Figure 1-figure supplement 9).

Another important figure missing is a histogram showing the ratios between the unmethylated and methylated libraries for all active windows, with the significantly differentially active windows marked.

We have added a supplementary figure showing this information (Figure 1-Supplementary Figure 10).

Perhaps I missed it, but what is the distribution of effect sizes (differential activity) following the various stimuli?

This information is provided in table form in Supplementary Files 3, 10, and 11, which we now reference in the Figure 2 legend (lines 365-366).

Minor changes

It is unclear what the lines connecting the two groups in Fig.3C represent, as these are two separate groups of regions.

We now clarify in the figure legend that values connected by a line are the same regions, not two different sets of regions. They show the correlation between DNA methylation and gene expression at mSTARR-seq-identified enhancers in individuals before and after IAV stimulation, separately for enhancers that are shared between conditions (left) versus those that are IFNAspecific (right). The two plots therefore do show two different sets of regions, which we have depicted to visualize the contrast in the effect of stimulation on the correlation on IFNA-specific enhancers versus shared enhancers. We have revised the figure legend to clarify these points (line 458-460).

L235-242 are unclear. Specifically - isn't the same filter mentioned in L241-242 applied to all regions?

Yes, the same filter for minimal RNA transcription was applied to all regions. We have modified the text (lines 264-265, 271, 275-277) to clarify that the enrichment analyses were performed twice, to test whether the target types were: 1) enriched in the dataset passing the RNA filter (i.e., the dataset showing plasmid-derived RNA reads in at least half the sham or methylated replicates; n = 216,091 windows) and 2) enriched in the set of windows showing significant regulatory activity (at FDR < 1%; n = 3,721 windows).

To improve cohesiveness, the section about most CpG sites associated with early life adversity not showing regulatory activity in K562s can be moved to the supplementary in my opinion.

Thank you for this suggestion. Because ELA and the biological embedding hypothesis (via DNA methylation) were major motivations for our analysis (see Introduction lines 42-48; 75-79), and we also discuss these results in the Discussion (lines 518-520), we have respectfully elected to retain this section in the main manuscript. We have added text in the Discussion explaining why we think experimental tests of methylation effects on regulation are relevant to the literature on early life adversity (lines 520-522), and have added discussion on limits to these analyses (lines 527-533).

References:

Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A (2013) Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science, 339, 1074-1077.

Cecil CA, Zhang Y, Nolte T (2020) Childhood maltreatment and DNA methylation: A systematic review. Neuroscience & Biobehavioral Reviews, 112, 392-409.

Dubois M, Louvel S, Le Goff A, Guaspare C, Allard P (2019) Epigenetics in the public sphere: interdisciplinary perspectives. Environmental Epigenetics, 5, dvz019.

Eisenberger NI, Cole SW (2012) Social neuroscience and health: neurophysiological mechanisms linking social ties with physical health. Nature neuroscience, 15, 669-674.

Houtepen L, Hardy R, Maddock J, Kuh D, Anderson E, Relton C, Suderman M, Howe L (2018) Childhood adversity and DNA methylation in two population-based cohorts. Translational Psychiatry, 8, 1-12.

Johnson GD, Barrera A, McDowell IC, D’Ippolito AM, Majoros WH, Vockley CM, Wang X, Allen AS, Reddy TE (2018) Human genome-wide measurement of drug-responsive regulatory activity. Nature communications, 9, 1-9.

Klein JC, Agarwal V, Inoue F, Keith A, Martin B, Kircher M, Ahituv N, Shendure J (2020) A systematic evaluation of the design and context dependencies of massively parallel reporter assays. Nature Methods, 17, 1083-1091.

Koss KJ, Gunnar MR (2018) Annual research review: Early adversity, the hypothalamic–pituitary– adrenocortical axis, and child psychopathology. Journal of Child Psychology and Psychiatry, 59, 327-346.

Marzi SJ, Sugden K, Arseneault L, Belsky DW, Burrage J, Corcoran DL, Danese A, Fisher HL, Hannon E, Moffitt TE (2018) Analysis of DNA methylation in young people: limited evidence for an association between victimization stress and epigenetic variation in blood. American journal of psychiatry, 175, 517-529.

Muerdter F, Boryń ŁM, Woodfin AR, Neumayr C, Rath M, Zabidi MA, Pagani M, Haberle V, Kazmar T, Catarino RR (2018) Resolving systematic errors in widely used enhancer activity assays in human cells. Nature methods, 15, 141-149.

Author Response

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

Reviewer #1:

1) Can the authors statistically define the egg-laying classes? In some parts of the manuscript, the division between the different classes could be more ambiguous. I understand that the class III strains are divided by the kcnl-1 genotype, but given the different results for diverse traits, it could be more clear to keep them as one class. Also, overall, the authors choose a collection of 15 strains across the different classes to phenotype for many traits and perform genome edits. It is understandable that they cannot test all strains, but given the variation across traits and classes, it might be good to add a few more caveats about how these strains might not be representative of all strains across the species.

Response: The egg-laying classes were defined as in Figure 1A by arbitrarily chosen cut-offs (at 10, 10-25, and 25 eggs in utero) to simplify subsequent analyses. We added this explanation to the first paragraph of the results section. However, the differences in average egg retention are significantly different between the four defined classes using the 15 selected strains (Fig. 2A).

We think that the distinction between Class IIIA and IIIB strains is important and justified because the two Classes significantly differ in mean egg retention (Fig. 2A) and because Class IIIB harbour the large-effect variant KCNL-1 V530L whereas Class IIIA do not.

We agree that the 15 selected strains are not necessarily representative of all strains across the species. We have added a note of caution regarding this point to the first paragraph of the section “Temporal progression of egg retention and internal hatching”: “Note that this strain selection, especially concerning the largest Class II, is unlikely to reflect the overall strain diversity observed across the species". In addition, we have reworded the first sentence of this paragraph as follows: “ To better characterize natural variation in C. elegans egg retention, we focused on a subset of 15 strains from divergent phenotypic Classes I-III, with an emphasis on Class III strains exhibiting strong egg retention (at mid-L4 + 30h) (Fig. 2A and 2B).”

2) For the GWAS experiments, the authors should describe if any of the QTL overlap with hyper-divergent regions in the strain set. The QTL could be driven by these less well defined regions.

Response: We have added the following sentence: “The three QTLs do not align with any of the recently identified hyper-divergent regions of the genome (Lee et al., 2021).

3) The authors should look at correlations between the mod-5(n822) edit phenotypes and the exogenous 5-HT and SSRI phenotypes to demonstrate how the traits can differ. Some correlation plots might help that point as well.

Response: We examined all possible correlations as suggested: none are significant and strain effects on trait differences are idiosyncratic, as written in our results section. The correlational analyses remain of limited value due to small samples: N=10 for mean strain values for measured phenotypes. We therefore feel that these analyses do not provide any additional insights beyond our figures (4C, 4D, 5C, 5D, S5A-C ) and our statement on page 15: “As in previous experiments (Fig. 4C and 5C), we find again that strains sharing the same egg retention phenotype may differ strongly in egg-laying behaviour in response to modulation of both exo- and endogenous serotonin levels (Class IIIA: ED3005 and JU2829) (Fig. 5D and S5C).”

4) Figure 6D, was there any censoring of the data? Normally, these types of studies are plagued by an increase in censored animals that can decrease significance. The effects among the classes seem large, but statistical comparisons might help as well.

Response: There was no censoring of animals (censoring of animals in lifespan studies is usually done by removing “bags of worms”, which here was our study phenotype). We now mention this in the corresponding figure legend. We also added a statistical analysis showing that mean survival was significantly different between all Classes.

5) Many of the traits, edits, and deeper analyses are performed on the JU751 genetic background. This choice is sensible, otherwise, the work can increase exponentially. However, the authors should add a caveat about how these results might be limited to JU751 and other strains might respond differently.

Response: For certain experiments, it was not feasible to include multiple strains from all phenotypic classes, so we selected JU751 (Class IIIB) and JU1200 (Class II), for which we had established CRISPR-engineered lines to modulate the egg retention phenotype by a single amino acid change in KCNL-1. To emphasize that these experimental observations cannot be generalized, we added the following statement in the relevant results section: “These experimental results offer preliminary evidence (bearing in mind that our analysis was primarily centered on a single genetic background) that laying of advanced-stage embryos may enhance intraspecific competitive ability, particularly in scenarios where multiple genotypes compete for colonization and exploitation of limited, patchily distributed resources.”

6) The authors argue that evolution could be acting on specific parts of the egg-laying machinery (e.g., muscledirected signaling components). It might be useful to look at levels of standing variation and selection at groups of loci compared to genomic controls to see if this conclusion can be strengthened.

Response: This is a good idea but how to select pertinent candidate loci is unclear (there are over 300 genes with effects on egg laying, www.wormbase.org). In addition, the genetics of muscle-directed signalling components in egg laying is only starting to be explored, with no specific candidate genes having been identified (Medrano & Collins, 2023, Curr Biol). We therefore think that such an analysis is currently not possible.

7) Completely optional: The authors present a compelling and interesting case for transitions and trade-offs between oviparity and viviparity. The C. vivipara species has a different egg-laying mode than other Caenorhabditis species. The authors could add a short section describing their expectations about the neuronal morphology, 5-HT circuits, and muscle function in this species given their results. What genes or circuits should be the focus of future studies to address this question in Caenorhabditis. Also, Loer and Rivard present some similar ideas based on the differences in 5-HT staining neurons across diverse nematodes. Those results can be incorporated and discussed as well.

Response: Our current research focuses on the evolution of egg laying in different Caenorhabditis species. So far, however, it remains difficult to provide specific hypotheses on how the egg-laying circuit has changed in C. vivipara. We rephrased the final paragraph of the discussion to incorporate some of the reviewer’s suggestions: “Nematodes display frequent transitions from oviparity to obligate viviparity in many distinct genera (Sudhaus, 1976; Ostrovsky et al., 2015), including in the genus Caenorhabditis, with at least one viviparous species, C. vivipara (Stevens et al., 2019). Although evidence exists for the evolution of egg-laying circuitry across oviparous Caenorhabditis species (Loer and Rivard, 2007), the specific cellular and genetic changes responsible for the transition to obligate viviparity in C. vivipara have yet to be examined. Resolving the genetic basis of intraspecific variation in C. elegans egg retention, including partial or facultative viviparity, may thus shed light on the molecular changes underlying the initial steps of evolutionary transitions from oviparity to obligate viviparity in invertebrates.”

Specific edits:

1) Perhaps a silly point, but "parity" (to my knowledge) does not have a biological meaning on its own. I suggest "egg-laying mode" or "birth mode".

Response: This term has been used previously in the literature (e.g.https://onlinelibrary.wiley.com/doi/10.1111/jeb.13886 or https://doi.org/10.1101/2023.10.22.563505). However, as the referee rightly points out, this is not a standard term. We therefore replaced “parity mode” with “egg-laying mode”.

2) "Against fluctuating environmental fluctuations" is a bit strange

Response: Corrected.

3) The first publications of Egl mutants were by the Horvitz lab so some citations are not in all of the first descriptions of the trait (early in Results)

Response: We have added the relevant work (Trent 1982, Trent 1983, Desai & Horvitz 1989) to this paragraph in the early results section.

4) "Strong egg retention usually strongly..." is a bit strange

Response: Corrected.

1. Figure 8G font looks smaller than the others.

Response: Corrected.

Reviewer #2:

1) In Figure 1A, I infer that in the graph class I measurements are represented by dark blue dots and class II by purple dots. I am having a really hard time distinguishing between these two colors in the graph. In the pie chart I have no problem, but in the graph the black lines around the colored dots seem to obscure the colors. Not sure how to fix this graphical problem, but it is preventing the graph from communicating the results effectively.

Response: We have changed the colours, spacing and format of this figure to resolve this problem.

2) The behavioral analysis of Figure 3B-3F is problematic. The experimental methods used and the interpretation of the results each have issues. This is cause for concern since this is the most direct analysis of the actual variations in egg-laying behavior across strains presented in this paper.

This experiment is modeled after the work of Waggoner et al. 1998, who recorded egg laying events of individual worms on video over several hours and noted the exact time of individual egg laying events. Waggoner et al. found in the reference C. elegans strain N2 that egg-laying events occurred in ~2 minute clusters ("active phases") separated by ~20 minute silent periods ("inactive phases"). Mignerot et al. did not take continuous videos of animals, but rather examined plates bearing a single worm only every 5 minutes and noted the number of new eggs that appeared on the plate in each 5-minute interval. From these data, the authors claim they have measured the intervals between "egg-laying phases" (the term used in the Figure 3 legend). In the Results, the authors explicitly claim they are measuring the timing and frequency of actual active and inactive egg-laying phases. Apparently, all the eggs laid within one 5-minute interval are considered to have been laid in a single active phase, and the time between 5-minute intervals containing egg laying events is considered an "inactive phase" and is measured only with a resolution of 5 minutes. It is not explained anywhere how the authors handle the situation of seeing eggs laid in two consecutive 5-minute intervals. Is that one active phase that is 10 minutes long, or is that two separate active phases with a 5-minute active phase in between? Because of this ambiguity in how they define active and inactive phases, I find it impossible to understand and judge the data presented in Fig. 3D-3F. The authors in the results state that "Class I and Class IIIB displayed significantly accelerated and reduced egg laying activity respectively (Fig. 3C to 3E)" . I assume they are referring to the statistical analysis described in the figure legend, which is quite difficult to understand. Frankly, just looking at the graphs in Fig. 3D3F, it is hard for the reader to identify specific features shown in the graphs can explain why, for example, Class I strains have fewer retained eggs than Class III strains. So, I found this analysis very unsatisfying.

I also feel the authors are making an unwarranted assumption that their non-N2 strains will have distinguishable active and inactive phases of egg-laying behavior analogous to those seen in the N2 strain. Given the possibly large variations in egg-laying behavior in the various strains examined, that assumption should be questioned. Thus, framing the entire analysis of behavior patterns in terms of the length of active and inactive phases might not be appropriate.

Response: This comment validly highlights important problems and limitations of our scan-sampling method to quantify strain differences in egg-laying behaviour. We acknowledge that we failed to present the data with due diligence, and clarity regarding terminology and interpretation. However, we think that some of these results are still of value after revised presentation. Our biggest mistake was to use the terms “active and inactive phase”, as coined by Waggoner et al. 1998. We are aware that our measures are not equivalent to these previously defined measures but have been sloppy with terminology. We therefore carefully reworded this entire results section, using clear definitions to indicate differences between the Waggoner assay and our assay (including a graphical representation of our assay design in the revised Fig. 3B). In brief, our simplified assay is useful to estimate the frequency and approximate duration of prolonged inactive periods of egg laying because we can unambiguously determine intervals in which eggs were laid or not. In contrast, as pointed out by the reviewer, we cannot determine if multiple active phases occurred within a 5-min interval, nor can we estimate the duration of an active “phase”. We now state this limitation explicitly in the manuscript. What our results do show is that the number of intervals during which egg laying occurred is significantly different between strains and Classes: Class I (low retention) have a higher number of intervals with egg-laying events, whereas Class IIIB showed a reduced number of such events (Fig. 3D). We can therefore also roughly estimate the mean time (per individual) between two egg-laying intervals, giving us a proxy for prolonged periods when egg-laying is inactive (Fig. 3E); we note that our estimate for N2 is very close to what has been previously measured (~20 min). Therefore, we can confidently conclude that there are natural strains which have both shorter (Class I) and longer (Class IIIB) inactive periods of egg laying. These results partly align with observed variation in egg retention. However, we agree with the reviewer – as we had stated both in results and discussion sections – that these behavioural differences act together with differences in the sensing of egg accumulation in utero (as suggested by results shown in Fig. 3G and 3H). We also agree that it seems very plausible that the observed behavioural differences, as revealed by scan-sampling, may only have a secondary role in accounting for natural variation in egg retention. We will be testing these hypotheses specifically in our future research.

Note: The statistical analyses are nested ANOVAs to ask (a) does the value differ between strains within a given class and (b) does the value differ between Classes? Classes labelled with different letters in the figures therefore significantly differ in their mean values, demonstrating that measured behavioural phenotypes consistently differ between some (but not all) phenotypic classes, yet largely in line with their egg retention phenotypes (Fig. 3D and 3E).

3) Figure 4A is a schematic diagram of how the egg-laying circuit works based on previous literature, and the authors cite Collins et al. 2015 and Kopchock et al. 2021 as their sources. One feature of this figure seems unwarranted, namely the part indicating that egg accumulation acts on the UM muscles, and the statement in the legend that "mechanical excitation of uterine muscles (UM) in response to egg accumulation favours exit from the inactive state (Collins et al., 2016)". I believe Collins et al. 2016 showed that egg accumulation favors egg laying and may have speculated that it does so by stretching the um muscles, but this idea remains speculative and has not been established by any experimental data. I point out this issue,in particular, because it may bear on the nice data the authors of this manuscript show in Figure 3G and 3H, which show that some strains accumulate many eggs in the uterus before they initiate egg laying.

Also, in Figure 4A and 4B, the legend does not explain the logic of the green areas labeled "egg-laying active phase" and the yellow area labeled "egg-laying inactive state". I was not sure what sure how to interpret these features of the graphics.

Response: The input from uterine muscles remains indeed hypothetical, and we have corrected the figure accordingly, now simply referring to the feedback of egg accumulation on egg laying activity, as recently characterized in more detail by Medrano & Collins (2023, Curr Biol).

The green/yellow backgrounds shown in figures 4A (and 4B) are not useful and we have removed them.

4) Results, page 11: "We used standard assays, in which animals are reared in liquid M9 buffer without bacterial food." In the standard assays, animals are reared on NGM agar plates with bacterial food, and then at the start of the egg-laying assay, are transferred to liquid M9 buffer without bacterial food. I assume that is what these authors did, and they should correct the language of the text to make it more accurate.

Response: The reviewer is correct. We have incorporated this change to improve accuracy.

5) The authors note that "serotonin induced a much stronger egg-laying responds in the Class IIIA strain ED3005 than in other strains (Fig. 4C)". I would like to point out to the authors that strains such as ED3005 that have a very large number of unlaid eggs in their uterus are prone to lay a very large number of eggs when treated with exogenous serotonin, simply for the trivial reason that they have more eggs to release. This was previously seen in, for example, in Desai and Horvitz (1989) in certain egg-laying defective mutants.

Response: This is an important point and our comparison of ED3005 to ALL other strains is problematic. We changed this result description by stating that ED3005 shows possible serotonin hypersensitivity compared to strains with similar levels of egg retention (Class IIIA): “In addition, serotonin induced a much stronger egg-laying response in the strain ED3005 than in other Class IIIA strains with similar levels of egg retention (Fig. 4B). ED3005 may thus exhibit serotonin hypersensitivity, which has been observed in certain egg-laying mutants where perturbed synaptic transmission impacts serotonin signalling (Schafer and Kenyon, 1995; Schafer et al., 1996).”

6) In Figure 4 the authors show that all strains lay eggs in response to fluoxetine and imipramine, but some strains (Class IIIB) do not lay eggs in response to serotonin. They then cite a series of papers, starting with Trent et al. 1983, that they claim show that this specific phenotype demonstrates that the HSN neurons are functionally releasing serotonin (bottom of page 11). This statement needs to be removed - it is incorrect. It is true that egg laying in response to fluoxetine and/or imipramine AS WELL AS egg laying in response to serotonin has been interpreted as indicating the presence of HSN neurons that functionally release serotonin to stimulate egg laying (these were referred to as Category C by Trent et al., 1983). However, the mutants that Mignerot et al. are talking about (those that don't respond to serotonin but do respond to imipramine/fluoxetine) were called Category D by Trent et al., 1983, and to my knowledge these have never been interpreted as necessarily having functionally intact HSN neurons. Mutants such as these that can lay eggs in some circumstances but cannot lay eggs in response to exogenous serotonin have usually been interpreted as having egg-laying muscles that are defective in responding to serotonin.

How can we interpret strains that respond to imipramine/fluoxetine and not serotonin? Mignerot et al. cite some of the papers (Kullyev et al. 2010; Wenishenker et al., 1999; Yue et al., 2018) showing that imipramine and fluoxetene have off-target effects and can stimulate egg laying by acting through proteins other than the serotonin-reuptake inhibitor. The authors later in their discussion at the top of Page 24 also cite Dempsey et al 2005, a paper that also argues that imipramine and fluoxetene act via off target effects. However, currently in Figure 4B Mignerot et al. emphasize that the serotonin reuptake inhibitor is the target of these drugs. Since the results presented for Class IIIB strains are not in accord with this interpretation, this seems misleading to me. The bottom line for me is that class IIIB strains cannot respond to exogenous serotonin, but can lay eggs in other conditions, so perhaps there is something specifically wrong with their ability to respond to serotonin.

Response: We thank the reviewer for this important comment – we misinterpreted some of these past findings and our statements were either inexact or incorrect. We have revised this section accordingly: “Both drugs also stimulated egg laying in the Class IIIB strains and the Class IIIA strain JU2829 for which exogenous serotonin either inhibited egg laying or had no effect on it (Fig. 4B). In the past, mutants unresponsive to serotonin yet responsive to other drugs, including fluoxetine and imipramine, have been interpreted as being defective in the serotonin response of vulval muscles (Trent et al., 1983; Reiner et al., 1995; Weinshenker et al., 1995). This is indeed the likely case of Class IIIB strains carrying the KCNL-1 V530L variant thought to specifically reduce excitability of vulval muscles (Vigne et al., 2021). Our results therefore suggest that JU2829 (Class IIIA) may exhibit a similar defect in vulval muscle activation via serotonin caused by an alternative genetic change. Overall, these pharmacological assays do not allow us to conclude if and how HSN function has diverged among strains because the mode of action and targets of tested drugs has not been fully resolved. Nevertheless, our results are consistent with previous models proposing that these drugs do not simply block serotonin reuptake but can stimulate egg laying, to some extent, through mechanisms independent of serotonergic signaling (Trent et al., 1983; Desai and Horvitz, 1989; Reiner et al., 1995; Weinshenker et al., 1995, 1999; Dempsey et al., 2005; Kullyev et al., 2010; Branicky et al., 2014; Yue et al., 2018).”

We removed the oversimplified Fig. 4B to avoid any misinterpretation.

8) In Figure 7B and 7C, the authors should add some type of error bars to the graphs to and give the readers an idea of whether the differences between strains that they write about are statistically significant or not.

Response: These are frequency data to describe temporal dynamics of hatching (N=45-72 eggs per strain) (Fig. 7B) and development in single cohorts (N=48-177 eggs per strain) (Fig. 7C), hence, the absence of error bars.

We agree that this representation of the data is not very telling. We therefore changed the data representation in these two figures to show that there are clear, statistically significant, negative correlations between egg retention and time to hatching / egg-to-adult developmental time.

9) When the authors reference a list of papers in a single list, e.g. "(Burton et al., 2021; Fausett et al., 2021; Garsin et al., 2001; Padilla et al., 2002; Van Voorhies and Ward, 2000)" they seem to do so in alphabetical order by the first author's last name. I believe the usual practice is to list references by year of publication, with the earliest first.

Response: We corrected citation style according to eLIFE format.

10) At the top of page 24, the authors write "It seems unlikely, however, that any of these variants strongly alter central function of HSN and HSN-mediated signalling because fluoxetine and imipramine, known to act via HSN (Dempsey et al., 2005; Trent et al., 1983; Weinshenker et al., 1995), triggered a robust stimulatory effect on egg laying in all examined strains (Fig. 4C)." I believe that the Weinshenker paper in fact showed that imipramine does not act via the HSN, and the Dempsey paper suggested that both drugs can act at least in part independently of the HSN. Therefore, the authors should revise their statement.

Response: We have removed the sentence.

Reviewing Editor:

Minor suggestions:

1) p. 2, fifth line from bottom: "lead" instead of "leads";

2) p. 2, last line: "muscle" instead of "muscles";

3) p. 3, first full paragraph, 17th line: "populations" instead of "population";

4) p. 5, fourth line from bottom: Delete first comma;

5) p. 6, Figure 1D: "of" instead of "off";

6) p. 7, fifth line: "KCNL-1";

7) p. 9, third paragraph, second line: please clarify "late mid-L4";

8) p. 16, first line: "exogenous";

9) p 20, first paragraph, beginning of second sentence: "Whether" instead of "If";

10) p. 22, ninth line from bottom: delete "shaped by";

11) p. 23, last paragraph, third and eighth lines from bottom: change "between" to "among"

Response: Thank you. All corrected.

Additional changes:

Figure 5A: We removed figure 5A showing a cartoon of mod-5/SERT and its effects on serotonin signalling. This figure was incorrectly showing that MOD-5 is expressed in HSN (Jafari et al 2011 J. Neuroscience, Hammarlund et al 2018 Neuron).

Abstract: We reworded the abstract to reduce its length.

Author Response

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

eLife assessment

This work describes new validated conditional double KO (cDKO) mice for LRRK1 and LRRK2 that will be useful for the field, given that LRRK2 is widely expressed in the brain and periphery, and many divergent phenotypes have been attributed previously to LRRK2 expression. The manuscript presents solid data demonstrating that it is the loss of LRRK1 and LRRK2 expression within the SNpc DA cells that is not well tolerated, as it was previously unclear from past work whether neurodegeneration in the LRRK double Knock Out (DKO) was cell autonomous or the result of loss of LRRK1/LRRK2 expression in other types of cells. Future studies may pursue the biochemical mechanisms underlying the reason for the apoptotic cells noted in this study, as here, the LRRK1/LRRK2 KO mice did not replicate the dramatic increase in the number of autophagic vacuoles previously noted in germline global LRRK1/LRRK2 KO mice.

We thank the editors for handling our manuscript and for the succinct summary that recognizes the significance of our findings and points out interesting directions for future studies. We also thank the reviewers for their helpful comments and positive evaluation of our work. Below, we have provided point-by-point responses to the reviewers’ comments.

Reviewer #1 (Public Review):

Summary:

This is an important work showing that loss of LRRK function causes late-onset dopaminergic neurodegeneration in a cell-autonomous manner. One of the LRRK members, LRRK2, is of significant translational importance as mutations in LRRK2 cause late-onset autosomal dominant Parkinson's disease (PD). While many in the field assume that LRRK2 mutant causes PD via increased LRRK2 activity (i.e., kinase activity), it is not a settled issue as not all disease-causing mutant LRRK2 exhibit increased activity. Further, while LRRK2 inhibitors are under clinical trials for PD, the consequence of chronic, long-term LRRK2 inhibition is unknown. Thus, studies evaluating the long-term impact of LRRK deficit have important translational implications. Moreover, because LRRK proteins, particularly LRRK2, are known to modulate immune response and intracellular membrane trafficking, the study's results and the reagents will be valuable for others interested in LRRK function.

Strengths:

This report describes a mouse model where the LRRK1 and LRRK2 gene is conditionally deleted in dopaminergic neurons. Previously, this group showed that while loss of LRRK2 expression does not cause brain phenotype, loss of both LRRK1 and LRRK2 causes a later onset, progressive degeneration of catecholaminergic neurons and dopaminergic (DAergic) neurons in the substantia nigra (SN), and noradrenergic neurons in the locus coeruleus (LC). However, because LRRK genes are widely expressed with some peripheral phenotypes, it was unknown if the neurodegeneration in the LRRK double knockout (DKO) was cell autonomous. To rigorously test this question, the authors have generated a double conditional (cDKO) allele where both LRRK1 and LRRK2 genes were targeted to contain loxP sites. In my view, this was beyond what is usually required, as most investigators might might combine one KO allele with another floxed allele. The authors provide a rigorous validation showing that the Driver (DAT-Cre) is expressed in most DAergic neurons in the SN and that LRRK levers are decreased selectively in the ventral midbrain. Using these mice, the authors show that the number of DAergic neurons is normal at 15 but significantly decreased at 20 months of age. Moreover, the authors show that the number of apoptotic neurons is increased by ~2X in aged SN, demonstrating increased ongoing cell death, as well as an increase in activated microglia. The degeneration is limited to DAergic neurons as LC neurons are not lost as this population does not express DAT. Overall, the mouse genetics and experimental analysis were performed rigorously, and the results were statistically sound and compelling.

Weaknesses:

I only have a few minor comments. First is that in PD and other degenerative conditions, loss of axons and terminals occurs prior to cell bodies. It might be beneficial to show the status of DAergic markers in the striatum. Second, previous studies indicate that very little, if any, LRRK1 is expressed in SN DAergic neurons. This also the case with the Allen Brain Atlas profile. Thus, authors should discuss the discrepancy as authors seem to imply significant LRRK1 expression in DA neurons.

We appreciate the reviewer’s recognition of the importance of the study as well as our rigorous experimental approaches and compelling results. Our responses to the reviewer's two minor comments are below.

1) DAergic markers in the striatum: We performed TH immunostaining in the striatum and quantified TH+ DA terminals in the striatum of DA neuron-specific LRRK cDKO and littermate control mice at the ages of 15 and 24 months. We found similar levels of TH immunoreactivity in the striatum of LRRK cDKO and littermate control mice at the age of 15 months (p = 0.6565, unpaired Student’s t-test) and significantly reduced levels of TH immunoreactivity in the striatum of LRRK cDKO, compared to control mice at the age of 24 months (~19%, p = 0.0215), suggesting an age-dependent loss of dopaminergic terminals in the striatum of DA neuron-specific LRRK cDKO mice. These results are now included as Figure 5 of the revised manuscript.

2) LRRK1 expression in the SNpc: It is shown in the Mouse brain RNA-seq dataset and the Allen Mouse brain ISH dataset (https://www.proteinatlas.org/ENSG00000154237-LRRK1/brain) that LRRK1 is broadly expressed in the mouse brain and is expressed at modest levels in the midbrain, comparable to the cerebral cortex. Indeed, our Western analysis also showed that levels of LRRK1 detected in the dissected ventral midbrain and the cerebral cortex of control mice are similar (40µg total protein loaded per lane; Figure 2E). Furthermore, we previously demonstrated that deletion of LRRK2 (or LRRK1) alone does not cause age-dependent loss of DA neurons in the SNpc, but deletions of both LRRK1 and LRRK2 result in age-dependent loss of DA neurons in LRRK DKO mice, indicating the functional importance of LRRK1 in the protection of DA neuron survival in the aging mouse brain (Tong et al., PNAS 2010, 107: 9879-9884, Giaime et al., Neuron 2017, 96: 796-807).

Reviewer #2 (Public Review):

Summary:

In this manuscript, Shen and collaborators described the generation of cDKO mice lacking LRRK1 and LRRK2 selectively in DAT-positive DAergic neurons. The Authors asked whether selective deletion of both LRRK isoforms could lead to a Parkinsonian phenotype, as previously reported by the same group in germline double LRRK1 and LRRK2 knockout mice (PMID: 29056298). Indeed, cDKO mice developed a late reduction of TH+ neurons in SNpc that partially correlated with the reduction of NeuN+ cells. This was associated with increased apoptotic cell and microglial cell numbers in SNpc.

Unlike the constitutive DKO mice described earlier, however, cDKO mice did not replicate the dramatic increase in the number of autophagic vacuoles. The study supports the authors' hypothesis that loss of function rather than gain of function of LRRK2 leads to PD.

Strengths:

The study described for the first time a model where both the PD-associated gene LRRK2 and its homolog LRRK1 are deleted selectively in DAergic neurons, offering a new tool to understand the physiopathological role of LRRK2 and the compensating role of LRRK1 in modulating DAergic cell function.

Weaknesses:

The model has no construct validity since loss of function mutations of LRRK2 are well-tolerated in humans and do not lead to PD. The evidence of a Parkinsonian phenotype in these cDKO mice is limited and should be considered preliminary.

We thank the reviewer for commenting on the usefulness of this new PD mouse model.

The reviewer did not include a reference citation for the statement "loss of function mutations of LRRK2 are well-tolerated in humans and do not lead to PD." It is possible that the reviewer was referring to a human population study (Whiffin et al., Nat Med 2020, 26: 869-877), entitled "The effect of LRRK2 lossof-function variants in humans." In this study, the authors analyzed 141,456 individuals sequenced in the Genome Aggregation Database, 49,960 exome-sequenced individuals from the UK Biobank, and more than 4 million participants in the 23andMe genotyped dataset, and they looked for human genetic variants predicted to cause loss-of-function of protein-coding genes (pLoF variants). The reported findings were interesting, and the authors were careful in stating their conclusions. However, this is not a linkage study of large pedigrees carrying a single, clear-cut loss-of-function mutation (e.g. large deletions of most exons and coding sequences). Therefore, the experimental evidence is not compelling enough to conclude whether loss-of-function mutations in LRRK2 cause PD or do not cause PD.

The current report is an unbiased genetic study in an effort to reveal the normal physiological role of LRRK in dopaminergic neurons. It was not intended to produce Parkinsonian phenotypes in LRRK cDKO mice, which would be a biased effort. However, the unequivocal discovery of the cell intrinsic role of LRRK in the protection of DA neurons from age-dependent degeneration and apoptotic cell death should be considered seriously, while we contemplate the disease mechanism and how LRRK2 mutations may cause DA neuron loss and PD.

Reviewer #3 (Public Review):

Kang, Huang, and colleagues investigated the impact of LRRK1 and LRRK2 deletion, specifically in dopaminergic neurons, using a novel cDKO mouse model. They observed a significant reduction in DAergic neurons in the substantia nigra in their conditional LRRK1 and LRRK2 KO mice and a corresponding increase in markers of apoptosis and gliosis. This work set out to address a longstanding question within the field around the role and importance of LRRK1 and LRRK2 in DAergic neurons and suggests that the loss of both proteins triggers some neurodegeneration and glial activation.

The studies included in this work are carefully performed and clearly communicated, but additional studies are needed to strengthen further the authors' claims around the consequences of LRRK2 deletion in DAergic neurons.

1) In Figures 2E and F, the authors assess the protein levels of LRRK1 and LRRK2 in their cDKO mouse model to confirm the deletion of both proteins. They observe a mild loss of LRRK1 and LRRK2 signals in the ventral midbrain compared to wild-type animals. While this is not surprising given other cell types that still express LRRK1 and LRRK2 would be present in their dissected ventral midbrain samples, it does not sufficiently confirm that LRRK1 and LRRK2 are not expressed in DAergic neurons. Additional data is needed to more directly demonstrate that LRRK1 and LRRK2 protein levels are reduced in DAergic neurons, including analysis of LRRK1 and LRRK2 protein levels via immunohistochemistry or FACS-based analysis of TH+ neurons.

We thank the reviewer for highlighting this incredibly important but often overlooked issue. We agree that the data in Figure 2E, F alone would be inadequate to validate DA neuron-specific LRRK cDKO mice.

Cell type-specific conditional knockouts are a mosaic with KO cells mixed with other cell types expressing the gene normally. DA neuron-specific cDKO is particularly challenging, as DA neurons are a subset of cells embedded in the ventral midbrain. Rather than using immunostaining, which relies upon specific, good LRRK1 and LRRK2 antibodies for IHC, or FACS sorting of TH+ neurons followed by Western blotting (few cells, mixed cell populations, etc.), we chose a clean genetic approach by generating germline mutant mice carrying the deleted LRRK1 and LRRK2 alleles in all cells from the floxed LRRK1 and LRRK2 alleles. This approach permits characterization of these deletion mutations in germline mutant mice using molecular approaches that yield unambiguous results.

We crossed CMV-Cre deleter mice with floxed LRRK1 and LRRK2 mice to generate respective germline LRRK1 KO and LRRK2 KO mice, in which all cells carry the LRRK1 or LRRK2 deleted alleles that are identical to those in DA neurons of cDKO mice. We then performed Northern, extensive RTPCR followed by sequencing, and Western analyses to show the absence of the full length LRRK1 and LRRK2 mRNA (Figure 1G, H, Figure 1-figure supplement 8 and 10), and the expected truncation of LRRK1 and LRRK2 mRNA (Figure 1-figure supplement 9 and 11), and the absence of LRRK1 and LRRK2 proteins (Figure 1I). These analyses together demonstrate that in the presence of Cre, either CMV-Cre expressed in all cells or DAT-Cre expressed selectively in DA neurons, the floxed LRRK1 and LRRK2 exons are deleted, resulting in null alleles. We further demonstrated the specificity of DAT-Cremediated recombination (deletion) by crossing DAT-Cre mice with a GFP reporter, showing that 99% TH+ DA neurons in the SNpc are also GFP+ (Figure 2A, B), indicating that DAT-Cre-mediated recombination of the floxed alleles occurs in essentially all TH+ DA neurons in the SNpc.

2) The authors observed a significant but modest effect of LRRK1 and LRRK2 deletion on the number of TH+ neurons in the substantia nigra (12-15% loss at 20-24 months of age). It is unclear whether this extent of neuron loss is functionally relevant. To strengthen the impact of these data, additional studies are warranted to determine whether this translates into any PD-relevant deficits in the mice, including motor deficits or alterations in alpha-synuclein accumulation/aggregation.

Yes, the reduction of DA neurons in the SNpc of cDKO mice at the age of 20-24 months is modest. At 15 months of age, the number of TH+ DA neurons in the SNpc is similar between LRRK cDKO mice (10,000 ± 141) and littermate controls (10,077 ± 310, p > 0.9999). At 20 months of age, the number of DA neurons in the SNpc of LRRK cDKO mice (8,948 ± 273) is significantly reduced (-12.7%), compared to control mice (10,244 ± 220, F1,46 = 16.59, p = 0.0002, two-way ANOVA with Bonferroni’s post hoc multiple comparisons, p = 0.0041). By 24 months of age, the number of DA neurons in the SNpc of LRRK cDKO mice (8,188 ± 452) relative to controls (9,675 ± 232, p = 0.0010) is further reduced (15.4%).

Similar results were obtained by an independent quantification by another investigator, also conducted in a genotype blind manner, using the fractionator and optical dissector method, by which TH+ cells were quantified in 25% areas. These results are included as Figure 3-figure supplement 1 in the revised manuscript. Because of the more limited sampling, the quantification data are more variable, compared to quantification of TH+ cells in all areas of the SNpc, shown in Figure 3. With both methods, we quantified TH+ cells in every 10th sections encompassing the entire SNpc (3D structure), as sampling using every 5th or every 10th sections yielded similar results.

We also performed behavioral analysis of LRRK cDKO mice and littermate controls at the ages of 10 and 25 months using the beam walk test (10 mm and 20 mm beam) and the pole test, which are sensitive to impairment of motor coordination. We found that LRRK cDKO mice at 10 months of age showed significantly more hindlimb errors (p = 0.0005, unpaired two-tailed Student’s t-test) and longer traversal time (p = 0.0075) in the 10mm beam walk test, compared to control mice, though their performance is similar in the 20 mm beam walk (hindlimb slips: p = 0.0733, traversal time: p = 0.9796) and in the pole test. At 22 months of age, the performance of LRRK cDKO mice and littermate controls is more variable and worse, compared to the younger mice, and is not significantly different between the genotypic groups. These results are now included as Figure 9 of the revised manuscript.

3) The authors demonstrate that, unlike in the germline LRRK DKO mice, they do not observe any alterations in electron-dense vacuoles via EM. Given their data showing increased apoptosis and gliosis, it remains unclear how the loss of LRRK proteins leads to DAergic neuronal cell loss. Mechanistic studies would be insightful to understand better potential explanations for how the loss of LRRK1 and LRRK2 may impair cellular survival, and additional text should be added to the discussion to discuss potential hypotheses for how this might occur.

We agree that this phenotypic difference between germline DKO and DA neuron-specific cDKO mice is intriguing, suggesting a non-cell autonomous contribution of LRRK in age-dependent accumulation of autophagic and lysosomal vacuoles in SNpc neurons of germline LRRK DKO mice. We will discuss the phenotypic difference further in the revised manuscript. We are generating microglial specific LRRK cDKO mice to investigate the role of LRRK in microglia and whether microglia contribute in a cell extrinsic manner to the regulation of the autophagy-lysosomal pathway in DA neurons.

4) The authors discuss the potential implications of the neuronal cell loss observed in cDKO mice for LRRK1 and LRRK2 for therapeutic approaches targeting LRRK2 and suggest this argues that LRRK2 variants may exert their effects through a loss-of-protein function. However, all of the data generated in this work focus on a mouse in which both LRRK1 and LRRK2 have been deleted, and it is therefore difficult to make any definitive conclusions about the consequences of specifically targeting LRRK2. The authors note potential redundancy between the two LRRK proteins, and they should soften some of their conclusions in the discussion section around implications for the effects of LRRK2 variants. Human subjects that carry LRRK2 loss-of-function alleles do not have an increased risk for developing PD, which argues against the author's conclusions that LRRK2 variants associated with PD are loss-o-ffunction. Additional text should be included in their discussion to better address these nuances and caution should be used in terms of extrapolating their data to effects observed with PD-linked variants in LRRK2.

We will modify the discussion accordingly in the revised manuscript.

Author Response

eLife assessment

This valuable paper presents a thoroughly detailed methodology for mesoscale-imaging of extensive areas of the cortex, either from a top or lateral perspective, in behaving mice. While the examples of scientific results to be derived with this method are in the preliminary stages, they offer promising and stimulating insights. Overall, the method and results presented are convincing and will be of interest to neuroscientists focused on cortical processing in rodents.

Authors’ Response: We thank the reviewers for the helpful and constructive comments. They have helped us plan for significant improvements to our manuscript. Our preliminary response and plans for revision are indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors introduce two preparations for observing large-scale cortical activity in mice during behavior. Alongside this, they present intriguing preliminary findings utilizing these methods. This paper is poised to be an invaluable resource for researchers engaged in extensive cortical recording in behaving mice.

Strengths:

-Comprehensive methodological detailing:

The paper excels in providing an exceptionally detailed description of the methods used. This meticulous documentation includes a step-by-step workflow, complemented by thorough workflow, protocols, and a list of materials in the supplementary materials.

-Minimal movement artifacts:

A notable strength of this study is the remarkably low movement artifacts. To further underscore this achievement, a more robust quantification across all subjects, coupled with benchmarking against established tools (such as those from suite2p), would be beneficial.

Authors’ Response: This is a good suggestion. Since we used suite2p for our data analysis, and have records of the fast-z correction applied by the microscope, we can supply these as quantifications of movement corrections that were applied across our sample of mice. We hope to supply this information as a supplement in the revised manuscript.

Currently, we have chosen to show that the corrected, post- suite2p registration movement artifacts are very close to zero. We will revise the manuscript with clear descriptions of methods that we have found important, such as fully tightening all mounting devices, utilizing the air table properly, implanting the cranial window with proper, even pressure across its entire extent, and mounting the mouse so that it is not too close or far from the surface of the running wheel.

Insightful preliminary data and analysis:

The preliminary data unveiled in the study reveal interesting heterogeneity in the relationships between neural activity and detailed behavioral features, particularly notable in the lateral cortex. This aspect of the findings is intriguing and suggests avenues for further exploration.

Weaknesses:

-Clarification about the extent of the method in the title and text:

The title of the paper, using the term "pan-cortical," along with certain phrases in the text, may inadvertently suggest that both the top and lateral view preparations are utilized in the same set of mice. To avoid confusion, it should be explicitly stated that the authors employ either the dorsal view (which offers limited access to the lateral ventral regions) or the lateral view (which restricts access to the opposite side of the cortex). For instance, in line 545, the phrase "lateral cortex with our dorsal and side mount preparations" should be revised to "lateral cortex with our dorsal or side mount preparations" for greater clarity.

Authors’ Response: We will revise the manuscript so that it is clear that we made use of two imaging configurations for the 2-photon mesoscope data and the benefits and limitations of these two preparations. The dorsal mount and the side mount each have their advantages and disadvantages, but together form a powerful tool for imaging much of the dorsal and lateral cortex in awake, behaving mice.

-Comparison with existing methods:

A more detailed contrast between this method and other published techniques would add value to the paper. Specifically, the lateral view appears somewhat narrower than that described in Esmaeili et al., 2021; a discussion of this comparison would be useful.

Authors’ Response: We will modify the manuscript so that a more detailed comparison with other published techniques is included. The preparation by Esmaeili et al. 2021 has some similarities, but also differences, from our preparation. Our preliminary reading is that their through-the-skull field of view is approximately the same as our through-the-skull field of view that exists between our first (headpost implantation) and second (window implantation) surgeries, although our preparation appears to include more anterior areas both near to and on the contralateral side of the midline. We will compare these preparations more accurately in the revised manuscript.

If you compare the imageable extent of our cranial window for mesoscale 2-photon imaging to that of their through-the-skull widefield preparation, which is a bit of an “apples to oranges” comparison, then you are likely correct that their field of view is larger than ours, if you are referring to our 10 mm radius-bend glass. However, use of our 9 mm radius bend glass (i.e. a tighter bend) allows us to image additional ventral auditory areas. We could show an example of this, perhaps, although we did not make as much use of this alternative window in the large FOV experiments, because the increased curvature of the glass relative to the 10 mm radius bend window prevents imaging of the entire preparation in a single 2-photon z-plane. With the 9 mm radius bend glass we mostly imaged in the multiple, small FOV configuration (see Fig. S2).

Furthermore, the number of neurons analyzed seems modest compared to recent papers (50k) - elaborating on this aspect could provide important context for the readers.

Authors’ response: With respect to the “modest” number of neurons analyzed (between 2000 and 8000 neurons per session for our dorsal and side mount preparations with medians near 4500; See Fig. S2e) we would like to point out that factors such as use of dual-plane imaging or multiple imaging planes, different mouse lines, use of different duration recording sessions (see our Fig S2c), use of different imaging speeds and resolutions (see our Fig S2d), use of different Suite2p run-time parameters, and inclusion or areas with blood vessels and different neuron cell densities, may all impact the count of total analyzed neurons. We could provide additional documentation of these issues, but we would like to point out that, in our case, we were not trying to maximize neuron count at the expense of other factors such as imaging speed and total spatial FOV extent.

-Discussion of methodological limitations:

The limitations inherent to the method, such as the potential behavioral effects of tilting the mouse's head, are not thoroughly examined. A more comprehensive discussion of these limitations would enhance the paper's balance and depth.

Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this situation (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, will be discussed more thoroughly in the revised manuscript.

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparison across mice indicates that side and dorsal mount mice show similar behavioral variability.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective to 20 degrees to the right, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

-Preliminary nature of results:

The results are at a preliminary stage; for example, the B-soid analysis is based on a single mouse, and the validation data are derived from the training data set. The discrepancy between the maps in Figures 5e and 6e might indicate that a significant portion of the map represents noise. An analysis of variability across mice and a method to assign significance to these maps would be beneficial.

Authors’ Response: In this methods paper, we have chosen to supply proof of principle examples, without a complete analysis of animal-to-animal variance. The dataset for this paper contains both neural and behavioral data for 91 sessions across 18 mice from both dorsal and side mount preparations. The complete analysis of this dataset exceeds the capacity of the present study. We will include more individual examples in the revised version, along with data showing the amount of between session and across mouse variance. We will include in the revised manuscript a comparison of the stability of B-SOiD measures across sessions, as a demonstration of what may be expected with this method.

-Analysis details:

More comprehensive details on the analysis would be beneficial for replicability and deeper understanding. For instance, the statement "Rigid and non-rigid motion correction were performed in Suite2p" could be expanded with a brief explanation of the underlying principles, such as phase correlation, to provide readers with a better grasp of the methodologies employed.

Authors’ Response: We are revising the manuscript to give more detail without reducing readability, so as to increase clarity of presentation. Since this is a methods paper, we are modifying the manuscript to include more details and clear explanations so that the reader may replicate our methods and results.

Reviewer #2 (Public Review):

Summary:

The authors present a comprehensive technical overview of the challenging acquisition of large-scale cortical activity, including surgical procedures and custom 3D-printed headbar designs to obtain neural activity from large parts of the dorsal or lateral neocortex. They then describe technical adjustments for stable head fixation, light shielding, and noise insulation in a 2-photon mesoscope and provide a workflow for multisensory mapping and alignment of the obtained large-scale neural data sets in the Allen CCF framework. Lastly, they show different analytical approaches to relate single-cell activity from various cortical areas to spontaneous activity by using visualization and clustering tools, such as Rastermap, PCA-based cell sorting, and B-SOID behavioral motif detection.

Authors’ Response: Thank you for this excellent summary of the scope of our paper.

The study contains a lot of useful technical information that should be of interest to the field. It tackles a timely problem that an increasing number of labs will be facing as recent technical advances allow the activity measurement of an increasing number of neurons across multiple areas in awake mice. Since the acquisition of cortical data with a large field of view in awake animals poses unique experimental challenges, the provided information could be very helpful to promote standard workflows for data acquisition and analysis and push the field forward.

Authors’ Response: We very much support the idea that our work here will contribute to the development of standard workflows across the field including multiple approaches to large-scale neural recordings.

Strengths:

The proposed methodology is technically sound and the authors provide convincing data to suggest that they successfully solved various problems, such as motion artifacts or high-frequency noise emissions, during 2-photon imaging. Overall, the authors achieved their goal of demonstrating a comprehensive approach for the imaging of neural data across many cortical areas and providing several examples that demonstrate the validity of their methods and recapitulate and further extend some recent findings in the field.

Weaknesses:

Most of the descriptions are quite focused on a specific acquisition system, the Thorlabs Mesoscope, and the manuscript is in part highly technical making it harder to understand the motivation and reasoning behind some of the proposed implementations. A revised version would benefit from a more general description of common problems and the thought process behind the proposed solutions to broaden the impact of the work and make it more accessible for labs that do not have access to a Thorlabs mesoscope. A better introduction of some of the specific issues would also promote the development of other solutions in labs that are just starting to use similar tools.

Authors’ Response: We will re-write the motivation behind the study to clarify the general problems that are being addressed. As the 2-photon imaging component of these experiments were performed on a Thorlabs mesoscope, the imaging details will necessarily deal specifically with this system. We will briefly compare the methods and results from our Thorlabs system to that of other systems, based on what we are able to glean from the literature on their strengths and weaknesses.

Reviewer #3 (Public Review):

Summary

In their manuscript, Vickers and McCormick have demonstrated the potential of leveraging mesoscale two-photon calcium imaging data to unravel complex behavioural motifs in mice. Particularly commendable is their dedication to providing detailed surgical preparations and corresponding design files, a contribution that will greatly benefit the broader neuroscience community as a whole. The quality of the data is high, but it is not clear whether this is available to the community, some datasets should be deposited. More importantly, the authors have acquired activity-clustered neural ensembles at an unprecedented spatial scale to further correlate with high-level behaviour motifs identified by B-SOiD. Such an advancement marks a significant contribution to the field. While the manuscript is comprehensive and the analytical strategy proposed is promising, some technical aspects warrant further clarification. Overall, the authors have presented an invaluable and innovative approach, effectively laying a solid foundation for future research in correlating large-scale neural ensembles with behaviour. The implementation of a custom sound insulator for the scanner is a great idea and should be something implemented by others.

Authors’ Response: Thank you for the kind words.

We intend to make the data set used in making our main figures available to the public, perhaps using FigShare, so that they may check the validity of the methods and analysis. We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with a second in-depth analysis paper that is currently in preparation.

This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other. This is described in the methods, but a visual representation would greatly benefit the readers looking to implement something similar.

Authors’ Response: This is an excellent suggestion. We will include a workflow diagram in the revised manuscript for the methods, data collection, and analysis.

The authors should cite sources for the claims stated in lines 449-453 and cite the claim of the mouse's hearing threshold mentioned in lines 463.

Authors’ Response: For the claim stated in lines 449-453, “The unattenuated or native high-frequency background noise generated by the resonant scanner causes stress to both mice and experimenters, and can prevent mice from achieving maximum performance in auditory mapping, spontaneous activity sessions, auditory stimulus detection, and auditory discrimination sessions/tasks,” we can provide the following references: (i) for mice: Sadananda et al, 2008 (“Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain”, Neuroscience Letters, Vol 435, Issue 1, p 17-23), and (ii) for humans: Fletcher et al, 2018 (“Effects of very high-frequency sound and ultrasound on humans. Part I: Adverse symptoms after exposure to audible very-high frequency sound”, J Acoust Soc A, 144, 2511-2520). We will include these references in the revised paper.

For line 463, “i.e. below the mouse hearing threshold at 12.5 kHz of roughly 15 dB”, we can provide the following reference: Zheng et al, 1999 (“Assessment of hearing in 80 inbred strains of mice by ABR threshold analyses”, Vol 130, Issues 1-2, p 94-107). We will also include this reference in the paper. Thank you for identifying these citation omissions.

No stats for the results shown in Figure 6e, it would be useful to know which of these neural densities for all areas show a clear statistical significance across all the behaviors.

Authors’ Response: There are two statistical comparisons that we feel may be useful to add to the single session data displayed in this figure, in order to address the point that you raise. The first would allow us to assess whether for each Rastermap group, the distribution of neuron densities across CCF areas differs from a null, uniform distribution. The second would allow us to examine differences between Rastermap groups associated with different qualitative behaviors in order to know with which patterns of neural activity they are reliably associated.

For the first comparison, we could provide a statistic similar to what we provide for Fig. S6c and f, in which for each CCF area we compare the observed mean correlation values to a null of 0, or, in this case, the population densities of each Rastermap group for each CCF area to a null value equal to the total number of CCF areas divided by the total number of recorded neurons for that group (i.e. a Rastermap group with 500 neurons evenly distributed across ~30 CCF areas would contain ~17 neurons (or ~6% density) per CCF area.) Our current figure legend states that the maximum of the scale bar look-up value (reds) for each group ranges from ~8% to 32%. So indeed, adding these significances would be informative in this case.

For the second comparison, we could compare the density of neurons for each CCF area across Rastermap groups for this session. For example, it may be the case that the density of neurons in primary and secondary visual areas belonging to Rastermap groups that predominate during the “walk” behavior is higher than in the Rastermap group that predominates during the “whisk” behavior, or that the density of neurons in the “whisk” and “twitch” Rastermap groups in primary and secondary motor areas is higher than in the Rastermap groups that are active during the “walk” and “oscillate” behaviors.

Such a comparison should in fact be robust to Rastermap group variability across sessions and mice, as long as the same qualitative behaviors recur. However, our current qualitative methods for discretization of the Rastermap groups likely limits our ability to extend such an analysis accurately across our entire dataset. We are pursuing more rigorous analysis methods in this vein for our second, results oriented paper.

While I understand that this is a methods paper, it seems like the authors are aware of the literature surrounding large neuronal recordings during mouse behavior. Indeed, in lines 178-179, the authors mention how a significant portion of the variance in neural activity can be attributed to changes in "arousal or self-directed movement even during spontaneous behavior." Why then did the authors not make an attempt at a simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc). These models are straightforward to implement, and indeed it would benefit this work if the model extracts information on par with what is known from the literature.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the current methods paper. We are following up this methods paper with an in depth analysis of neural activity and corresponding behavior across the cortex during spontaneous and trained behaviors, but this analysis goes well beyond the scope of the present manuscript. Here, we prefer to present examples of the types of results that can be expected to be obtained using our methods, and how these results compare with those obtained by others in the field.

Specific strengths and weaknesses with areas to improve:

The paper should include an overall cartoon diagram that indicates how the various modules are linked together for the sampling of both behaviour and mesoscale GCAMP. This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other.

Authors’ Response: This is an excellent suggestion and will be included in the revised manuscript, so that readers can more readily follow our workflow, data collection, and analysis.

The paper contains many important results regarding correlations between behaviour and activity motifs on both the cellular and regional scales. There is a lot of data and it is difficult to draw out new concepts. It might be useful for readers to have an overall figure discussing various results and how they are linked to pupil movement and brain activity. A simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc) may help in this regard.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the present methods paper. Such an analysis is a significant undertaking with such large and heterogeneous datasets, and we provide proof-of-principle data here so that the reader can understand the type of data to be expected using our methods. We hope to provide a more complete analysis of data obtained using our methodology in the near future in a second manuscript.

However, we may be amenable to including preliminary linear model fit results, as supplementary material, for the two example sessions highlighted in this paper (i.e. the one dorsal mount session in Fig. 4, and the one side mount session shown in Figs. 5 and 6).

Previously, widefield imaging methods have been employed to describe regional activity motifs that correlate with known intracortical projections. Within the authors' data it would be interesting to perhaps describe how these two different methods are interrelated -they do collect both datasets. Surprisingly, such macroscale patterns are not immediately obvious from the authors' data. Some of this may be related to the scaling of correlation patterns or other factors. Perhaps there still isn't enough data to readily see these and it is too sparse.

Authors’ Response: Unfortunately, we are unable to directly compare widefield GCaMP6s activity with mesoscope 2-photon GCaMP6s activity. During widefield data acquisition, animals were stimulated with visual, auditory, or somatosensory stimuli, while 2-photon mesoscope data collection occurred during spontaneous changes in behavioral state, without sensory stimulation. The suggested comparison is, indeed, an interesting project for the future.

In lines 71-71, the authors described some disadvantages of one-photon widefield imaging including the inability to achieve single-cell resolution. However, this is not true. In recent years, the combination of better surgical preparations, camera sensors, and genetically encoded calcium indicators has enabled the acquisition of single-cell data even using one-photon widefield imaging methods. These methods include miniscopes (Cai et al., 2016), multi-camera arrays (Hope et al., 2023), and spinning disks (Xie et al., 2023).

Cai, Denise J., et al. "A shared neural ensemble links distinct contextual memories encoded close in time." Nature 534.7605 (2016): 115-118.

Hope, James, et al. "Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton." bioRxiv (2023).

Xie, Hao, et al. "Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution." Nature Biomedical Engineering (2023): 1-14.

Authors’ Response: We will correct these statements and incorporate these, and other relevant, references. There are advantages and disadvantages to each chosen technique, such as ease of use, field of view, accuracy, speed, etc., and we will highlight a few of these without an extensive literature review.

Even the best one-photon imaging techniques typically have ~10-20 micrometer resolution in xy (we image at 5 micrometer resolution for our large FOV configuration, but the xy point-spread function for the Thorlabs mesoscope is 0.61 x 0.61 micrometers in xy with 970 nm excitation) and undefined z-resolution (4.25 micrometers for Thorlabs mesoscope). A coarser resolution increases the likelihood that activity data from neighboring cells may contaminate the fluorescence observed from imaged neurons. Reducing the FOV and using sparse expression of the indicator lessens this overlap problem.

We do appreciate these recent advances, however, particularly for use in cases where more rapid imaging is desired over a large field of view (CCD acquisition can be much faster than that of standard 2-photon galvo-galvo or even galvo-resonant scanning, as the Thorlabs mesoscope uses). This being said, there are few currently available genetically encoded Ca2+ sensors that are able to measure fluctuations faster than ~10 Hz, which is a speed achievable on the Thorlabs 2-photon mesoscope with our techniques using the “small, multiple FOV” method (Fig. S2d, e).

The authors' claim of achieving optical clarity for up to 150 days post-surgery with their modified crystal skull approach is significantly longer than the 8 weeks (approximately 56 days) reported in the original study by Kim et al. (2016). Since surgical preparations are an integral part of the manuscript, it may be helpful to provide more details to address the feasibility and reliability of the preparation in chronic studies. A series of images documenting the progression optical quality of the window would offer valuable insight.

Authors’ Response: As you suggest, we will include images and data demonstrating the average changes in the window preparation, as well as the degree of variability and a range of outcome scenarios that we observed over the prolonged time periods of our study. We will also include methodological details that we found were useful for facilitating long term use of these preparations.

Author Response

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

eLife assessment

This study addresses how protein synthesis in activated lymphocytes keeps up with their rapid division, with important findings that are of significance to cell biologists and immunologists endeavouring to understand the 'economy' of the immune system. The work is supported by solid data but because it proposes non-conventional mechanisms, it requires additional explanation and justification to align with the current understanding in the field.

Public Reviews:

Reviewer #1 (Public Review):

The authors examine the fascinating question of how T lymphocytes regulate proteome expression during the dramatic cell state change that accompanies the transition from the resting quiescent state to the activated, dividing state. Orthogonal, complementary assays for translation (RPM/RTA, metabolic labeling) are combined with polyribosome profiling and quantitative, biochemical determinations of protein and ribosome content to explore this question, primarily in the OT-I T lymphocyte model system. The authors conclude that the ratio of protein levels to ribosomes/protein synthesis capacity is insufficient to support activation-coupled T cell division and cell size expansion. The authors hint at cellular mechanisms to explain this apparent paradox, focusing on protein acquisition strategies, including emperipolesis and entosis, though these remain topic areas for future study.

The strengths of the paper include the focus on a fundamental biological question - the transcriptional/translational control mechanisms that support the rapid, dramatic cell state change that accompanies lymphocyte activation from the quiescent to activated state, the use of orthogonal approaches to validate the primary findings, and the creative proposal for how this state change is achieved.

The weakness of the work is that several cellular regulatory processes that could explain the apparent paradox are not explored, though they are accessible for experimental analysis. In the accounting narrative that the authors highlight, a thorough accounting of the cellular process inventory that could support the cell state change should be further explored before committing to the proposal, provocative as it is, that protein acquisition provides a principal mechanism for supporting lymphocyte activation cell state change.

Appraisal and Discussion:

1) relating to the points raised above, two recent review articles explore this topic area and highlight important areas of study in RNA biology and translational control that likely contribute to the paradox noted by the authors: Choi et al. 2022, doi.org/10.4110/in.2022.22.e39 ("RNA metabolism in T lymphocytes") and Turner 2023, DOI: 10.1002/bies.202200236 ("Regulation and function of poised mRNAs in lymphocytes"). These should be cited, and the broader areas of RNA biology discussed by these authors integrated into the current manuscript.

Good suggestion. We have added these references with a short discussion.

2) The authors cite the Wolf et al. study from the Geiger lab (doi.org/10.1038/s41590-020-07145, ref. 41) though largely to compare determined values for ribosome number. Many other elements of the Wolf paper seem quite relevant, for example, the very high abundance of glycolytic enzymes (and whose mRNAs are quite abundant as well), where (and as others have reported) there is a dramatic activation of glycolytic flux upon T cell activation that is largely independent of transcription and translation, the evidence for "pre-existing, idle ribosomes", the changes in mRNA copy number and protein synthesis rate Spearman correlation that accompanies activation, and that the efficiencies of mRNA translation are heterogeneous. These data suggest that more accounting needs to be done to establish that there is a paradox.

As one example, what if glycolytic enzyme protein levels in the resting cell are in substantial excess of what's needed to support glycolysis (likely true) and so translational upregulation can be directed to other mRNAs whose products are necessary for function of the activated cell? In this scenario, the dilution of glycolytic enzyme concentration that would come with cell division would not necessarily have a functional consequence. And the idle ribosomes could be recruited to key subsets of mRNAs (transcriptionally or post-transcriptionally upregulated) and with that a substantial remodeling of the proteome (authors ref. 44). The study of Ricciardi et al. 2018 (The translational machinery of human CD4+ T cells is poised for activation and controls the switch from quiescence to metabolic remodeling (doi.org/10.1016/j.cmet.2018.08.009) is consistent with this possibility. That study, and the short reviews noted above, are useful in highlighting the contributions of selective translational remodeling and the signaling pathways that contribute to the cell state change of T cell activation.

Our study focuses on the central issue of whether measured ribosome translation rates support rapid division. The abundance of glycolytic enzymes, mRNA copy numbers etc., are clearly interesting and critical to cell metabolism, but are irrelevant to measuring the overall translation rate and capacity of T cells.

From this perspective, an alternative view can be posited, where the quiescent state is biologically poised to support activation, where subsets of proteins and mRNAs are present in far higher levels than that necessary to support basal function of the quiescent lymphocyte. In such a model, the early stages of lymphocyte activation and cell division are supported by this surplus inventory, with transcriptional activation, including ribosomal genes, primarily contributing at later stages of the activation process. An obvious analogy is the developing Drosophila embryo where maternal inheritance supports early-stage development and zygotic transcriptional contributions subsequently assuming primary control (e.g. DOI 10.1002/1873-3468.13183 , DOI: 10.1126/science.abq4835). To pursue that biological logic would require quantifying individual mRNAs and their ribosome loading states, mRNA-specific elongation rates, existing individual protein levels, turnover rates of both mRNAs and proteins, ribosome levels, mean ribosome occupancy state, and how each of these parameters is altered in response to activation. Such accounting could go far to unveil the paradox. This is a considerable undertaking, though, and outside the scope of the current paper.

The reviewer is essentially proposing RiboSeq analysis of pre- and post-activation T cells, whereby individual mRNAs can be queried for ribosome occupancy, and where translation inhibitors could be used to quantify mRNA-specific transit rates. This is important information but would not provide a more accurate accounting of protein synthesis rates than our much more direct measurement. We note that other labs have begun to work on this exact topic, however – see both PMID: 36002234 and PMID: 32330465.

Reviewer #2 (Public Review):

This paper takes a novel look at the protein economy of primary human and mouse T-cells - in both resting and activated state. Their findings in primary human T-cells are that:

1) A large fraction of ribosomes are stalled in resting cultured primary human lymphocytes, and these stalled ribosomes are likely to be monosomes.

2) Elongation occurs at similar rates for HeLa cells and lymphocytes, with the active ribosomes in resting lymphocytes translating at a similar rate as fully activated lymphocytes.

They then turn their attention to mouse OT-1 lymphocytes, looking at translation rates both in vitro and in vivo. Day 1 resting T-cells also show stalling - which curiously wasn't seen on freshly purified cells - I didn't understand these differences.

This is clarified and discussed starting in the third paragraph of “Protein synthesis in mouse lymphocytes ex vivo” section. Cells cultured ex vivo for 1 day with no activation show signs of stalling, as we observed in isolated human cells. But cells immediately out of an animal show a measurable decay rate since they are obviously synthesizing proteins in vivo and are processed rapidly.

In vivo, they show that it is possible to monitor accurate translation and measure rates. Perhaps most interestingly they note a paradoxically high ratio of cellular protein to ribosomes insufficient to support their rapid in vivo division, suggesting that the activated lymphocyte proteome in vivo may be generated in an unusual manner.

This was an interesting and provocative paper. Lots of interesting techniques and throwing down challenges to the community - it manages to address a number of important issues without necessarily providing answers.

Reviewer #3 (Public Review):

This manuscript provides a more or less quantitative analysis of protein synthesis in lymphocytes. I have no issue with the data as presented, as I'm sure all measurements have been expertly done. I see no need for additional experimental work, although it would be helpful if the authors could comment on the possibility of measuring the rate of synthesis of a defined protein, say a histone, in cells prior to and after activation. The conclusion the authors leave us with is the idea that the rates of protein synthesis recorded here are incompatible with observed rates of T cell division in vivo. Indeed, in the final paragraph of the discussion, the authors note the mismatch between what they consider a requirement for cell division, and the observed rates of protein synthesis. They then invoke unconventional mechanisms to make up for the shortfall, without -in this reviewer's opinion- discussing in adequate detail the technical limitations of the methodology used.

Points #1-3 in the Discussion relate to potential pitfalls of our analyses; in point #3 we now add further limitations of RTA based on non-random detection of nascent chains due either to bias in either puromycylation or antibody detection of puromycylated nascent chains.

A key question is the broad interest, novelty, and extension of current knowledge, in comparison with Argüello's (reference 27) 'SunRise' method. It would be helpful for the authors to stake out a clear position as to the similarities and differences with reference 27: what have we learned that is new? The authors could cite reference 27 in the introduction of their manuscript, given the similarity in approach. That said, the findings reported here will generate further discussion.

We did cite this reference (27) in the section “Flow RPM measures ribosome elongation rate in live cells” giving credit where credit is due. We independently devised the method in 2014, and uniquely, to our knowledge, have applied it in vivo. We now further discuss the importance of our CHX modification to limit dissociation and increase the accuracy of RTA (second and third paragraphs of “Protein synthesis in mouse lymphocytes and innate immune cells in vivo”).

The manuscript would increase in impact if the authors were to clearly define why a particular measurement is important and then show the actual experiment/result. As an example, it would be helpful to explain to the non-expert why the distinction between monosomes, polysomes, and stalled versions of the same is important, and then explain the rationale of the actual experiment: how can these distinctions be made with confidence, and what are confounding variables?

We believe this is addressed in the section “Resting human lymphocytes have a dominant monosome population”.

The initial use of human cells, later abandoned in favor of the OT-1 in vitro and in vivo models, requires contextualization. If the goal is to address the relationship between rates of translation and cell division of antigen-activated T cells in vivo, then a lot of the work on the human model and the in vitro experiments becomes more of a distraction, unless properly contextualized. Is there any reason to assume that antigen-specific activation in vivo will impact translation differently than the use of the PMA/ionomycin/IL2 cocktail? The way the work is presented leaves me with the impression that everything that was done is included, regardless of whether it goes to the core of the question(s) of interest.

Donor PBMCs are clearly the more relevant model for understanding human T cell biology, which is why started our studies with this model. Had the manuscript strictly described mouse studies it is likely that we would be criticized for not studying human cells: Catch 22! However, as we state in the manuscript, the human cell model has a variety of technical downsides, including donor heterogeneity. PMA/ionomycin activation is also physiologically questionable, and while we could deliver a defined TCR to redirect their specificity, this is typically done after cells have been activated, since lentiviral delivery is poor in resting lymphocytes. A main point we try to make from this work is that cells derived from human blood donors show signs of ribosomal stalling by the time they are isolated and put into culture. This may limit the usefulness of studying them preactivation, although based on our mouse data, some level of stalled ribosomes may be a feature as well – to prime T cells to be ready for their massive expansion. The move to the OT-I system gave us complete control over the system, including in vivo delivery of translation inhibitors.

It would be helpful if the authors made explicit some of the assumptions that underlie their quantitative comparisons. Likewise, the authors should discuss the limitations of their methods and provide alternative interpretations where possible, even if they consider them less/not plausible, with justification. As they themselves note, improvements in the RPM protocols raised the increase in translating ribosomes upon activation from 10-fold to 15-fold. Who's to say that is the best achievable result? What about the reliability/optimization of the other measurements?

We expanded discussion of potential pitfalls of the RPM techniques and others in the Discussion section. Regarding RPM per se, we use it as a readout of ribosome time decay, so even if further optimizations can be made, the decay rates we have made should still be accurate. In addition, for our cell accounting measurements in Figure 6, we do not use RPM data and rather calculate based on the assumption that every ribosome is used for protein synthesis at a “maximal” rate of mRNA transit.

The composition of the set of proteins produced upon activation will differ from cell to cell (CD4, CD8, B, resting vs. dividing). Even if analyses are performed on fixed cells, the ability of the monoclonal anti-puromycin antibody to penetrate the matrix of the various fixed cell types may not be equal for all of them, depending on protein composition, susceptibility to fixation etc. Is it possible for puromycin to occupy the ribosome's A site and terminate translation without forming a covalent bond with the nascent chain? This could affect the staining with anti-puromycin antibodies and also underestimate the number of nascent chains.

Yes, the method (like every other one) is imperfect. Harringtonine run-off experiments show that RPM staining only detects nascent chains. Note that reference 47 reports that 75% of translation in activated T cells is devoted to synthesizing ~250 housekeeping proteins, which are likely to be highly similar between lymphocyte subsets.

I believe that the concept of FACS-based quantitation also requires an explanation for the nonexpert. For the FACS plots shown, the differences between the highest and lowest RPM scores for cells that divided and that have a similar CFSE score is at least 10-fold. Does that mean that divided cells can differ by that margin in terms of the number of nascent chains present? If I make the assumption that cells stimulated with PMA/ionomycin/IL2 respond more or less synchronously, why would there be a 10-fold difference in absolute fluorescence intensity (anti=puromycin) for randomly chosen cells with similar CFSE values? While the use of MFI values is standard practice in cytofluorimetry, the authors should devote some comments to such variation at the population level.

We believe that the referee is referring to Sup Fig. 1B. In this experiment the T cells are polyclonal and represent the full range of naïve to potentially exhausted differentiation states. Looking at our initial in vivo RPM study (reference 22) and comparing Figure 2 (OTI’s) to Figure 3 (endogenous CD4s or CD8s), reveals more spread in the RPM values polyclonal vs. monoclonal T cells - now clarified in the third paragraph of “Protein synthesis in mouse lymphocytes and innate immune cells in vivo”). Flow cytometry is by far the most accurate method for measuring fluorescence in individual cells. It is likely to be an accurate measure of the variation of nascent chains in cells in the same division cohort but likely represents the diversity of T cell activation profiles in blood of healthy donors.

It is assumed that for cells to complete division, they must have produced a full and complete copy of their proteome and only then divide. What if cells can proceed to divide even when expressing a subset of the proteome of departure (=the threshold set required for initiation of division), only to complete synthesis of the 'missing ' portion once cell division is complete? Would this obviate the requirement for an unusual mechanism of protein acquisition (trogocytosis; other)?

There must be a steady state level of translation and proteome replenishment, though. If a cell can divide when it affords daughter cells with 90% of its G0 proteome (as an example), that daughter cell would either 1) be 10% smaller, or 2) require extra translation to make up for the missing proteome during its own division cycle. Though T cells do typically shrink slightly after an initial activation, cell size stabilizes over time. Requiring each daughter cell to make more and more missing proteome could be plausible, considering that initial bursts of division do take longer over time, but still, even in vitro activated T cells divide rapidly for weeks without large decreases in their division rates.

Translation is estimated to proceed at a rate of ~6 amino acids per second, but surely there is variability in this number attributable to inaccuracies of the methods used, in addition to biological variability. Were these so-called standard values determined for a range of different tissues? It stands to reason that there might be variation depending on the availability of initiation/elongation factors, NTPs, aminoacyl tRNAs etc. What is the margin of error in calculating chain elongation rates based on the results shown here?

We refer to all relevant studies we know of, including new in vivo estimates of elongation rates (reference 40).

Reviewer #1 (Recommendations For The Authors):

A "limitations of study" section would be a helpful way to detail potential contributing mechanisms that were not explored in the current study.

We have expanded the methodological limitations in the Discussion section.

Major:

1) Broaden the scope of biological models that could explain the paradox.

In the Discussion, we suggest that T cells acquire some fraction of their proteome through external sources and highlight some examples of this occurring.

Minor:

1) Include Mr markers for Fig. 2C.

Done.

2) Though commonly used interchangeably, historically the term protein synthesis was the consequence of mRNA translation. In other words, proteins are not translated.

Good point! We have changed the text accordingly.

3) Include more meaningful X-axis legend in polysome gradient panels i.e., Fig. S2, e.g., fraction number.

In most experiments, fractions were not collected. Rather, the x-axis refers to time that the sample took to be queried by the detector.

4) Figure 3A does not report polysome profiles as described in the text, pg. 5, though this is reported in Fig S2D.

The figure callouts were correct but confusing. We now separately refer to out each result to clarify.

5) In Fig 5A, SDS-PAGE/anti-Puro blots would be more convincing and contain more information. The dot-blot is difficult to interpret.

Disagree. To quantitate total anti-puromycin signal a dot blot is far better than immunoblotting, which is compromised by unequal transfer of different protein species.

6) It's not clear why a degree of monosome translation is necessarily surprising (pg. 7).

It’s surprising since for many decades it was believed that translation by monosomes is a tiny fraction of translation. But separately, with this particular mode of activation, activated T cells displayed a preponderance of monosomes during their burst of division. When the activation method was improved, polysomes dominated. But monosome translation clearly supported T cell division during activation without cognate peptide, which was interesting.

Reviewer #2 (Recommendations For The Authors):

1) One concern is the dose of puromycin used. My understanding is that puromycin acts as a chain termination inhibitor - but is being used here predominantly as a label for nascent polypeptide chains. My concern, therefore, is the dose being used - here at 50ug/ml - which seems high and I would be concerned that at this dose it would act as a translational inhibitor rather than just labelling nascent chains, and is therefore resulting in a lower signal/background ration than expected. In human cell lines 0.1ug/ml is optimal and doses published (in cell lines) range between 1 and 10ug/ml so it will be interesting to understand why this high dose was used.

Do they have a dose-response curve - is this high dose necessary because these are primary Tcells. Can the authors show that 50 µg/mL of puromycin is optimal for studying protein translation in primary human T cells? A titration curve will help answer this question and could be included in Suppl Figure 1. This experiment is critical as the authors use a higher dose than previous studies (commonly between 1 and 10 µg/mL).

The reviewer is referencing puromycin concentrations typically used in the selection of cells – for the RPM assay, puromycin is used at saturating doses to label the maximal number of nascent chains stalled by CHX or EME pretreatment.

2) None of the figures show statistical significance.

Statistics on relevant comparisons are now indicated on figures and in legends.

3) The authors mention: "We performed RPM on cells labelled with CFSE to track cell division by dye dilution (Supplemental Figure 1B). On day 2, activated cells exhibited multiple populations, with nearly all divided cells showing a high RPM signal.". However, on day 2 it is hard to see any dividing cells in the dot plot included in the supplemental figure. Dividing cells only appear on day 5? Their statements make the subsequent paragraphs also difficult to follow.

We modified the text to clarify this data – there is likely activation-induced cell death occurring which is why there are relatively few CFSE-low cells at this timepoint, and they do exhibit a fairly wide range of RPM staining. The main point is that by day 5, nearly all divided cells exhibit high RPM.

4) "Many divided cells exhibited near baseline RPM signals, however, consistent with their return to the resting state. Interestingly, although non-activated cells did not divide, ~50% demonstrated increased RPM staining.". Again, it is hard to see the ~50% of cells with increased RPM the authors refer to in the provided supplemental figure.

This is from quantification of the flow data and is described more fully later when we discuss ribosome stalling.

5) The authors say "Thus, we cannot attribute the persistence of flow RPM staining in translation initiation inhibitor-treated cells to incomplete inhibition of protein synthesis.' - but it's unclear what this refers to as in the previous paragraph they also say: 'Initiation inhibitors, however, clearly discriminated between day 1 resting and activated cells. RPM signal was diminished by up to 8090% on day 5 post-activation.' - this is all somewhat confusing. It would be helpful to have this clarified and in the text to make more liberal use of referring to specific figures.

Figure 1B shows that RPM is maintained at fairly high levels during treatment with EME or CHX (in contrast to the initiation inhibitors HAR/PA). To rule out that the drugs were simply not active, tritiated leucine labeling was conducted to confirm that incorporation of the radiolabeled amino acid dropped to near-baseline (Figure 1C). Therefore, we can conclude that the drugs are indeed working as intended, but EME/CHX does not decrease RPM signal to the same extent that they prevent leucine incorporation.

6) Page 5 Fig 3A - I don't understand the difference between freshly isolated OT-1 cells - which don't stall and day 1 OT-1 cells which do. Why are freshly isolated cells not behaving like the naïve cells- isn't this what they would predict? Also - I accept that there is a move from monosome to polysome population between day 1 and 2 - the effect isn't huge - it would be helpful/interesting to know what has happened by day 5 - is the effect much more significant?

Freshly isolated cells are harvested from animals and immediately queried, whereas day 1 cells are cultured for 24h in the absence of any activation. Presumably, the ex vivo culture without any activation causes the mouse T cell ribosomes to stall, just as we observed in cells obtained from human donors that took hours to collect and bring to the bench. The appearance of polysomes is really related to how the activation of the cells is done… refer to Figure 5B to see how significant the polysome buildup can be!

7) Fig S3C - I don't understand how they reach the conclusion from this figure that: '~15-fold increase in translating ribosomes in activated OT-I T cells in vivo (Supplemental Figure 3C) as compared to the 10-fold increase we previously reported using the original protocol. It would very much help the reader if these calculations could be better explained.

These are simply quantifications of the RPM staining done in Supplemental Figure 3C compared to experiments done in the absence of the CHX-modified method.

8) Page 7 - They conclude that the Tan paper has superior lymphocyte activation - but presumably this depends on the signal as to whether there is more activation and how this affects the shift from monosome to polysome -ie maybe a stronger activation signal affects the distribution more - perhaps their method is the more physiological? Is their conclusion fair - that 'These findings indicate that monosomes make a major contribution to translation in resting T cells but are likely to make a minor contribution in fully activated cells.'

Yes, we believe that their published method would be more physiological with the use of the natural OT-I peptide. We conclude that although monosome translation is present (as others have published), there are relatively few monosomes in fully activated T cells. Therefore, the monosome contribution to overall translation in activated T cells appears to be minor.

9) Contrary to observations in vitro, ribosomes are not stalled in naïve mouse T cells in vivo, as we show via RTA analysis of non-activated T cells. - yes - this seems somewhat surprising - what is the explanation?

We presume this is due to the stress/non-native environment that ex vivo cultured cells are subjected to.

10) Whilst I understand the point that the authors are trying to make in Figure 1D about resting T cells having high background RPM staining due to stalled ribosomes, it is intriguing that there is almost no difference (no statistical significance provided) after 2 or 5 days of activation. Isn't this finding contrary to the one provided in Figure 1A and Suppl Figure 1B?

Figure 1A is showing the difference between no activation and activation conditions. Figure 1D is predominantly meant to show that the increase in RPM from activated cells at day 1 and day 5 are not as different as one might predict. The reason, as we describe in further experiments, is likely that cells exhibiting ribosomal stalling can incorporate puromycin, damping the “fold change” we calculate (unlike what we observe in metabolic labeling experiments in the same figure panel). Statistics have now been displayed on the graphs in Figure 1D for further clarification.

11) "Including EME with HAR prevented decay of the RPM signal, as predicted, since EME blocks elongation while enabling (even enhancing) puromycylation21,26." I find this very confusing. I understand that emetine blocks protein elongation whilst enabling puromycilation, but why does it block the effect of the protein initiation inhibitor Harringtonin? Do they compete with each other?

When ribosomes are frozen with emetine, they cannot transit mRNA and “fall off”. Therefore, the inclusion of EME in these experiments is a control to ensure that we are looking at true transit and runoff of ribosomes with harringtonine treatment (explanation in the second paragraph of “Flow RPM measures ribosome elongation rates in live cells” section)

12) Can the authors explain why the RPM signal of activated OT-I cells (PMA/Iono) increases 20fold compared to resting cells, but there is only a ~2-fold increase in signal in human cells? The authors previously mentioned: "We noted that the RPM signal in activated cells was only 2- to 5fold higher than in non-activated cells. This increase is modest compared to the ~15-fold activation-induced increase in protein synthesis in original studies 10,11. To examine this discrepancy, we incubated cells for 15 min with harringtonin (HAR) or pactamycin (PA) to block translation initiation or emetine (EME) or cycloheximide (CHX) to block elongation." Would the authors have followed the same path if they had started the paper with OT-I cells?

Human cells are not as well activated as OT-I in our study. The last question is beyond the scope of our reasoning as empirical evidence-based scientists, but we have applied for funding from the HG Wells Foundation for a time machine to answer this question.

13) Authors should include representative raw data of the flow cytometries used to perform the "Ribosome Transit Assay (RTA) in Figures 2 and 3 as supplemental data.

Done; now included in Supplemental Figures 1 and 3.

14) It would be interesting to compare RPM in T cells activated with a more physiological stimulus, such as beads anti-CD3 anti-CD28 vs PMA/Iono. Particularly after showing that peptide-specific stimulation (with SIINFEKL) is more effective than PMA/Iono in activating OT-I cells and inducing polysome formation (Figures 5B and Suppl Figure 4A).

We tried plate bound anti- CD3 and anti-CD28 early in these studies, and they didn’t induce as much early activation.

15) Can the authors include the gating strategy to call "activated OT-I cells" to the cells shown in Suppl Figure 3c?

A new Supplemental Figure 3D has been added showing the exact gating strategy for the OT-I cell RTA assays described in Supplemental Figure 3C and elsewhere.

16) In Figure 6B, the authors mention an increase in the volume of the cells based on the assumption of spherical morphology but then show an increase in diameter. It would be more consistent to show both parameters in the same graph.

The graph was changed to volume calculations instead of diameter for clarity. But they are linked as volume scales by radius cubed.

17) The paper's main conclusion (i.e., that the ratio of proteins to ribosomes in T cells activated in-vivo does not support their doubling time) is exciting. They conclude this after measuring cell volume, protein abundance, and ribosomes per cell. As no changes in cell volume and protein abundance between T cells activated in vitro vs in vivo were observed (Figures 6B and 6C), the difference is exclusively attributable to a reduced number of ribosomes per cell in T cells activated in vivo (Figure 6F). Critically, the measurement of ribosomes per cell in T cells activated in vivo (Figure 6F, "ex vivo day 2") includes only two data points. It is hard to understand how the authors calculated this figure's means and standard deviations as it is not described in the figure legend. From the dispersion observed for "day 1" and "day 2" in vitroactivated T cells, it seems that the variability of the assay to measure ribosome content could explain part of the phenotype. Additionally, there are several missing data points in Figure 6H. As this figure is just a transformation of Figures 6D and 6G, it isn't easy to understand why. Can I suggest that they include more data points for Figures 6F, G, and H in the ex vivo day 2' category as the two data points shown with little variability is out of keeping with the rest of the data, and may be skewing their data?

Figure 6F does not have the same number of data points as other panels because it required measurement of both protein content and ribosome number. Since the ribosome quantification method described here was developed later than some of our earlier protein measurements, not all experiments had both sets of data to properly calculate the proteins per ribosome. All data that had both values are included, though.

Reviewer #3 (Recommendations For The Authors):

Minor points:

If an increase in cell diameter is recorded upon activation, why not also provide the value for the increase in volume?

Done

Regarding the writing, the erratic punctuation/hyphenation - or lack thereof - doesn't improve readability. One example: "....consistent with the idea that the flow RPM signal in day 1 resting lymphocytes...." Perhaps better: "... consistent with the idea that the RPM signal, obtained by flow cytometry for lymphocytes analyzed on day 1 and maintained in the absence of any activating agent,..." I understand that this can make for longer sentences, but I object to the use of 'flow' as shorthand for 'flow cytometry', and to the use of day 1 as an adverb or adjective. That works as lab jargon, it's less effective in a written text. The abbreviation 'DRiPs' is not defined. Words like 'notably', and 'surprisingly' can be eliminated.

This work would benefit from the inclusion of a section describing 'Limitations of the study'.

This is now expanded in the Discussion, as described above.

Author Response

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

Reviewer #1 (Public Review):

The association of vitamin D supplementation in reducing Asthma risk is well studied, although the mechanistic basis for this remains unanswered. In the presented study, Kilic and co-authors aim to dissect the pathway of Vitamin D-mediated amelioration of allergic airway inflammation. They use initial leads from bioinformatic approaches, which they then associate with results from a clinical trial (VDAART) and then validate them using experimental approaches in murine models. The authors identify a role of VDR in inducing the expression of the key regulator Ikzf3, which possibly suppresses the IL-2/STAT5 axis, consequently blunting the Th2 response and mitigating allergic airway inflammation.

The major strength of the paper lies in its interdisciplinary approach, right from hypothesis generation, and linkage with clinical data, as well as in the use of extensive ex vivo experiments and in vivo approaches using knock-out mice. The study presents some interesting findings including an inducible baseline absence/minimal expression of VDR in lymphocytes, which could have physiological implications and needs to be explored in future studies. However, the study presents a potential for further dissection of relevant pathophysiological parameters using additional techniques, to explain certain seemingly associative results, and allow for a more effective translation.

Several results in the study suggest multiple factors and pathways influencing the phenotype seen, which remain unexplored. The inferences of this study also need to be read in the context of the different sub-phenotypes and endotypes of Asthma, where the Th2 response may not be predominant. While this does not undermine the importance of this elegant study, it is essential to emphasize a holistic picture while interpreting the results.

Reviewer #2 (Public Review):

Summary:

This study seeks to advance our knowledge of how vitamin D may be protective in allergic airway disease in both adult and neonatal mouse models. The rationale and starting point are important human clinical, genetic/bioinformatic data, with a proposed role for vitamin D regulation of 2 human chromosomal loci (Chr17q12-21.1 and Chr17q21.2) linked to the risk of immune-mediated/inflammatory disease. The authors have made significant contributions to this work specifically in airway disease/asthma. They link these data to propose a role for vitamin D in regulating IL-2 in Th2 cells implicating genes associated with these loci in this process.

Strengths:

Here the authors draw together evidence form. multiple lines of investigation to propose that amongst murine CD4+ T cell populations, Th2 cells express high levels of VDR, and that vitamin D regulates many of the genes on the chromosomal loci identified to be of interest, in these cells. The bottom line is the proposal that vitamin D, via Ikfz3/Aiolos, suppresses IL-2 signalling and reduces IL-2 signalling in Th2 cells. This is a novel concept and whilst the availability of IL-2 and the control of IL-2 signalling is generally thought to play a role in the capacity of vitamin D to modulate both effector and especially regulatory T cell populations, this study provides new data.

Weaknesses:

Overall, this is a highly complicated paper with numerous strands of investigation, methodologies etc. It is not "easy" reading to follow the logic between each series of experiments and also frequently fine detail of many of the experimental systems used (too numerous to list), which will likely frustrate immunologists interested in this. There is already extensive scientific literature on many aspects of the work presented, much of which is not acknowledged and largely ignored. For example, reports on the effects of vitamin D on Th2 cells are highly contradictory, especially in vitro, even though most studies agree that in vivo effects are largely protective. Similarly other reports on adult and neonatal models of vitamin D and modulation of allergic airway disease are not referenced. In summary, the data presentation is unwieldy, with numerous supplementary additions, that makes the data difficult to evaluate and the central message lost. Whilst there are novel data of interest to the vitamin D and wider community, this manuscript would benefit from editing to make it much more readily accessible to the reader.

Wider impact: Strategies to target the IL-2 pathway have long been considered and there is a wealth of knowledge here in autoimmune disease, transplantation, GvHD etc - with some great messages pertinent to the current study. This includes the use of IL-2, including low dose IL-2 to boost Treg but not effector T cell populations, to engineered molecules to target IL-2/IL-2R.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

In the revised manuscript, the authors have addressed a significant number of concerns raised. The restructuring and incorporation of a number of discussion points have improved the readability. Moreover, the authors have also incorporated some more figures to address certain questions raised.

However, the authors could reconsider a few more points which would improve the readability of the manuscript.

For e.g.

1) While it is appreciated that the authors have provided the schematic of the study design for the VDAART trial, the visualization for the RNA-seq analysis may be helpful.

We have created a visualization of the workflow for the RNA seq analysis as part of Figure 1 – figure supplement 1C.

2) Quantification of images would not require any additional experiments, yet can reinforce the results with objectivity.

We appreciate this comment. We chose to display histology images to allow a glimpse at the inflammatory condition in the lung tissue. For histological quantification, lung tissue should have been harvested and analyzed in a systematic and randomized way as well as in sufficient animal numbers to allow statistical analyses. This has not been done for these mouse models since the focus was in analyzing cytokine production by lung tissue CD4+ T cells as the driver of inflammation.

3) The authors have not addressed the discrepancy of the sample sizes in the experiments. Some dot plots still don't match the legends, and there is a wide variation in the numbers chosen for different experiments and different groups in the same experiments.

We appreciate the thorough screening of our manuscript and apologize for this oversight. We corrected the errors in the respective figure legends.

The in vivo experiments comprise studies performed in (A) VDR-KO mice and (B) WT mice fed with vit-D supplemented chow.

Sample size calculations for the mouse models of allergic airway inflammation based on BAL cell numbers revealed a minimum of n=8 per group for correct statistical analysis. In both experimental settings, the respective mouse lines were bred in the mouse facilities of MGH (A) and BWH (B). Depending on the litter sizes, additional mice were added in the HDM group, since bigger variability was expected in this group than the saline group.

Intracellular CD4+ cytokine staining was performed for all mice, however some stainings failed and could not be reliably interpreted and were therefore excluded.

Reviewer #2 (Recommendations For The Authors):

The authors have largely replied to the reviewer comments, amended some noted typos & figure legend issues, as well as discussed the reviewers concerns in text and in their rebuttal.

The data presented are novel and of significant interest, conceptually moving this field forward, but in this reviewer's opinion reflect one pathway, of likely several, linked to protective effects of vitamin D on airway disease. This reviewer recommends a further slight editing of the text to present this broader scenario.

i) Treg cells are highly dependent on IL-2 (both Foxp3+ and IL-10+ cells, not always the same population), constitutively express the IL-2R, and there is already a significant literature regarding vitamin D and IL-10/Treg in control of immune-mediated conditions. A simple statement acknowledging this and that there are likely more than one mechanisms by which vitamin D may regulate allergic airway disease (directly or indirectly) would be appreciated - this is no way detracts from the novelty and contribution of the current findings.

We thank the reviewer for this suggestion. We have added the following statement to the manuscript (lines 623-625):

“Additional pathways, including the induction of IL-10 production by CD4+ T cells as well as a direct induction of Foxp3+ T reg cells could have further contributed to the observed protective effect of vitamin D supplementation (PMID: 21047796; 22529297).”

ii) More comprehensive referencing of earlier papers proposing effects of vitamin D in controlling Treg/IL-10 and dampening Th2 responses in mouse (and human) models

(e.g. Taher, Y. A., van Esch, B. C. A. M., Hofman, G. A., Henricks, P. A. J. & van Oosterhout, A. J. M. 1alpha,25-dihydroxyvitamin D3 potentiates the beneficial effects of allergen immunotherapy in a mouse model of allergic asthma: role for IL-10 and TGF-beta. J. Immunol. 180, 5211-21 (2008). Vassiliou JE et al, 2014. Vitamin D deficiency induces Th2 skewing and eosinophilia in neonatal allergic airways disease. Allergy DOI10.1111/all.12465).

We have included the reference in the discussion section of our manuscript in lines 617-619:

“Similar findings regarding the effects of vitamin D in controlling Treg/IL-10 and dampening Th2 responses have been reported, e.g., in (PMID: 18390702) and in offspring of mice that had been subjected to vitamin D deficiency in the third trimester of their pregnancy (PMID: 24943330).”

Author Response

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

Reviewer #1 (Public Review):

The authors of the manuscript "High-resolution kinetics of herbivore-induced plant volatile transfer reveal tightly clocked responses in neighboring plants" assessed the effects of herbivory induced maize volatiles on receiver plants over a period of time in order to assess the dynamics of the responses of receiver plants. Different volatile compound classes were measured over a period of time using PTR-ToF-MS and GC-MS, under both natural light:dark conditions, and continuous light. They also measured gene expression of related genes as well as defense related phytohormones. The effects of a secondary exposure to GLVs on primed receiver plants was also measured.

The paper addresses some interesting points, however some questions arise regarding some of the methods employed. Firstly, I am wondering why VOCs (as measured by GC-MS) were not quantified. While I understand that quantification is time consuming and requires more work, it allows for comparisons to be made between lines of the same species, as well as across other literature on the subject. Simply relying on the area under the curve and presenting results using arbitrary units is not enough for analyses like these. AU values do not allow for conclusions regarding total quantities, and while I understand that this is not the main focus of this paper, it raises a lot of uncertainty for readers (for example, the references cited show that TMTT has been found to accumulate at similar levels of caryophyllene, however the AU values reported are an order of magnitude higher for TMTT. Again, without actual quantification this is meaningless, but for readers it is confusing).

With regards to the correlation analyses shown in figure 6, the results presented in many of the correlation plots are not actually informative. While there is a trend, I do not think that this is an appropriate way to show the data, as there are clearly other relationships at play. The comparison between plants under continuous light and normal light:dark conditions is interesting.

This paper addresses a very interesting idea and I look forward to seeing further work that builds on these ideas.

As mentioned in our previous response, we have added the quantification of GLVs in order to increase the comparability of our work to other studies.

Regarding the comment about TMTT (only measured as internal pools), the purpose of the inclusion of these internal pool data, was simply to determine whether terpenes were accumulating in leaf tissue during the night when emissions are hindered (likely due to closed stomata). The data clearly show that internal terpene pools do not accumulate above daytime levels during darkness – this is further supported by gene expression data that show downregulation of terpene synthase genes during darkness. While quantification would certainly increase the ability to compare internal pools, it would not change the interpretation of our results. Also note that absolute quantification is challenging for compounds such as TMTT, which are not readily available.

Regarding the comment on Figure 6, while we agree there may be interesting patterns beyond linear relationships, as stated in our previous response, the purpose of our analysis was to determine if the higher terpene burst in receiver plants on the second day may be explained by sender plants emitting more GLVs on the second day. Figure 6 shows that this is not the case. Further analyses would not provide additional significant insights into the hypothesis that we tested here.

We thank the reviewer for their overall positive outlook on our paper and for the constructive comments.

Reviewer #2 (Public Review):

The exact dynamics of responses to volatiles from herbivore-attacked neighbouring plants have been little studied so far. Also, we still lack evidence whether herbivore-induced plant volatiles (HIPVs) induce or prime plant defences of neighbours. The authors investigated the volatile emission patterns of receiver plants that respond to the volatile emission of neighbouring sender plants which are fed upon by herbivorous caterpillars. They applied a very elegant approach (more rigorous than the current state-of-the-art) to monitor temporal response patterns of neighbouring plants to HIPVs by measuring volatile emissions of senders and receivers, senders only and receivers only. Different terpenoids were produced within 2 h of such exposure in receiver plants, but not during the dark phase. Once the light turned on again, large amounts of terpenoids were released from the receiver plants. This may indicate a delayed terpene burst, but terpenoids may also be induced by the sudden change in light. As one contrasting control, the authors also studied the time-delay in volatile emission when plants were just kept under continuous light. Here they also found a delayed terpenoid production, but this seemed to be lower compared to the plants exposed to the day-night-cycle. Another helpful control was now performed for the revision in which the herbivory treatment was started in the evening hours and lights were left on. This experiment revealed that the burst of terpenoid emission indeed shifted somewhat. Circadiane and diurnal processes must thus interact.

Interestingly, internal terpene pools of one of the leaves tested here remained more comparable between night and day, indicating that their pools stay higher in plants exposed to HIPVs. In contrast, terpene synthases were only induced during the light-phase, not in the dark-phase. Moreover, jasmonates were only significantly induced 22 h after onset of the volatile exposure and thus parallel with the burst of terpene release.

An additional experiment exposing plants to the green leaf volatile (glv) (Z)-3-hexenyl acetate revealed that plants can be primed by this glv, leading to a stronger terpene burst. The results are discussed with nice logic and considering potential ecological consequences. All data are now well discussed.

Overall, this study provides intriguing insights in the potential interplay between priming and induction, which may co-occur, enhancing (indirect and direct) plant defence. Follow-up studies are suggested that may provide additional evidence.

We thank the reviewer for their positive outlook on our paper and for their constructive comments.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

The authors did a great job with the revision. The additional experiments strengthened their conclusions. Thanks also for performing the suggested test for potential differences in induction capacity at different times of day, the new data are very interesting.

Thank you very much.

Line 49-52: The newly added sentence could be clarified in wording.

We will clarify the sentence.

Line 254-255: The newly added sentence needs to be corrected. This is no full sentence and it is not clear what the authors wanted to say here.

We will clarify this sentence.

Figure 6: In those instances, in which the correlation is not significant, the line should not be shown.

We will remove the lines when correlations are not significant.

The names of chemical compounds and terpene synthases should be written in lower case letters (see legend Fig 6, e.g. hexenal, not Hexenal; legend fig. 2: terpene synthase, not Terpene synthase)

In the last round of revisions, I commented on Line 23: consequences on community dynamics are not investigated here, so this is a bit misleading. ... Your response was "We have deleted the sentence about community dynamics ..." which, however, in fact was not done! Please change!

Apologies for that, we will delete mention of community dynamics in that sentence (for real).

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

eLife assessment

This important study examines the effects of herbivory-induced maize volatiles on neighboring plants and their responses over time. Measurements of volatile compound classes and gene expression in receiver plants exposed to these volatiles led to the conclusion that the delayed emission of certain terpenes in receiver plants after the onset of light may be a result of stress memory, highlighting the role of priming and induction in plant defenses triggered by herbivore-induced plant volatiles (HIPVs). Most experimental data are compelling but additional experiments and accurate quantifications of the compounds would be required to confirm some of the main claims.

Response: We thank the editors for their overall positive feedback on our MS. We have added additional experiments to quantify green leaf volatile emissions in both sender plants and synthetic dispensers (Reviewer 1) and address the importance of the precise time of day plants are induced (Reviewer 2). These additions strengthen the main conclusions of our study.

Public Reviews:

Reviewer #1 (Public Review):

The authors of the manuscript "High-resolution kinetics of herbivore-induced plant volatile transfer reveal tightly clocked responses in neighboring plants" assessed the effects of herbivory-induced maize volatiles on receiver plants over a period of time in order to assess the dynamics of the responses of receiver plants. Different volatile compound classes were measured over a period of time using PTR-ToF-MS and GC-MS, under both natural light:dark conditions, and continuous light. They also measured gene expression of related genes as well as defence-related phytohormones. The effects of a secondary exposure to GLVs on primed receiver plants were also measured.

The paper addresses some interesting points, however, some questions arise regarding some of the methods employed. Firstly, I am wondering why VOCs (as measured by GC-MS) were not quantified. While I understand that quantification is time-consuming and requires more work, it allows for comparisons to be made between lines of the same species, as well as across other literature on the subject. As experiments with VOC dispensers were also used in this experiment, I find it even more baffling that the authors didn't confirm the concentration of the emission from the plants they used to make sure they matched. The references cited justifying the concentration used (saying it was within the range of GLVs emitted by their plants) to prepare the dispenser were for either a different variety of maize (delprim versus B73) or arabidopsis. Simply relying on the area under the curve and presenting results using arbitrary units is not enough for analyses like these.

Response: We thank the reviewer for their comment. We have now quantified both the emission of dispensers and maize seedlings infested with 3 4th-instar Spodoptera exigua larvae. Averaged across 1 h, HAC dispensers emitted roughly 2x higher molar concentrations than total GLV molar concentrations emitted by plants infested by 3 caterpillars. Of note, GLV emissions induced by caterpillars vary over time, and can be more than 2-fold higher than the average during times of strong active feeding (Supplemental Fig 4). Thus, the release rate of the dispensers is well within the plant’s physiological range.

Note that the references cited were included to support the claim of the biological activity of all three GLVs rather than to justify concentration of our dispensers. We have rephrased this sentence to reflect this (see L330-333).

With regards to the correlation analyses shown in Figure 6, the results presented in many of the correlation plots are not actually informative. By blindly reporting the correlation coefficient important trends are being ignored, as there are clearly either bimodal relationships (e.g. upper left panel, HAC/TMTT, HAC/MNT) or even stranger relationships (e.g. upper left panel, IND/SQT, IND/MNT) that are not being well explained by a correlation plot. It is not appropriate to discuss the correlation factors presented here and to draw such strong conclusions on emission kinetics. The comparison between plants under continuous light and normal light:dark conditions is interesting, but I think there are better ways to examine these relationships, for example, multivariate analysis might reveal some patterns.

Response: We thank the reviewer for their comment. With our analysis we aimed at testing specifically whether the high release of known bioactive volatiles (GLVs and indole) by sender plants on the second day can explain the higher terpene emissions in the receiver plants. We explicitly mention this in the text (L176-L186). Indeed, under normal light conditions (light and dark phase), there are clear positive correlations between the GLV release of sender plants and the terpene release of receiver plants over time (see also Fig 1 and Fig 5). However, under continuous light conditions, GLV emissions in sender plants no longer correlate with terpene emissions in receiver plants (also apparent by comparison of Fig 4 and Fig 5). This shows that temporal variation in GLV emissions are insufficient to explain the delayed terpene burst. This is the relevant conclusion we draw from this analysis. As presented, we find the data to provide strong evidence that the delayed burst in receiver plant terpene emissions cannot be solely explained by higher availability of active signals on the second day. The priming experiment in Figure 7 then provides a direct additional test for this concept. While more complex analyses could indeed reveal additional patterns, these would not be particularly informative for the question at hand.

In Figure 2, the elevated concentrations of beta-caryophyllene found in the control plants at 8h and 16.75h measurement timepoints are curious. Is this something that is commonly seen in B73?

Response: We thank the reviewer for this comment. A small number of untreated plants indeed accumulated β -caryophyllene at night, which is likely the result of biological variability between samples. Our plants were soil-grown, and it is for instance possible that variation in soil biota may account for this variability. Alternatively, some plants may have been slightly stressed during handling. Note that this variability does not affect any of the conclusions in our manuscript.

While there can be discrepancies between emissions and compounds actually present within leaf tissue, it is a little bit odd that such high levels of b-caryophyllene were found at these timepoints, however, this is not reflected in the PTR-ToF-MS measurements of sesquiterpenes. It would be beneficial to include an overview of the mechanism of production and storage of sesquiterpenes in maize leaves, which would clarify why high amounts were found only in the GC-MS analysis and not the PTR-ToF-MS analysis, which is a more sensitive analytical tool. It is possible that the amounts of b-caryophyllene present in the leaf are actually extremely low, however as the values are not given as a concentration but rather arbitrary units, it is not possible to tell. I would include a line explaining what is seen with b-caryophyllene.

Response: Thank you for this comment. It is important to note that accumulation in maize leaves can differ substantially from emission, especially at night when stomata are closed. This has been observed before in maize leaves (Seidl-Adams et al., 2015). As the reviewer suspects, earlier work indeed found that β-caryophyllene is a minor sesquiterpene compared to β-farnesene and α-bergamotene in B73 ( Block et al., 2018). The PTR-ToF-MS does not discriminate between terpenes with the same m/z and thus measures total sesquiterpene emissions. Given that sesquiterpene emissions are strongly regulated by stomatal aperture and that overall sesquiterpene accumulation in control plants is low, it is not surprising that we measure only minor amounts of sesquiterpene emissions in general, and in control plants in particular. We now text to the manuscript to explain these aspects (L116-L122). Block, A.K., Hunter, C.T., Rering, C. et al. Contrasting insect attraction and herbivore-induced plant volatile production in maize. Planta 248, 105–116 (2018).

Seidl-Adams I, Richter A, Boomer KB, Yoshinaga N, Degenhardt J, Tumlinson JH. Emission of herbivore elicitor-induced sesquiterpenes is regulated by stomatal aperture in maize (Zea mays) seedlings. Plant Cell Environ. 38, 23-34 (2015).

Additionally, it seems like the amounts of TMTT within the leaf are extraordinarily high (judging only by the au values given for scale), far higher than one would expect from maize.

Response: We are unsure about the reviewer’s interpretation here. The AU values do not allow for conclusions regarding total quantities. An earlier study found that TMTT in induced B73 plants accumulates to similar amounts as β-caryophyllene (Block et al., 2018), thus it is not surprising to detect significant TMTT pools in induced maize leaves. It is important to note that the aim of the experiment here was to test the hypothesis that plants may be hyperaccumulating volatiles when the stomata are closed at night, which could potentially explain the delayed terpene burst on the second day. We do not observe such a hyperaccumulation, thus ruling out this as the primary factor responsible for the observed phenomenon. This is further supported by the continuous light experiments, where the delayed burst in terpene emission is not hindered by the lack of a dark phase.

Block, A.K., Hunter, C.T., Rering, C. et al. Contrasting insect attraction and herbivore-induced plant volatile production in maize. Planta 248, 105–116 (2018).

Reviewer #2 (Public Review):

The exact dynamics of responses to volatiles from herbivore-attacked neighbouring plants have been little studied so far. Also, we still lack evidence of whether herbivore-induced plant volatiles (HIPVs) induce or prime plant defences of neighbours. The authors investigated the volatile emission patterns of receiver plants that respond to the volatile emission of neighbouring sender plants which are fed upon by herbivorous caterpillars. They applied a very elegant approach (more rigorous than the current state-of-the-art) to monitor temporal response patterns of neighbouring plants to HIPVs by measuring volatile emissions of senders and receivers, senders only and receivers only. Different terpenoids were produced within 2 h of such exposure in receiver plants, but not during the dark phase. Once the light turned on again, large amounts of terpenoids were released from the receiver plants. This may indicate a delayed terpene burst, but terpenoids may also be induced by the sudden change in light. A potential caveat exists with respect to the exact timing and the day-night cycle. The timing may be critical, i.e. at which time-point after onset of light herbivores were placed on the plants and how long the terpene emission lasted before the light was turned off. If the rhythm or a potential internal clock matters, then this information should also be highly relevant. Moreover, light on/off is a rather arbitrary treatment that is practical for experiments in the laboratory but which is not a very realistic setting. Particularly with regard to terpene emission, the sudden turning on of light instead of a smooth and continuous change to lighter conditions may trigger emission responses that are not found in nature.

Response: We thank the reviewer for their comment. Although not explicitly mentioned it in the initial draft of the MS, we employed 15 min transition periods for light and dark phase transitions with a light intensity of 60 µmol m-2 s-1 (compared to 300 µmol m-2 s-1 at full light) to achieve a more gradual transition. We now included this information in the manuscript (L291-L292).

As one contrasting control, the authors also studied the time-delay in volatile emission when plants were just kept under continuous light (just for the experiment or continuously?). Here they also found a delayed terpenoid production, but this seemed to be lower compared to the plants exposed to the day-night-cycle. Another helpful control would be to start the herbivory treatment in the evening hours and leave the light on. If then again plants only release volatiles after a 17 h delay, the response is indeed independent of the diurnal clock of the plant.

Response: This is a very interesting point raised by the reviewer. We now conducted an additional experiment under continuous light where we started the herbivory treatment just before the start of the dark phase (ca. 20:00 PM). We found a similar pattern: a distinct delay in the highest burst. However, interestingly, the burst was shifted from 12-18 hr to 10-12 hr (Supplemental Fig 1). This burst aligned reasonably well with the point at which lights would normally be turned on again. In light of this, and, as the herbivore additions typically started ca. 5 hrs after the onset of light following a dark period (Figures 1-7), we wanted to rule out the possibility that the lack of a burst on the first day, was simply due to a difference in induction capacity depending on how shortly after the onset of light plants became exposed to GLVs. As such, we designed an additional experiment to examine whether exposure to GLVs immediately after the lights come on induce higher terpene emissions than plants exposed to GLVs ca. 5 hr after lights come on (Supplemental Fig 2). Interestingly, emissions across the terpenes were similar, regardless how long after the onset of lights on plants were exposed to GLVs. This suggests that the delayed burst is not due to the fact that, on the second day, plants are exposed to GLVs immediately after the lights come on whereas the first day they are only exposed 5 hr after the lights come on. Both continuous light experiments (normal timing and shifted timing) show bursts that occur slightly earlier than we observe with under normal day : night light conditions (L159-L166 and L207-L211), suggesting an interaction between circadian and diurnal processes. For instance, it is possible that plants would start producing volatiles slightly earlier than the onset of the day, however, light and stomatal opening limits the exact timing of the burst under normal light:dark transitions. The additional data provide further evidence for the delayed burst as a timed response in maize plants.

Additionally, we have added explanation the continuous light figure legends that plants were grown under normal conditions and lights were only left on following treatment.

Interestingly, internal terpene pools of one of the leaves tested here remained more comparable between night and day, indicating that their pools stay higher in plants exposed to HIPVs. In contrast, terpene synthases were only induced during the light-phase, not in the dark-phase. Moreover, jasmonates were only significantly induced 22 h after the onset of the volatile exposure and thus parallel with the burst of terpene release. An additional experiment exposing plants to the green leaf volatile (glv) (Z)-3-hexenyl acetate revealed that plants can be primed by this glv, leading to a stronger terpene burst. The results are discussed with nice logic and considering potential ecological consequences. Some data are not discussed, e.g. the jasmonate and gene induction pattern.

Response: Thanks for this comment. We have added a sentence regarding the jasmonate data suggesting that, in addition to providing an additional layer of evidence for the observed delay, suggest that other JA-dependent defenses in maize may follow similar temporal patterns (L254-L257).

Overall, this study provides intriguing insights into the potential interplay between priming and induction, which may co-occur, enhancing (indirect and direct) plant defence. Follow-up studies are suggested that may provide additional evidence.

Reviewer #1 (Recommendations For The Authors):

Could the authors please explain why they chose not to calculate concentrations for VOCs? Perhaps it is that B73 is a very unique variety in that it contains very high levels of TMTT, even in control plants? This should be clarified by the authors.

Response: We address this comment in the public review portion

For the legend within Figure 2, I would move it to be in the upper left or right corners of the figure. It is not easy to see in its current position.

Response: We have moved the figure legend based on the reviewers recommendation

Figures depicting PTR-ToF-MS data: add m/z values to either the figures themselves and/or the legends.

Response: We have added m/z values to the legends and added molecular formulas of protonated compounds to each panel.

Overall, here are some other suggestions: I am slightly weary of the term "clocked response". I'm not sure this is the correct fit for what you are trying to convey. I think "regulated" is a better term than "clocked". I understand that it is likely a stylistic choice to use this word, however, I advise reconsidering for the sake of clarity of the results.

Response: Thank you. We find clocked to be an appropriate term, as it highlights the temporal aspect of the burst, and have thus left the title as is.

Have another look at the references as some are not in the correct format (i.e., species not in italics).

Response: We have checked and corrected the references

Reviewer #2 (Recommendations For The Authors):

Line 23: consequences on community dynamics are not investigated here, so this is a bit misleading.

Last sentence of the abstract: It would be nice to read the answer to this long-standing question here.

Response: We have deleted he sentence about community dynamics and provided a more concrete final sentence (L38-L40)

Lines 48-50: The example does not fit so well with the first sentence and is not entirely clear (relation to temporal dynamics; similar to what?).

Response: We have reworded the sentence for clarity (L49-L52)

Line 56: "volatiles" should be plural.

Response: Changed (L58)

Line 58: "to be produced" rather than "to produce"

Response: This seems a stylistic choice, and have left it as is.

End of abstract: Did you have any hypotheses? These should be stated here.

Response: The listing of hypotheses is also a stylistic choice, which is in some cases required by journals, but not eLife. As such we have not included a discrete list of hypotheses and instead describe what we aimed to investigate and what we found.

Line 93: "This response disappeared at night." Does this mean: "No volatiles were emitted during night"? Or was this a gradual disappearance? How many hours after the onset of light did the herbivore treatment start and how many hours after the first emission of volatiles was the light turned off?

Response: We have added when herbivory began (L92-L93) and changed the text to ‘as soon as light was restored’ (L97-L98).

Line 93: "as soon as the night was over" means practically rather "as soon as the light was switched on".

Response: See above

Line 91: "small induction" - do you mean "low amounts of xxx"?

Response: We mean a small induction. Terpene emission is relatively low (hence small), but still induced relative controls.

Line 91: which mono- and sesquiterpenes were monitored?

Response: It is PTR-ToF-MS a thus we cannot identify individual sesquiterpenes and monoterpenes (as they all have the same mass), and thus group them generally.

Figure 1: What exactly is the "control"? And what does the vertical hatched line in the beginning represent?

Response: We have defined the control and added a sentence describing the vertical hatched line

"Black points represent the same but with undamaged sender plants" - what is "the same" here? I find that a bit confusing!

Response: We have rephrased

Line 104: how do you define an "overaccumulation"?

Response: We have added ‘above daytime levels’ to clarify that we mean over daytime levels (L106)

Why was the oldest developing leaf chosen? Is this the largest one when plants are two weeks old? How many leaves do they have then? Is this the leaf with the highest biomass?

Response: We chose this leaf as it is the largest and also highly responsive to HIPVs. We have added this sentence (with a reference) in the methods section (L369-L370)

Line 107: "started increasing after 3 hours" - they may already have started before. The following description also sounds like the dynamics were investigated here. However, instead the authors measured samples at four distinct time-points and cannot say whether something "began" or "remained" etc. The wording should be changed to a more appropriate description, describing the differences at a given time-point.

Response: We changed the wording to ‘were marginally induced after 3 hr’ see L110

Line 113: What do you mean by "delete BELOW NIGHTTIME levels"?

Response: The word we used was ‘deplete’ to ‘drop’ (L116)

Line 114: "the expression of terpene synthases" add "in the receiver plants exposed to HIPVs."

Response: Added

Figure 2ff: The situation of receiver plants exposed to control plant volatiles is not explained in the method section and also not depicted in the Suppl. Fig. 1. Here, the sender plants seem to always have been induced (if the red star-like structure should resemble an induction - a legend may be helpful here).

Response: We have changed to ‘connected to undamaged sender plants’. We additionally added a sentence to the methods section describing controls L300

Line 140: This treatment is not described in the methods section. Were the plants only kept under constant conditions for the 2 experimental days? Compared to the induction shown in Fig. 1, the amount of released volatiles seems less here.

Response: We have added explanation of this to the figure legends, explaining that plants were grown under normal conditions and lights were only left on following treatment

Another helpful control would be to start the herbivory treatment in the evening hours and leave the light on. If then again plants only release volatiles after a 17 h delay, the response is indeed independent of the diurnal clock of the plant.

Response: See public review comment. We have added this experiment and discuss it accordingly in the MS (L159-L166 and L207-L211)

Line 157: Check sentence/grammar

Response: Checked and modified

Figure 5: I suggest using a different colour for volatiles released from the sender plants, not again the green also used in the other figures for the receiver plants. This would help the reader to quickly see which plants are in focus in each figure.

Response: We have changed the color of the figures for clarity

Figure 6 legend: check grammar in several sentences (use of singular vs. plural)

Response: We have made the tense uniform

The diurnal rhythm of jasmonates (and potentially also terpene synthases?) is not considered in the discussion.

Response: See above, and we have added a sentence to the discussion mentioning the jasmonates (L254-L257)

Line 230-231: check grammar. Given the complexity, the response pattern may not be so predictable.

Response: We do not understand this comment, but have checked the grammar throughout the manuscript.

Line 235: I like the discussion on potential ecological consequences.

While some interpretation for each experiment is already given in the results section, not all results are discussed in the discussion section. For example, the jasmonate data are not discussed. This should be added.

Response: See above

Line 266: To get an idea about the plant size: How many leaves do the plants have in that stage?

Response: Added a sentence describing the size L287-L288

Line 321: change to "as in the greenhouse"

Response: Changed

Line 334: How were the terpenoids identified and, in particular, quantified?

Response: Added (L379-L380)

Line 354: Maybe rather change to: "Plant treatments and tissue collection for phytohormone sampling were identical as described above for terpene and gene expression analysis.

Response: Changed

Line 357: add "material" or "leaf tissue" after "flash frozen"

Response: Added

Line 359: What was the source of the isotopically labelled phytohormones?

Response: Added (L400-L403)

Line 360: The phytohormones are "analyzed" using UPLC. The "quantification" is then done afterward. Please correct.

Response: Corrected (L404)

Overall: a great approach and a wonderful idea!

Thanks

Author Response

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

Reviewer 1

Strengths:

The major strength of this paper is the series of laser cutting experiments supporting that asters position via pushing forces acting both on the boundary (see below for a relevant comment) and between asters. The combination of imaging, data analysis and mathematical modeling is also powerful.

Author Response: We thank the Reviewer for the positive comments, especially in recognising the power of our quantitative approaches.

Weaknesses:

This paper has weaknesses, mainly in the presentation but also in the quality of the data which do not always support the conclusions satisfactorily (this might in part be a presentation issue).

Author Response>: We address these concerns below.

My overall suggestion for the authors is to explain better the motivation and interpretation of their experiments and also to remove some of the observations which seem to be there because they could be done rather than because they add to the main message of the paper, which I find straightforward, valuable and supported by the data in Figure 4.

Author Response: We have extended the motivation of the study in the Introduction, and at the beginning of appropriate Results sections. We better motivate the force potential and especially the key results from Figure 4. We outline specific changes below.

In Figure 2, it is difficult for me to understand what is being tracked. I believe that the authors track the yolk granules (visible as large green blobs) and not lipid droplets. There is some confusion between the text, legends and methods so I could not tell. If the authors are tracking yolk granules as a proxy for hydrodynamics flows it seems appropriate to cite previous papers that have used and verified these methods. More notably, this figure is somewhat disconnected with the rest of the paper. I find the analysis interesting in principle but would urge the authors to propose some interpretation of the experiments in the context of their big-picture message. At this point, I cannot understand what the Figure adds.

Author Response: Indeed, we track the yolk droplets that move around the aster. In the extraction protocol, we likely get a mixture of lipid droplets and yolk granules; this is due to the extraction procedure involving shear forces within the pipette. We are not certain about the exact nature of these droplets, but they are likely to a large extent yolk. We have clarified the terminology in the text, the legend and methods section. In this figure, we now show that the droplets do not move towards the aster center as the hydrodynamic pulling model would suggest. Instead, they appear to passively respond to a repulsive force, that results in them streaming around the aster. We have added additional panels to the figure that illustrates the directionality of yolk granule movements (lines 159-164). We agree with the Reviewer that the context could have been clarified. The role of fluid flows in biological systems is, as the Reviewer highlights, well studied. We have added additional contextualisa8on in the text (lines 140-146). We also motivate more clearly the figure, as it provides evidence that the asters generate forces over 20µm scale (lines 159-164). This is highly relevant for one of the paper’s main conclusions – that the Drosophila blastocyst asters generate pushing forces that enable regular packing.

In Figure 3, it is not surprising that the aster-aster interactions are different from interactions with the boundary which is likely more rigid. It is also hard to understand why the force and thus velocity should scale as microtubule length. This Figure should be better conceptualized. I think that it becomes clear at the end of the paper that the authors are trying to derive an effective potential to use in a mathematical model in Figure 5 to test their hypotheses. I think that should be told from the start, so a reader understands why these experiments are being shown.

Author Response: We don’t claim that the force scales with microtubule length on a single microtubule. However, at larger distances from the aster, the microtubule density decreases, and hence the effective force decreases.

The Reviewer is correct that we use these results to motivate our effective potential. We have brought this motivation forward in the manuscript to guide the reader (lines 169-171) and included a further note at the end of the section (lines 216-218).

The experiments in Figure 4 are very nice in suppor8ng a pushing model. However, it would help if the authors could speculate what the single aster is pushing against in this experiment. The experiments reported in Figure 1 seemed to suggest that the aster mainly pushed against the boundary. In the experiments in Figure 4 do the individual asters touch the boundary on both sides? I think that readers need more information on what the extract looks like for those experiments.

Author Response: We now include an additional panel B in Figure 4– that shows an example of an explant during aster ablation. The distance between asters is typically less than the distance to the explant boundary. Boundary effects likely play a small role in the aster-aster separation, in terms of potentially determining the axis of separation. However, the separation of asters occurs along a straight line for a substan8al period (>1 min) of separation; if boundary effects were more dominant, we may expect to see curving of the aster-aster separation trajectories as they also receive feedback from the boundary.

Figure 4F could use some statistics. I doubt that the acceleration in the pink curves would be significant. I believe that the decelera8on is and that is probably the most crucial result. Since the authors present only 3 asters pairs it is important to be sure that these conclusions are solid.

Author Response: We agree with the Reviewer. These experiments are challenging to do, as they require carefully controlled conditions. In two out of three experiments we see significant increase in acceleration in the pink curves. Of course, the interpretation of this must be caveated as our experimental number is low. These details are now provided in the revision (lines 263267).

Reviewer 2

Strengths:

This study reveals a unique aster positioning mechanics in the syncytial embryo explant, which leads to an understanding of the mechanism underlying the positioning of multiple asters associated with nuclei in the embryo. The use of explants enabled accurate measurement of aster motility and, therefore, the construc8on of a quantitative model. This is a notable achievement.

Author Response: We thank the Reviewer for their review, and in highlighting how our quantitative model is a clear step forward in our understanding of aster dynamics.

Weaknesses:

The main conclusion that aster repulsion predominates in this system has already been drawn by the same authors in their recent study (de-Carvalho et al., Development, 2022). As the present work provides additional support to the previous study using different experimental system, the authors should emphasize that the present manuscripts adds to it (but the conceptual novelty is limited).

Author Response: While this study is related to the previous work, there are major differences. First, here we quantitatively assess aster dynamics within a “clean” system. Such accurate measurements are not possible in vivo currently. Further, experiments like laser ablation are much better defined within the explant system. We do recognise more clearly the previous work in the Introduc8on and lines 291-293, 299-300. Combined, with the different perspectives provided in these papers on the problem of aster positioning in syncytia, we believe these papers provide new and well-supported insights.

The molecular mechanisms underlying aster repulsion remain unexplored since the authors were unable to identify specific factor(s) responsible for aster repulsion in the explant.

Author Response: Given that the nature of the aster dynamics were not previously characterised, our work presents a major step forward. We show compelling evidence that an effective pushing force potential plays a role in aster interactions. With this critical knowledge, we can now explore for the potential molecular mechanisms – but such information lies beyond the current manuscript scope. This is particularly challenging due to the lack of specific microtubule drug inhibitors in Drosophila. We highlight related issues in the Discussion: paragraph starting on line 340 and lines 367-370.

Specific suggestions:

Microtubules should be visualized more clearly (either in live or fixed samples). This is particularly important in Figure 4E and Video 4 (laser ablation experiment to create asymmetric asters).

Author Response: This is similar to Reviewer 1 final comment above. These experiments are very challenging and being able to see the microtubules with sufficient clarity is not straightforward. Given our controls and previous experience, we are confident we are ablating the microtubules.

Minor points:

1) The authors explain the roles of microtubule asters in several model systems in the first paragraph of the introduction part. Please specify the species and/or cell types in each description.

Author Response: We have provided as suggested.

2) In lines 164 and 172, the citing figure numbers should be modified to Supplementary Fig. 1A and 1B, respectively.

Author Response: We thank the Reviewer for spotting this error. It has now been corrected.

3) The authors showed in the previous study that the boundary in the explant does not have an intact cell cortex and f-actin compartments (de-Carvalho et al., Development, 2022). This important informa8on should also be described in the current manuscript. It is also valuable to mention whether the pulling force mechanism operates in embryos where the intact cell cortex is present.

Author Response: This is an interesting point We have added a sentence in the discussion with this information. We have now added additional text in the Discussion (lines 324-327).

Author Response

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

Reviewer #1 (Recommendations For The Authors):

It is somewhat speculative that the structure represents the EIIa-bound regulatory state. There's a strong enough case that it should be analyzed in the discussion, but I don't think it is firmly established. Therefore, the title of the paper should be changed.

Our answer: Thank you for the comment. We have changed the title to “Mobile barrier mechanisms for Na+-coupled symport in an MFS sugar transporter”

Reading through the manuscript, it was challenging to distinguish what is new in the current manuscript and what has been done previously. There were a lot of parts where it was hard for me to identify the main point of the current study among all the details of previous studies. It would also benefit from shortening. For example:

-Page 6: Nb725 binding has already been characterized extensively in the very nice JBC paper earlier this year. It's important to test 725-4 for binding, but since it doesn't change the binding interaction, and probably wouldn't be expected to, the entire section could be written more succinctly. The main point, which is that 725-4 behaves like 725, is lost among all the details

Our answer: Thanks for this instructive suggestion. We have shortened the description in this section.

-Page 9-10. I don't understand what summarizing all of the results from the previous D59C studies adds to the current story. It's important because it provides an indication of the substrate binding site, but its mechanism of action does not seem relevant to the current work.

Our answer: We have shortened the description of the sugar-binding site and moved the previous Fig. 3b to supplementary figure sFig. 11. According to your comment about showing the location of the binding sites, which is also suggested by Reviewer #2, we modified Fig. 3 and added two panels to map the location of the bound Na+ in the inward-facing structure and the bound sugar in the outward-facing structure.

The sugar-binding site identified in the published structure is critical to construct the mobile barrier mechanism. The sugar-binding residues identified in the published structure provided essential data to support the conclusion that the sugar-binding pocket is broken in the inward-facing structure. Thus, this published structure is mechanistically relevant to the current study.

-Page 12. Too much summary of the previous outward structure. Since this is already part of the literature, it would be more efficient to reference the previous data when it is important to interpret the new data (or show as a figure).

Our answer: The introduction of the previous sugar-binding sit is important for the detailed comparison between the two states as discussed above, but we agree with this reviewer and have significantly shortened the paragraph by moving the detailed description into the legend to the sFig. 11.

-Instead of providing the PDB ID in figures of the current structure, just say "current work" or similar. Then it is obvious you are not citing a previous structure.

Our answer: To distinguish clearly the new data and published results, the citation of the cryoEM structure [PDP ID 8T60] has been completely removed from the main text but kept in sTable 1.

-An entire panel of Figure 3 is dedicated to ligand binding in a previous outward-facing structure.

Showing it in the overlay would be sufficient.

Our answer: It is the first time for us to show a structure with a bound-Na+. Fig. 3 also illustrates the spatial relationship between the sugar-binding pocket and the cation-binding pocket since both binding sites are determined now. As stated above, according to two reviewers’ comments, we have modified the Figures and the Fig. 3d is the overlay.

Please increase the size of the font in all figures. It should be 6-8 point when printed on a standard sheet of paper. Labels in Figure 3, distances in Figure 4, and everything in Figure 5 is hard to see.

Our answer: Thank you for the comments and the enlargement of the figure size and label font in all figures have been made.

Figure 2: would be helpful to show Figure S8 in the main text, orienting the reader to the approximate location of substrate binding. What is known about the EIIA-Glc binding interface? Has anyone probed this by mutagenesis? Where are these residues on the overall structure, and are they somewhere other than the nanobody interface?

Our answer: Thank you for this comment. We have added a panel for orienting the readers about the substrate location in MelB in Figure 3c. The sFig. 8 actually focuses on the details of Nb interactions with MelB. Our current data strongly supported the notion that the Nb-bound MelBSt structure mimics the EIIAGlc-bound MelB but is not structurally resolved, so we have tuned down our statement on EIIAGlc. There is one study suggesting the C-terminal tail helix may be involved in the EIIAGlc binding, which has been added to the discussion.

Can Figure 5 be split into 2 figures and simplified?

Our answer: thanks for the suggestion. We have split it into Figs. 5b and 6 and also moved the peptide mapping to the Fig 5a.

What is the difference between cartoon and ribbon rendering?

Our answer: Ribbon: illustrating the structure; cartoon: highlighting the positions with statistically significant protection or deprotection. The statistically significant changes are implied by the ribbon representation; Sphere: not covered by labeled peptides.

Can the panels showing the kinetic data be enlarged? I don't think they need to surround the molecule. An array underneath would be fine.

Our answer: We have enlarged all figures and labels. The placement of selected plots around the model could clearly show the difference in deuterium uptake rates between the transmembrane domain and extra-membrane regions. We will maintain this arrangement.

Do colors in panel A correspond with colors in panel B?

Our answer: The color usage in both are different. Now the two panels have been separated.

Do I understand correctly that in the HDX experiments, negative values indicate positions that exchange more quickly in the nanobody-free protein relative to the nanobody-bound protein?

Our answer: Your understanding is correct.

I assume some of this is due to the protein changing conformation, but some of it might be due to burial at the nanobody-binding interface. Can those peptides be indicated?

Our answer: Thank you for this comment. We have marked the peptide carrying the Nb-binding residues on uptake plots in Figs.6 and Extended Fig. 1. There are only three Nb-binding residues covered by many overlapping peptides. Most are not covered, either not carried by the labeled peptides (Tyr205, Ser206, and Ser207) or with insignificant changes (Pro132 and Thr133), except for Asp137, Lys138, and Arg141 which are presented in 8 labeled peptides.

Few buried positions in the outward-facing state are expected to be solvent in the inward-facing state; unfortunately, inward-facing state they are buried by Nb binding.

Make figure legends easier to interpret by removing non-essential methods details (like buffer conditions).

Our answer: We removed the detailed method descriptions in most figure legends. Thank you.

Check throughout for typos.

ie page 9 Lue Leu

Page 9 like likely

Our answer: We have corrected them. Thank you!

Reviewer #2 (Recommendations For The Authors):

I have mostly minor questions/remarks.

• Why not do the hdx-ms experiments in the presence of sugar? That would give a proper distinction between two conformational states, instead of an ensemble of states vs one state.

Our answer: MelB conformation induced by sugar is also multiple states, and likely most are outward-facing states and occluded intermediate states. This is also supported by the new finding of an inward state with low sugar affinity. The ideal design should be one inward and one outward to understand the inward-outward transition. We have not identified an outward-facing mutant while we can obtain the inward by the Nb. WT MelBSt with bound Na+ favors the outward-facing state. Although our design is not ideal, we do have one state vs a predominant outward-facing WT with bound Na+.

Minor comments:

• Fig 5 is misleading as the peptide number does not match with the amino acid sequence. I would suggest putting a heat map with coverage on top. Or showing deuterium uptake per peptide. See examples below.

Our answer: The peptide number should not match with sequence number. We have 155 overlapping peptides that cover the entire amino acid sequence including the 10-His tag, and there are 60 residues with no data because they are not covered by a labeled peptide. The residue positions that are covered by peptides are estimated by bars on the top. The cylinder length does not correspond to the length of the transmembrane helix, just for mapping purposes.

• Can the authors explain how they found that the Nbs bind to the cytoplasmic side (before obtaining the structure)?

Our answer: Our in vivo two-hybrid assay between the Nb and MelBSt indicated their interaction on the cytoplasmic surface of MelBSt, which is further confirmed by the melibiose fermentation and transport assay, where the transport activities were completely inhibited by intracellularly coexpressed Nb and MelBSt. Thanks for raising this question.

• The authors use the word "substrate" indifferently for sugar and Na+ binding, which is a bit confusing. Technically, only sugar is the substrate and Na+ is a ligand, or cotransported-ion, that powers the reaction of transport. This might sound like nit-picking but it can lead to misunderstandings (at some point I thought two sugars were transported, and then I was looking for the second Na+ binding site).

Our answer: We used to call the sugar and Na as co-substrate but we agree with this comment.

We have changed by using substrate for the cargo sugar and coupling cation for the driving cation.

• Abstract "only the inner barrier" - the is missing.

Thanks. We have corrected this.

• p.3 intro "and identified that the positive cooperativity of cation and melibiose, " something is missing.

Thanks again. We missed the “as the core symport mechanism”.

• P.6 Nb275_4 instead of Nb725_4

Thank you very much for your careful reading.

• P.7. Also, affinity affinities

Thank you very much. We changed to “; and also, the -NPG affinity decreased by 21~32-fold for both Nbs”

• P.8 " contains 417 MelBSt residues (positions 2-210, 219-355, and 364-432). This does not sum up to 417 residues.

Thanks for your critical reading. We changed 364-432 to 262-432.

• p.9 Lue 54

We have corrected it to Leu54.

• I find fig.3 hard to read. Can the authors show the Na+ binding pockets and sugar binding pockets within the structure? Especially figure 3b. why are the residues in different colors?

Our answer: We have moved Fig 3b into sFig. 11. We colored the residues in the previous Fig 3B to match the hosting helices. We have added two panels to show the location of both sugar and Na in the molecular. Thank you for your comments.

• Fig4 bcef. Colored circles at the end of the helices. What are they for?

Our answer: We revised the legend. “The paired helices involved in either barrier formation were highlighted in the same colored circles.”

• 86% coverage includes the his-tag - it would be good to clarify that.

Our answer: Yes, it includes the 10-His tag.

• Fig.7 - anti clockwise cycle of transport is counter-intuitive.

Our answer: We have re-arranged. Our model was constructed originally to explain efflux due to limited information at the earlier state. Now more data are available allowing us to explain inflow and active transport.

• Where are all the uptake plots per peptide for the HDX-MS data?

Our answer: We have added the course raw data and prepared all uptake plots for all 71 peptides with statistically significant changes as an Extended Fig. 1.

• P.22 protein was concentrated to 50 mg/mL. Really? That is a lot.

This is correct. We can even concentrate MelBSt protein to greater than 50 mg/ml.

• Have the authors looked into the potential role of lipids in regulating the conformational transition? Since the structure was obtained in nanodiscs, have they observed some unexplained densities? The role of lipid-protein interactions in regulating such transitions was observed for several transporters including MFS (Gupta K, et al. The role of interfacial lipids in stabilizing membrane protein oligomers. Nature. 2017 10.1038/nature20820. Martens C, et al. Direct protein-lipid interactions shape the conformational landscape of secondary transporters. Nat Commun. 2018 10.1038/s41467-018-06704-1.). Furthermore, I see the authors have already observed lipid specific functional regulation of MelB (ref: Hariharan, P., et al BMC Biol 16, 85 (2018). https://doi.org/10.1186/s12915-018-0553-0). A few words about this previous work, and even commenting on the absence of lipid-protein interactions in this current work is worthwhile.

Our answer: Thanks for this very relevant comment. We paid attention to the unmodelled densities. There is one with potential but it is challenging to model it. We have added a sentence “There is no unexplained density that can be clearly modeled by lipids.” in the method to address this concern.

Reviewer #3 (Recommendations For The Authors):

1) In the following sentence, the authors report high errors for the Kd value. The anti-Fab Nb binding to NabFab was two-fold poorer than Nb725_4 at a Kd value of 0.11 {plus minus} 0.16 μM. The figure however indicates that the error value is 0.016 µM. Pls correct.

Our answer: Thank you. You are correct. The error has been corrected. 0.16 ± 0.02 uM. In this revised manuscript, we present the data in nM units.

2) Is the stoichiometry of the MelB:Na+ symport clearly known in this transporter. It can be mentioned in the discussion with appropriate references.

Our answer: Yes, the stoichiometry of unity has been clearly determined, which was included in the second paragraph of the previous version.

3) In the last section of results, the authors seem to suggest a greater movement within their Cterminal helical bundle compared to N-terminal helices. Is there evidence to suggest an asymmetry in the rocker switch between the two states of the transporter?

Our answer: Our structural data revealed that the C-terminal bundle is more dynamic compared with the N-terminal bundle where hosts the residues for specific binding of galactoside and Na+. The HDX data showed that the most dynamic regions are the structurally unresolved C-terminal tail by either method, the conserved tail helix and the middle-loop helix. transmembrane helices are relatively less dynamic with similar distributions on both transmembrane bundles. Since the most dynamic regions are peripheral element associated with the C-terminal domain, it might give a wrong impression. With regard to the symmetric or asymmetric movement, which will certainly affect the dynamic interactions between the transporter and the lipids, we favor the notion that MelBSt performs symmetric movement during the rocker switch between inward and outward states at the least cost for the protein-lipids interaction.

4) Figure 1. Are the thermograms exothermic or endothermic? clarify

Our answer: In our thermograms, all positive peaks are exothermic due to the direct detection of the heat release by the TA instrument. We clarified this in Method and now we stress this in figure legends to avoid confusion.

5) Figure 4a,d. Please put in a membrane bilayer and depict cytosolic and extracellular compartments for clarity.

Thank you. We have added a bilayer and labeled the sidedness in this figure and other related figures.

6) Fig 7. Melibiose symport cannot be referred to as Melibiose efflux transport in the legend as the latter refers to antiport. Pls rectify.

Our answer: Influx and efflux are conventionally used to describe the direction of movement of a substrate. The use of symport and antiport indicates the directions of the coupling reaction for the cargo and cation. For the symporter MelB, melibiose efflux means that sugar with the coupled cation moves out, which is driven by the melibiose concentration. During the steady state of melibiose active transport, efflux rate = influx rate.

7) Page 11 "A common feature of carrier transporters". The authors can use either carriers or transporters. Need not use both simultaneously.

Sorry for overlooking this. We have deleted carriers. Thank you very much for your time.

8) Several typos were noticed in this manuscript. some are listed below. pls correct.

Page 4- last paragraph "Furthermore"

We have corrected it. Thank you again!

Page 7 - second para one repharse "affinity reduced by 21~32 fold/units.." pls clarify

Added 21~32 fold.

Page 9 - "so it is highly likely that inward-open conformation" pls correct.

We have corrected to “likely”.

Fig. S9c - correct the spelling "Distance".

We have corrected to “Distance”

Author Response

eLife assessment

In this valuable study, the authors investigate the transcriptional landscape of tuberculous meningitis, revealing key molecular differences contributed by HIV co-infection. Whilst some of the evidence presented is compelling, the bioinformatics analysis is limited to a descriptive narrative of gene-level functional annotations, which are somewhat basic and fail to define aspects of biology very precisely. Whilst the work will be of broad interest to the infectious disease community, validation of the data is critical for future utility.

Response: We appreciate eLife’s positive assessment, although we challenge the conclusion that we ‘fail to define aspects of biology very precisely’. Our stated objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis and the eLife assessment affirms we have investigated ‘the transcriptional landscape of tuberculous meningitis’. To more precisely define aspects of the biology will require another study with different design and methods. Therefore the criticism seems unnecessarily harsh given the limitations of our stated objective.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Tuberculous meningitis (TBM) is one of the most severe forms of extrapulmonary TB. TBM is especially prevalent in people who are immunocompromised (e.g. HIV-positive). Delays in diagnosis and treatment could lead to severe disease or mortality. In this study, the authors performed the largest-ever host whole blood transcriptomics analysis on a cohort of 606 Vietnamese participants. The results indicated that TBM mortality is associated with increased neutrophil activation and decreased T and B cell activation pathways. Furthermore, increased angiogenesis was also observed in HIV-positive patients who died from TBM, whereas activated TNF signaling and down-regulated extracellular matrix organisation were seen in the HIV-negative group. Despite similarities in transcriptional profiles between PTB and TBM compared to healthy controls, inflammatory genes were more active in HIV-positive TBM. Finally, 4 hub genes (MCEMP1, NELL2, ZNF354C, and CD4) were identified as strong predictors of death from TBM.

Strengths:

This is a really impressive piece of work, both in terms of the size of the cohort which took years of effort to recruit, sample, and analyse, and also the meticulous bioinformatics performed. The biggest advantage of obtaining a whole blood signature is that it allows an easier translational development into a test that can be used in the clinical with a minimally invasive sample. Furthermore, the data from this study has also revealed important insights into the mechanisms associated with mortality and the differences in pathogenesis between HIV-positive and HIV-negative patients, which would have diagnostic and therapeutic implications.

Weaknesses:

The data on blood neutrophil count is really intriguing and seems to provide a very powerful yet easy-to-measure method to differentiate survival vs. death in TBM patients. It would be quite useful in this case to perform predictive analysis to see if neutrophil count alone, or in combination with gene signature, can predict (or better predict) mortality, as it would be far easier for clinical implementation than the RNA-based method. Moreover, genes associated with increased neutrophil activation and decreased T cell activation both have significantly higher enrichment scores in TBM (Figure 9) and in morality (Figure 8). While I understand the basis of selecting hub genes in the significant modules, they often do not represent these biological pathways (at least not directly associated in most cases). If genes were selected based on these biologically relevant pathways, would they have better predictive values?

Response: Blood neutrophil count was not found to be a predictor for TBM mortality in our previous studies. We agree it could be useful to perform predictive analysis with neutrophil count as suggested by reviewer. Regarding hub genes versus genes representative of the biological pathways, we cannot know which have better predictive values without performing variable selection for the sets of all genes including both hub genes and pathway representative genes, additional analysis which we will undertake.

Reviewer #2 (Public Review):

Summary:

This manuscript describes the analysis of blood transcriptomic data from patients with TB meningitis, with and without HIV infection, with some comparison to those of patients with pulmonary tuberculosis and healthy volunteers. The objectives were to describe the comparative biological differences represented by the blood transcriptome in TBM associated with HIV co-infection or survival/mortality outcomes and to identify a blood transcriptional signature to predict these outcomes. The authors report an association between mortality and increased levels of acute inflammation and neutrophil activation, but decreased levels of adaptive immunity and T/B cell activation. They propose a 4-gene prognostic signature to predict mortality.

Strengths:

-Biological evaluations of blood transcriptomes in TB meningitis and their relationship to outcomes have not been extensively reported previously.

-The size of the data set is a major strength and is likely to be used extensively for secondary analyses in this field of research.

Weaknesses:

The bioinformatic analysis is limited to a descriptive narrative of gene-level functional annotations curated in GO and KEGG databases. This analysis can not be used to make causal inferences. In addition, the functional annotations are limited to 'high-level' terms that fail to define biology very precisely. At best, they require independent validation for a given context. As a result, the conclusions are not adequately substantiated. The identification of a prognostic blood transcriptomic signature uses an unusual discovery approach that leverages weighted gene network analysis that underpins the bioinformatic analyses. However, the main problem is that authors seem to use all the data for discovery and do not undertake any true external validation of their gene signature. As a result, the proposed gene signature is likely to be overfitted to these data and not generalisable. Even this does not achieve significantly better prognostic discrimination than the existing clinical scoring.

Response: As explained in response to the eLife assessment, our objective was to use bioinformatics tools to identify the biological pathways and hub genes associated with TBM pathogenesis. We agree that ‘This analysis can not be used to make causal inferences’: that would require different study design and approaches. The proposed gene signature has higher AUC values than the existing clinical model. We agree that validation of the gene signature in an independent sample set will be a crucial next step.

Author Response

Author responses to the original review:

The data we produce are not criticized as such and thus, do not require revision; the criticisms concern our interpretation of them. General themes of the reviews are that i) genetic signatures do not matter for defining neuronal types (here sympathetic versus parasympathetic); ii) that a cholinergic postganglionic autonomic neuron must be parasympathetic; and iii) that some physiology of the pelvic region would deserve the label “parasympathetic”. We answered the latter argument in (Espinosa-Medina et al., 2018) to which we refer the interested reader; and we fully disagree with the first two. Of note, part of the last sentence of the eLife assessment is misleading and does not reflect the referees’ comments. Our paper analyses genetic differences between the cranial and sacral outflow and uses them to argue that they cannot be both parasympathetic. The eLife assessment acknowledges the “genetic differences” but concludes that, somehow, they don’t detract from a common parasympathetic identity. We take issue with this paradox, of course, but it is coherent with the referee’s comments. On the other hand, the eLife assessment alone pushes the paradox one step further by stating that “functional differences” between the cranial and sacral outflows can’t either prevent them from being both parasympathetic. We would also object to this, but the only “functional differences” used by the referees to dismiss our diagnostic of a sympathetic-like character (rather than parasympathetic) for the sacral outflow are between noradrenergic and cholinergic, and between sympathetic and parasympathetic (and we also disagree with those, see above, and below) —not between cranial and sacral.

We will thus use the opportunity offered by eLife to keep the paper as it is (with a few minor stylistic changes). We respond below to the referees’ detailed remarks and hope that the publication, as per eLife new model, of the paper, the referees’ comments and our response will help move the field forward.

Public review by Referee #1

“Consistently, the P3 cluster of neurons is located close to sympathetic neuron clusters on the map, echoing the conventional understanding that the pelvic ganglia are mixed, containing both sympathetic and parasympathetic neurons”.

The greater closeness of P3 than of P1/2/4 to the sympathetic cluster can be used to judge P1/2/4 less sympathetic than P3 (and more… something else), but not more parasympathetic. There is no echo of the “conventional understanding” here.

“A closer look at the expression showed that some genes are expressed at higher levels in sympathetic neurons and in P2 cluster neurons ” [We assume that the referee means “in sympathetic neurons and in P3 cluster neurons”] but much weaker in P1, P2, and P4 neurons such as Islet1 and GATA2, and the opposite is true for SST. Another set of genes is expressed weakly across clusters, like HoxC6, HoxD4, GM30648, SHISA9, and TBX20.

These statements are inaccurate; On the one hand, the classification is not based on impression by visual inspection of the heatmap, but by calculations, using thresholds. Admittedly, the thresholds have an arbitrary aspect, but the referee can verify (by eye inspection of heatmap) that genes which we calculate as being at “higher levels in sympathetic neurons and in P3 cluster neurons, but much weaker in P1, P2, and P4 neurons” or vice versa, i.e. noradrenergic or cholinergic neurons (genes from groups V and VI, respectively), have a much bigger difference than those cited by the referee, indeed are quasi-absent from the weaker clusters or ganglia. In addition, even by subjective eye inspection:

Islet is equally expressed in P4 and sympathetics.

SST is equally expressed in P1 and sympathetics.

Tbx20 is equally expressed in P2 and sympathetics.

HoxC6, HoxD4, GM30648, SHISA9 are equally expressed in all clusters and all sympathetic ganglia.

“Since the pelvic ganglia are in a caudal body part, it is not surprising to have genes expressed in pelvic ganglia, but not in rostral sphenopalatine ganglia, and vice versa (to have genes expressed in sphenopalatine ganglia, but not in pelvic ganglia), according to well recognized rostro-caudal body patterning, such as nested expression of hox genes.”

We do not simply show “genes expressed in pelvic ganglia, but not in rostral sphenopalatine ganglia, and vice versa”, i.e. a genetic distance between pelvic and sphenopalatine, but many genes expressed in all pelvic cells and sympathetic ones, i.e. a genetic proximity between pelvic and sympathetic. This situation can be deemed “unsurprising”, but it can only be used to question the parasympathetic nature of pelvic cells (as we do), or considered irrelevant (as the referee does, because genes would not define cell types, see our response to an equivalent stance by Referee#2). Concerning Hox genes, we do take them into account, and speculate in the discussion that their nested expression is key to the structure of the autonomic nervous system, including its division into sympathetic and parasympathetic outflows.

It is much simpler and easier to divide the autonomic nervous system into sympathetic neurons that release noradrenaline versus parasympathetic neurons that release acetylcholine, and these two systems often act in antagonistic manners, though in some cases, these two systems can work synergistically. It also does not matter whether or not pelvic cholinergic neurons could receive inputs from thoracic-lumbar preganglionic neurons (PGNs), not just sacral PGNs; such occurrence only represents a minor revision of the anatomy. In fact, it makes much more sense to call those cholinergic neurons located in the sympathetic chain ganglia parasympathetic.

This “minor revision of the anatomy” would make spinal preganglionic neurons which are universally considered sympathetic (in the thoraco-lumbar chord), synapse onto large numbers of parasympathetic neurons (in the paravertebral chains for sweat glands and periosteum, and in the pelvic ganglion), robbing these terms of any meaning.

Thus, from the functionality point of view, it is not justified to claim that "pelvic organs receive no parasympathetic innervation".

There never was any general or rigorous functional definition of the sympathetic and parasympathetic nervous systems — it is striking, almost ironic, that Langley, creator of the term parasympathetic and the ultimate physiologist, provides an exclusively anatomic definition in his Autonomic Nervous System, Part I. Hence, our definition cannot clash with any “functionality point of view”. In fact, as we briefly say in the discussion and explore in (Espinosa-Medina et al., 2018), it is the “sacral parasympathetic” paradigm which is unjustified from a functionality point of view, for implying a functional antagonism across the lumbo-sacral gap, which has been disproven repeatedly. It remains to be determined which neurons are antagonistic to which on the blood vessels of the external genitals; antagonism within one division of the autonomic nervous system would not be without precedent (e.g. there exist both vasoconstrictor and vasodilator sympathetic neurons, and both, inhibitor and activator enteric motoneurons). The way to this question is finally open to research, and as referee#2 says “it is early days”.

Public review by Referee #2

This work further documents differences between the cranial and sacral parasympathetic outflows that have been known since the time of Langley - 100 years ago.

We assume that the referee means that it is the “cranial and sacral parasympathetic outflows” which “have been known since the time of Langley”, not their differences (that we would “further document”): the differences were explicitly negated by Langley. As a matter of fact, the sacral and cranial outflows were first likened to each other by Gaskell, 140 years ago (Gaskell, 1886). This anatomic parallel (which is deeply flawed (Espinosa-Medina et al., 2018)) was inherited wholesale by Langley, who added one physiological argument (Langley and Anderson, 1895) (which has been contested many times (Espinosa-Medina et al., 2018) and references within).

In addition, the sphenopalatine and other cranial ganglia develop from placodes and the neural crest, while sympathetic and sacral ganglia develop from the neural crest alone.

Contrary to what the referee says, the sphenopalatine has no placodal contribution. There is no placodal contribution to any autonomic ganglion, sympathetic or parasympathetic (except an isolated claim concerning the ciliary ganglion (Lee et al., 2003)). All autonomic ganglia derive from the neural crest as determined a long time ago in chicken. For the sphenopalatine in mouse, see our own work (Espinosa-Medina et al., 2016).

One feature that seems to set the pelvic ganglion apart is […] the convergence of preganglionic sympathetic and parasympathetic synapses on individual ganglion cells (Figure 3). This unusual organization has been reported before using microelectrode recordings (see Crowcroft and Szurszewski, J Physiol (1971) and Janig and McLachlan, Physiol Rev (1987)). Anatomical evidence of convergence in the pelvic ganglion has been reported by Keast, Neuroscience (1995).

Contrary to what the referee says, we do not provide in Figure 3 any evidence for anatomic convergence, i.e. for individual pelvic ganglion cells receiving dual lumbar and sacral inputs. We simply show that cholinergic neurons figure prominently among targets of the lumbar pathway. This said, the convergence of both pathways on the same pelvic neurons, described in the references cited by the referee, is another major problem in the theory of the “sacral parasympathetic” (as we discussed previously (Espinosa-Medina et al., 2018)).

It should also be noted that the anatomy of the pelvic ganglion in male rodents is unique. Unlike other species where the ganglion forms a distributed plexus of mini-ganglia, in male rodents the ganglion coalesces into one structure that is easier to find and study. Interestingly the image in Figure 3A appears to show a clustering of Chat-positive and Th-positive neurons. Does this result from the developmental fusion of mini ganglia having distinct sympathetic and parasympathetic origins?

The clustering of Chat-positive and Th-positive cells could arise from a number of developmental mechanisms, that we have no idea of at the moment. This has no bearing on sympathetic and parasympathetic.

In addition, Brunet et al dismiss the cholinergic and noradrenergic phenotypes as a basis for defining parasympathetic and parasympathetic neurons. However, see the bottom of Figure S4 and further counterarguments in Horn (Clin Auton Res (2018)).

The bottom of Figure S4 simply indicates which cells are cholinergic and adrenergic. We have already expounded many times that noradrenergic and cholinergic do not coincide with sympathetic and parasympathetic. Henry Dale (Nobel Prize 1936) demonstrated this. Langley himself devoted several pages of his final treatise to this exception to his “Theory on the relation of drugs to nerve system” (Langley, 1921) (p43) (which was actually a bigger problem for him than it is for us, for reason which are too long to recount here; it is as if the theoretical difficulties experienced by Langley had been internalized to this day in the form of a dismissal of the cholinergic sympathetic neurons as a slightly scandalous but altogether forgettable oddity). (Horn, 2018) reviews the evidence that the thoracic cholinergic sympathetic phenotype is brought about by a secondary switch upon interaction with the target and argues that this would be a fundamental difference with the sacral “parasympathetic”. But in fact the secondary switch is preceded by co-expression of ChAT and VAChT with Th in most sympathetic neurons (reviewed in (Ernsberger and Rohrer, 2018)); and we have no idea of the dynamic in the pelvic ganglion. It may also be mentioned in this context that target-dependent specification of neuronal identity has also been demonstrated of other types of sympathetic neurons ((Furlan et al., 2016)

What then about neuropeptides, whose expression pattern is incompatible with the revised nomenclature proposed by Brunet et al.?

There was never any neuropeptide-inspired criterion for a nomenclature of the autonomic nervous system.

Figure 1B indicates that VIP is expressed by sacral and cranial ganglion cells, but not thoracolumbar ganglion cells.

Contrary to what the referee says, there are VIP-positive cells in our sympathetic data set and even strongly positive ones, except they are scattered and few (red bars on the UMAP). They correspond to cholinergic sympathetics, likely sudomotor, which are known to contain VIP (e.g.(Anderson et al., 2006)(Stanke et al., 2006)). In other words, VIP is probably part of what we call the cholinergic synexpression group (but was not placed in it by our calculations, probably because of a low expression level in sympathetic noradrenergic cells).

The authors do not mention neuropeptide Y (NPY). The immunocytochemistry literature indicates that NPY is expressed by a large subpopulation of sympathetic neurons but never by sacral or cranial parasympathetic neurons.

Contrary to what the referee says, Keast (Keast, 1995) finds 3.7% of pelvic neurons double stained for NPY and VIP in male rats, and says (Keast, 2006) that in females “co-expression of NPY and VIP is common” ( thus in cholinergic neurons that the referee calls “parasympathetic”). Single cell transcriptomics is probably more sensitive than immunochemistry, and in our dichotomized data set (table S1), NPY is expressed in all pelvic clusters and all sympathetic ganglia. In other words, it is one more argument for their kinship. It does not appear in the heatmap because it ranks below the 100 top genes.

Answer to the original recommendations by Referee #2

Introduction - the use of the words 'consensual' and 'promiscuity' are not clear and rather loaded in the context of the pelvic ganglia. Pick alternative words.

There is no sexual innuendo inherent in “promiscuity”: “condition of elements of different kinds grouped or massed together without order” (Oxford English Dictionary). We replaced “never consensual” by “never generally accepted”.

Results - Page 2 - what sex were the mice? Previous works indicate significant sexual dimorphism in the pelvic ganglion.

The mice included both males and females, and male and female cells are represented in all ganglia and clusters. This is now mentioned in the Material and Methods. Thus, however unsuited to analyze sexual dimorphism, our data set ensures that all the cell types we describe are qualitatively present in both sexes.

Results line 3 - the celiac and mesenteric ganglia are prevertebral ganglia and not part of the sympathetic chain. The chain refers to the paravertebral ganglia.

We replaced “part of the prevertebral chain” by “belonging to prevertebral ganglia”. This said, there are precedents for “prevertebral chain ganglia” to designate the rostro-caudal series of prevertebral ganglia. Rita Levi-Montalcini, for example, who devoted her glorious career to sympathetic ganglia, writes in 1972 “The nerve cell population of para- and prevertebral chain ganglia is reduced to 3–5% of that of controls”. (10.1016/0006-8993(72)90405-2).

Page 3 - "as the current dogma implies". Dogma often refers to opinion or church doctrine. The current nomenclature is neither. Pick another word.

There is little in science that is proven to the point of eliminating any element of opinion. “Dogma” refers to “that which is held as a principle or tenet […], especially a tenet authoritatively laid down by […] a school of thought” (OED). And “dogma” is used in science to designate tenets better experimentally supported than the “sacral parasympathetic”, such as the “central dogma of molecular biology”.

Page 3 - "To give justice" implies the classical notion is unjust. How about, 'to further explore previous evidence indicating that ....'

The term is indeed not proper English for the meaning intended, and the right expression is “to do justice”, to mean: “to treat [a subject or thing] in a manner showing due appreciation, to deal with [it] as is right or fitting” (OED). We have corrected the paper accordingly.

Page 4 top - the convergence indicated by Figure 3 does not justify excluding cholinergic and noradrenergic genes from the analysis.

Contrary to what the referee says, Figure 3 does not show any “convergence”, see our answer to Referee#1. What Figure 3 shows is that cells that are targeted by the lumbar pathway (a pathway universally deemed “sympathetic”) are cholinergic in massive proportion. Therefore, by an uncontroversial criterion, the pelvic ganglion contains lots of sympathetic cholinergic neurons. The only other option is to declare that sympathetic preganglionic neurons synapse onto parasympathetic postganglionic ones (which is what Referee#1 proposes, and considers “much simpler”. We beg to differ).

Our justification for excluding cholinergic and noradrenergic genes from the definition of “sympathetic” and “parasympathetic” is simply that sympathetic neurons can be cholinergic (to sweat glands and periosteum; and — as we show in Figure 3 — many targets of the lumbar pathway); One can also note that anywhere else in the nervous system, classifying cell types as a function of neurotransmitter phenotype would lead to non-sensical descriptions, such as putting together pyramidal cells and cerebellar granules, or motor neurons and basal forebrain cholinergic neurons. Indeed Referee#1 proposes such a revolutionary revision, by calling all cholinergic autonomic neurons “parasympathetic” (see our answer above).

Keast (1995) did similar experiments and used presynaptic lesions to draw a different conclusion indicating preferential innervation pelvic subpopulations.

Keast found “preferential” innervation of pelvic subpopulations based on lesion experiments; Nevertheless, she concluded (at the time) that “the correct definition of these two components of the nervous system is based on neuroanatomy rather than chemistry” (Keast, 2006).

Page 4 - "In the aggregate, the pelvic ganglion is best described as a divergent sympathetic ganglion devoid of parasympathetic neurons" The notion of a divergent ganglion is completely unclear!

We take “divergent” in a developmental or evolutionary meaning: related to sympathetic ganglia, yet somewhat differing from them. Elsewhere we use the word “modified”. Importantly (and as cited in the paper), a similar situation emerges from the single cell transcriptomic analysis of the lumbar and sacral preganglionics (by other research groups).

Granted, it is devoid of neurons having the signature of cranial parasympathetics, but that is insufficient to conclude that they are not parasympathetics.

If a genetic signature which is not only un-parasympathetic, but sympathetic-like remains compatible with some version of the label “parasympathetic”, we get dangerously close to dismissing the molecular make-up of a neuron as a definition of its type. This goes against any contemporary understanding of neuron types (take (Zeisel et al., 2018) among hundreds of other examples).

Page 4 - "the entire taxonomy of autonomic ganglia could be a developmental readout of Hox genes." This reader completely agrees! We appreciate this would be difficult to test but it helps to explain possible differences along the rostro-caudal axis. Consider making this a key implication of the study!

If the reader agrees, then his/her previous points become mysterious: we speculate that the Hox code determines the structure of the autonomic nervous system, i.e. the array, along the rostrocaudal axis, of a bulbar parasympathetic, a thoracolumbar sympathetic and lumbo-sacral “pelvo-sympathetic”. The existence of caudal parasympathetic neurons, on the contrary, would subvert any role for Hox genes: similar neurons (similar enough to be called by the same name) would arise at completely different rostro-caudal levels, i.e. with a different Hox code.

Page 5 - "It is thus remarkable ...that we uncover in no way contradicts the physiology." Not really. The 'classical' sympathetic system innervates the limbs, and the skin and it participates in thermoregulation and in cardiovascular adjustments to exercise. The parasympathetic system does none of these things. Reclassing the pelvic outflow as pseudo-sympathetic contradicts this physiology.

We do not say that the sacral outflow is classically sympathetic; We go all the way to proposing the special name “pelvo-sympathetic”; And we insist that these special sympathetic-like neurons have special targets (detrusor muscle, helicine arteries…): there is no contradiction. Not only is there no contradiction, but we remove the mind-twister of an anatomical/genetic/cell type-based “sacral parasympathetic” combined with a lack of physiological lumbosacral antagonism (we provide a short history of this dissonance in (Espinosa-Medina et al., 2018)), which led Wilfrid Jänig to write (Jänig, 2006)(p. 357): “Thus, functions assumed to be primarily associated with sacral (parasympathetic) are well duplicated by thoracolumbar (sympathetic) pathways. This shows that the division of the spinal autonomic systems into sympathetic and parasympathetic with respect to sexual functions is questionable”. We could not agree more: this division is questionable in terms of physiology and inexistent in terms of cell types. In other words, we reconcile cell types with physiology (but “it is early days”).

Answer to the novel recommendations by Referee #2

In addition to my original comments, important anatomical and functional distinctions are not explained by the data in this paper. ANATOMY- Sympathetic ganglia are located in close proximity to major branches of the aorta. Cranial and sacral parasympathetic ganglia are located next to or within the structures they innervate (e.g. eye, lung, heart, bladder).

The pelvic ganglion, including some of its cholinergic neurons, that the referee insist are parasympathetic, is further removed from one of its major targets (the helicine arteries of the external genitals) than the sympathetic prevertebral ganglia are of some of theirs (like the gut or kidney). We discussed this issue in (Espinosa-Medina et al., 2018).

FUNCTION- The sympathetic system controls state variables (e.g. body temperature, blood pressure, serum electrolytes and fluid balance), parasympathetic neurons do not.

Even in the classical view, the sympathetic system controls the blood vessels of the external genitals or the size of the pupil, for example, which are not state variables.

[…] The data in the paper are a useful next step in defining the genetic diversity of autonomic neurons but do not justify or improve upon existing nomenclature. The future challenge is to understand distinctions between subsets of autonomic ganglion cells that innervate different targets and the principles that govern the integrative function of the autonomic motor system that controls behavior.

We thank the referee for finding our data useful; and we fully agree with the latter statement. However, neurons, like many other cell types, are hierarchically organized (Zeng and Sanes, 2017), i.e. subsets of neurons belong to sets, with defining traits. Our data argue that there is no parasympathetic neuronal set that includes any pelvic ganglionic neuron. In contrast, there is a ganglionic sympathetic set (defined by our analysis of gene expression) which includes all of them — as there is a preganglionic sympathetic set that includes sacral preganglionics (Alkaslasi et al., 2021; Blum et al., 2021)(although the direct comparison with cranial preganglionics is yet to be made).

References

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Alkaslasi, M. R., Piccus, Z. E., Hareendran, S., Silberberg, H., Chen, L., Zhang, Y., Petros, T. J. and Le Pichon, C. E. (2021). Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord. Nat Commun 12, 2471.

Blum, J. A., Klemm, S., Shadrach, J. L., Guttenplan, K. A., Nakayama, L., Kathiria, A., Hoang, P. T., Gautier, O., Kaltschmidt, J. A., Greenleaf, W. J., et al. (2021). Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons. Nat Neurosci 24, 572–583.

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Keast, J. R. (1995). Visualization and immunohistochemical characterization of sympathetic and parasympathetic neurons in the male rat major pelvic ganglion. Neuroscience 66, 655–662.

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Author Response

Reviewer #2 (Public Review):

Manassaro et al. present an extensive three-session study in which they aimed to change defensive responses (skin conductance; SCR) to an aversively conditioned stimulus by targeting medial prefrontal cortex (their words) using repetitive TMS prior to retrieval. They report that stimulating mPFC using TMS abolishes SCR responses to the conditioned stimulus, and that this effect is specific for the stimulated region and the specific CS-US association, given that SCR responses to a different modality US are not changed.

I like how the authors have clearly attempted to control for several potential confounds by including multiple stimulation sites, measured SCR responses to several unconditioned stimuli, and applied the experiment in multiple contexts. However, several conceptual and practical issues remain that I think limit the value of potential conclusions drawn from this work.

The first issue that I have with this study concerns the relationship between the TMS manipulation and the theoretical background the authors present in their rationale. In the introduction the authors sketch that what they call 'mPFC' is involved in regulation of threat responses. They make a convincing case, however, almost all of the evidence they present concerns the ventromedial part of the prefrontal cortex (refs 18-25). The authors then mention that no one has ever studied the effects of 'mPFC'-TMS on threat memories. That is not surprising given that stimulating vmPFC with TMS is very difficult, if not impossible. Simulation of the electrical field that develops as a consequence from the authors manipulation (using the same TMS coil and positioning the authors use) shows that vmPFC (or mPFC for that matter) is not stimulated. The authors then continue in the methods section stating that the region they aimed for was BA10. This region they presumably do stimulate, however, that does not follow logically from their argument. BA10 is anatomically, cytoarchitectonically and functionally a wholly different area than vmPFC and I wonder if their rationale would hold given that they stimulate BA10.

We would like to thank the Reviewer for highlighting this very important point. The Reviewer is right in stating that the Brodmann area 10 (BA 10) is anatomically, cytoarchitectonically, and functionally distinct from the ventromedial PFC. As we reported in the Methods section, the coil placement over the frontopolar midline electrode (Fpz) according to the international 10‒20 EEG coordinate system directly focused the stimulation over the medial portion of the BA 10. In the literature, the aPFC is also known as the “frontopolar cortex” or the “rostral frontal cortex” and encompasses the most anterior portion of the prefrontal cortex, which corresponds to the BA 10. In line with this observation, we have corrected “medial prefrontal cortex” (mPFC) with “medial anterior prefrontal cortex” (aPFC) throughout the manuscript. We also have corrected the theoretical background and the rationale in the Introduction section by mentioning several studies that: i) Reported the involvement of the aPFC in emotional down-regulation (Volman et al., 2013; Koch et al., 2018; Bramson et al., 2020). ii) Traced anatomical connections between the medial/lateral aPFC and the amygdala (Peng et al., 2018; Folloni et al., 2019; Bramson et al., 2020). iii) Detected functional connections between the aPFC and the vmPFC during fear down-regulation (Klumpers et al., 2010). iv) Found hypoactivation, reduced connectivity, and altered thickness of aPFC in PTSD patients (Lanius et al., 2005; Morey et al., 2008; Sadeh et al., 2015; Sadeh et al., 2016). v) Revealed that strong activation of the aPFC may promote a higher resilience against PTSD onset (Kaldewaij et al., 2021) and that enhanced aPFC activity and potentiated aPFC-vmPFC connectivity is detectable after effective therapy in PTSD patients (Fonzo et al., 2017). Furthermore, we discussed our results in light of this evidence in the Discussion section. We really thank the Reviewer for this key implementation of our study.

The second concern I have is that although I think the authors should be praised for including both sham and active control regions, the controls might not be optimally chosen to control for the potential confounds of their condition of interest (mPFC-TMS). Namely, TMS on the forehead can be unpleasant, if not painful, whereas sham-TMS or TMS applied to the back of the head or even over dlPFC is not (or less so at the very least). Given that the SCR results after mPFC TMS show exactly the same temporal pattern as the sham-TMS but with a lower starting point, one could wonder whether a painful stimulation prior to the retrieval might have already caused habituation to painful stimulation observed in SCR in consequent CS presentations. A control region that would have been more obvious to take is the lateral part of BA10, by moving the TMS coil several centimeters to the left or right, circumventing all things potentially called medial but giving similar unpleasant sensations (pain etc).

We would also like to thank the Reviewer for bringing to light this issue and allowing us to strengthen our results. The Reviewer is right in pointing out that rTMS application over the forehead can be subjectively perceived as unpleasant, relative to other head coordinates or sham stimulation. The question of whether an unpleasant stimulation prior to the retrieval might provoke habituation to discomfort sensations and lead to weaker SCRs in the consequent CS presentations is valid and reasonable. We also thank the Reviewer for advising us to stimulate the lateral part of BA 10 as an active control site. However, given the potential involvement of the lateral BA 10 in the fear network (see previous point) and the potential risks due to the anatomical proximity of lateral BA 10 with the temporal lobe, we reasoned to adopt an alternative approach to investigate whether “a painful stimulation prior to the retrieval might have already caused habituation to painful stimulation observed in SCR in consequent CS presentations”. We repeated the entire experiment in one further group (ctrl discomfort, n = 10) by replacing the rTMS procedure with a 10-min discomfort-inducing procedure over the same site of the forehead (Fpz) to mimic the rTMS-evoked unpleasant sensations in the absence of neural stimulation effects (see the new version of the Methods section). The electrical stimulation intensity was individually calibrated through a staircase procedure (0 = no discomfort; 10 = high discomfort). The shock amplitude was set at the current level corresponding to the mean rating of ‘4’ on the subjective scale because, in the new experiments that we performed targeting the aPFC with rTMS (n = 9), we collected participants’ rTMS-induced discomfort ratings obtaining a mean rating of 3.833 ± 0.589 SEM on the same scale. We found CS-evoked SCR levels not significantly different to those of the sham group during the test session as well as during the follow-up session, suggesting that the discomfort experienced during the rTMS procedure did not contribute to the reduction of electrodermal responses observed in the aPFC group. We reported the results of this experiment in the Results section and Figure 2-figure supplement 2.

My final concern is that the main analyses are performed on single trials of SCR responses, which is a relatively noise measure to use on single trials. This is also done in relatively small groups (n=21). I would have liked to see both the raw or at least averaged timeseries SCR data plotted, and a rationale explaining how the authors decided on the current sample sizes, if that was based on a power analyses one must have expected quite strong effects.

Following the Reviewer’s suggestion, we decided to remove the analysis on single trials, and we apologize for not including SCR timeseries. To quantify the amount of effect induced by the rTMS protocol, we have now added within-group comparisons (through 2 × 2 mixed ANOVAs) that show, for each group, the amount of change in CS-evoked SCRs from the conditioning phase to the test phase, as well as from the conditioning phase to the follow-up phase. Furthermore, to directly and simply depict these changes, in addition to dot plots, we have also represented them with line charts (Figs. 2C, 2H, 4C, 4H, 5C, 5H). To estimate the sample size, we had previously performed a power analysis through G*Power 3.1.9.2 and it had resulted in n = 21 per experimental group. However, by correcting data pre-processing procedures (in accordance with Reviewer 1), we obtained data that were not normally distributed. Thus, we reasoned to enlarge our sample width by re-performing a power analysis (with the new suggested statistical analyses) and then repeating the experiments. For the main statistics, i.e. mixed ANOVA (within-between interaction) with two groups and two measurements, with the following input parameters: α equal to 0.05, power (1-β) equal to 0.95, and a hypothesized effect size (f) equal to 0.25, the new estimated sample size resulted in n = 30 per experimental group.

Author Response

Reviewer #1 (Public Review):

In this manuscript, the Authors implement a delayed feedback control method and use it for the first time in biological neuronal networks. They extend a well-established computational theory and expand it into the biological realm. With this, they obtain novel evidence, never considered before, that showcases the difference between simulated neuronal networks and biological ones. Furthermore, they optimize the DFC method to achieve optimal results in the control of cell excitability in the content of biological neuronal networks, taking advantage of a closed-loop stimulation setup that, by itself, is not trivial to build and operate and that will certainly have a positive impact the fields of cellular and network electrophysiology.

Regarding the results, it would be very constructive if the Authors could share the code for the quasi-real-time interface with the Multichannel Systems software (current and older hardware versions), as this represents likely a bottleneck preventing more researchers to implement such an experimental paradigm.

On the data focusing on the effects of the DFC algorithms on neuronal behavior, the evidence is very compelling, although more care should be devoted to the statistical analyses, since some of the applied statistical tests are not appropriate. In a more biological sense, further discussion and clarification of the experimental details would improve this manuscript, making it more accessible and clearer for researchers across disciplines (i.e., ranging from computational to experimental Neuroscience) and increasing the impact of this research.

In summary, this work represents a necessary bridge between recent advances in computational neuroscience and the biological implementation of neuronal control mechanisms.

Regarding sharing the control code, our application for closed-loop stimulation using aDFC, DFC and Poisson is now available in GitHub (https://github.com/NCN-Lab/aDFC). This was, in fact, our initial intention following the reviewing process. With this application, the user can run the developed algorithms with the MEA2100-256 System from Multi Channel Systems MCS GmbH.

Same with the data. The dataset with the spike data from all experiments is also now publicly available in Zenodo. The data can be found in https://doi.org/10.5281/zenodo.10138446.

Regarding the improvements in the statistical analysis, the tests are now performed following Reviewer #1 suggestions. Important to emphasize that this did not change the results/ conclusions of the work.

Author Response

Reviewer #1 (Public Review):

The manuscript by Grove and colleagues analyzes the role of TEAD1 transcription factors in all events regulating PNS myelin formation and maintenance and regeneration. Throughout the manuscript, the authors compare the results obtained to those they previously described in YAP/TAZ double knockout mice. Strengths of the manuscript are combined in vivo analyses by generating mutants constitutively lacking TEAD1 expression in myelinating Schwann cells (P0Cre//TEAD1f/f mice: cKO) and mutants in which TEAD1 expression can be ablated after tamoxifen-mediated recombination is myelinating Schwann cells (PlpCreER//TEAD1f/f mice: iKO). Using this approach the authors were able to assess the role of TEAD1 in all aspects related to PNS myelin: formation as well as maintenance and remyelination after injury. By exploiting these models, they were able to define the role of TEAD1 in regulating Schwann cell proliferation as well as in the cholesterol biosynthetic pathway. Collectively, their data indicate that TEAD 1 has a composite role in PNS myelination being required for developmental myelination, but dispensable for myelin maintenance. Further, they also describe a role for TEAD1 in promoting PNS remyelination after an injury event.

Despite these strengths, there are some weaknesses that should be addressed by the authors:

1) The manuscript would benefit from better and more detailed analysis of the role of the other TEAD transcription factors, as they are likely redundant in function to TEAD1. For example, since in cKO mice some fibers can escape the sorting defect and eventually myelinate, albeit at a lower level, could they determine whether TEAD2-4 transcription factors might compensate for TEAD1 absence in this setting?

We speculate that other TEADs, most likely both TEAD2 and TEAD3, compensate TEAD1 in myelinating some developing axons. We also speculate that TEAD4 counteracts TEAD1, resulting in excessive proliferation of Schwann cells in Tead1 cKO. Unfortunately, because, unlike TEAD1, floxed/congenic alleles and IHC-compatible antibodies are not yet available for TEAD2-4, it is difficult to determine their roles. We attempted to knock down TEAD2-4 by injecting AAV-shRNAs into the sciatic nerves of WT and Tead1 iKO, but this intervention was not successful. Our future studies will determine compensatory and/or opposing roles of other TEADs during development and homeostasis and after nerve injury.

2) A striking result of the study is the morphological defects observed in the process of axonal sorting and in the Remak fibers formation of TEAD1 cKO mice. To explain the sorting defect, the authors correctly analyze Schwann cell proliferation. However, since axonal sorting is mediated by the interaction between the extracellular matrix and intracellular cytoskeleton rearrangement, they should address also these two aspects. As per the Remak bundles and the poly-axonal myelination they observe, it is difficult to reconcile this "abnormal" myelination with the fact that TEAD1 cKO mice have a very severe myelinating phenotype, which is persistent in adulthood.

It is noteworthy that we found radial sorting to be delayed, but not blocked, in Tead1 cKO, as we had previously reported for Yap/Taz cDKO mice in our earlier publication (Grove et al., eLIFE 2017). The primary reason that myelin development fails in Schwann cells lacking YAP/TAZ (or TEAD1 in the present report) is because they do not initiate myelination of sorted axons, not because of defective radial sorting. We showed that radial sorting was delayed in Schwann cells lacking YAP/TAZ because of their late S phase entry (Figure 4 in Grove et al., eLIFE 2017). In addition, our earlier report demonstrated that the key laminin receptor, integrin 6, is strongly downregulated but axons are nevertheless sorted out by Schwann cells in Yap/Taz cDKO (Figure 4-figure supplement 2 in Grove et al., eLIFE 2017). Our current view, therefore, is that extracellular matrix may contribute to reducing Schwann cell proliferation (Berti et al., 2011; Pellegatta et al., 2013; Yu, Feltri, Wrabetz, Strickland, & Chen, 2005), which helps to delay radial sorting, but that it is not required for Schwann cells lacking YAP/TAZ (or TEAD1) to sort axons (see the author response #2 in Grove et al., eLIFE 2017). Based on this information, we disagree with the reviewer that it is essential for us to address the role of extracellular matrix in delaying radial sorting in Tead1 cKO.

Regarding Remak bundles, ‘thinly’ myelinated Remak bundles are only ‘occasionally’ observed in Tead1 cKO mice. Given that some large axons are still myelinated in Tead1 cKO mice, likely due to compensation by other TEADs, we speculate that Remak bundles are occasionally myelinated by other TEADs in Tead1 cKO. We have clarified our description and expanded our discussion of TEAD1 regulation of Remak bundles, including abnormal polyaxonal myelination.

3) In the analyses of the cholesterol biosynthetic pathway, TEAD1 seems to be only partly involved. Again, which is the role of any of the other TEADs?

Examining cholesterol biosynthesis pathways (SREBP1 and 2) and their target enzymes (SCD1, HMGCR, FDPS, IDI1) in Tead1 cKO and Yap/Taz cDKO, we showed that TEAD1 is required for upregulating FDPS and IDI1. These data suggest that TEAD1 plays a major role in mediating YAP/TAZ-driven cholesterol synthesis by upregulating FDPS and IDI1. It is also important to note that FDPS and IDI1 levels are reduced in TEAD1 cKO as ‘greatly’ as those in Yap/Taz cDKO (Figure 5). We therefore speculate that other TEADs compensate TEAD1 modestly, if at all, in upregulating FDPS and IDI1. We do not rule out the possibility, however, that other TEADs fully compensate TEAD1 in ‘maintaining’ cholesterol synthesis in adult Schwann cells. We will address these important questions in the future when the key resources mentioned above become available to study TEAD2-4.

4) Why do cKO mice die before P60?

In accordance with IACUC guidelines, we humanely euthanized Tead1 cKO mice before P60 because, like Yap/Taz cKO mice, they develop severe peripheral neuropathy.

Author Response

Reviewer #2 (Public Review):

In this paper, the authors discover that postsynaptic mitochondria in C. elegans govern glutamate receptor trafficking dynamics. The core results are two-fold. For one, they find that loss or inhibition of mcu-1 - the C. elegans mitochondrial calcium uniporter - increases GLR-1 glutamate receptor accumulation at the postsynaptic dendritic sites and enhances its trafficking dynamics. The authors hypothesize that this effect on glutamate receptors may have something to do with mitochondrial ROS production. This is because ROS is a by-product of normal oxidative phosphorylation, downstream of calcium import. Indeed, the generation of artificially high amounts of mitochondrial ROS has the opposite effect of mcu-1 loss: decreased glutamate receptor subunit accumulation. Collectively, the results support the idea that mitochondrial function can control receptor dynamics at synaptic sites. This is interesting because tight control of synaptic function likely combines several mitochondrial functions: energy production, calcium buffering, and (here) ROS signaling.

STRENGTHS

• The C. elegans genetic model is a strength because the authors are able to make refined conclusions by classical loss-of-function mutants (e.g., mcu-1) along with an impressive cytological toolkit to examine GLR-1 dynamics.

• The use of pharmacology as a second means to test those genetic conclusions is a strength.

• The authors' careful reagent verification of reporters (Ca2+, ROS, etc.) is a strength.

• The ability to link fundamental mitochondrial processes to GLR-1 exocytosis will expand how the field thinks about mitochondrial synapse function.

WEAKNESSES

For the most part, the data in the paper support the conclusions, and the authors were careful to try experiments in multiple ways. But please see below:

• (Main Point) The data are good, but they fall short of mechanism (e.g., Line 322). Figure 6 is accurate as drawn. But calcium and ROS are not abstract signals. They are likely exerting affirmative actions on specific targets. The Discussion does acknowledge this in terms of ROS and it speculates on possible targets.

We thank the reviewer for their analytical review of our manuscript. We agree that all molecular players involved in the proposed mechanism were not identified by the data presented, so we modified the text to remove overstatements. We also agree that Ca2+ and ROS signaling is not abstract. Rather, there are specific and diverse targets of both Ca2+ and ROS signaling. Follow-up experiments are underway to identify and provide evidence for the necessity of potential ROS/Ca2+ targets in this proposed mechanism. For the current manuscript, we have modified our verbiage in an attempt to not mislead or overstate what our results suggest (e.g., changes/additions to the beginning of the ‘Discussion’, lines 365-377 and 385-388) and updated the illustration of the proposed model to include dashed lines that, as mentioned in the figure legend, indicate indirect action by ROS and Ca2+ (see revised Figure 7).

The general idea seems to be that mitochondria import calcium through MCU-1 (and interacting factors). As a result, oxidative phosphorylation successfully occurs and mitochondrial ROS is a signaling by-product that signals glutamate receptors not to undergo exocytosis. But there are other interpretations of what might happen in between. In fact, if OXPHOS is disrupted, it is known that this can generate a lot more mitochondrial ROS than the normal by-product levels.

We do agree that an alternative explanation could be that genetic or pharmacological inhibition of mitochondrial Ca2+ uptake disrupts oxidative phosphorylation, and as a result, inefficiencies or uncoupling in the electron transport chain would lead to an even greater increase in mitochondrial ROS production. Although oxidative phosphorylation was not directly measured, one of our post hoc analyses of GLR-1 transport suggests ATP levels are comparable between controls, mcu-1 mutants, and with Ru360 treatment: the velocity of GLR-1 transport is unchanged between these experimental groups. The processivity of molecular motors (which dictates transport velocity) is highly sensitive to relative ATP abundance. Thus, if ATP levels were dramatically decreased in mcu-1 mutants or following Ru360 treatment, then one would expect a detectable change in GLR-1 transport velocities, but we observed no change (see revised Figure S2E and related discussion at lines 183-190). Although these results do not directly indicate whether ATP production is altered with loss or inhibition of MCU-1, it does suggest that basal ATP levels remain sufficient to support the metabolic demands of GLR-1 transport.

This reviewer wonders if excess ROS would cause an extreme response. Or alternatively, if scavenging ROS via pharmacological scavengers or SOD expression would reverse the effects.

These are good points, and we have previously published experiments that address each of them. First, we have seen that globally increasing ROS with various concentrations of H2O2 within the physiological range (<100 nM) decreased GLR-1 transport to a similar extent (PMID: 32847966) indicating that there is not a dose-dependent decrease in GLR-1 transport. We have also assessed GLR-1 transport after treatment with concentrations of H2O2 well above the physiological range (e.g., 500 nM), but these high concentrations obliterated all GLR-1 transport. Contrary to what one may expect, we showed that decreasing ROS via pharmacological or genetic means (probably below physiological range) decreased GLR-1 transport (PMID: 35622512) via a Ca2+ independent mechanism. In other words, ROS scavenging did not have the opposite effect on GLR-1 transport, but we have not combined ROS scavenging with optical induction of ROS production (e.g., via KillerRed) nor have we assessed the potential influence of ROS scavenging on synaptic recruitment. Although we agree that these are important follow-up experiments, they will require a more sensitive ROS indicator because current genetically encoded in vivo ROS sensors cannot detect decreases in ROS levels below the physiological range (< 10 nM) (PMID: 31586057).

Small Points

• 33.3 mHz - just making sure, do the authors mean once every 30 seconds? That would be more straightforward.

Yes, we do mean a 1-second pulse of light every 30 seconds. We have clarified this in the manuscript text (line 115).

• Figure 2 is confusing. The text says that the mcu-1 mutants have a GLR-1::GFP FRAP rate that is comparable to controls (Lines 165-167). But Figure 2E suggests that it is markedly less, which is the opposite result of the slight increase in rate resulting from Ru360 treatment. And is the explanation why the GLR-1::GFP results differ from the SEP::GLR-1 results a difference between total GFP vs. surface GFP?

The confusion is due to an incorrect statement in the results text. We have corrected this error and appreciate the reviewer for bringing it to our attention (lines 173-174).

• I could not watch Video 2 (not sure if it is the file or just the copy I downloaded).

We thank the reviewer for bringing this to our attention and we believe we have remedied the issue.

• It is good that the authors tried both optical stimulation and mechanical stimulation (dropping culture plates to stimulate the worms, Figure 3). Why was the mechanical stimulation set aside for further tests in the paper?

Mechanical stimulation consisted of dropping culture plates containing 2-3 C. elegans onto a lab bench every 30 seconds for 5 or 10 minutes. This mechanical stimulation paradigm was technically cumbersome and was less effective at inducing changes in mito-roGFP fluorescence that optical stimulation. This is likely due to habituation to the mechanical stimulus which has been well-characterized in C. elegans. The optical stimulation was therefore used as it is a more reliable and repeatable method for stimulating the AVA neuron.

• Does this process affect all kinds of transport, or is it just the glutamate receptors? Was anything else examined?

Transport of other proteins has not been examined in the context of mitoROS signaling. Our attempts at visualizing and quantifying the transport, synaptic delivery and exocytosis of other synaptic proteins in vivo has proven to be more technically challenging likely due to relatively lower expression in the C. elegans neurons suitable for transport analysis.

Reviewer #3 (Public Review):

Reactive oxygen species (ROS) have been previously shown to regulate glutamate receptor phosphorylation, long-distance transport, and delivery of glutamate receptors to synapses, however, the source of ROS is unclear. In this study, the authors test if mitochondria act as a signaling hub and produce ROS in response to neuronal activity in order to regulate glutamate receptor trafficking. The authors use a variety of optogenetic tools including the calcium reporter mitoGCaMP and the ROS reporter mito-roGFP to monitor changes in calcium and ROS, respectively, in mitochondria after activating neurons with ChRimson in the genetic model organism C. elegans. Repeated stimulation of interneurons called AVA with ChRimson leads to increased calcium uptake into mitochondria in dendrites and increased mitochondrial ROS production. The mitochondrial calcium uniporter mcu-1 is required for these effects because mcu-1 genetic loss of function or treatment with Ru360, a drug that inhibits mcu-1, inhibits the uptake of calcium into mitochondria and ROS production after neuronal activation. Mcu-1 genetic loss of function is correlated with an increase in exocytosis of glutamate receptors but a decrease in glutamate receptor transport and delivery to dendrites. This study suggests that mitochondria monitor neuronal activity by taking up calcium and downregulating glutamate receptor trafficking via ROS, as a means to negatively regulate excitatory synapse function.

Strengths

-The use of multiple optogenetic tools and approaches to monitor mitochondrial calcium, reactive oxygen species, and glutamate receptor trafficking in live organisms.

-Identifying a novel signaling role for dendritic mitochondria which is to monitor neuronal activity (via calcium uptake into mitochondria) and generate a signal (reactive oxygen species) that regulates glutamate receptors at synapses.

Weaknesses

-Although the use of KillerRed to generate ROS downstream of mcu-1 is a clever approach, the fact that activation of KillerRed results in reduced GLR-1 exocytosis, delivery, and transport raises the concern that KillerRed is generating a high level or ROS that might be toxic to cellular processes. Experiments showing that other cellular processes are not affected by KillerRed activation and testing if reduced ROS production mimics the effects of blocking mcu-1 would strengthen the conclusions in this study.

We thank the reviewer for their careful analyses of our findings. It is plausible that KillerRed could cause toxic levels of ROS, in fact, it was originally used to instigate oxidative stress-induced apoptosis to achieve cell-specific ablation. These cell ablation protocols required 20+ minutes of KillerRed activation with substantially higher levels of irradiation (e.g., 3.8 mW/mm [PMID: 24209746] vs. our light dosage of 25 µW/mm2). Additionally, our transgenic C. elegans strains expressing KillerRed were designed to have a relatively low KillerRed expression and were screened for low expression based on KillerRed’s fluorescence. Using these strains, we were able to minimally activate KillerRed in the AVA neuron resulting in ROS elevations at mitochondria that were comparable to neuronal activity-induced increases in mitochondrial ROS as measured by mito-roGFP. Specifically, we found that 10 minutes of mechano-stimulation and 5 minutes of ChRimson stimulation increased the fluorescence ratio (Fratio) of mito-roGFP nearly two-fold (Figure 4A-B and 4C-E). A 15-second pulse of light focused on a small region activating mitoKR in the AVA neurite also caused similar two-fold increase in the mito-roGFP Fratio (Figure 4C-E) comparable to what neuronal activity induced. Our 5-minute global KillerRed activation less effectively increased the mito-roGFP Fratio at mitochondria in the AVA neurite compared to neuronal activity (revised Figure 4B and 4H) but was sufficient in decreasing GLR-1 transport (revised Figure 5G-H). So, we decided to do all experiments with 5 minutes of global KillerRed activation since lower activation levels of KillerRed were more likely to achieve non-toxic, signaling levels of ROS. Since we strongly agree that this data is important for tool validation, we have reorganized the manuscript such that these data are now a primary figure (see revised Figure 4 and new results sub-section starting at line 252).

Additionally, we added supplemental transport velocity data. This data shows that local photoactivation as well as whole-cell activation of KillerRed does not alter transport velocity of GLR-1 vesicles within the neurite (revised Figure S4A and S4B and lines 272-276 and 287-289), which would be the case if ATP, microtubules, or actin dynamics were affected. This supports that our local and whole-cell activation protocol does not cause toxic levels of ROS production.

Lastly, the reviewer questions whether decreasing ROS alters GLR-1 transport, synaptic delivery and exocytosis in a similar fashion to loss or inhibition of mcu-1, and if so, would further support the proposed mechanism. We have decreased ROS via genetic (catalase overexpression) and pharmacological (using the mitochondria-targeted antioxidant MitoTEMPO) means and seen that diminished ROS levels decrease GLR-1 transport albeit to a lesser degree than that caused by loss/inhibition of mcu-1 (PMID: 35622512). To determine if decreased GLR-1 transport during diminished ROS levels involves mcu-1, we would need to assess GLR-1 transport in mcu-1 mutants while ROS is decreased (e.g., using MitoTEMPO treatment) to see if their combined effect phenocopies the effect of mcu-1(lf) or decreased ROS alone. However, as mentioned previously, we are unable to measure ROS levels below the sensitivity of roGFP but within physiological range so we cannot currently calibrate or validate our methods for scavenging ROS in vivo. This is why we have not yet analyzed synaptic delivery or exocytosis rates of GLR-1 in the context of decreased ROS, but these would be interesting follow-up experiments that may further support our model once more sensitive ROS sensors are available.

Reviewer #4 (Public Review):

Using optogenetic stimulation, the authors presented compelling evidence that neuronal activity increases mitochondrial calcium levels, facilitated by the mitochondrial uniporter MCU-1. Through ratiometric measurements, they showed that mitochondrial ROS levels also increase due to neuronal activity via MCU-1. Subsequent FRAP studies were employed to investigate the trafficking of the AMPA receptor, GLR-1. By integrating genetic and pharmacological methodologies, the recovery rate of GLR-1 was assessed. The authors concluded that increased mitochondrial ROS due to neuronal activity reduces the trafficking and exocytosis of AMPA receptors. They proposed that mitochondrial ROS serves as a homeostatic mechanism regulating AMPA receptor trafficking and abundance, thus maintaining synaptic strength. This research is crucial as it provides a direct link between mitochondrial signaling and AMPA receptor trafficking.

However, there are several significant concerns regarding the methodologies and quantifications employed in this manuscript. The authors utilized GLR-SEP to label surface AMPA receptors and relied on the "FRAP rate" as an indicator of the exocytosis rate. The absence of direct visualization of exocytosis using GLR-SEP, and the lack of direct measurements of exocytosis events, casts doubt on the conclusions about ROS's impact on AMPA receptor exocytosis. Furthermore, the "FRAP rate" determined in this study is a combination of recovery rates (incorporating both endosomal trafficking and diffusion) with the mobile fractions of AMPA receptors, potentially weakened interpretations of the findings. A more comprehensive discussion addressing the conflicting effects of MCU-1 and ROS on GLR-GFP FRAP recovery and dendritic trafficking would enable readers to grasp the intricate roles of mitochondrial calcium and ROS in modulating synaptic receptors.

We appreciate the reviewer’s attention to detail while reviewing our article. Their major concern about directly visualizing exocytosis events is valid since changes in exocytosis and endocytosis would dictate the amount of SEP::GLR-1 at the synaptic membrane. However, streaming imaging of SEP in vivo is technically difficult showing only few exocytosis events and provides short “snapshots” (1-2 minutes, longer streaming imaging causes photobleaching and photo-toxicity) which must be extrapolated to longer time frames. Our 16-minute SEP::GLR-1 FRAP protocol allows us to capture all plasma membrane recruitment and quantify the relative balance between exo- and endocytosis. It also allows for longer observational periods during which we can detect changes in GLR-1 recruitment to and retention at the synaptic membrane in genetic mutants and with drug treatments. In addition, our photobleaching approach involves photobleaching a ~40-60 µm region proximally and distally to the imaging region which limits the influence of receptor diffusion on the FRAP rate. The reviewer makes a valid point that receptor endocytosis rates would also influence the SEP::GLR-1 FRAP rate. We have now changed the text in the results and discussion to include this information (lines 155-161, and changing “exocytosis” to “synaptic recruitment” throughout the manuscript when discussing SEP::GLR-1 FRAP results [e.g, at lines 169, 208, and 321]).

Author Response

Reviewer #1 (Public Review):

Payne et al. have investigated the neural basis of VOR adaptation with the goal of constraining sites and mechanisms of plasticity supporting cerebellar learning. This has been an area of intense debate for decades; previous competing models have argued extensively about the sites of plasticity and the strength of eye velocity feedback/ efference copy signals to Purkinje cells has been central to the debate. This paper nicely explores the consequences of varying the strength of this feedback and in so doing, provides a potential explanation for why Purkinje cell responses during VOR cancellation could exhibit stronger responses following learning, despite net depression of the strength of their vestibular inputs. In that sense it provides some reconciliation of existing models. The work appears to be well done and the paper is well written. The manuscript could be improved and the significance of the work clarified and enhanced by contextualizing the work more appropriately within the existing literature in this area.

We thank the reviewer for the nice summary of this work’s contribution to the long-standing debate regarding sites and mechanisms of plasticity underlying cerebellar learning.

We have revised the manuscript to address several key points raised by the reviewer. We now emphasize that the main evidence for weak feedback arises from interpreting our model in the context of the existing experimental evidence for plasticity rules in the cerebellar cortex, and we have clarified the commonalities and differences from the Miles-Lisberger model. Several missing references are now included. Additionally, we clarify the comparison of our model to data after learning, and explain how altered signaling through the visual pathways drives paradoxical changes in neural activity without requiring plasticity in the visual pathways. We hope that these changes better situate the work to be interpreted appropriately in the context of the existing literature.

Reviewer #2 (Public Review):

Payne et al. use a computational approach to predict the sites and directions of plasticity within the vestibular cerebellum that explain an unresolved controversy regarding the basis of VOR learning. Specifically, the conclusion by Miles and Lisberger (1981) that vestibular inputs onto Purkinje cells (PCs) must potentiate, rather than depress (as in the Marr/Albus/Ito model), following gain-increase learning because when the VOR is cancelled, PC firing increases rather than decreases. Payne et al. provide a novel model solution that recapitulates the results of Miles and Lisberger but, paradoxically, uses plasticity in the cerebellar cortex that weakens PC output rather than strengthens it. However, the model only succeeds when efference copy feedback to the cerebellar cortex is relatively weak thereby allowing a second feedback pathway to drive PC activity during VOR cancellation to counteract the learned change in gain. Because the model is biologically constrained, the findings are well supported. This work will likely benefit the field by providing a number of potentially experimentally testable conclusions. The findings will be of interest to a wider audience if the results can be extrapolated to other cerebellar-dependent learning behaviors rather then just VOR gain-increase learning. Overall, the manuscript is very well written with clearly delineated results and conclusions.

We appreciate the reviewer’s comments that the model is well-constrained and provides a solution to the long-standing debate surrounding sites and directions of plasticity underlying VOR learning.

The reviewer raises an important question: do our results generalize across the cerebellum? We note first that we are studying the cerebellum to illustrate a core problem in modeling systems throughout the brain, namely, how to disambiguate plasticity in the face of ubiquitous feedback loops, both within the brain and between the brain and the environment. Within the cerebellum, we focused on VOR learning due to the wealth of experimental data available. While the specific effect of feedback strength on plasticity will depend on the details of the relevant cerebellar circuit, our general approach can be applied to other areas, given sufficient data, in order to determine how plasticity is distributed in the face of potential feedback loops. Importantly, error-driven LTD of the parallel fiber-Purkinje cell synapse is a fundamental hypothesized mechanism for cerebellar learning which has been generally accepted elsewhere in the cerebellum, but was called into question for VOR learning in the flocculus by the Miles-Lisberger model. Thus, our study of VOR learning has broad implications for reconciling plasticity mechanisms across the cerebellum.

We also note that, even within the VOR circuit, the direction of plasticity and the relative dependence on plasticity at each site may depend on the timescale of learning. On longer timescales, there is thought to be consolidation of learning from a cerebellar cortical site to a brainstem site. Such consolidation from a faster-learning site to a slower-learning site is known as systems consolidation and has been shown theoretically to mitigate the ‘plasticity-stability dilemma’ of having fast learning without over-writing longer-term learning. Our model is compatible with both error-driven plasticity in the cerebellar cortex and a site of plasticity in the brainstem, with brainstem plasticity potentially mediating consolidation of earlier learned changes in the cerebellar cortex. We have now updated the text significantly to discuss the broader implications of the results and to address the reviewer’s specific comments.

Reviewer #3 (Public Review):

Summary: In this study, the authors attempt to determine what is the role (and strength) of feedback in a closed-loop (cerebellar) system.

Strengths:

1) By combining extensive data fitting of cerebellar experimental observations this study provides deep insights into existing questions and more broadly on the role of feedback and what are the limitations when inferring feedback in (plastic) neural circuits.

2) Another strength of this study is the gradual build-up of evidence by using models of different complexities to help build the argument that weak feedback is sufficient to explain experimental observations.

3) The paper is well-written and structured.

Weaknesses:

1) In principle feedback can (i) drive dynamics or/and (ii) drive learning directly. Throughout the paper, the authors refer to only the first case (i.e. dynamics). However, the role of feedback in learning is already implicitly assumed by the authors when jointly fitting the model before and after learning. Note that the general conclusion that feedback (in general) is weak may be to the first view (i.e. dynamics), but not the second. Given that a key conclusion of the paper is that no feedback is sufficient to explain the data, this suggests that feedback may instead be used for learning/plasticity.

We fully agree with the reviewer that our conclusions do not preclude an important role for many other types of feedback, including as an instructive signal for learning. Instead of explicitly considering feedback for learning in our model, we consider static snapshots before and after learning to infer plasticity, while remaining agnostic to the neural algorithm used to achieve such plasticity. A widely held hypothesis is that motor error signals carried by climbing fibers instruct LTD at co-active parallel fiber inputs to Purkinje cells; this is indeed a form of feedback, operating on a slower timescale than “feedback for dynamics.” This “feedback for learning” is not modeled here but is fully consistent with our results, as discussed in a new paragraph of our Discussion (end of Section 3.4.1 “Pathways undergoing plasticity”).

2) There are some potential limitations of the conclusions drawn due to the model inference methods used. The methods used (fmincon) can easily get stuck in local minima and more importantly they do not provide an overview of the likelihood of parameters given the data. A few studies have now shown that it is important to apply more powerful inference techniques both to infer plasticity (Bykowska et al. Frontiers 2019) and neural dynamics (Gonçalves et al. eLife 2020). As highlighted by Costa et al. Frontiers 2013 using more standard fitting methods can lead to misleading interpretations. Given the large range of experimental data used to constrain the model, this may not be an issue, but it is not explicitly shown.

The reviewer correctly points out that we used a deterministic model-fitting procedure. To address this concern, we complemented the full dynamic model with a simple analytic model ( Figure 5 ) for which we could fully derive the cost function landscape and analytically show that there is a line of parameters corresponding to a perfect degeneracy in the model. Thus, the challenge in the model we analyze is that there are too many solutions, rather than it being difficult to find a solution. Given this degeneracy, we chose to fix the level of efference copy feedback and then find the (now non-degenerate) solutions, and to then compare these different solutions with regards to their implications for the correlated strengths and changes in strengths of different pathways. We have edited the relevant section of the Discussion for clarity on this topic, and have added references to the additional strategies for model inference mentioned above, in Section 3.3 “Relation to other sloppy models”.

3) There is some lack of clarity on how the feedback pathways as currently presented should be interpreted in the brain.

We interpret this comment as referring to the questions of (1) whether our model includes a pathway for learning through feedback, (2) what is the anatomical implementation of the efference copy feedback pathway and visual pathways, and (3) how should the positive weights on the efference copy feedback pathway k PE be interpreted. We address these below.

(1) Feedback for learning was discussed in point 1 above.

(2) Anatomical implementation of efference copy pathway: We have edited the Discussion to clarify that there is anatomical evidence for efference copy input to the cerebellum, but that a key aspect of ‘feedback’ is that activity functionally loops back onto itself. Instead, neurons carrying eye movement commands (such as in the vestibular nucleus) could send signals to the cerebellum, without receiving output from the same cerebellar neurons – this would correspond to a ‘spiraling’ pathway that does not form a closed feedback loop (Figure 8). Thus we argue that the existence of the gross anatomical pathways does not necessitate a role for strong, functional, efference copy feedback (Discussion, Section 3.1, lines 481-491).

Anatomical implementation of visual pathway: The visual feedback pathways considered here are those that would receive visual motion information from the environment. This visual feedback is itself changed by eye movements, thus providing a net overall negative feedback loop that helps to stabilize gaze. This pathway has been proposed to involve cortical regions such as MST (discussed in Materials and Methods, Model Implementation, lines 769-774).

(3) Interpretation of positive feedback loop: In our model, the efference copy feedback filter, k PE , has positive weight. This corresponds to the positive net sign of the Purkinje cell to brainstem to Purkinje cell feedback loop. Specifically, the Purkinje cell to brainstem pathway is inhibitory (because Purkinje cells are inhibitory), the brainstem to eye velocity command pathway is inhibitory (to achieve counter-rotation of the eyes in response to head turns), and the feedback of this eye velocity command back to Purkinje cells (k PE ) is positive. Thus this loop in our model represents positive feedback. This is now clarified in Materials and Methods, Model Implementation, lines 748.

4) The functional benefits of having (or not) feedback could be better discussed (related to point 1 above).

Related to point 1 above, it is certainly the case that feedback is necessary for learning. We do not explicitly model the climbing fiber feedback thought to be involved in learning/plasticity of the parallel fiber pathway.

We instead focus on the role of efference copy feedback, and how it functionally impacts the required sites and signs of plasticity in the circuit. As shown in the paper, if the efference copy pathway is strong, then this is most consistent with learned changes in eye movements being driven primarily by plasticity in the brainstem pathway (as in the Miles-Lisberger hypothesis), whereas if the efference copy pathway is weak, then this is most consistent with learned changes in eye movements being driven by net depression in the parallel fiber to Purkinje cell pathway (as in the classic Marr-Albus-Ito model and as suggested by most cellular and molecular studies of parallel fiber-Purkinje cell plasticity), in addition to a role of plasticity in the brainstem pathway. We also note that, in the ‘Strong Feedback’ model, the feedback is so strong that the system is on the brink of instability – this has been argued to have the functional benefit of providing ‘inertia’ to eye movements that could help to maintain eye movements during smooth pursuit when a target goes behind an occluder, but it also has the disadvantage of placing the system at a level of positive feedback near the brink of instability. We also note that the visual feedback pathway through the environment, emphasized in this work, serves as a negative feedback loop that reduces deviations between the eye and target velocity. We have extensively re-written the first section of the Discussion (Section 3.1), in order to more clearly lay out the implications of each model for circuit plasticity and feedback.

5) Some of the key conclusions of the work are not described in the abstract, namely that feedback is weak in the cerebellar system.

Thank you for raising this point, we have added this key conclusion to the end of the abstract: “Our results address a long-standing debate regarding cerebellum-dependent motor learning, suggesting a reconciliation in which error-driven plasticity of synaptic inputs to Purkinje cells is compatible with seemingly oppositely directed changes in Purkinje cell activity. More broadly, the results demonstrate how learning-related changes in neural activity can appear to contradict the sign of the underlying plasticity when either internal feedback or feedback through the environment is present.”

Claims:

The argument is well-built throughout the paper, but there are some potential caveats with the general interpretation (see weaknesses).

Impact:

This work has the potential to bring important messages on how best to interpret and infer the role of feedback in neural systems. For the field of the cerebellum, it also proposes solutions to long-standing problems.

Author Response

Reviewer #1 (Public Review):

Summary:

Cyclic Nucleotide Binding (CNB) domains are pervasive structural components involved in signaling pathways across eukaryotes and prokaryotes. Despite their similar structures, CNB domains exhibit distinct ligand-sensing capabilities. The manuscript offers a thorough and convincing investigation that clarifies numerous puzzling aspects of nucleotide binding in Trypanosoma.

Strengths:

One of the strengths of this study is its multifaceted methodology, which includes a range of techniques including crystallography, ITC (Isothermal Titration Calorimetry), fluorimetry, CD (Circular Dichroism) spectroscopy, mass spectrometry, and computational analysis. This interdisciplinary approach not only enhances the depth of the investigation but also offers a robust cross-validation of the results.

Weaknesses:

None noticed.

Reviewer #2 (Public Review):

Summary:

This manuscript clearly shows that Trypanosoma PKA is controlled by nucleoside analogues rather than cyclic nucleotides, which are the primary allosteric effectors of human PKA and PKG. The authors demonstrate that the inosine, guanosine, and adenosine nucleosides bind with high affinity and activate PKA in the tropical pathogens T. brucei, T. cruzi and Leishmania. The underlying determinants of nucleoside binding and selectivity are dissected by solving the crystal structure of T. cruzi PKAR(200-503) and T. brucei PKAR(199-499) bound to inosine at 1.4 Å and 2.1 Å resolution and through comparative mutational analyses. Of particular interest is the identification of a minimal subset of 2-3 residues that controls nucleoside vs. cyclic nucleotide specificity.

Strengths:

The significance of this study lies not only in the structure-activity relationships revealed for important targets in several parasite pathogens but also in the understanding of CNB's evolutionary role.

Weaknesses:

The main missing piece is the model for activation of the kinetoplastid PKA which remains speculative in the absence of a structure for the trypanosomatid PKA holoenzyme complex. However, this appears to be beyond the scope of this manuscript, which is already quite dense.

We fully agree that insight into the activation mechanism and its possible deviation from the mammalian paradigm requires a holoenzyme structure revealing the details of R-C interaction. We have attempted Cryo-EM from LEXSY-produced holoenzyme, yet upscaling the purification procedures described in this manuscript have repeatedly failed in spite of numerous protocol changes and optimizations. Much more work is required to achieve this.

Reviewer #2 (Recommendations For The Authors):

Some minor points to consider for enhancing the impact of this interesting manuscript:

1) The nucleoside affinities measured are mainly for the regulatory subunits unbound to the kinase domain. How would nucleoside affinities change when the regulatory subunits are bound to the kinase domain, which is presumably the case under resting conditions? An estimation of this change in affinity is important because it more closely relates to the variations in cellular nucleoside concentrations needed for activation.

This is an important question and we have given an indirect answer in the manuscript, but not very explicit. The EC50 values for kinase activation of the purified holoenzyme complexes are very similar or almost identical to the kD values measured by ITC with free regulatory subunits. By inference, the binding kD for the holoenzyme and for the free R-subunit cannot be very different. In addition, we have recently determined the EC50 for PKA activation in vivo in trypanosomes using a bioluminescence complementation reporter assay. The values fit perfectly to the values obtained with purified holoenzyme (Wu et al. in preparation). A sentence in Results (lines 201-203) has been added.

2) The authors should point out that a major implication of nucleoside vs. cyclic nucleotide activation is in terms of signal termination. If phosphodiesterases (PDEs) are responsible for cAMP/cGMP signal termination, what terminates nucleoside-dependent signaling? Although the answer to this question may not be known at this stage, it is important to highlight this critical implication of the authors' study.

The mechanism of signal termination is indeed unknown so far. We speculate that some enzymes of the purine salvage pathways are differentially localized in subcellular compartments and thereby able to establish microdomains that enable nucleoside signaling. In addition, PKA subunit phosphorylations/dephosphorylations and/or protein turnover may also regulate signal termination. As an example, free PKAC1 is rapidly degraded upon depletion of the PKAR subunit by RNAi. We have now mentioned signal termination in Discussion and have revised the last part of Discussion (lines 567-602). A possible approach to monitor compartmentalized signaling would be using the FluoSTEPs technology (Tenner et al., Sci. Adv. 2021; 7: eabe4091), but adapting this to the trypanosome system will not be a short-term task.

Author Response

Reviewer #1 (Public Review):

Summary:

The investigators have performed a state-of-the art systematic review and meta-analysis of studies that may help to answer the research question: if administration of multiple antibiotics simultaneously prevents antibiotic resistance development in individuals. The amount of studies eligible for analysis is very low, and within that low number, there is huge variability in bug-drug combinations studied and most studies had a high risk of bias, further limiting the capability of meta-analysis to answer the research question. In addition, based on I2 values there is also huge statistical heterogeneity between outcomes of studies compared, further limiting the predictive value of meta-analysis. In fact, the only 2 studies meeting all eligibility criteria addressed the treatment of mycobacterium tuberculosis, for which the research question is hardly applicable. The authors, therefore, conclude that "our analysis could not identify any benefit or harm of using a higher or a lower number of antibiotics regarding within-patient resistance development." Apart from articulating this knowledge gap, the findings will not have consequences for patient care, but may stimulate the scientific community to better address this research question in future studies.

Strengths:

The systematic and rigorous approach for the review and meta-analysis.

Weaknesses:

None identified.

We thank the reviewer for this thoughtful and positive appraisal of our work.

Reviewer #2 (Public Review):

Summary:

The authors performed a systematic review and meta-analysis to investigate whether the frequency of emergence of resistance is different if combination antibiotic therapy is used compared to fewer antibiotics. The review shows that there is currently insufficient evidence to reach a conclusion due to the limited sample size. High-quality studies evaluating appropriate antimicrobial resistance endpoints are needed.

Strengths:

The strengths of the manuscript are that the article addresses a relevant research question that is often debated. The article is well-written and the methodology used is valid. The review shows that there is currently insufficient evidence to reach a conclusion due to the limited sample size. High-quality studies evaluating appropriate antimicrobial resistance endpoints are needed. I have several comments and suggestions for the manuscript.

Weaknesses:

Weaknesses of the manuscript are the large clinical and statistical heterogeneity and the lack of clear definitions of acquisition of resistance. Both these weaknesses complicate the interpretation of the study results.

We thank the reviewer for the positive comments and pointing out where our work can be improved.

Major comments:

My main concern about the manuscript is the extent of both clinical and statistical heterogeneity, which complicates the interpretation of the results. I don't understand some of the antibiotic comparisons that are included in the systematic review. For instance the study by Paul et al (50), where vancomycin (as monotherapy) is compared to co-trimoxazole (as combination therapy). Emergence (or selection) of co-trimoxazole in S. aureus is in itself much more common than vancomycin resistance. It is logical and expected to have more resistance in the co-trimoxazole group compared to the vancomycin group, however, this difference is due to the drug itself and not due to co-trimoxazole being a combination therapy. It is therefore unfair to attribute the difference in resistance to combination therapy. Another example is the study by Walsh (71) where rifampin + novobiocin is compared to rifampin + co-trimoxazole. There is more emergence of resistance in the rifampin + co-trimoxazole group but this could be attributed to novobiocin being a different type of antibiotic than co-trimoxazole instead of the difference being attributed to combination therapy. To improve interpretation and reduce heterogeneity my suggestion would be to limit the primary analyses to regimens where the antibiotics compared are the same but in one group one or more antibiotic(s) are added (i.e. A versus A+B). The other analyses are problematic in their interpretation and should be clearly labeled as secondary and their interpretation discussed.

We acknowledge the presence of statistical and clinical heterogeneity in our overall analysis. The decision to pursue this comprehensive examination was predefined in our previously published study protocol (PROSPERO CRD42020187257) and driven by our interest whether, despite some differences, we could either identify an overarching effect of combination therapy on resistance or identify factors that explain potential differences of the effect of combination therapy across pathogens/drugs. We indeed, find that heterogeneity is high, however identifying the driving factors of this heterogeneity is difficult as evidence is limited.

We carried out several subgroup analyses, e.g. explicitly focusing on specific pathogen groups and medical conditions or exploring heterogeneity in treatment arms (figure 3, supplementary materials section 6). However, it is important to highlight that the number of studies available for these subgroup analyses was low. Additionally, recognizing the high heterogeneity within treatment arms, we performed a subgroup analysis focusing solely on resistances of antibiotics common to both arms (supplementary material section 6.1.8; which would avoid comparisons such as the one between vancomycin and co-trimoxazole raised by the reviewer). Unfortunately, this also revealed substantial heterogeneity. While we aimed to address heterogeneity through these subgroup analyses, limitations arose due to the number of studies meeting specific criteria and the nature of data provided by these studies.

Moreover, regarding the concern on interpretation of co-trimoxazole as combination therapy, we acknowledge the confusion surrounding its classification as one or two antibiotics. Despite the common contemporary view of co-trimoxazole as a single antibiotic, we chose to consider it as two antibiotics due to historical practices, as observed in Black et al. (1982), where trimethoprim was compared to trimethoprim and sulfamethoxazole. We recognize that this decision may lead to confusion and we consider conducting a further sensitivity analysis in the future version of this manuscript, exploring the possibility of considering co-trimoxazole as a single antibiotic. We agree that the slight trend of less antibiotics performing better overserved for MRSA, should not be over interpreted as this is driven by the two studies Walsh et al 1993 and Paul et al 2015 as pointed out by the reviewer. In lines 183-186 we discuss this issue that for better evaluation of antibiotic combination therapy, more studies which use identical antibiotics (i.e. A versus A+B) are needed. We will try to clarify and highlight this in the future version of the manuscript.

Another concern is about the definition of acquisition of resistance, which is unclear to me. If for example meropenem is administered and the follow-up cultures show Enterococcus species (which is intrinsically resistant to meropenem), does this constitute acquisition of resistance? If so, it would be misleading to determine this as an acquisition of resistance, as many people are colonized with Enterococci and selection of Enterococci under therapy is very common. If this is not considered as the acquisition of resistance please include how the acquisition of resistance is defined per included study.

Thank you for pointing out this potential ambiguity. Our definition of “acquisition of resistance” is agnostic to bacterial species and hence intrinsically resistant species can be included if they were only detected during the follow-up culture by the studies. We will clarify this in the definition of “acquisition of the resistance” in the manuscript (see l. 259-260). However, it was not always clear from the studies which pathogens were acquired or whether intrinsically resistant species were not reported. Therefore, we rely on the studies' specifications of resistant and non-resistant without further classifying data into intrinsic and non-intrinsic resistance. The outcome “acquisition of resistance” can be seen more of a risk assessment for having any resistant bacterium during or after treatment. In contrast, the outcome “emergence of resistance” is more rigorous, demanding the same species to be measured as more resistant during or after treatment.

Table S1 is not sufficiently clear because it often only contains how susceptibility testing was done but not which antibiotics were tested and how a strain was classified as resistant or susceptible.

In Table S1, we omitted the listing of antibiotics for which susceptibility testing was performed, as this information is already presented in the main text (Table 1). However, we agree that linking this information better in a future version would benefit the understanding. Given the variability in methods used to assess resistance and the variability in drugs, the comparability of breakpoints is limited. Hence, we decided not to provide further details on this aspect so far.

Line 85: "Even though within-patient antibiotic resistance development is rare, it may contribute to the emergence and spread of resistance."

Depending on the bug-drug combination, there is great variation in the propensity to develop within-patient antibiotic resistance. For example: within-patient development of ciprofloxacin resistance in Pseudomonas is fairly common while within-patient development of methicillin resistance in S. aureus is rare. Based on these differences, large clinical heterogeneity is expected and it is questionable where these studies should be pooled.

We agree that our formulation neglects differences in prevalence of within-host resistance emergence depending on bug-drug combinations. We will correct this in our upcoming version. (i.e. we will correct our statement to: “Within-patient antibiotic resistance development, even if rare, can contribute to the emergence and spread of resistance.”)

Line 114: "The overall pooled OR for acquisition of resistance comparing a lower number of antibiotics versus a higher one was 1.23 (95% CI 0.68 - 2.25), with substantial heterogeneity between studies (I2=77.4%)"

What consequential measures did the authors take after determining this high heterogeneity? Did they explore the source of this large heterogeneity? Considering this large heterogeneity, do the authors consider it appropriate to pool these studies?

Thank you for highlighting this lack of clarity. In our upcoming version, we will emphasize the sub-analyses conducted to explore heterogeneity (i.e., figure 3 and supplementary materials section 6). Nevertheless, these analyses faced limitations due to the scarcity of evidence and the data provided by the studies. Given the lack of appropriate evidence, it is hard to identify the source of heterogeneity. The decision to pool all studies was pre-specified in our previously published study protocol (PROSPERO CRD42020187257) and was motivated by the question whether there is a general effect of combination therapy on resistance development or identify factors that explain potential differences of the effect of combination therapy across bug-drug combinations.

Author Response

We are grateful to the reviewers for their positive feedback with their comments and suggestions on the manuscript. Reviewer 1 has indicated two weaknesses and Reviewer 2 has none. With this provisional reply, we address the two concerns of the Reviewer 1:

1) Data obtained from a single aminoacyl-tRNA (D-Tyr-tRNATyr) have been generalized to imply that what is relevant to this model substrate is true for all other D-aa-tRNAs. This is not a risk-free extrapolation. Why do the authors believe that the length of the amino acid side chain will not matter in the activity of DTD2?

We thank the reviewer for bringing up this important point. We wish to clarify that only a few of the aminoacyl-tRNA synthetases are known to charge D-amino acids and only D-Leu (Yeast), D-Asp (Bacteria, Yeast), D-Tyr (Bacteria, Cyanobacteria, Yeast) and D-Trp (Bacteria) show toxicity in vivo in the absence of known DTD (Soutourina J. et al., JBC, 2000; Soutourina O. et al., JBC, 2004; Wydau S. et al., JBC, 2009). D-Tyr-tRNATyr is used as a model substrate to test the DTD activity in the field because of the conserved toxicity of D-Tyr in various organisms. DTD2 has been shown to recycle D-Asp-tRNAAsp and D-Tyr-tRNATyr with the same efficiency both in vitro and in vivo (Wydau S. et al., NAR, 2007). Moreover, we have previously shown that it recycles acetaldehyde-modified D-Phe-tRNAPhe and D-Tyr-tRNATyr in vitro (Mazeed M. et al., Science Advances, 2021). We have earlier shown that DTD1, another conserved chiral proofreader across bacteria and eukaryotes, acts via a side chain independent mechanism (Ahmad S. et al., eLife, 2013). Considering the action on multiple side chains with different chemistry and size, it can be proposed with reasonable confidence that DTD2 also operates based on a side chain independent manner.

2) While the use of EFTu supports that the ternary complex formation by the elongation factor can resist modifications of L-Tyr-tRNATyr by the aldehydes or other agents, in the context of the present work on the role of DTD2 in plants, one would want to see the data using eEF1alpha. This is particularly relevant because there are likely to be differences in the way EFTu and eEF1alpha may protect aminoacyl-tRNAs (for example see description in the latter half of the article by Wolfson and Knight 2005, FEBS Letters 579, 3467-3472).

We thank the reviewer for bringing another important point. We analysed the aa-tRNA bound elongation factor structures from both bacteria (PDB id: 1TTT) and mammal (PDB id: 5LZS) and found that the amino acid binding site is highly conserved where side chain of amino acid is projected outside. Modelling of D-amino acid in the same site shows serious clashes, indicating D-chiral rejection during aa-tRNA binding by elongation factor. In addition, the amino group of amino acid is tightly selected by the main chain atoms of elongation factor thereby lacking a space for aldehydes to enter and then modify the L-aa-tRNAs and Gly-tRNAs. Minor differences near the amino acid side chain binding site (as indicated in Wolfson and Knight, FEBS Letters, 2005) might induce the amino acid specific binding differences. However, those changes will have no influence when the D-chiral amino acid enters the pocket, as the whole side chain would clash with the active site. We will present a sequence and structural conservation analysis to clarify this important point in our revised manuscript. Overall, our structural analysis suggests a conserved mode of aa-tRNA selection by elongation factor across life forms and therefore, our biochemical results with bacterial elongation factor Tu (EF-Tu) reflect the protective role of elongation factor in general across species.

In our revised manuscript, we will provide a thorough point-by-point response to the above as well as all the specific reviewer comments. We also intend to include new analysis with updated data that would address the key questions raised by the reviewers.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

This nice study by Miyano combines slice electrophysiology and superresolution microscopy to address the role of RBP2 in Ca2+ channel clustering and neurotransmitter release at hippocampal mossy fiber terminals. While a number of studies demonstrated a critical role for RBPs in clustering Ca2+ channels at other synapses and some provided evidence for a role of the protein in molecular coupling of Ca2+ channels and release sites, the present study targets another key synapse that is an important model for presynaptic studies and offers access to a microdomain controlled synaptic vesicle (SV) release mechanism with low initial release probability.

Summarizing a large body of high-quality work, the authors demonstrate reduced Ca2+ currents and a reduced release probability. They attribute the latter to the reduced Ca2+ influx and can restore release by increasing Ca2+ influx. Moreover, they propose an altered fusion competence of the SVs, which is not so strongly supported by the data in my view.

The effects are relatively small, but I think the careful analysis of the RBP role at the mossy fiber synapse is an important contribution.

We thank the reviewer for careful assessment of the paper. We agree that while reduced Ca influx in KO is relatively straightforward, impaired priming is somewhat indirect, remaining as suggestion. We also noted that Moser and colleagues have analyzed the function of RIM-BP2 at hair cell synapses and also showed reduced Ca influx. In cortical synapses, there have been no study using direct presynaptic recording. In the revision, we carefully cited previous studies and tried to be fair. We hope that the current revision is much improved.

Reviewer #2 (Public Review):

The proper expression and organization of CaV channels at the presynaptic release sites are subject to coordinative and redundant control of many active zone-specific molecules including RIM-BPs. Previous studies have demonstrated that ablation of RIM-BPs in various mammalian synapses causes significant impairment of synaptic transmission, either by reducing CaV expression or decoupling CaV from synaptic vesicles. The mechanisms remain unknown.

In the manuscript, Sakaba and colleagues aimed to examine the specific role of RIM-BP2 at the hippocampal mossy fiber-CA3 pyramidal cell synapse, which is well-characterized by low initial release probability and strong facilitation during repetitive stimulation. By directly recording Ca2+ currents and capacitance jumps from the MF boutons, which is very challenging but feasible, they showed that depolarization-evoked Ca2+ influx was reduced significantly (~39%) by KO of RIM-BP2, but no impacts on Ca-induced exocytosis and RRP (measured by capacitance change). They used STED microscopy to image the spatial distribution of the CaV2.1 cluster but found no change in the cluster number with a slight decrease in cluster intensity (~20%). They concluded that RIM-BP2 functions in tonic synapses by reducing CaV expression and thus differentially from phasic synapses by decoupling CaV-SV.

In general, they provide solid data showing that RIM-BP2 KO reduces Ca influx at MF-CA3 synapse, but the phenotype is not new as Moser and colleagues have also used presynaptic recording and capacitance measurement and shown that RIM-BP2 KO reduces Ca2+ influx at hair cell active zone (Krinner et al., 2017), although at different synapse model expressing CaV1.3 instead of CaV2.1. Further, the concept that RIM-BP2 plays diverse functions in transmitter release at different central synapses has also been proposed with solid evidence (Brockmann et al., 2019).

We thank the reviewer for careful reading of the ms. We agree that previous studies have sown reduced Ca influx at hair cells, and diverse function of RIM-BP2 in different central synapses have been proposed by Brockman et al. The new point of this study is we firmly and quantitatively show the reduced Ca currents using direct presynaptic recording, which has not been done in mossy fiber synapses or cortical synapses in general. Quantitative and time-resolved measurements of the presynaptic currents cannot be done by other methods, so far. In this revision, we point this out carefully.  

Reviewer #1 (Recommendations For The Authors):

The MS is overall carefully prepared and I have only a few minor comments to help with further improving the manuscript.

Abstract:

I think the notion of different RBP function at tonic and phasic synapses is not so well founded. The reduced number of Ca2+ channels and their altered topography have been shown in multiple synapses that also include those with phasic release. Quantitative structural and functional analysis of presynaptic Ca2+ channels of RBP-2 and RBP1-2 DKO deficient AZs closely related to the present study has e.g. been provided for auditory synapses (e.g. hair cells, endbulb/calyx of end synapses that provide both phasic and sustained release.

In abstract, we have omitted description of phasic vs tonic synapses, because it is not well founded as the reviewer pointed out. Specifically, in abstract (Line 13~):

“Synaptic vesicles dock and fuse at the presynaptic active zone (AZ), the specialized site for transmitter release. AZ proteins play multiple roles such as recruitment of Ca2+ channels as well as synaptic vesicle docking, priming and fusion. However, the precise role of each AZ protein type remains unknown. In order to dissect the role of RIM-BP2 at mammalian cortical synapses having low release probability, we applied direct electrophysiological recording and super-resolution imaging to hippocampal mossy fiber terminals of RIM-BP2 KO mice. By using direct presynaptic recording, we found the reduced Ca2+ currents. The measurements of EPSCs and presynaptic capacitance suggested that the initial release probability was lowered because of the reduced Ca2+ influx and impaired fusion competence in RIM-BP2 KO. Nevertheless, larger Ca2+ influx restored release partially. Consistent with presynaptic recording, STED microscopy suggested less abundance of P/Q-type Ca2+ channels at AZs deficient in RIM-BP2. Our results suggest that the RIM-BP2 regulates both Ca2+ channel abundance and transmitter release at mossy fiber synapses.”

Intro:

Line 48: consider adding Butola et al., 2021 /endbuld of Held to reference which concurs on the notion made for Calyx. However, a contrasting finding was made for another synapse with tight coupling: RBP2 deletion did not alter tight coupling in hair cells (Krinner et al., 2017). Line 51: RBP-DKO/lack of additional effect of RBP1 deletion: suggest adding Krinner et al., 2021 to reference, which concurs with the notion made for hair cells.

We cited Butola et al., 2021 (Line 49) and Krinner et al., 2021 (Line 52), as the reviewer suggested.

Results:

STED microscopy: I am concerned with two aspects of the analysis/presentation. I) I recommend replacing density with abundance as the authors do not resolve single channels. II) I appreciate the note of caution about the fact that STED nanoscopy due to the non-linear nature of the depletion process should/could not be easily used to quantify copy numbers based on immunofluorescence. I would recommend the authors perform 2D Gaussian fitting to at least the Cav2.1 immunofluorescent spots neighboring Munc13-1 spots and report the short and long axis estimates as well as potentially the area. Should the authors have confocal Cav2.1 and Cav2.2 immunofluorescent data co-acquired with STED of Munc13-1, this would be very valuable additional information, but I do not think the experiment is essential for the sake of publication if it was not done already, given the large body of high-quality physiology data.

I) We have changed the term from density to abundance as the reviewer suggested throughout the manuscript.

II) As the reviewer suggested, we have carried out 2D Gaussian fitting of Cav2.1 spots. The length, width, and area of Cav2.1 clusters in the AZ were not different between WT and RIM-BP2 KO terminals (Line 431-433, Figure 7-figure supplement 4). The spatial resolution of STED, especially at mossy fiber synapses in the tissue, and a small difference between WT and KO (~30 % expected from electrophysiology) could prevent detection of the difference, unlike ribbon synapses and fly NMJ where release sites and Ca channel clusters are well defined. We should also note that the intensity was calculated similar to previous studies (integral of signal intensity, Krinner et al., 2017), and not absolute peak intensity. 　

As the reviewer suggested, we have added confocal data ((Line 434-436, Figure 7-figure supplement 5). We have determined the AZ area from the Munc13-1 STED data, and Munc13-1, Cav2.1 and Cav2.2 intensities were quantified. As shown in the figure, only Ca2.1 intensity was reduced in KO, consistent with the STED data.

Nevertheless, we should be cautious about interpretation of the intensity as the reviewer suggested, and are aware that the data are just consistent with electrophysiology. From imaging, we only see a qualitative rather than quantitative difference between WT and KO.

Discussion:

I think the focus on alterations of presynaptic Ca channels could be further strengthened along with the discussion of the relevant previous studies.

Thank you for the suggestion. We have added a paragraph as shown below in the discussion (Line 531~).