Author response:

We thank the reviewers for their thorough evaluation and constructive feedback on our manuscript.

We think that their valuable suggestions will strengthen the manuscript and help us clarify several important points.

All reviewers acknowledged the importance of our theoretical results and network classification in making pattern formation analysis a more tractable problem. At the same time, they have also raised a number of important concerns that we shall carefully consider.

A. A major clarification that the reviewers found important concerns the definition of non-trivial pattern transformations and its generalization to higher dimensions. In this regard, the reviewers’ comments are:

Reviewer #1:

(on non-trivial pattern transformations):

(3) All modelling is confined to one spatial dimension, and the very definition of a "non-trivial" transformation is framed in terms of peak positions along a line, which clearly must be reformulated for higher dimensions. It's well-known that diffusions in 1, 2, and 3 dimensions are also dramatically different, so the relevance of the three-class taxonomy to real multicellular tissues remains unclear, or at least should be explained in more detail. Reviewer #2 (on non-trivial pattern transformations):

(5) The definition of non-trivial pattern formation is provided only in the Supplementary Information, despite its central importance for interpreting the main results. It would significantly improve clarity if this definition were included and explained in the main text. Additionally, it remains unclear how the definition is consistently applied across the different initial conditions. In particular, the authors should clarify how slope-based measures are determined for both the random noise and sharp peak/step function initial states. Furthermore, the authors do not specify how the sign function is evaluated at zero. If the standard mathematical definition sgn(0)=0 is used, then even a simple widening of a peak could fulfill the criterion for nontrivial pattern transformation.

We agree with Reviewer #2 that including a more detailed definition of non-trivial pattern transformation in the main text would enhance the clarity of the paper. The one-dimensional (1D) definition currently provided in the Supplementary Information was chosen because all computations presented therein involve exclusively one-dimensional patterns. However, we acknowledge that this definition, as it was, did not have a totally unambiguous generalization  to higher dimensions. Therefore, in a revised version of the manuscript, we will incorporate an expanded definition applicable to higher-dimensional cases.

This general definition of a non-trivial pattern transformation should make no reference to the sign of spatial derivatives of either the initial or resulting patterns. Specifically, a pattern transformation is considered non-trivial if it satisfies the following criteria:

- It is heterogeneous: The resulting pattern is heterogeneous in space.

- It is rearranging: The arrangement of critical points (i.e. peaks, valleys and saddle points in a gene product concentration) along the domain in the resulting pattern of a gene product is different to the arrangement of critical points in its initial pattern. This includes the emergence of new critical points, the disappearance of existing ones, or the spatial displacement of critical points from one location to another.

- It is non-replicating: The spatial arrangement of critical points in the pattern of one gene product must differ from that of any other upstream gene product.

Nonetheless, our two initial patterns are spatially discontinuous functions: in homogeneous initial patterns, the white noise is discontinuous by definition; and for the spike and spike+homogeneous initial patterns, we use sharp spikes defined by the rectangular function, which is discontinuous at the spike boundaries. Therefore, the aforementioned definition should be supplemented with the following two ad hoc assumptions:

- Homogeneous initial patterns do not comprise any critical point. White noise in this type of initial patterns represents small thermodynamic fluctuations around the steady state and, for the purpose of pattern transformation, this is equivalent to a constant concentration along the domain.

- Spike and spike+homogeneous initial patterns each contain a single critical point located at the center of the spike. The sharp spikes, modeled using the rectangular function, serve as a theoretical idealization to facilitate mathematical analysis. Once diffusion begins to act, these sharp boundaries are smoothed into differentiable gradients, maintaining a unique critical point at the center of the initial spike, which is the most relevant information for pattern transformation.

Finally, it is worth recalling that our gene network classification is fundamentally based on an analysis of the dispersion relation associated with the gene network, and the construction of this dispersion relation is independent of the spatial dimensionality of the domain (i.e. it does not require assuming any specific number of dimensions). The fact that the description of this dispersion relation was in the SI may have been non-ideal for the understandability of the article and will, consequently, be moved to the main text in an upcoming version of the article. Thus, the gene networks that can lead to pattern transformation are the same in 1D, 2D or 3D. As for the resulting patterns, the broad description we provide also applies to any number of dimensions; these would be periodic, non periodic as in the amplified noise patterns or non periodic as in the hierarchic networks. For the latter notice that, except for boundary effects that we later discuss, the spike initial condition is radially symmetric and thus, the patterns resulting from it will also be radially symmetric. We will make this point more explicit in a revised version of the article, especially since, as suggested, this important portion of the Supplementary Information will be incorporated into the main text.

Reviewer 2 suggests that with our definition of non-trivial pattern transformation, the simple widening of a concentration peak would constitute a non-trivial pattern transformation. This is not the case, as already shown in the figures as a example, since in a widening there is no change in the position of the critical point. A different situation applies if a wide and completely flat concentration peak (i.e. a plateau) forms. As we will explain in the coming version this is not possible because of requirement R5.

We think that this clarification of the definition of non-trivial pattern transformation will also help clarify the next point (B below) since it would make it clearer that this article does not intend to explain which specific resulting pattern would arise from any given gene network.

B. The main concern among these relates to the validity of our linearization of the model equations and the extension of the results obtained for the linear system to the fully nonlinear system. In this regard, the reviewers’ comments are:

Reviewer #1:

(on linearization):

(2) A central step in the model formulation is the linearisation of the reaction term around a homogeneous steady state; higher-order kinetics, including ubiquitous bimolecular sinks such as A + B → AB, are simply collapsed into the Jacobian without any stated amplitude bound on the perturbations. Because the manuscript never analyses how far this assumption can be relaxed, the robustness of the three-class taxonomy under realistic nonlinear reactions or large spike amplitudes remains uncertain.

Reviewer #2:

(on linearization):

(2) Most of the proofs presented in the Supplementary Information rely on linearized versions of the governing equations, and it remains unclear how these results extend to the fully nonlinear system. We are concerned that the generality of the conclusions drawn from the linear analysis may be overstated in the main text. For example, in Section S3, the authors introduce the concept of dynamic equivalence of transitive chains (Proposition S3.1) and intracellular transitive M-branching (Proposition S3.2), which pertains to the system's steady-state behavior. However, the proof is based solely on the linearized equations, without additional justification for why the result should hold in the presence of nonlinearities. Moreover, the linearized system is used to analyze the response to a "spike initial pattern of arbitrary height C" (SI Chapter S5.1), yet it is not clear how conclusions derived from the linear regime can be valid for large perturbations, where nonlinear effects are expected to play a significant role. We encourage the authors to clarify the assumptions under which the linearized analysis remains valid and to discuss the potential limitations of applying these results to the nonlinear regime.

In this article, we address two main questions: first, which gene network topologies can give rise to non-trivial pattern transformations; and second, which broad types of resulting patterns can these gene network topologies give rise to resulting pattern. Thus, we are not intending to explain which exact resulting patterns would arise from any given gene network (i.e. a gene network topology with specific functions and interaction strengths or weights), a question for which non-linearities do indeed matter.

For most known gene regulatory networks, available empirical information is typically limited to the nature of gene product regulations -indicating whether they act as activators or inhibitors- while details about the specific functional form of these regulations are rare. For instance, given two gene products, i and j, the network may indicate that i acts as an activator of j, implying that the concentration of j increases with that of i. However, this increase could follow a variety of functional forms: it may be quadratic (e.g., ), cubic (e.g., ), or any other function f j(gi). As we explain in the description of our model, we restrict our study to functions with a monotonicity constraint: higher concentrations of i lead to increased production of j (i.e., ).  In other words, a given gene interaction is always inhibitory or activatory, it does not change of sign. This monotonicity constraint corresponds to requirement (R5) in our main text. This requirement it is based on the biologically plausible idea that the complexity of gene regulation in development stems more from the topology of gene networks than from the complexity of the regulation by which a gene product may regulate another (i.e. we use simple monotonic functions).

Question 1: A critical part to understand question 1 is in the dispersion relation that was explained in SI. From the reviewers’ comments it is clear that having this crucial part in the main text of an upcoming version of the article would improve understandability, specially for question 1.

In brief, any pattern transformation requires the initial pattern to change. The trigger of such change is a change in the concentration of some gene product, either conceptualized as a noise fluctuation (in the homogeneous initial pattern) or a regulated change in a specific point (in the spike initial pattern). Mathematically, both can be conceptualized as perturbations and, for pattern transformation to be possible, such perturbation should grow so that the initial pattern becomes unstable and can change to another resulting pattern.

If the perturbation is small, one can use the standard linear perturbation analysis in S6.2 of our Supplementary Information. In other words, the linear analysis is enough to ascertain if a small perturbation would grow or not. A gene network in which this will not happen would be unable to lead to pattern transformation, whichever the nonlinear part of f(g). In that sense, the linear approximation provides a necessary condition that any gene network needs to fulfill to lead to pattern transformation.

However, the linear analysis would not ascertain whether a specific gene network will actually lead to pattern transformation (i.e., the condition is not sufficient). This, as well as the shape of the specific resulting pattern, may actually depend on the non-linear parts too. As we discuss, based on the dispersion relation, and other complementing arguments along the article, we can also get some insights on the possible patterns from the linear approximation alone (question 2). This arguments hold thanks to the imposition of requirements (R1-R5) on function f(g), which prevent strange behaviors stemming from the nonlinear part of the equation.

The amplitude bound of perturbations mentioned by Reviewer #1 is addressed by requirements (R2) and (R4). Although the solution to the linear system predicts unbounded growth of unstable eigenmodes, the assume functions f(g) on which the nonlinear terms  eventually halt this growth, thereby ensuring the boundedness of solutions as imposed by (R4). This assumption on the nonlinear part is literally requirement R2 on f(g) in the main text.

The transitive chains and branchings in section S3 of the Supplementary Information mentioned by the Reviewer #2 are topological properties of gene networks and therefore they influence only the linear part of the reaction-diffusion equations. This is why the proofs in that section are based on the linearized equations. We agree that clarifying this point in the text, as suggested by the reviewer, would improve the reader’s understanding of the section.

Regarding Reviewer #2’s concerns about large perturbations, we acknowledge that the phrasing using “arbitrary height” may be confusing. For the homogeneous initial conditions these perturbations are assumed to be small because they are actually molecular noise (otherwise the initial condition could not be considered homogenous in the classical sense of developmental biology models). In the spike initial conditions in hierarchic networks the perturbation is not necessarily small. For the analysis provided in the SI we indeed assume that the perturbations are small enough for the linear approximation to be possible. Notice, however, that since these networks require an intracellular self-activating loop upstream of the first extracellular signal, the effective perturbation would rapidly grow to a value determined by such loop.

In general the height of the initial spike does not affect the fact that hierarchic networks can lead to non-trivial pattern transformation. By definition these networks require the secretion of an extracellular signal from the cells in the spike (otherwise no change in gene product concentrations can occur over space). By definition this signal is not produced by any other cells and, thus, its concentration is governed by diffusion from the spike and its production in the cells in the spike. Thus, whichever the initial height of the spike and whichever the non-linearities in f(g), the signal’s concentration would decrease with the distance from the spike. As explained in the main text, this would lead to non-trivial pattern transformations if other general conditions are met. In general, the height of the initial perturbation can affect which specific pattern transformation would arise from a specific gene network but not which gene network topologies can lead to pattern transformation. This will be more clearly stated in an upcoming version of the article. C. In the following, we respond to the remaining concerns raised by the reviewers:

Reviewer #1:

(1) The Results section is difficult to follow. Key logical steps and network configurations are described shortly in prose, which constantly require the reader to address either SI or other parts of the text (see numerous links on the requirements R1-R5 listed at the beginning of the paper) to gain minimal understanding. As a result, a scientifically literate but non-specialist reader may struggle to grasp the argument with a reasonable time invested.

We acknowledge that the current version of the main text may not be as clear as we intended. Initially, we believed that placing the more technical mathematical passages in the Supplementary Information would make the main text more accessible to readers. However, we agree with the reviewer that including some of these computations in the main text could improve clarity. We also believe that adding a summary table outlining all the model’s requirements would further contribute to that goal.

Reviewer #2:

(1) We have serious concerns regarding the validity of the simulation results presented in the manuscript. Rather than simulating the full nonlinear system described by Equation (1), the authors base their results on a truncated expansion (Equation S.8.2) that captures only the time evolution of small deviations around a spatially homogeneous steady state. However, it remains unclear how this reduced system is derived from the full equations specifically, which terms are retained or neglected and why- and how the expansion of the nonlinear function can be steady-state independent, as claimed. Additionally, in simulations involving the spike plus homogeneous initial condition, it is not evident -or, where equations are provided, it is not correct- that the assumed global homogeneous background actually corresponds to a steady state of the full dynamics. We elaborate on these concerns in the following:

We believe there has been a misunderstanding regarding the presentation of the model equations (S8.2) used throughout our simulations. Accordingly, we agree that this relevant section of the Supplementary Information should be rewritten in a revised version of the manuscript to clarify this issue. Below, we address all the concerns raised by the reviewer.

Equation (S8.2) represents the full nonlinear system described in Equation (1). While we recognize that the model may oversimplify real biological processes, its purpose is to illustrate our general statements about pattern formation rather than to capture any specific or detailed mechanism. In this context, model (S8.2) offers three key advantages for our goals: it allows rapid manipulation of gene network topology simply by modifying the matrix J, making it ideal for illustrating pattern formation across different network classes; it accommodates gene networks of arbitrary size -unlike other models, such as the classical Gierer-Meinhardt model, which are limited to two-element Turing or noise-amplifying networks-; and, due to the simplicity of its nonlinear terms, this model involves relatively few free parameters, facilitating the fine-tuning needed to identify parameter regions where non-trivial pattern transformations occur.

Indeed, we find that the ability of model (S8.2) to illustrate our results despite having such simple nonlinear terms -bearing in mind that at least some nonlinearity is always necessary for selforganization- strongly supports the claim that the capacity of a gene network to produce pattern transformations is fully determined by the linear part of Equation (1). In this sense, nonlinear terms primarily influence the precise parameter values at which these transformations occur and contribute to shaping specific features of the resulting patterns.

Model (S8.2) has been successfully employed in pattern formation studies elsewhere in the literature; accordingly, we provide relevant bibliographic references to support its widespread use.

We believe the misunderstanding arises from our explanation of the biological interpretation of the model. As noted in the accompanying bibliography, the model is based on a general reactiondiffusion mechanism assuming the existence of a steady state. However, this conceptual reactiondiffusion framework is not the same as our Equation (1); rather, it was introduced by the original proponents of the model in the seminal paper cited in our text. In this context, Equation (S8.2) describes small concentration perturbations around that steady state, where the variables represent deviations in concentration relative to the general steady state.

The aforementioned general steady state corresponds to the trivial equilibrium point g≡0 in equations (S8.2). Consequently, all our simulations based on model (S8.2) start from this steady state, to which we add white noise to generate homogeneous initial patterns or a sharp spike for the two types of spike initial patterns.

It is also worth noting that Equations (S8.2) represent a non-dimensional model.

It is assumed that the homogeneous steady states are given by g_i=0 and g_i=c_i, where 1/c_i = \mu_i or \hat{\mu}_i, independently of the specific network structure. However, the basis for this assumption is unclear, especially since some of the functions do not satisfy this condition -for example, f5 as defined below Eq. S8.10.5. Moreover, if g_i=c_i does not correspond to a true steady state, then the time evolution of deviations from this state is not correctly described by Eq. S8.2, as the zeroth-order terms do not vanish in that case.

From the explanations above, it is important to distinguish two scales in the process: the scale of small perturbations, where equations (S8.2) apply; and the global scale, where the conceptual general reaction-diffusion system operates. Since the specific form of this general system does not affect equations (S8.2), we assume that it follows any of the models cited in the text, which yield a non-zero steady state at .

In this sense, Equation (S8.2) represent a small concentration deviation of such global system and g(t ,x) is a relative concentration where g≡0 represents the steady-state at are concentrations above , and g<0 are concentrations below .

As previously mentioned, simulations are performed using Equations (S8.2) on the basis of the equilibrium point g≡0. The result of these simulations is then superimposed on the non-zero steady state and presented in the figures along the article.

Using the full model instead of the simplified Equations (S8.2) may result in slightly different resulting patterns, but it does not affect the gene network’s ability to produce pattern transformations, nor does it alter the main structural properties of the patterns—for example, the periodic nature of patterns generated by Turing networks.

Additionally, the equations used contain only linear terms and a cubic degradation term for each species g_i, while neglecting all quadratic terms and cubic terms involving cross-species interactions (i≠j). An explanation for this selective truncation is not provided, and without knowledge of the full equation (f), it is impossible to assess whether this expansion is mathematically justified. If, as suggested in the Supplementary Information, the linear and cubic terms are derived from f, then at the very least, the Jacobian matrix should depend on the background steady-state concentration. However, the equations for the small deviation around a steady state (including the Jacobian matrix) used in the simulations appear to be independent of the particular steady state concentration.

The Jacobian of Equation (S8.2) is independent of g because g represents a small perturbation around a steady state of a general reaction-diffusion system. Consequently, the matrix J corresponds to the Jacobian of the general system evaluated at that steady state. Evaluating the Jacobian of equations (S8.2) at the equilibrium point g≡0 -which represents the general steady state- recovers the matrix J.

This is why we believe that the differences observed between the spike-only initial condition and the spike superimposed on a homogeneous background are not due to the initial conditions themselves, but rather result from a modified reaction scheme introduced through a questionable cutoff.

"In simulations with spike initial patterns, the reference value g≡0 represents an actual concentration of 0 and therefore, we must add to (S8.2) a Heaviside function Φ acting of f (i.e., Φ(f(g))=f(g) if f(g)>0 , Φ(f(g))=0 if f(g){less than or equal to}0 ) to prevent the existence of negative concentrations for any gene product (i.e., g_i<0 for some i )." (SI chapter S8).

This cutoff alters the dynamics (no inhibition) and introduces a different reaction scheme between the two simulations. The need for this correction may itself reflect either a problem in the original equations (which should fulfill the necessary conditions and prevent negative concentrations (R4 in main text)) or the inappropriateness of using an expanded approximation which assumes independence on the steady state concentration. It is already questionable if the linearized equations with a cubic degradation term are valid for the spike initial conditions (with different background concentration values), as the amplitude of this perturbation seems rather large.

For homogeneous and spike+homogeneous initial conditions, we interpret equations (S8.2) as small perturbations around a non-zero steady state of a general reaction-diffusion system. For spike-only initial conditions, that steady state is zero. As we mention before, g≡0 will then represent such steady-state of zero concentration, g>0 are positive concentrations of the general system, and g<0 would represent unfeasible negative concentrations of the general system. Therefore, the use of a cutoff function to handle such initial conditions is justified. Moreover, this cutoff function is the same as the one employed in the reference general system cited in our paper.

We acknowledge that the cutoff influences the simulations and accounts for the differences observed between spike and spike+homogeneous initial conditions. However, this distinction reflects what occurs in real biological systems, which is precisely why we differentiate these two types of initial states. For instance, the emergence of a periodic pattern in a noise-amplifying network depends critically on the formation of regions with concentrations below the steady state near the initial spike. Such regions can form in spike-plus-homogeneous initial patterns but not in spike-only initial patterns, where concentrations below the steady state would correspond to biologically unfeasible negative values.

Lastly, we note that under the current simulation scheme, it is not possible to meaningfully assess criteria RH2a and RH2b, as they rely on nonlinear interactions that are absent from the implemented dynamics.

It is explicitly stated in the relevant subsections of Section S7 in the Supplementary Information that, for the simulations involving RH2a and RH2b, the function f(g) in equation (S8.2) is modified by adding an ad hoc quadratic term to enable the assessment of these criteria.

(3) Several statements in the main text are presented without accompanying proof or sufficient explanation, which makes it difficult to assess their validity. In some cases, the lack of justification raises serious doubts about whether the claims are generally true. Examples are:

"For the purpose of clarity we will explain our results as if these cells have a simple arrangement in space (e.g., a 1D line or a 2D square lattice) but, as we will discuss, our results shall apply with the same logic to any distribution of cells in space." (Main text l.145-l.148).

We believe that the confusion in this statement arises from the ambiguous use of the phrase “our results”. We will revise the text to provide a more precise description. Specifically, by “our results,” we refer to the conclusion that it is possible to determine whether a gene network leads to nontrivial pattern transformations based solely on its topology. This conclusion is independent of the dimensionality of space, as none of our arguments rely on assumptions specific to spatial dimensions. While one-dimensional examples are used for clarity and illustration, the underlying reasoning applies generally. In an improved version of the article, we will clarify this point explicitly and move relevant arguments from the Supplementary Information into the main text.

Critically, our classification of gene networks is ultimately based on an argument concerning the dispersion relation associated with the network, and the construction of this dispersion relation is independent of the spatial dimensionality of the domain. In this sense, the networks identified in the text as capable of producing pattern transformations will be able to generate non-trivial pattern transformations in any spatial domain and in any number of dimensions. While the specific parameter values that permit such transformations may vary depending on the geometry and dimensionality of the domain, the existence of at least one such parameter set remains unaffected.

The geometry of the domain can influence the specific form of the resulting patterns, but it does not alter the broader class of patterns (e.g., periodic patterns, peaks emerging around a spike, etc.) that a given gene network topology can produce. One such geometric influence, commonly observed in simulations, involves boundary effects. For example, structures such as peaks or rings forming near the boundaries may appear higher, broader, or spatially shifted compared to those arising in the central regions of the domain. However, we think a pattern consisting of a periodic train of peaks where only those near the boundary are slightly different can still be classified as a periodic pattern.

"For any non-trivial pattern transformation (as long as it is symmetric around the initial spike), there exists an H gene network capable of producing it from a spike initial pattern." (Main text l.366f).

A justification for this statement is provided shortly after the claim, although we acknowledge that the current explanation is somewhat cumbersome and would benefit from a clearer presentation in a revised version of the main text.

A more detailed justification is provided in the Supplementary Information, based on three key ideas. First, any pattern (provided it is symmetric with respect to the initial spike) can be described as an arrangement of peaks with varying heights and spatial positions along a one-dimensional domain. Second, there exists a simple gene network—the diamond network—that, through parameter tuning, can produce two peaks of arbitrary height and symmetric position relative to the initial spike. Third, by placing multiple diamond networks positively upstream of a common gene product, that gene product can express peaks at each location where the upstream diamond networks induce them. Under mild additional conditions, this mechanism allows the formation of essentially any symmetric pattern. These mild conditions, along with a detailed analysis of the diamond network’s ability to generate peaks with controllable height and position, are discussed in the Supplementary Information.

"In 2D there are no peaks but concentric rings of high gene product concentration centered around the spike, while in 3D there are concentric spherical shells." (Main text l. 447ff).

This result pertains specifically to pattern transformations arising from spike initial patterns. As defined in the text, spike initial patterns are radially symmetric. Since diffusion preserves radial symmetry, pattern transformations from spike initial patterns in two or three dimensions reduce to effectively one-dimensional transformations along each radial direction. In this framework, each pair of concentration peaks symmetric with respect to the spike in one dimension corresponds to a ring surrounding the spike in two dimensions, and each ring in two dimensions becomes a hollow spherical shell around the spike in three dimensions.

We agree that including a brief section in the Supplementary Information to clarify these subtleties would be helpful for readers to better understand the generalization of certain patterns to higher dimensions.

(4) The study identifies one-signal networks and examines how combinations of these structures can give rise to minimal pattern-forming subnetworks. However, the analysis of the combinations of these minimal pattern-forming subnetworks remains relatively brief, and the manuscript does not explore how the results might change if the subnetworks were combined in upstream and downstream configurations. In our view, it is not evident that all possible gene regulatory networks can be fully characterized by these categories, nor that the resulting patterns can be reliably predicted. Rather, the approach appears more suited to identifying which known subnetworks are present within a larger network, without necessarily capturing the full dynamics of more complex configurations.

We acknowledge that our explanation regarding the combination of sub-networks was relatively brief, and we intend to address this in a revised version. Our argument that combining sub-networks does not produce qualitatively new types of pattern transformations -beyond those already described- is based on the dispersion relation. Although this relation was only detailed in the Supplementary Information, it is central to our argument and will therefore be moved to the main text. Below, we provide an outline of this argument:

Our study identifies two distinct behaviors of the principal branch of the dispersion relation at large wavenumbers. Based on this, gene networks capable of pattern formation can be classified into two categories: networks of the first kind, where the real part of the principal branch diverges to infinity as the wavenumber increases; and networks of the second kind, where the real part of the principal branch converges to a positive finite value for large wavenumbers. Naturally this argument applies to any gene network irrespectively of which, or how many, sub-networks are used to built it.

Any gene regulatory network capable of pattern formation falls into one of these two categories. We identified that networks of the first kind contain at least one Turing sub-network, whereas networks of the second kind include either an H sub-network or a noise-amplifying sub-network. In this way, the primary objective of our study -namely, achieving a topological classification of gene regulatory networks capable of pattern formation- is fulfilled. It is important to note that while the dispersion relation provides broad information about the possible resulting patterns a gene network topology can produce (e.g., periodic versus noisy), it does not specify the exact patterns that emerge for each particular set of parameter values.

Finally, regarding the shape of the resulting patterns, Figure S10 in the Supplementary Information exemplifies the notion that the behavior of combined networks can be understood as a combination of the individual behaviors of each constituent sub-network (note that the contribution of each type of sub-network in the resulting pattern is readily distinguishable). Consequently, we focus our detailed analysis on the patterning properties of the fundamental classes.

(6) The manuscript lacks a clear and detailed explanation of the underlying model and its assumptions. In particular, it is not well-defined what constitutes a "cell" in the context of the model, nor is it justified why spatial features of cells -such as their size or boundaries- can be neglected. Furthermore, the concept of the extracellular space in the one-dimensional model remains ambiguous, making it unclear which gene products are assumed to diffuse.

The size of cells is ignored in our model because we assume that they are small enough with respect to the total size of the domain that the space continuous reaction-diffusion equation (equation (1) in the main text) holds. Conceptually, one could understand cells in our model each of the pieces in an even partition of the domain into small subdomains surrounding each position x. This is anyway the standard procedure in most models of pattern formation by reaction-diffusion in embryonic development.

For extracellular signals, we assume that g(t ,x) corresponds to the concentration of the signal in the extracellular space surrounding the cell located at position x. The extracellular space is any fluid medium for which Fick Laws apply and, therfore, the Fickian diffusion term in equation (1) is valid.

For intracellular gene products, we assume that g(t ,x) corresponds to the concentration of such gene product within the cell at position x (if the gene product in hand is a transcription factor, for example), or on its surface (if it is a membrane-bound receptor). When collapsed in the continuous equations there is not such difference between being strictly within the cell or on its boundary. The only important fact is that these gene products cannot diffuse.

Regarding cell boundaries, let us consider an extracellular signal s that regulates a transcriptor factor i within cells (in our model, i is an intracellular gene product). Such regulation shall be mediated by a membrane-bound receptor, which corresponds to intracellular gene product j. In terms of the gene regulatory network this is s→ j→i. Cell boundary effects mentioned by the reviewer should be encapsulated in the specific functional form of the regulation function f(g), but they have no effect in the actual topology of the network. Consequently, they are out of the scope of this study: as we mentioned before, considering different non-linear terms for f(g) will affect the parameter range for which a gene network is capable of producing non-trivial pattern transformations, but not their overall ability to produce non-trivial pattern transformations (i.e., the existence of at least one choice of model parameters for which such transformations take place).

Finally, we would like to once again express our sincere gratitude to all reviewers for their insightful and constructive feedback. We are confident that the thorough peer review process will significantly enhance both the clarity and depth of our work. We greatly value the detailed comments provided and will carefully incorporate them in the preparation of a revised manuscript, which we intend to submit in the coming months.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately predict which residues will be in contact in the 3D structure of the expected complex. To this effect, they train a deep learning model that takes as inputs the geometric structures of the individual monomers, per-residue features (PSSMs) extracted from MSAs for each monomer, and rich representations of the amino acid sequences computed with the pre-trained protein language models ESM-1b, MSA Transformer, and ESM-IF. Predicting inter-protein contacts in complexes is an important problem. Multimer variants of AlphaFold, such as AlphaFold-Multimer, are the current state of the art for full protein complex structure prediction, and if the three-dimensional structure of a complex can be accurately predicted then the inter-protein contacts can also be accurately determined. By contrast, the method presented here seeks state-of-the-art performance among models that have been trained end-to-end for inter-protein contact prediction.

Strengths:

The paper is carefully written and the method is very well detailed. The model works both for homodimers and heterodimers. The ablation studies convincingly demonstrate that the chosen model architecture is appropriate for the task. Various comparisons suggest that PLMGraph-Inter performs substantially better, given the same input than DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter. As a byproduct of the analysis, a potentially useful heuristic criterion for acceptable contact prediction quality is found by the authors: namely, to have at least 50% precision in the prediction of the top 50 contacts.

We thank the reviewer for recognizing the strengths of our work!

Weaknesses:

My biggest issue with this work is the evaluations made using bound monomer structures as inputs, coming from the very complexes to be predicted. Conformational changes in protein-protein association are the key element of the binding mechanism and are challenging to predict. While the GLINTER paper (Xie & Xu, 2022) is guilty of the same sin, the authors of CDPred (Guo et al., 2022) correctly only report test results obtained using predicted unbound tertiary structures as inputs to their model. Test results using experimental monomer structures in bound states can hide important limitations in the model, and thus say very little about the realistic use cases in which only the unbound structures (experimental or predicted) are available. I therefore strongly suggest reducing the importance given to the results obtained using bound structures and emphasizing instead those obtained using predicted monomer structures as inputs.

We thank the reviewer for the suggestion! We evaluated PLMGraph-Inter with the predicted monomers and analyzed the result in details (see the “Impact of the monomeric structure quality on contact prediction” section and Figure 3). To mimic the real cases, we even deliberately reduced the performance of AF2 by using reduced MSAs (see the 2nd paragraph in the ““Impact of the monomeric structure quality on contact prediction” section). We leave some of the results in the supplementary of the current manuscript (Table S2). We will move these results to the main text to emphasize the performance of PLMGraph-Inter with the predicted monomers in the revision.

In particular, the most relevant comparison with AlphaFold-Multimer (AFM) is given in Figure S2, not Figure 6. Unfortunately, it substantially shrinks the proportion of structures for which AFM fails while PLMGraph-Inter performs decently. Still, it would be interesting to investigate why this occurs. One possibility would be that the predicted monomer structures are of bad quality there, and PLMGraph-Inter may be able to rely on a signal from its language model features instead. Finally, AFM multimer confidence values ("iptm + ptm") should be provided, especially in the cases in which AFM struggles.

We thank the reviewer for the suggestion! Yes! The performance of PLMGraph-Inter drops when the predicted monomers are used in the prediction. However, it is difficult to say which is a fairer comparison, Figure 6 or Figure S2, since AFM also searched monomer templates (see the third paragraph in 7. Supplementary Information : 7.1 Data in the AlphaFold-Multimer preprint: https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2.full) in the prediction. When we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) employed at least 20 templates in their predictions, and 87.8% of the targets employed the native templates. We will provide the AFM confidence values of the AFM predictions in the revision.

Besides, in cases where any experimental structures - bound or unbound - are available and given to PLMGraph-Inter as inputs, they should also be provided to AlphaFold-Multimer (AFM) as templates. Withholding these from AFM only makes the comparison artificially unfair. Hence, a new test should be run using AFM templates, and a new version of Figure 6 should be produced. Additionally, AFM's mean precision, at least for top-50 contact prediction, should be reported so it can be compared with PLMGraph-Inter's.

We thank the reviewers for the suggestion! We would like to notify that AFM also searched monomer templates (see the third paragraph in 7. Supplementary Information : 7.1 Data in the AlphaFold-Multimer preprint: https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2.full) in the prediction. When we checked our AFM runs, we found that 99% of the targets in our study (including all the targets in the four datasets: HomoPDB, HeteroPDB, DHTest and DB5.5) employed at least 20 templates in their predictions, and 87.8% of the targets employed the native template.

It's a shame that many of the structures used in the comparison with AFM are actually in the AFM v2 training set. If there are any outside the AFM v2 training set and, ideally, not sequence- or structure-homologous to anything in the AFM v2 training set, they should be discussed and reported on separately. In addition, why not test on structures from the "Benchmark 2" or "Recent-PDB-Multimers" datasets used in the AFM paper?

We thank the reviewer for the suggestion! The biggest challenge to objectively evaluate AFM is that as far as we known, AFM does not release the PDB ids of its training set and the “Recent-PDB-Multimers” dataset. “Benchmark 2” only includes 17 heterodimer proteins, and the number can be further decreased after removing targets redundant to our training set. We think it is difficult to draw conclusions from such a small number of targets. In the revision, we will analyze the performance of AFM on targets released after the date cutoff of the AFM training set, but with which we cannot totally remove the redundancy between the training and the test sets of AFM.

It is also worth noting that the AFM v2 weights have now been outdated for a while, and better v3 weights now exist, with a training cutoff of 2021-09-30.

We thank the reviewer for reminding the new version of AFM. The only difference between AFM V3 and V2 is the cutoff date of the training set. Our test set would have more overlaps with the training set of AFM V3, which is one reason that we think AFM V2 is more appropriate to be used in the comparison.

Another weakness in the evaluation framework: because PLMGraph-Inter uses structural inputs, it is not sufficient to make its test set non-redundant in sequence to its training set. It must also be non-redundant in structure. The Benchmark 2 dataset mentioned above is an example of a test set constructed by removing structures with homologous templates in the AF2 training set. Something similar should be done here.

We agree with the reviewer that testing whether the model can keep its performance on targets with no templates (i.e. non-redundant in structure) is important. We will perform the analysis in the revision.

Finally, the performance of DRN-1D2D for top-50 precision reported in Table 1 suggests to me that, in an ablation study, language model features alone would yield better performance than geometric features alone. So, I am puzzled why model "a" in the ablation is a "geometry-only" model and not a "LM-only" one.

Using the protein geometric graph to integrate multiple protein language models is the main idea of PLMGraph-Inter. Comparing with our previous work (DRN-1D2D_Inter), we consider the building of the geometric graph as one major contribution of this work. To emphasize the efficacy of this geometric graph, we chose to use the “geometry-only” model as the base model. We will further clarity this in the revision.

Reviewer #2 (Public Review):

This work introduces PLMGraph-Inter, a new deep-learning approach for predicting inter-protein contacts, which is crucial for understanding protein-protein interactions. Despite advancements in this field, especially driven by AlphaFold, prediction accuracy and efficiency in terms of computational cost) still remains an area for improvement. PLMGraph-Inter utilizes invariant geometric graphs to integrate the features from multiple protein language models into the structural information of each subunit. When compared against other inter-protein contact prediction methods, PLMGraph-Inter shows better performance which indicates that utilizing both sequence embeddings and structural embeddings is important to achieve high-accuracy predictions with relatively smaller computational costs for the model training.

The conclusions of this paper are mostly well supported by data, but test examples should be revisited with a more strict sequence identity cutoff to avoid any potential information leakage from the training data. The main figures should be improved to make them easier to understand.

We thank the reviewer for recognizing the significance of our work! We will revise the manuscript carefully to address the reviewer’s concerns.

1. The sequence identity cutoff to remove redundancies between training and test set was set to 40%, which is a bit high to remove test examples having homology to training examples. For example, CDPred uses a sequence identity cutoff of 30% to strictly remove redundancies between training and test set examples. To make their results more solid, the authors should have curated test examples with lower sequence identity cutoffs, or have provided the performance changes against sequence identities to the closest training examples.

We thank the reviewer for the valuable suggestion! Using different thresholds to reduce the redundancy between the test set and the training set is a very good suggestion, and we will perform the analysis in the revision. In the current version of the manuscript, the 40% sequence identity is used as the cutoff for many previous studies used this cutoff (e.g. the Recent-PDB-Multimers used in AlphaFold-Multimer (see: 7.8 Datasets in the AlphaFold-Multimer paper); the work of DSCRIPT: https://www.cell.com/action/showPdf?pii=S2405-4712%2821%2900333-1 (see: the PPI dataset paragraph in the METHODS DETAILS section of the STAR METHODS)). One reason for using the relatively higher threshold for PPI studies is that PPIs are generally not as conserved as protein monomers.

We performed a preliminary analysis using different thresholds to remove redundancy when preparing this provisional response letter:

Author response table 1.

Table1. The performance of PLMGraph-Inter on the HomoPDB and HeteroPDB test sets using native structures(AlphaFold2 predicted structures).

Method:

To remove redundancy, we clustered 11096 sequences from the training set and test sets (HomoPDB, HeteroPDB) using MMSeq2 with different sequence identity threshold (40%, 30%, 20%, 10%) (the lowest cutoff for CD-HIT is 40%, so we switched to MMSeq2). Each sequence is then uniquely labeled by the cluster (e.g. cluster 0, cluster 1, …) to which it belongs, from which each PPI can be marked with a pair of clusters (e.g. cluster 0-cluster 1). The PPIs belonging to the same cluster pair (note: cluster n - cluster m and cluster n-cluster m were considered as the same pair) were considered as redundant. For each PPI in the test set, if the pair cluster it belongs to contains the PPI belonging to the training set, we remove that PPI from the test set.

We will perform more detailed analyses in the revised manuscript.

1. Figures with head-to-head comparison scatter plots are hard to understand as scatter plots because too many different methods are abstracted into a single plot with multiple colors. It would be better to provide individual head-to-head scatter plots as supplementary figures, not in the main figure.

We thank the reviewer for the suggestion! We will include the individual head-to-head scatter plots as supplementary figures in the revision.

3) The authors claim that PLMGraph-Inter is complementary to AlphaFold-multimer as it shows better precision for the cases where AlphaFold-multimer fails. To strengthen the point, the qualities of predicted complex structures via protein-protein docking with predicted contacts as restraints should have been compared to those of AlphaFold-multimer structures.

We thank the reviewer for the suggestion! We will add this comparison in the revision.

4) It would be interesting to further analyze whether there is a difference in prediction performance depending on the depth of multiple sequence alignment or the type of complex (antigen-antibody, enzyme-substrates, single species PPI, multiple species PPI, etc).

We thank the reviewer for the suggestion! We will perform such analysis in the revision.

Author response:

eLife Assessment 

This valuable study investigates how the neural representation of individual finger movements changes during the early period of sequence learning. By combining a new method for extracting features from human magnetoencephalography data and decoding analyses, the authors provide incomplete evidence of an early, swift change in the brain regions correlated with sequence learning, including a set of previously unreported frontal cortical regions. The addition of more control analyses to rule out that head movement artefacts influence the findings, and to further explain the proposal of offline contextualization during short rest periods as the basis for improvement performance would strengthen the manuscript. 

We appreciate the Editorial assessment on our paper’s strengths and novelty.  We have implemented additional control analyses to show that neither task-related eye movements nor increasing overlap of finger movements during learning account for our findings, which are that contextualized neural representations in a network of bilateral frontoparietal brain regions actively contribute to skill learning.  Importantly, we carried out additional analyses showing that contextualization develops predominantly during rest intervals.

Public Reviews:

We thank the Reviewers for their comments and suggestions, prompting new analyses and additions that strengthened our report.

Reviewer #1 (Public review): 

Summary: 

This study addresses the issue of rapid skill learning and whether individual sequence elements (here: finger presses) are differentially represented in human MEG data. The authors use a decoding approach to classify individual finger elements and accomplish an accuracy of around 94%. A relevant finding is that the neural representations of individual finger elements dynamically change over the course of learning. This would be highly relevant for any attempts to develop better brain machine interfaces - one now can decode individual elements within a sequence with high precision, but these representations are not static but develop over the course of learning. 

Strengths: The work follows a large body of work from the same group on the behavioural and neural foundations of sequence learning. The behavioural task is well established and neatly designed to allow for tracking learning and how individual sequence elements contribute. The inclusion of short offline rest periods between learning epochs has been influential because it has revealed that a lot, if not most of the gains in behaviour (ie speed of finger movements) occur in these so-called micro-offline rest periods. The authors use a range of new decoding techniques, and exhaustively interrogate their data in different ways, using different decoding approaches. Regardless of the approach, impressively high decoding accuracies are observed, but when using a hybrid approach that combines the MEG data in different ways, the authors observe decoding accuracies of individual sequence elements from the MEG data of up to 94%. 

We have previously showed that neural replay of MEG activity representing the practiced skill correlated with micro-offline gains during rest intervals of early learning, 1 consistent with the recent report that hippocampal ripples during these offline periods predict human motor sequence learning2.  However, decoding accuracy in our earlier work1 needed improvement.  Here, we reported a strategy to improve decoding accuracy that could benefit future studies of neural replay or BCI using MEG.

Weaknesses: 

There are a few concerns which the authors may well be able to resolve. These are not weaknesses as such, but factors that would be helpful to address as these concern potential contributions to the results that one would like to rule out. Regarding the decoding results shown in Figure 2 etc, a concern is that within individual frequency bands, the highest accuracy seems to be within frequencies that match the rate of keypresses. This is a general concern when relating movement to brain activity, so is not specific to decoding as done here. As far as reported, there was no specific restraint to the arm or shoulder, and even then it is conceivable that small head movements would correlate highly with the vigor of individual finger movements. This concern is supported by the highest contribution in decoding accuracy being in middle frontal regions - midline structures that would be specifically sensitive to movement artefacts and don't seem to come to mind as key structures for very simple sequential keypress tasks such as this - and the overall pattern is remarkably symmetrical (despite being a unimanual finger task) and spatially broad. This issue may well be matching the time course of learning, as the vigor and speed of finger presses will also influence the degree to which the arm/shoulder and head move. This is not to say that useful information is contained within either of the frequencies or broadband data. But it raises the question of whether a lot is dominated by movement "artefacts" and one may get a more specific answer if removing any such contributions. 

Reviewer #1 expresses concern that the combination of the low-frequency narrow-band decoder results, and the bilateral middle frontal regions displaying the highest average intra-parcel decoding performance across subjects is suggestive that the decoding results could be driven by head movement or other artefacts.

Head movement artefacts are highly unlikely to contribute meaningfully to our results for the following reasons. First, in addition to ICA denoising, all “recordings were visually inspected and marked to denoise segments containing other large amplitude artifacts due to movements” (see Methods). Second, the response pad was positioned in a manner that minimized wrist, arm or more proximal body movements during the task. Third, while head position was not monitored online for this study, the head was restrained using an inflatable air bladder, and head position was assessed at the beginning and at the end of each recording. Head movement did not exceed 5mm between the beginning and end of each scan for all participants included in the study. Fourth, we agree that despite the steps taken above, it is possible that minor head movements could still contribute to some remaining variance in the MEG data in our study. The Reviewer states a concern that “it is conceivable that small head movements would correlate highly with the vigor of individual finger movements”. However, in order for any such correlations to meaningfully impact decoding performance, such head movements would need to: (A) be consistent and pervasive throughout the recording (which might not be the case if the head movements were related to movement vigor and vigor changed over time); and (B) systematically vary between different finger movements, and also between the same finger movement performed at different sequence locations (see 5-class decoding performance in Figure 4B). The possibility of any head movement artefacts meeting all these conditions is extremely unlikely.

Given the task design, a much more likely confound in our estimation would be the contribution of eye movement artefacts to the decoder performance (an issue appropriately raised by Reviewer #3 in the comments below). Remember from Figure 1A in the manuscript that an asterisk marks the current position in the sequence and is updated at each keypress. Since participants make very few performance errors, the position of the asterisk on the display is highly correlated with the keypress being made in the sequence. Thus, it is possible that if participants are attending to the visual feedback provided on the display, they may move their eyes in a way that is systematically related to the task.  Since we did record eye movements simultaneously with the MEG recordings (EyeLink 1000 Plus; Fs = 600 Hz), we were able to perform a control analysis to address this question. For each keypress event during trials in which no errors occurred (which is the same time-point that the asterisk position is updated), we extracted three features related to eye movements: 1) the gaze position at the time of asterisk position update (or keyDown event), 2) the gaze position 150ms later, and 3) the peak velocity of the eye movement between the two positions. We then constructed a classifier from these features with the aim of predicting the location of the asterisk (ordinal positions 1-5) on the display. As shown in the confusion matrix below (Author response image 1), the classifier failed to perform above chance levels (Overall cross-validated accuracy = 0.21817):

Author response image 1.

Confusion matrix showing that three eye movement features fail to predict asterisk position on the task display above chance levels (Fold 1 test accuracy = 0.21718; Fold 2 test accuracy = 0.22023; Fold 3 test accuracy = 0.21859; Fold 4 test accuracy = 0.22113; Fold 5 test accuracy = 0.21373; Overall cross-validated accuracy = 0.2181). Since the ordinal position of the asterisk on the display is highly correlated with the ordinal position of individual keypresses in the sequence, this analysis provides strong evidence that keypress decoding performance from MEG features is not explained by systematic relationships between finger movement behavior and eye movements (i.e. – behavioral artefacts).

In fact, inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. A similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. The minimal participant engagement with the visual task display observed in this study highlights another important point – that the behavior in explicit sequence learning motor tasks is highly generative in nature rather than reactive to stimulus cues as in the serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when designing investigations and comparing findings across studies.

We observed that initial keypress decoding accuracy was predominantly driven by contralateral primary sensorimotor cortex in the initial practice trials before transitioning to bilateral frontoparietal regions by trials 11 or 12 as performance gains plateaued.  The contribution of contralateral primary sensorimotor areas to early skill learning has been extensively reported in humans and non-human animals. 1,3-5  Similarly, the increased involvement of bilateral frontal and parietal regions to decoding during early skill learning in the non-dominant hand is well known.  Enhanced bilateral activation in both frontal and parietal cortex during skill learning has been extensively reported6-11, and appears to be even more prominent during early fine motor skill learning in the non-dominant hand12,13.  The frontal regions identified in these studies are known to play crucial roles in executive control14, motor planning15, and working memory6,8,16-18 processes, while the same parietal regions are known to integrate multimodal sensory feedback and support visuomotor transformations6,8,16-18, in addition to working memory19. Thus, it is not surprising that these regions increasingly contribute to decoding as subjects internalize the sequential task.  We now include a statement reflecting these considerations in the revised Discussion.

A somewhat related point is this: when combining voxel and parcel space, a concern is whether a degree of circularity may have contributed to the improved accuracy of the combined data, because it seems to use the same MEG signals twice - the voxels most contributing are also those contributing most to a parcel being identified as relevant, as parcels reflect the average of voxels within a boundary. In this context, I struggled to understand the explanation given, ie that the improved accuracy of the hybrid model may be due to "lower spatially resolved whole-brain and higher spatially resolved regional activity patterns".

We strongly disagree with the Reviewer’s assertion that the construction of the hybrid-space decoder is circular. To clarify, the base feature set for the hybrid-space decoder constructed for all participants includes whole-brain spatial patterns of MEG source activity averaged within parcels. As stated in the manuscript, these 148 inter-parcel features reflect “lower spatially resolved whole-brain activity patterns” or global brain dynamics. We then independently test how well spatial patterns of MEG source activity for all voxels distributed within individual parcels can decode keypress actions. Again, the testing of these intra-parcel spatial patterns, intended to capture “higher spatially resolved regional brain activity patterns”, is completely independent from one another and independent from the weighting of individual inter-parcel features. These intra-parcel features could, for example, provide additional information about muscle activation patterns or the task environment. These approximately 1150 intra-parcel voxels (on average, within the total number varying between subjects) are then combined with the 148 inter-parcel features to construct the final hybrid-space decoder. In fact, this varied spatial filter approach shares some similarities to the construction of convolutional neural networks (CNNs) used to perform object recognition in image classification applications. One could also view this hybrid-space decoding approach as a spatial analogue to common time-frequency based analyses such as theta-gamma phase amplitude coupling (PAC), which combine information from two or more narrow-band spectral features derived from the same time-series data.

We directly tested this hypothesis – that spatially overlapping intra- and inter-parcel features portray different information – by constructing an alternative hybrid-space decoder (HybridAlt) that excluded average inter-parcel features which spatially overlapped with intra-parcel voxel features, and comparing the performance to the decoder used in the manuscript (HybridOrig). The prediction was that if the overlapping parcel contained similar information to the more spatially resolved voxel patterns, then removing the parcel features (n=8) from the decoding analysis should not impact performance. In fact, despite making up less than 1% of the overall input feature space, removing those parcels resulted in a significant drop in overall performance greater than 2% (78.15% ± SD 7.03% for HybridOrig vs. 75.49% ± SD 7.17% for HybridAlt; Wilcoxon signed rank test, z = 3.7410, p = 1.8326e-04) (Author response image 2).

Author response image 2.

Comparison of decoding performances with two different hybrid approaches. HybridAlt: Intra-parcel voxel-space features of top ranked parcels and inter-parcel features of remaining parcels. HybridOrig:  Voxel-space features of top ranked parcels and whole-brain parcel-space features (i.e. – the version used in the manuscript). Dots represent decoding accuracy for individual subjects. Dashed lines indicate the trend in performance change across participants. Note, that HybridOrig (the approach used in our manuscript) significantly outperforms the HybridAlt approach, indicating that the excluded parcel features provide unique information compared to the spatially overlapping intra-parcel voxel patterns.

Firstly, there will be a relatively high degree of spatial contiguity among voxels because of the nature of the signal measured, i.e. nearby individual voxels are unlikely to be independent. Secondly, the voxel data gives a somewhat misleading sense of precision; the inversion can be set up to give an estimate for each voxel, but there will not just be dependence among adjacent voxels, but also substantial variation in the sensitivity and confidence with which activity can be projected to different parts of the brain. Midline and deeper structures come to mind, where the inversion will be more problematic than for regions along the dorsal convexity of the brain, and a concern is that in those midline structures, the highest decoding accuracy is seen. 

We definitely agree with the Reviewer that some inter-parcel features representing neighboring (or spatially contiguous) voxels are likely to be correlated. This has been well documented in the MEG literature20,21 and is a particularly important confound to address in functional or effective connectivity analyses (not performed in the present study). In the present analysis, any correlation between adjacent voxels presents a multi-collinearity problem, which effectively reduces the dimensionality of the input feature space. However, as long as there are multiple groups of correlated voxels within each parcel (i.e. - the effective dimensionality is still greater than 1), the intra-parcel spatial patterns could still meaningfully contribute to the decoder performance. Two specific results support this assertion.

First, we obtained higher decoding accuracy with voxel-space features [74.51% (± SD 7.34%)] compared to parcel space features [68.77% (± SD 7.6%)] (Figure 3B), indicating individual voxels carry more information in decoding the keypresses than the averaged voxel-space features or parcel-space features.  Second, Individual voxels within a parcel showed varying feature importance scores in decoding keypresses (Author response image 3). This finding supports the Reviewer’s assertion that neighboring voxels express similar information, but also shows that the correlated voxels form mini subclusters that are much smaller spatially than the parcel they reside in.

Author response image 3.

Feature importance score of individual voxels in decoding keypresses: MRMR was used to rank the individual voxel space features in decoding keypresses and the min-max normalized MRMR score was mapped to a structural brain surface. Note that individual voxels within a parcel showed different contribution to decoding.

Some of these concerns could be addressed by recording head movement (with enough precision) to regress out these contributions. The authors state that head movement was monitored with 3 fiducials, and their time courses ought to provide a way to deal with this issue. The ICA procedure may not have sufficiently dealt with removing movement-related problems, but one could eg relate individual components that were identified to the keypresses as another means for checking. An alternative could be to focus on frequency ranges above the movement frequencies. The accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment. 

We have already addressed the issue of movement related artefacts in the first response above. With respect to a focus on frequency ranges above movement frequencies, the Reviewer states the “accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment”. First, it is important to note that cortical delta-band oscillations measured with local field potentials (LFPs) in macaques is known to contain important information related to end-effector kinematics22,23 muscle activation patterns24 and temporal sequencing25 during skilled reaching and grasping actions. Thus, there is a substantial body of evidence that low-frequency neural oscillatory activity in this range contains important information about the skill learning behavior investigated in the present study. Second, our own data shows (which the Reviewer also points out) that significant information related to the skill learning behavior is also present in higher frequency bands (see Figure 2A and Figure 3—figure supplement 1). As we pointed out in our earlier response to questions about the hybrid space decoder architecture (see above), it is likely that different, yet complimentary, information is encoded across different temporal frequencies (just as it is encoded across different spatial frequencies). Again, this interpretation is supported by our data as the highest performing classifiers in all cases (when holding all parameters constant) were always constructed from broadband input MEG data (Figure 2A and Figure 3—figure supplement 1).  

One question concerns the interpretation of the results shown in Figure 4. They imply that during the course of learning, entirely different brain networks underpin the behaviour. Not only that, but they also include regions that would seem rather unexpected to be key nodes for learning and expressing relatively simple finger sequences, such as here. What then is the biological plausibility of these results? The authors seem to circumnavigate this issue by moving into a distance metric that captures the (neural network) changes over the course of learning, but the discussion seems detached from which regions are actually involved; or they offer a rather broad discussion of the anatomical regions identified here, eg in the context of LFOs, where they merely refer to "frontoparietal regions". 

The Reviewer notes the shift in brain networks driving keypress decoding performance between trials 1, 11 and 36 as shown in Figure 4A. The Reviewer questions whether these substantial shifts in brain network states underpinning the skill are biologically plausible, as well as the likelihood that bilateral superior and middle frontal and parietal cortex are important nodes within these networks.

First, previous fMRI work in humans performing a similar sequence learning task showed that flexibility in brain network composition (i.e. – changes in brain region members displaying coordinated activity) is up-regulated in novel learning environments and explains differences in learning rates across individuals26.  This work supports our interpretation of the present study data, that brain networks engaged in sequential motor skills rapidly reconfigure during early learning.

Second, frontoparietal network activity is known to support motor memory encoding during early learning27,28. For example, reactivation events in the posterior parietal29 and medial prefrontal30,31 cortex (MPFC) have been temporally linked to hippocampal replay, and are posited to support memory consolidation across several memory domains32, including motor sequence learning1,33,34.  Further, synchronized interactions between MPFC and hippocampus are more prominent during early learning as opposed to later stages27,35,36, perhaps reflecting “redistribution of hippocampal memories to MPFC” 27.  MPFC contributes to very early memory formation by learning association between contexts, locations, events and adaptive responses during rapid learning37. Consistently, coupling between hippocampus and MPFC has been shown during, and importantly immediately following (rest) initial memory encoding38,39.  Importantly, MPFC activity during initial memory encoding predicts subsequent recall40. Thus, the spatial map required to encode a motor sequence memory may be “built under the supervision of the prefrontal cortex” 28, also engaged in the development of an abstract representation of the sequence41.  In more abstract terms, the prefrontal, premotor and parietal cortices support novice performance “by deploying attentional and control processes” 42-44 required during early learning42-44. The dorsolateral prefrontal cortex DLPFC specifically is thought to engage in goal selection and sequence monitoring during early skill practice45, all consistent with the schema model of declarative memory in which prefrontal cortices play an important role in encoding46,47.  Thus, several prefrontal and frontoparietal regions contributing to long term learning 48 are also engaged in early stages of encoding. Altogether, there is strong biological support for the involvement of bilateral prefrontal and frontoparietal regions to decoding during early skill learning.  We now address this issue in the revised manuscript.

If I understand correctly, the offline neural representation analysis is in essence the comparison of the last keypress vs the first keypress of the next sequence. In that sense, the activity during offline rest periods is actually not considered. This makes the nomenclature somewhat confusing. While it matches the behavioural analysis, having only key presses one can't do it in any other way, but here the authors actually do have recordings of brain activity during offline rest. So at the very least calling it offline neural representation is misleading to this reviewer because what is compared is activity during the last and during the next keypress, not activity during offline periods. But it also seems a missed opportunity - the authors argue that most of the relevant learning occurs during offline rest periods, yet there is no attempt to actually test whether activity during this period can be useful for the questions at hand here. 

We agree with the Reviewer that our previous “offline neural representation” nomenclature could be misinterpreted. In the revised manuscript we refer to this difference as the “offline neural representational change”. Please, note that our previous work did link offline neural activity (i.e. – 16-22 Hz beta power and neural replay density during inter-practice rest periods) to observed micro-offline gains49.

Reviewer #2 (Public review): 

Summary 

Dash et al. asked whether and how the neural representation of individual finger movements is "contextualized" within a trained sequence during the very early period of sequential skill learning by using decoding of MEG signal. Specifically, they assessed whether/how the same finger presses (pressing index finger) embedded in the different ordinal positions of a practiced sequence (4-1-3-2-4; here, the numbers 1 through 4 correspond to the little through the index fingers of the non-dominant left hand) change their representation (MEG feature). They did this by computing either the decoding accuracy of the index finger at the ordinal positions 1 vs. 5 (index_OP1 vs index_OP5) or pattern distance between index_OP1 vs. index_OP5 at each training trial and found that both the decoding accuracy and the pattern distance progressively increase over the course of learning trials. More interestingly, they also computed the pattern distance for index_OP5 for the last execution of a practice trial vs. index_OP1 for the first execution in the next practice trial (i.e., across the rest period). This "off-line" distance was significantly larger than the "on-line" distance, which was computed within practice trials and predicted micro-offline skill gain. Based on these results, the authors conclude that the differentiation of representation for the identical movement embedded in different positions of a sequential skill ("contextualization") primarily occurs during early skill learning, especially during rest, consistent with the recent theory of the "micro-offline learning" proposed by the authors' group. I think this is an important and timely topic for the field of motor learning and beyond. <br /> Strengths 

The specific strengths of the current work are as follows. First, the use of temporally rich neural information (MEG signal) has a large advantage over previous studies testing sequential representations using fMRI. This allowed the authors to examine the earliest period (= the first few minutes of training) of skill learning with finer temporal resolution. Second, through the optimization of MEG feature extraction, the current study achieved extremely high decoding accuracy (approx. 94%) compared to previous works. As claimed by the authors, this is one of the strengths of the paper (but see my comments). Third, although some potential refinement might be needed, comparing "online" and "offline" pattern distance is a neat idea. 

Weaknesses 

Along with the strengths I raised above, the paper has some weaknesses. First, the pursuit of high decoding accuracy, especially the choice of time points and window length (i.e., 200 msec window starting from 0 msec from key press onset), casts a shadow on the interpretation of the main result. Currently, it is unclear whether the decoding results simply reflect behavioral change or true underlying neural change. As shown in the behavioral data, the key press speed reached 3~4 presses per second already at around the end of the early learning period (11th trial), which means inter-press intervals become as short as 250-330 msec. Thus, in almost more than 60% of training period data, the time window for MEG feature extraction (200 msec) spans around 60% of the inter-press intervals. Considering that the preparation/cueing of subsequent presses starts ahead of the actual press (e.g., Kornysheva et al., 2019) and/or potential online planning (e.g., Ariani and Diedrichsen, 2019), the decoder likely has captured these future press information as well as the signal related to the current key press, independent of the formation of genuine sequential representation (e.g., "contextualization" of individual press). This may also explain the gradual increase in decoding accuracy or pattern distance between index_OP1 vs. index_OP5 (Figure 4C and 5A), which co-occurred with performance improvement, as shorter inter-press intervals are more favorable for the dissociating the two index finger presses followed by different finger presses. The compromised decoding accuracies for the control sequences can be explained in similar logic. Therefore, more careful consideration and elaborated discussion seem necessary when trying to both achieve high-performance decoding and assess early skill learning, as it can impact all the subsequent analyses.

The Reviewer raises the possibility that (given the windowing parameters used in the present study) an increase in “contextualization” with learning could simply reflect faster typing speeds as opposed to an actual change in the underlying neural representation. The issue can essentially be framed as a mixing problem. As correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged (assuming this mixing of representations is used by the classifier to differentially tag each index finger press). If this were the case, it follows that such mixing effects reflecting the ordinal sequence structure would also be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A in the previously submitted manuscript do not show this trend in the distribution of misclassifications across the four fingers.

Moreover, if the representation distance is largely driven by this mixing effect, it’s also possible that the increased overlap between consecutive index finger keypresses during the 4-4 transition marking the end of one sequence and the beginning of the next one could actually mask contextualization-related changes to the underlying neural representations and make them harder to detect. In this case, a decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position might show decreased performance with learning as adjacent keypresses overlapped in time with each other to an increasing extent. However, Figure 4C in our previously submitted manuscript does not support this possibility, as the 2-class hybrid classifier displays improved classification performance over early practice trials despite greater temporal overlap.

We also conducted a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis affirmed that the possible alternative explanation put forward by the Reviewer is not supported by our data (Adjusted R2 = 0.00431; F = 5.62). We now include this new negative control analysis result in the revised manuscript.

Overall, we do strongly agree with the Reviewer that the naturalistic, self-paced, generative task employed in the present study results in overlapping brain processes related to planning, execution, evaluation and memory of the action sequence. We also agree that there are several tradeoffs to consider in the construction of the classifiers depending on the study aim. Given our aim of optimizing keypress decoder accuracy in the present study, the set of trade-offs resulted in representations reflecting more the latter three processes, and less so the planning component. Whether separate decoders can be constructed to tease apart the representations or networks supporting these overlapping processes is an important future direction of research in this area. For example, work presently underway in our lab constrains the selection of windowing parameters in a manner that allows individual classifiers to be temporally linked to specific planning, execution, evaluation or memory-related processes to discern which brain networks are involved and how they adaptively reorganize with learning. Results from the present study (Figure 4—figure supplement 2) showing hybrid-space decoder prediction accuracies exceeding 74% for temporal windows spanning as little as 25ms and located up to 100ms prior to the keyDown event strongly support the feasibility of such an approach.

Related to the above point, testing only one particular sequence (4-1-3-2-4), aside from the control ones, limits the generalizability of the finding. This also may have contributed to the extremely high decoding accuracy reported in the current study. 

The Reviewer raises a question about the generalizability of the decoder accuracy reported in our study. Fortunately, a comparison between decoder performances on Day 1 and Day 2 datasets does provide some insight into this issue. As the Reviewer points out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4-class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3—supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. Both changes in accuracy are important with regards to the generalizability of our findings. First, 87.11% performance accuracy for the trained sequence data on Day 2 (a reduction of only 3.36%) indicates that the hybrid-space decoder performance is robust over multiple MEG sessions, and thus, robust to variations in SNR across the MEG sensor array caused by small differences in head position between scans.  This indicates a substantial advantage over sensor-space decoding approaches. Furthermore, when tested on data from unpracticed sequences, overall performance dropped an additional 7.67%. This difference reflects the performance bias of the classifier for the trained sequence, possibly caused by high-order sequence structure being incorporated into the feature weights. In the future, it will be important to understand in more detail how random or repeated keypress sequence training data impacts overall decoder performance and generalization. We strongly agree with the Reviewer that the issue of generalizability is extremely important and have added a new paragraph to the Discussion in the revised manuscript highlighting the strengths and weaknesses of our study with respect to this issue.

In terms of clinical BCI, one of the potential relevance of the study, as claimed by the authors, it is not clear that the specific time window chosen in the current study (up to 200 msec since key press onset) is really useful. In most cases, clinical BCI would target neural signals with no overt movement execution due to patients' inability to move (e.g., Hochberg et al., 2012). Given the time window, the surprisingly high performance of the current decoder may result from sensory feedback and/or planning of subsequent movement, which may not always be available in the clinical BCI context. Of course, the decoding accuracy is still much higher than chance even when using signal before the key press (as shown in Figure 4 Supplement 2), but it is not immediately clear to me that the authors relate their high decoding accuracy based on post-movement signal to clinical BCI settings.

The Reviewer questions the relevance of the specific window parameters used in the present study for clinical BCI applications, particularly for paretic patients who are unable to produce finger movements or for whom afferent sensory feedback is no longer intact. We strongly agree with the Reviewer that any intended clinical application must carefully consider these specific input feature constraints dictated by the clinical cohort, and in turn impose appropriate and complimentary constraints on classifier parameters that may differ from the ones used in the present study.  We now highlight this issue in the Discussion of the revised manuscript and relate our present findings to published clinical BCI work within this context.

One of the important and fascinating claims of the current study is that the "contextualization" of individual finger movements in a trained sequence specifically occurs during short rest periods in very early skill learning, echoing the recent theory of micro-offline learning proposed by the authors' group. Here, I think two points need to be clarified. First, the concept of "contextualization" is kept somewhat blurry throughout the text. It is only at the later part of the Discussion (around line #330 on page 13) that some potential mechanism for the "contextualization" is provided as "what-and-where" binding. Still, it is unclear what "contextualization" actually is in the current data, as the MEG signal analyzed is extracted from 0-200 msec after the keypress. If one thinks something is contextualizing an action, that contextualization should come earlier than the action itself. 

The Reviewer requests that we: 1) more clearly define our use of the term “contextualization” and 2) provide the rationale for assessing it over a 200ms window aligned to the keyDown event. This choice of window parameters means that the MEG activity used in our analysis was coincident with, rather than preceding, the actual keypresses.  We define contextualization as the differentiation of representation for the identical movement embedded in different positions of a sequential skill. That is, representations of individual action elements progressively incorporate information about their relationship to the overall sequence structure as the skill is learned. We agree with the Reviewer that this can be appropriately interpreted as “what-and-where” binding. We now incorporate this definition in the Introduction of the revised manuscript as requested.

The window parameters for optimizing accurate decoding individual finger movements were determined using a grid search of the parameter space (a sliding window of variable width between 25-350 ms with 25 ms increments variably aligned from 0 to +100ms with 10ms increments relative to the keyDown event). This approach generated 140 different temporal windows for each keypress for each participant, with the final parameter selection determined through comparison of the resulting performance between each decoder.  Importantly, the decision to optimize for decoding accuracy placed an emphasis on keypress representations characterized by the most consistent and robust features shared across subjects, which in turn maximize statistical power in detecting common learning-related changes. In this case, the optimal window encompassed a 200ms epoch aligned to the keyDown event (t0 = 0 ms).  We then asked if the representations (i.e. – spatial patterns of combined parcel- and voxel-space activity) of the same digit at two different sequence positions changed with practice within this optimal decoding window.  Of course, our findings do not rule out the possibility that contextualization can also be found before or even after this time window, as we did not directly address this issue in the present study.  Ongoing work in our lab, as pointed out above, is investigating contextualization within different time windows tailored specifically for assessing sequence skill action planning, execution, evaluation and memory processes.

The second point is that the result provided by the authors is not yet convincing enough to support the claim that "contextualization" occurs during rest. In the original analysis, the authors presented the statistical significance regarding the correlation between the "offline" pattern differentiation and micro-offline skill gain (Figure 5. Supplement 1), as well as the larger "offline" distance than "online" distance (Figure 5B). However, this analysis looks like regressing two variables (monotonically) increasing as a function of the trial. Although some information in this analysis, such as what the independent/dependent variables were or how individual subjects were treated, was missing in the Methods, getting a statistically significant slope seems unsurprising in such a situation. Also, curiously, the same quantitative evidence was not provided for its "online" counterpart, and the authors only briefly mentioned in the text that there was no significant correlation between them. It may be true looking at the data in Figure 5A as the online representation distance looks less monotonically changing, but the classification accuracy presented in Figure 4C, which should reflect similar representational distance, shows a more monotonic increase up to the 11th trial. Further, the ways the "online" and "offline" representation distance was estimated seem to make them not directly comparable. While the "online" distance was computed using all the correct press data within each 10 sec of execution, the "offline" distance is basically computed by only two presses (i.e., the last index_OP5 vs. the first index_OP1 separated by 10 sec of rest). Theoretically, the distance between the neural activity patterns for temporally closer events tends to be closer than that between the patterns for temporally far-apart events. It would be fairer to use the distance between the first index_OP1 vs. the last index_OP5 within an execution period for "online" distance, as well. 

The Reviewer suggests that the current data is not convincing enough to show that contextualization occurs during rest and raises two important concerns: 1) the relationship between online contextualization and micro-online gains is not shown, and 2) the online distance was calculated differently from its offline counterpart (i.e. - instead of calculating the distance between last IndexOP5 and first IndexOP1 from a single trial, the distance was calculated for each sequence within a trial and then averaged).

We addressed the first concern by performing individual subject correlations between 1) contextualization changes during rest intervals and micro-offline gains; 2) contextualization changes during practice trials and micro-online gains, and 3) contextualization changes during practice trials and micro-offline gains (Author response image 4). We then statistically compared the resulting correlation coefficient distributions and found that within-subject correlations for contextualization changes during rest intervals and micro-offline gains were significantly higher than online contextualization and micro-online gains (t = 3.2827, p = 0.0015) and online contextualization and micro-offline gains (t = 3.7021, p = 5.3013e-04). These results are consistent with our interpretation that micro-offline gains are supported by contextualization changes during the inter-practice rest period.

Author response image 4.

Distribution of individual subject correlation coefficients between contextualization changes occurring during practice or rest with  micro-online and micro-offline performance gains. Note that, the correlation distributions were significantly higher for the relationship between contextualization changes during rest and micro-offline gains than for contextualization changes during practice and either micro-online or offline gain.

With respect to the second concern highlighted above, we agree with the Reviewer that one limitation of the analysis comparing online versus offline changes in contextualization as presented in the reviewed manuscript, is that it does not eliminate the possibility that any differences could simply be explained by the passage of time (which is smaller for the online analysis compared to the offline analysis). The Reviewer suggests an approach that addresses this issue, which we have now carried out.   When quantifying online changes in contextualization from the first IndexOP1 the last IndexOP5 keypress in the same trial we observed no learning-related trend (Author response image 5, right panel). Importantly, offline distances were significantly larger than online distances regardless of the measurement approach and neither predicted online learning (Author response image 6).

Author response image 5.

Trial by trial trend of offline (left panel) and online (middle and right panels) changes in contextualization. Offline changes in contextualization were assessed by calculating the distance between neural representations for the last IndexOP5 keypress in the previous trial and the first IndexOP1 keypress in the present trial. Two different approaches were used to characterize online contextualization changes. The analysis included in the reviewed manuscript (middle panel) calculated the distance between IndexOP1 and IndexOP5 for each correct sequence, which was then averaged across the trial. This approach is limited by the lack of control for the passage of time when making online versus offline comparisons. Thus, the second approach controlled for the passage of time by calculating distance between the representations associated with the first IndexOP1 keypress and the last IndexOP5 keypress within the same trial. Note that while the first approach showed an increase online contextualization trend with practice, the second approach did not.

Author response image 6.

Relationship between online contextualization and online learning is shown for both within-sequence (left; note that this is the online contextualization measure used in the reviewd manuscript) and across-sequence (right) distance calculation. There was no significant relationship between online learning and online contextualization regardless of the measurement approach.

A related concern regarding the control analysis, where individual values for max speed and the degree of online contextualization were compared (Figure 5 Supplement 3), is whether the individual difference is meaningful. If I understood correctly, the optimization of the decoding process (temporal window, feature inclusion/reduction, decoder, etc.) was performed for individual participants, and the same feature extraction was also employed for the analysis of representation distance (i.e., contextualization). If this is the case, the distances are individually differently calculated and they may need to be normalized relative to some stable reference (e.g., 1 vs. 4 or average distance within the control sequence presses) before comparison across the individuals. 

The Reviewer makes a good point here. We have now implemented the suggested normalization procedure in the analysis provided in the revised manuscript.

Reviewer #3 (Public review): 

Summary: 

One goal of this paper is to introduce a new approach for highly accurate decoding of finger movements from human magnetoencephalography data via dimension reduction of a "multi-scale, hybrid" feature space. Following this decoding approach, the authors aim to show that early skill learning involves "contextualization" of the neural coding of individual movements, relative to their position in a sequence of consecutive movements. Furthermore, they aim to show that this "contextualization" develops primarily during short rest periods interspersed with skill training and correlates with a performance metric which the authors interpret as an indicator of offline learning. <br /> Strengths: 

A clear strength of the paper is the innovative decoding approach, which achieves impressive decoding accuracies via dimension reduction of a "multi-scale, hybrid space". This hybrid-space approach follows the neurobiologically plausible idea of the concurrent distribution of neural coding across local circuits as well as large-scale networks. A further strength of the study is the large number of tested dimension reduction techniques and classifiers (though the manuscript reveals little about the comparison of the latter). 

We appreciate the Reviewer’s comments regarding the paper’s strengths.

A simple control analysis based on shuffled class labels could lend further support to this complex decoding approach. As a control analysis that completely rules out any source of overfitting, the authors could test the decoder after shuffling class labels. Following such shuffling, decoding accuracies should drop to chance level for all decoding approaches, including the optimized decoder. This would also provide an estimate of actual chance-level performance (which is informative over and beyond the theoretical chance level). Furthermore, currently, the manuscript does not explain the huge drop in decoding accuracies for the voxel-space decoding (Figure 3B). Finally, the authors' approach to cortical parcellation raises questions regarding the information carried by varying dipole orientations within a parcel (which currently seems to be ignored?) and the implementation of the mean-flipping method (given that there are two dimensions - space and time - what do the authors refer to when they talk about the sign of the "average source", line 477?). 

The Reviewer recommends that we: 1) conduct an additional control analysis on classifier performance using shuffled class labels, 2) provide a more detailed explanation regarding the drop in decoding accuracies for the voxel-space decoding following LDA dimensionality reduction (see Fig 3B), and 3) provide additional details on how problems related to dipole solution orientations were addressed in the present study.  

In relation to the first point, we have now implemented a random shuffling approach as a control for the classification analyses. The results of this analysis indicated that the chance level accuracy was 22.12% (± SD 9.1%) for individual keypress decoding (4-class classification), and 18.41% (± SD 7.4%) for individual sequence item decoding (5-class classification), irrespective of the input feature set or the type of decoder used. Thus, the decoding accuracy observed with the final model was substantially higher than these chance levels.  

Second, please note that the dimensionality of the voxel-space feature set is very high (i.e. – 15684). LDA attempts to map the input features onto a much smaller dimensional space (number of classes-1; e.g. –  3 dimensions, for 4-class keypress decoding). Given the very high dimension of the voxel-space input features in this case, the resulting mapping exhibits reduced accuracy. Despite this general consideration, please refer to Figure 3—figure supplement 3, where we observe improvement in voxel-space decoder performance when utilizing alternative dimensionality reduction techniques.

The decoders constructed in the present study assess the average spatial patterns across time (as defined by the windowing procedure) in the input feature space.  We now provide additional details in the Methods of the revised manuscript pertaining to the parcellation procedure and how the sign ambiguity problem was addressed in our analysis.

Weaknesses: 

A clear weakness of the paper lies in the authors' conclusions regarding "contextualization". Several potential confounds, described below, question the neurobiological implications proposed by the authors and provide a simpler explanation of the results. Furthermore, the paper follows the assumption that short breaks result in offline skill learning, while recent evidence, described below, casts doubt on this assumption. 

We thank the Reviewer for giving us the opportunity to address these issues in detail (see below).

The authors interpret the ordinal position information captured by their decoding approach as a reflection of neural coding dedicated to the local context of a movement (Figure 4). One way to dissociate ordinal position information from information about the moving effectors is to train a classifier on one sequence and test the classifier on other sequences that require the same movements, but in different positions50. In the present study, however, participants trained to repeat a single sequence (4-1-3-2-4). As a result, ordinal position information is potentially confounded by the fixed finger transitions around each of the two critical positions (first and fifth press). Across consecutive correct sequences, the first keypress in a given sequence was always preceded by a movement of the index finger (=last movement of the preceding sequence), and followed by a little finger movement. The last keypress, on the other hand, was always preceded by a ring finger movement, and followed by an index finger movement (=first movement of the next sequence). Figure 4 - Supplement 2 shows that finger identity can be decoded with high accuracy (>70%) across a large time window around the time of the key press, up to at least +/-100 ms (and likely beyond, given that decoding accuracy is still high at the boundaries of the window depicted in that figure). This time window approaches the keypress transition times in this study. Given that distinct finger transitions characterized the first and fifth keypress, the classifier could thus rely on persistent (or "lingering") information from the preceding finger movement, and/or "preparatory" information about the subsequent finger movement, in order to dissociate the first and fifth keypress. Currently, the manuscript provides no evidence that the context information captured by the decoding approach is more than a by-product of temporally extended, and therefore overlapping, but independent neural representations of consecutive keypresses that are executed in close temporal proximity - rather than a neural representation dedicated to context. 

Such temporal overlap of consecutive, independent finger representations may also account for the dynamics of "ordinal coding"/"contextualization", i.e., the increase in 2-class decoding accuracy, across Day 1 (Figure 4C). As learning progresses, both tapping speed and the consistency of keypress transition times increase (Figure 1), i.e., consecutive keypresses are closer in time, and more consistently so. As a result, information related to a given keypress is increasingly overlapping in time with information related to the preceding and subsequent keypresses. The authors seem to argue that their regression analysis in Figure 5 - Figure Supplement 3 speaks against any influence of tapping speed on "ordinal coding" (even though that argument is not made explicitly in the manuscript). However, Figure 5 - Figure Supplement 3 shows inter-individual differences in a between-subject analysis (across trials, as in panel A, or separately for each trial, as in panel B), and, therefore, says little about the within-subject dynamics of "ordinal coding" across the experiment. A regression of trial-by-trial "ordinal coding" on trial-by-trial tapping speed (either within-subject or at a group-level, after averaging across subjects) could address this issue. Given the highly similar dynamics of "ordinal coding" on the one hand (Figure 4C), and tapping speed on the other hand (Figure 1B), I would expect a strong relationship between the two in the suggested within-subject (or group-level) regression. Furthermore, learning should increase the number of (consecutively) correct sequences, and, thus, the consistency of finger transitions. Therefore, the increase in 2-class decoding accuracy may simply reflect an increasing overlap in time of increasingly consistent information from consecutive keypresses, which allows the classifier to dissociate the first and fifth keypress more reliably as learning progresses, simply based on the characteristic finger transitions associated with each. In other words, given that the physical context of a given keypress changes as learning progresses - keypresses move closer together in time and are more consistently correct - it seems problematic to conclude that the mental representation of that context changes. To draw that conclusion, the physical context should remain stable (or any changes to the physical context should be controlled for). 

The issues raised by Reviewer #3 here are similar to two issues raised by Reviewer #2 above and agree they must both be carefully considered in any evaluation of our findings.

As both Reviewers pointed out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4-class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3—supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. This classification performance difference of 7.67% when tested on the Day 2 data could reflect the performance bias of the classifier for the trained sequence, possibly caused by mixed information from temporally close keypresses being incorporated into the feature weights.

Along these same lines, both Reviewers also raise the possibility that an increase in “ordinal coding/contextualization” with learning could simply reflect an increase in this mixing effect caused by faster typing speeds as opposed to an actual change in the underlying neural representation. The basic idea is that as correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged (assuming this mixing of representations is used by the classifier to differentially tag each index finger press). If this were the case, it follows that such mixing effects reflecting the ordinal sequence structure would also be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A in the previously submitted manuscript do not show this trend in the distribution of misclassifications across the four fingers.

Following this logic, it’s also possible that if the ordinal coding is largely driven by this mixing effect, the increased overlap between consecutive index finger keypresses during the 4-4 transition marking the end of one sequence and the beginning of the next one could actually mask contextualization-related changes to the underlying neural representations and make them harder to detect. In this case, a decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position might show decreased performance with learning as adjacent keypresses overlapped in time with each other to an increasing extent. However, Figure 4C in our previously submitted manuscript does not support this possibility, as the 2-class hybrid classifier displays improved classification performance over early practice trials despite greater temporal overlap.

As noted in the above replay to Reviewer #2, we also conducted a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis affirmed that the possible alternative explanation put forward by the Reviewer is not supported by our data (Adjusted R2 = 0.00431; F = 5.62). We now include this new negative control analysis result in the revised manuscript.

Finally, the Reviewer hints that one way to address this issue would be to compare MEG responses before and after learning for sequences typed at a fixed speed. However, given that the speed-accuracy trade-off should improve with learning, a comparison between unlearned and learned skill states would dictate that the skill be evaluated at a very low fixed speed. Essentially, such a design presents the problem that the post-training test is evaluating the representation in the unlearned behavioral state that is not representative of the acquired skill. Thus, this approach would not address our experimental question: “do neural representations of the same action performed at different locations within a skill sequence contextually differentiate or remain stable as learning evolves”.

A similar difference in physical context may explain why neural representation distances ("differentiation") differ between rest and practice (Figure 5). The authors define "offline differentiation" by comparing the hybrid space features of the last index finger movement of a trial (ordinal position 5) and the first index finger movement of the next trial (ordinal position 1). However, the latter is not only the first movement in the sequence but also the very first movement in that trial (at least in trials that started with a correct sequence), i.e., not preceded by any recent movement. In contrast, the last index finger of the last correct sequence in the preceding trial includes the characteristic finger transition from the fourth to the fifth movement. Thus, there is more overlapping information arising from the consistent, neighbouring keypresses for the last index finger movement, compared to the first index finger movement of the next trial. A strong difference (larger neural representation distance) between these two movements is, therefore, not surprising, given the task design, and this difference is also expected to increase with learning, given the increase in tapping speed, and the consequent stronger overlap in representations for consecutive keypresses. Furthermore, initiating a new sequence involves pre-planning, while ongoing practice relies on online planning (Ariani et al., eNeuro 2021), i.e., two mental operations that are dissociable at the level of neural representation (Ariani et al., bioRxiv 2023). 

The Reviewer argues that the comparison of last finger movement of a trial and the first in the next trial are performed in different circumstances and contexts. This is an important point and one we tend to agree with. For this task, the first sequence in a practice trial (which is pre-planned offline) is performed in a somewhat different context from the sequence iterations that follow, which involve temporally overlapping planning, execution and evaluation processes.  The Reviewer is particularly concerned about a difference in the temporal mixing effect issue raised above between the first and last keypresses performed in a trial. However, in contrast to the Reviewers stated argument above, findings from Korneysheva et. al (2019) showed that neural representations of individual actions are competitively queued during the pre-planning period in a manner that reflects the ordinal structure of the learned sequence.  Thus, mixing effects are likely still present for the first keypress in a trial. Also note that we now present new control analyses in multiple responses above confirming that hypothetical mixing effects between adjacent keypresses do not explain our reported contextualization finding. A statement addressing these possibilities raised by the Reviewer has been added to the Discussion in the revised manuscript.

In relation to pre-planning, ongoing MEG work in our lab is investigating contextualization within different time windows tailored specifically for assessing how sequence skill action planning evolves with learning.

Given these differences in the physical context and associated mental processes, it is not surprising that "offline differentiation", as defined here, is more pronounced than "online differentiation". For the latter, the authors compared movements that were better matched regarding the presence of consistent preceding and subsequent keypresses (online differentiation was defined as the mean difference between all first vs. last index finger movements during practice).  It is unclear why the authors did not follow a similar definition for "online differentiation" as for "micro-online gains" (and, indeed, a definition that is more consistent with their definition of "offline differentiation"), i.e., the difference between the first index finger movement of the first correct sequence during practice, and the last index finger of the last correct sequence. While these two movements are, again, not matched for the presence of neighbouring keypresses (see the argument above), this mismatch would at least be the same across "offline differentiation" and "online differentiation", so they would be more comparable. 

This is the same point made earlier by Reviewer #2, and we agree with this assessment. As stated in the response to Reviewer #2 above, we have now carried out quantification of online contextualization using this approach and included it in the revised manuscript. We thank the Reviewer for this suggestion.

A further complication in interpreting the results regarding "contextualization" stems from the visual feedback that participants received during the task. Each keypress generated an asterisk shown above the string on the screen, irrespective of whether the keypress was correct or incorrect. As a result, incorrect (e.g., additional, or missing) keypresses could shift the phase of the visual feedback string (of asterisks) relative to the ordinal position of the current movement in the sequence (e.g., the fifth movement in the sequence could coincide with the presentation of any asterisk in the string, from the first to the fifth). Given that more incorrect keypresses are expected at the start of the experiment, compared to later stages, the consistency in visual feedback position, relative to the ordinal position of the movement in the sequence, increased across the experiment. A better differentiation between the first and the fifth movement with learning could, therefore, simply reflect better decoding of the more consistent visual feedback, based either on the feedback-induced brain response, or feedback-induced eye movements (the study did not include eye tracking). It is not clear why the authors introduced this complicated visual feedback in their task, besides consistency with their previous studies.

We strongly agree with the Reviewer that eye movements related to task engagement are important to rule out as a potential driver of the decoding accuracy or contextualization effect. We address this issue above in response to a question raised by Reviewer #1 about the impact of movement related artefacts in general on our findings.

First, the assumption the Reviewer makes here about the distribution of errors in this task is incorrect. On average across subjects, 2.32% ± 1.48% (mean ± SD) of all keypresses performed were errors, which were evenly distributed across the four possible keypress responses. While errors increased progressively over practice trials, they did so in proportion to the increase in correct keypresses, so that the overall ratio of correct-to-incorrect keypresses remained stable over the training session. Thus, the Reviewer’s assumptions that there is a higher relative frequency of errors in early trials, and a resulting systematic trend phase shift differences between the visual display updates (i.e. – a change in asterisk position above the displayed sequence) and the keypress performed is not substantiated by the data. To the contrary, the asterisk position on the display and the keypress being executed remained highly correlated over the entire training session. We now include a statement about the frequency and distribution of errors in the revised manuscript.

Given this high correlation, we firmly agree with the Reviewer that the issue of eye movement-related artefacts is still an important one to address. Fortunately, we did collect eye movement data during the MEG recordings so were able to investigate this. As detailed in the response to Reviewer #1 above, we found that gaze positions and eye-movement velocity time-locked to visual display updates (i.e. – a change in asterisk position above the displayed sequence) did not reflect the asterisk location above chance levels (Overall cross-validated accuracy = 0.21817; see Author response image 1). Furthermore, an inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. As pointed out above, a similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. Notably, the minimal participant engagement with the visual task display observed in this study highlights an important difference between behavior observed during explicit sequence learning motor tasks (which is highly generative in nature) with reactive responses to stimulus cues in a serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when comparing findings across studies. All elements pertaining to this new control analysis are now included in the revised manuscript.

The authors report a significant correlation between "offline differentiation" and cumulative micro-offline gains. However, it would be more informative to correlate trial-by-trial changes in each of the two variables. This would address the question of whether there is a trial-by-trial relation between the degree of "contextualization" and the amount of micro-offline gains - are performance changes (micro-offline gains) less pronounced across rest periods for which the change in "contextualization" is relatively low? Furthermore, is the relationship between micro-offline gains and "offline differentiation" significantly stronger than the relationship between micro-offline gains and "online differentiation"? 

In response to a similar issue raised above by Reviewer #2, we now include new analyses comparing correlation magnitudes between (1) “online differention” vs micro-online gains, (2) “online differention” vs micro-offline gains and (3) “offline differentiation” and micro-offline gains (see Author response images 4, 5 and 6 above). These new analyses and results have been added to the revised manuscript. Once again, we thank both Reviewers for this suggestion.

The authors follow the assumption that micro-offline gains reflect offline learning.

This statement is incorrect. The original Bonstrup et al (2019) 49 paper clearly states that micro-offline gains must be carefully interpreted based upon the behavioral context within which they are observed, and lays out the conditions under which one can have confidence that micro-offline gains reflect offline learning.  In fact, the excellent meta-analysis of Pan & Rickard (2015) 51, which re-interprets the benefits of sleep in overnight skill consolidation from a “reactive inhibition” perspective, was a crucial resource in the experimental design of our initial study49, as well as in all our subsequent work. Pan & Rickard stated:

“Empirically, reactive inhibition refers to performance worsening that can accumulate during a period of continuous training (Hull, 1943). It tends to dissipate, at least in part, when brief breaks are inserted between blocks of training. If there are multiple performance-break cycles over a training session, as in the motor sequence literature, performance can exhibit a scalloped effect, worsening during each uninterrupted performance block but improving across blocks52,53. Rickard, Cai, Rieth, Jones, and Ard (2008) and Brawn, Fenn, Nusbaum, and Margoliash (2010) 52,53 demonstrated highly robust scalloped reactive inhibition effects using the commonly employed 30 s–30 s performance break cycle, as shown for Rickard et al.’s (2008) massed practice sleep group in Figure 2. The scalloped effect is evident for that group after the first few 30 s blocks of each session. The absence of the scalloped effect during the first few blocks of training in the massed group suggests that rapid learning during that period masks any reactive inhibition effect.”

Crucially, Pan & Rickard51 made several concrete recommendations for reducing the impact of the reactive inhibition confound on offline learning studies. One of these recommendations was to reduce practice times to 10s (most prior sequence learning studies up until that point had employed 30s long practice trials). They stated:

“The traditional design involving 30 s-30 s performance break cycles should be abandoned given the evidence that it results in a reactive inhibition confound, and alternative designs with reduced performance duration per block used instead 51. One promising possibility is to switch to 10 s performance durations for each performance-break cycle Instead 51. That design appears sufficient to eliminate at least the majority of the reactive inhibition effect 52,53.”

We mindfully incorporated recommendations from Pan and Rickard51  into our own study designs including 1) utilizing 10s practice trials and 2) constraining our analysis of micro-offline gains to early learning trials (where performance monotonically increases and 95% of overall performance gains occur), which are prior to the emergence of the “scalloped” performance dynamics that are strongly linked to reactive inhibition effects. 

However, there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.

We strongly disagree with the Reviewer’s assertion that “there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.”  The initial Bönstrup et al. (2019) 49 report was followed up by a large online crowd-sourcing study (Bönstrup et al., 2020) 54. This second (and much larger) study provided several additional important findings supporting our interpretation of micro-offline gains in cases where the important behavioral conditions clarified above were met (see Author response image 7 below for further details on these conditions).

Author response image 7.

Micro-offline gains observed in learning and non-learning contexts are attributed to different underlying causes. (A) Micro-offline and online changes relative to overall trial-by-trial learning. This figure is based on data from Bönstrup et al. (2019) 49. During early learning, micro-offline gains (red bars) closely track trial-by-trial performance gains (green line with open circle markers), with minimal contribution from micro-online gains (blue bars). The stated conclusion in Bönstrup et al. (2019) is that micro-offline gains only during this Early Learning stage reflect rapid memory consolidation (see also 54). After early learning, about practice trial 11, skill plateaus. This plateau skill period is characterized by a striking emergence of coupled (and relatively stable) micro-online drops and micro-offline increases. Bönstrup et al. (2019) as well as others in the literature 55-57, argue that micro-offline gains during the plateau period likely reflect recovery from inhibitory performance factors such as reactive inhibition or fatigue, and thus must be excluded from analyses relating micro-offline gains to skill learning.  The Non-repeating groups in Experiments 3 and 4 from Das et al. (2024) suffer from a lack of consideration of these known confounds.

Evidence documented in that paper54 showed that micro-offline gains during early skill learning were: 1) replicable and generalized to subjects learning the task in their daily living environment (n=389); 2) equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (n=118);  3) reduced (along with learning rates) by retroactive interference applied immediately after each practice period relative to interference applied after passage of time (n=373), indicating stabilization of the motor memory at a microscale of several seconds consistent with rapid consolidation; and 4) not modified by random termination of the practice periods, ruling out a contribution of predictive motor slowing (N = 71) 54.  Altogether, our findings were strongly consistent with the interpretation that micro-offline gains reflect memory consolidation supporting early skill learning. This is precisely the portion of the learning curve Pan and Rickard51 refer to when they state “…rapid learning during that period masks any reactive inhibition effect”.

This interpretation is further supported by brain imaging evidence linking known memory-related networks and consolidation mechanisms to micro-offline gains. First, we reported that the density of fast hippocampo-neocortical skill memory replay events increases approximately three-fold during early learning inter-practice rest periods with the density explaining differences in the magnitude of micro-offline gains across subjects1. Second, Jacobacci et al. (2020) independently reproduced our original behavioral findings and reported BOLD fMRI changes in the hippocampus and precuneus (regions also identified in our MEG study1) linked to micro-offline gains during early skill learning. 33 These functional changes were coupled with rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that operates during rest periods of early learning undergoes structural plasticity over several minutes following practice58. Third, even more recently, Chen et al. (2024) provided direct evidence from intracranial EEG in humans linking sharp-wave ripple events (which are known markers for neural replay59) in the hippocampus (80-120 Hz in humans) with micro-offline gains during early skill learning. The authors report that the strong increase in ripple rates tracked learning behavior, both across blocks and across participants. The authors conclude that hippocampal ripples during resting offline periods contribute to motor sequence learning. 2

Thus, there is actually now substantial evidence in the literature directly supporting the assertion “that micro-offline gains really result from offline learning”.  On the contrary, according to Gupta & Rickard (2024) “…the mechanism underlying RI [reactive inhibition] is not well established” after over 80 years of investigation60, possibly due to the fact that “reactive inhibition” is a categorical description of behavioral effects that likely result from several heterogenous processes with very different underlying mechanisms.

On the contrary, recent evidence questions this interpretation (Gupta & Rickard, npj Sci Learn 2022; Gupta & Rickard, Sci Rep 2024; Das et al., bioRxiv 2024). Instead, there is evidence that micro-offline gains are transient performance benefits that emerge when participants train with breaks, compared to participants who train without breaks, however, these benefits vanish within seconds after training if both groups of participants perform under comparable conditions (Das et al., bioRxiv 2024). 

It is important to point out that the recent work of Gupta & Rickard (2022,2024) 55 does not present any data that directly opposes our finding that early skill learning49 is expressed as micro-offline gains during rest breaks. These studies are essentially an extension of the Rickard et al (2008) paper that employed a massed (30s practice followed by 30s breaks) vs spaced (10s practice followed by 10s breaks) to assess if recovery from reactive inhibition effects could account for performance gains measured after several minutes or hours. Gupta & Rickard (2022) added two additional groups (30s practice/10s break and 10s practice/10s break as used in the work from our group). The primary aim of the study was to assess whether it was more likely that changes in performance when retested 5 minutes after skill training (consisting of 12 practice trials for the massed groups and 36 practice trials for the spaced groups) had ended reflected memory consolidation effects or recovery from reactive inhibition effects. The Gupta & Rickard (2024) follow-up paper employed a similar design with the primary difference being that participants performed a fixed number of sequences on each trial as opposed to trials lasting a fixed duration. This was done to facilitate the fitting of a quantitative statistical model to the data.  To reiterate, neither study included any analysis of micro-online or micro-offline gains and did not include any comparison focused on skill gains during early learning. Instead, Gupta & Rickard (2022), reported evidence for reactive inhibition effects for all groups over much longer training periods. Again, we reported the same finding for trials following the early learning period in our original Bönstrup et al. (2019) paper49 (Author response image 7). Also, please note that we reported in this paper that cumulative micro-offline gains over early learning did not correlate with overnight offline consolidation measured 24 hours later49 (see the Results section and further elaboration in the Discussion). Thus, while the composition of our data is supportive of a short-term memory consolidation process operating over several seconds during early learning, it likely differs from those involved over longer training times and offline periods, as assessed by Gupta & Rickard (2022).

In the recent preprint from Das et al (2024) 61,  the authors make the strong claim that “micro-offline gains during early learning do not reflect offline learning” which is not supported by their own data.   The authors hypothesize that if “micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”.  The study utilizes a spaced vs. massed practice group between-subjects design inspired by the reactive inhibition work from Rickard and others to test this hypothesis. Crucially, the design incorporates only a small fraction of the training used in other investigations to evaluate early skill learning1,33,49,54,57,58,62.  A direct comparison between the practice schedule designs for the spaced and massed groups in Das et al., and the training schedule all participants experienced in the original Bönstrup et al. (2019) paper highlights this issue as well as several others (Author response image 8):

Author response image 8.

(A) Comparison of Das et al. Spaced & Massed group training session designs, and the training session design from the original Bönstrup et al. (2019) 49 paper. Similar to the approach taken by Das et al., all practice is visualized as 10-second practice trials with a variable number (either 0, 1 or 30) of 10-second-long inter-practice rest intervals to allow for direct comparisons between designs. The two key takeaways from this comparison are that (1) the intervention differences (i.e. – practice schedules) between the Massed and Spaced groups from the Das et al. report are extremely small (less than 12% of the overall session schedule) and (2) the overall amount of practice is much less than compared to the design from the original Bönstrup report 49  (which has been utilized in several subsequent studies). (B) Group-level learning curve data from Bönstrup et al. (2019) 49 is used to estimate the performance range accounted for by the equivalent periods covering Test 1, Training 1 and Test 2 from Das et al (2024). Note that the intervention in the Das et al. study is limited to a period covering less than 50% of the overall learning range.

First, participants in the original Bönstrup et al. study 49 experienced 157.14% more practice time and 46.97% less inter-practice rest time than the Spaced group in the Das et al. study (Author response image 8).  Thus, the overall amount of practice and rest differ substantially between studies, with much more limited training occurring for participants in Das et al.  

Second, and perhaps most importantly, the actual intervention (i.e. – the difference in practice schedule between the Spaced and Massed groups) employed by Das et al. covers a very small fraction of the overall training session. Identical practice schedule segments for both the Spaced & Massed groups are indicated by the red shaded area in Author response image 8. Please note that these identical segments cover 94.84% of the Massed group training schedule and 88.01% of the Spaced group training schedule (since it has 60 seconds of additional rest). This means that the actual interventions cover less than 5% (for Massed) and 12% (for Spaced) of the total training session, which minimizes any chance of observing a difference between groups.

Also note that the very beginning of the practice schedule (during which Figure R9 shows substantial learning is known to occur) is labeled in the Das et al. study as Test 1.  Test 1 encompasses the first 20 seconds of practice (alternatively viewed as the first two 10-second-long practice trials with no inter-practice rest). This is immediately followed by the Training 1 intervention, which is composed of only three 10-second-long practice trials (with 10-second inter-practice rest for the Spaced group and no inter-practice rest for the Massed group). Author response image 8 also shows that since there is no inter-practice rest after the third Training practice trial for the Spaced group, this third trial (for both Training 1 and 2) is actually a part of an identical practice schedule segment shared by both groups (Massed and Spaced), reducing the magnitude of the intervention even further.

Moreover, we know from the original Bönstrup et al. (2019) paper49 that 46.57% of all overall group-level performance gains occurred between trials 2 and 5 for that study. Thus, Das et al. are limiting their designed intervention to a period covering less than half of the early learning range discussed in the literature, which again, minimizes any chance of observing an effect.

This issue is amplified even further at Training 2 since skill learning prior to the long 5-minute break is retained, further constraining the performance range over these three trials. A related issue pertains to the trials labeled as Test 1 (trials 1-2) and Test 2 (trials 6-7) by Das et al. Again, we know from the original Bönstrup et al. paper 49 that 18.06% and 14.43% (32.49% total) of all overall group-level performance gains occurred during trials corresponding to Das et al Test 1 and Test 2, respectively. In other words, Das et al averaged skill performance over 20 seconds of practice at two time-points where dramatic skill improvements occur. Pan & Rickard (1995) previously showed that such averaging is known to inject artefacts into analyses of performance gains.

Furthermore, the structure of the Test in Das et. al study appears to have an interference effect on the Spaced group performance after the training intervention.  This makes sense if you consider that the Spaced group is required to now perform the task in a Massed practice environment (i.e., two 10-second-long practice trials merged into one long trial), further blurring the true intervention effects. This effect is observable in Figure 1C,E of their pre-print. Specifically, while the Massed group continues to show an increase in performance during test relative to the last 10 seconds of practice during training, the Spaced group displays a marked decrease. This decrease is in stark contrast to the monotonic increases observed for both groups at all other time-points.

Interestingly, when statistical comparisons between the groups are made at the time-points when the intervention is present (as opposed to after it has been removed) then the stated hypothesis, “If micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”, is confirmed.

The data presented by Gupta and Rickard (2022, 2024) and Das et al. (2024) is in many ways more confirmatory of the constraints employed by our group and others with respect to experimental design, analysis and interpretation of study findings, rather than contradictory. Still, it does highlight a limitation of the current micro-online/offline framework, which was originally only intended to be applied to early skill learning over spaced practice schedules when reactive inhibition effects are minimized49. Extrapolation of this current framework to post-plateau performance periods, longer timespans, or non-learning situations (e.g. – the Non-repeating groups from Experiments 3 & 4 in Das et al. (2024)), when reactive inhibition plays a more substantive role, is not warranted. Ultimately, it will be important to develop new paradigms allowing one to independently estimate the different coincident or antagonistic features (e.g. - memory consolidation, planning, working memory and reactive inhibition) contributing to micro-online and micro-offline gains during and after early skill learning within a unifying framework.

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Author Response

Reviewer #1 (Public Review):

Summary:

By examining the prevalence of interactions with ancient amino acids of coenzymes in ancient versus recent folds, the authors noticed an increased interaction propensity for ancient interactions. They infer from this that coenzymes might have played an important role in prebiotic proteins.

Strengths:

(1) The analysis, which is very straightforward, is technically correct. However, the conclusions might not be as strong as presented.

(2) This paper presents an excellent summary of contemporary thought on what might have constituted prebiotic proteins and their properties.

(3) The paper is clearly written.

We are grateful for the kind comments of the reviewer on our manuscript. However, we would like to clarify a possible misunderstanding in the summary of our study. Specifically, analysis of "ancient versus recent folds" was not really reported in our results. Our analysis concerned "coenzyme age" rather than the "protein folds age" and was focused mainly on interaction with early vs. late amino acids in protein sequence. While structural propensities of the coenzyme binding sites were also analyzed, no distinction on the level of ancient vs. recent folds was assumed and this was only commented on in the discussion, based on previous work of others.

Weaknesses:

(1) The conclusions might not be as strong as presented. First of all, while ancient amino acids interact less frequently in late with a given coenzyme, maybe this just reflects the fact that proteins that evolved later might be using residues that have a more favorable binding free energy.

We would like to point out that there was no distinction to proteins that evolved early or late in our dataset of coenzyme-binding proteins. The aim of our analysis was purely to observe trends in the age of amino acids vs. age of coenzymes. While no direct inference can be made from this about early life as all the proteins are from extant life (as highlighted in the discussion of our work), our goal was to look for intrinsic propensities of early vs. late amino acids in binding to the different coenzyme entities. Indeed, very early interactions would be smeared by the eons of evolutionary history (perhaps also towards more favourable binding free energy, as pointed out also by the reviewer). Nevertheless, significant trends have been recorded across the PDB dataset, pointing to different propensities and mechanistic properties of the binding events. Rather than to a specific evolutionary past, our data therefore point to a “capacity” of the early amino acids to bind certain coenzymes and we believe that this is the major (and standing) conclusion of our work, along with the properties of such interactions. In our revised version, we will carefully go through all the conclusions and make sure that this message stands out but we are confident that the following concluding sentences copied from the abstract and the discussion of our manuscript fully comply with our data:

“These results imply the plausibility of a coenzyme-peptide functional collaboration preceding the establishment of the Central Dogma and full protein alphabet evolution”

“While no direct inferences about distant evolutionary past can be drawn from the analysis of extant proteins, the principles guiding these interactions can imply their potential prebiotic feasibility and significance.”

“This implies that late amino acids would not be necessarily needed for the sovereignty of coenzyme-peptide interplay.”

We would also like to add that proteins that evolved later might not always have higher free energy of binding. Musil et al., 2021 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294521/) showed in their study on the example of haloalkane dehalogenase Dha A that the ancestral sequence reconstruction is a powerful tool for designing more stable, but also more active proteins. Ancestral sequence reconstruction relies on finding ancient states of protein families to suggest mutations that will lead to more stable proteins than are currently existing proteins. Their study did not explore the ligand-protein interactions specifically, but showed that ancient states often show more favourable properties than modern proteins.

(2) What about other small molecules that existed in the probiotic soup? Do they also prefer such ancient amino acids? If so, this might reflect the interaction propensity of specific amino acids rather than the inferred important role of coenzymes.

We appreciate the comment of the reviewer towards other small molecules, which we assume points mainly towards metal ions (i.e. inorganic cofactors). We completely agree with the reviewer that such interactions are of utmost importance to the origins of life. Intentionally, they were not part of our study, as these have already been studied previously by others (e.g. Bromberg et al., 2022; and reviewed in Frenkel-Pinter et al., 2020) and also us (Fried et al., 2022). For example, it is noteworthy that prebiotically relevant metal binding sites (e.g. of Mg2+) exhibit enrichment in early amino acids such as Asp and Glu while more recent metal (e.g. Cu and Zn) site in the late amino acids His and Cys (Fried et al., 2022). At the same time, comparable analyses of amino acid - coenzyme trends were not available.

Nevertheless, involvement of metal ions in the coenzyme binding sites was also studied here and pointed to their bigger involvement with the Ancient coenzymes. In the revised version of the manuscript, we will be happy to enlarge the discussion of the studies concerning inorganic cofactors.

(3) Perhaps the conclusions just reflect the types of active sites that evolved first and nothing more.

We partly agree on this point with the reviewer but not on the fact why it is listed as the weakness of our study and on the “nothing more” notion. Understanding what the properties of the earliest binding sites is key to merging the gap between prebiotic chemistry and biochemistry. The potential of peptides preceding ribosomal synthesis (and the full alphabet evolution) along with prebiotically plausible coenzymes addresses exactly this gap, which is currently not understood.

Reviewer #2 (Public Review):

I enjoyed reading this paper and appreciate the careful analysis performed by the investigators examining whether 'ancient' cofactors are preferentially bound by the first-available amino acids, and whether later 'LUCA' cofactors are bound by the late-arriving amino acids. I've always found this question fascinating as there is a contradiction in inorganic metal-protein complexes (not what is focused on here). Metal coordination of Fe, Ni heavily relies on softer ligands like His and Cys - which are by most models latecomer amino acids. There are no traces of thiols or imidazoles in meteorites - although work by Dvorkin has indicated that could very well be due to acid degradation during extraction. Chris Dupont (PNAS 2005) showed that metal speciation in the early earth (such as proposed by Anbar and prior RJP Williams) matched the purported order of fold emergence.

As such, cofactor-protein interactions as a driving force for evolution has always made sense to me and I admittedly read this paper biased in its favor. But to make sure, I started to play around with the data that the authors kindly and importantly shared in the supplementary files. Here's what I found:

Point 1: The correlation between abundance of amino acids and protein age is dominated by glycine. There is a small, but visible difference in old vs new amino acid fractional abundance between Ancient and LUCA proteins (Figure 3, Supplementary Table 3). However, the bias is not evenly distributed among the amino acids - which Figure 4A shows but is hard to digest as presented. So instead I used the spreadsheet in Supplement 3 to calculate the fractional difference FDaa = F(old aa)-F(new aa). As expected from Figure 3, the mean FD for Ancient is greater than the mean FD for LUCA. But when you look at the same table for each amino acid FDcofactor = F(ancient cofactor) - F(LUCA cofactor), you now see that the bias is not evenly distributed between older and newer amino acids at all. In fact, most of the difference can be explained by glycine (FDcofactor = 3.8) and the rest by also including tryptophan (FDcofactor = -3.8). If you remove these two amino acids from the analysis, the trend seen in Figure 3 all but disappears.

Troubling - so you might argue that Gly is the oldest of the old and Trp is the newest of the new so the argument still stands. Unfortunately, Gly is a lot of things - flexible, small, polar - so what is the real correlation, age, or chemistry? This leads to point 2.

We truly acknowledge the effort that the reviewer made in the revision of the data and for the thoughtful, deeper analysis. We agree that this deserves further discussion of our data. As invited by the reviewer, we indeed repeated the analysis on the whole dataset. First, we would like to point out that the reviewer was most probably referring to the Supplementary Fig. 2 (and not 3, which concerns protein folds). While the difference between Ancient and LUCA coenzyme binding is indeed most pronounced for Gly and Trp, we failed to confirm that the trend disappears if those two amino acids are removed from the analysis (additional FDcofactors of 3.2 and -3.2 are observed for the early and late amino acids, resp.), as seen in Table I below. The main additional contributors to this effect are Asp (FD of 2.1) and Ser (FD of 1.8) from the early amino acids and Arg (FD of -2.6) and Cys (FD of -1.7) of the late amino acids. Hence, while we agree with the reviewer that Gly and Trp (the oldest and the youngest) contribute to this effect the most, we disagree that the trend reduces to these two amino acids.

In addition, the most recent coenzyme temporality (the Post-LUCA) was neglected in the reviewer’s analysis. The difference between F (old) and F (new) is even more pronounced in PostLUCA than in LUCA, vs. Ancient (Table II) and depends much less on Trp. Meanwhile, Asp, Ser, Leu, Phe, and Arg dominate the observed phenomenon (Table I). This further supports our lack of agreement with the reviewer’s point. Nevertheless, we remain grateful for this discussion and we will happily include this additional analysis in the Supplementary Material of our revised manuscript.

Author response table 1.

Amino acid fractional difference of all coenzymes at residue level

Author response table 2.

Amino acid fractional difference of all coenzymes

Point 2 - The correlation is dominated by phosphate.

In the ancient cofactor list, all but 4 comprise at least one phosphate (SAM, tetrahydrofolic acid, biopterin, and heme). Except for SAM, the rest have very low Gly abundance. The overall high Gly abundance in the ancient enzymes is due to the chemical property of glycine that can occupy the right-hand side of the Ramachandran plot. This allows it to make the alternating alphaleftalpharight conformation of the P-loop forming Milner-White's anionic nest. If you remove phosphate binding folds from the analysis the trend in Figure 3 vanishes.

Likewise, Trp is an important functional residue for binding quinones and tuning its redox potential. The LUCA cofactor set is dominated by quinone and derivatives, which likely drives up the new amino acid score for this class of cofactors.

Once again, we are thankful to the reviewer for raising this point. The role of Gly in the anionic nests proposed by Milner-White and Russel, as well as the Trp role in quinone binding are important points that we would be happy to highlight more in the discussion of the revised manuscript.<br /> Nevertheless, we disagree that the trends reduce only to the phosphate-containing coenzymes and importantly, that “the trend in Figure 3 vanishes” upon their removal. Table III and IV (below) show the data for coenzymes excluding those with phosphate moiety and the trend in Fig. 3 remains, albeit less pronounced.

Author response table 3.

Amino acid fractional difference of non-phosphate containing coenzymes

Author response table 4.

Amino acid fractional difference of non-phosphate containing coenzymes at residue level

In summary, while I still believe the premise that cofactors drove the shape of peptides and the folds that came from them - and that Rossmann folds are ancient phosphate-binding proteins, this analysis does not really bring anything new to these ideas that have already been stated by Tawfik/Longo, Milner-White/Russell, and many others.

I did this analysis ad hoc on a slice of the data the authors provided and could easily have missed something and I encourage the authors to check my work. If it holds up it should be noted that negative results can often be as informative as strong positive ones. I think the signal here is too weak to see in the noise using the current approach.

We are grateful to the reviewer for encouraging further look at our data. While we hope that the analysis on the whole dataset (listed in Tables I - IV) will change the reviewer’s standpoint on our work, we would still like to comment on the questioned novelty of our results. In fact, the extraordinary works by Tawfik/Longo and Milner-While/Russel (which were cited in our manuscript multiple times) presented one of the motivations for this study. We take the opportunity to copy the part of our discussion that specifically highlights the relevance of their studies, and points out the contribution of our work with respect to theirs.

“While all the coenzymes bind preferentially to protein residue sidechains, more backbone interactions appear in the ancient coenzyme class when compared to others. This supports an earlier hypothesis that functions of the earliest peptides (possibly of variable compositions and lengths) would be performed with the assistance of the main chain atoms rather than their sidechains (Milner-White and Russel 2011). Longo et al., recently analyzed binding sites of different phosphate-containing ligands which were arguably of high relevance during earliest stages of life, connecting all of today’s core metabolism (Longo et al., 2020 (b)). They observed that unlike the evolutionary younger binding motifs (which rely on sidechain binding), the most ancient lineages indeed bind to phosphate moieties predominantly via the protein backbone. Our analysis assigns this phenomenon primarily to interactions via early amino acids that (as mentioned above) are generally enriched in the binding interface of the ancient coenzymes. This implies that late amino acids would not be necessarily needed for the sovereignty of coenzymepeptide interplay.”

Unlike any other previous work, our study involves all the major coenzymes (not just the phosphate-containing ones) and is based on their evolutionary age, as well as age of amino acids. It is the first PDB-wide systematic evolutionary analysis of coenzyme-amino acid binding. Besides confirming some earlier theoretical assertions (such as role of backbone interactions in early peptide-coenzyme evolution) and observations (such as occurrence of the ancient phosphatecontaining coenzymes in the oldest protein folds), it uncovers substantial novel knowledge. For example, (i) enrichment of early amino acids in the binding of ancient coenzymes, vs. enrichment of late amino acids in the binding of LUCA and Post-LUCA coenzymes, (ii) the trends in secondary structure content of the binding sites of coenzyme of different temporalities, (iii) increased involvement of metal ions in the ancient coenzyme binding events, and (iv) the capacity of only early amino acids to bind ancient coenzymes. In our humble opinion, all of these points bring important contributions in the peptide-coenzyme knowledge gap which has been discussed in a number of previous studies.

Author response:

eLife assessment

This potentially useful study involves neuro-imaging and electrophysiology in a small cohort of congenital cataract patients after sight recovery and age-matched control participants with normal sight. It aims to characterize the effects of early visual deprivation on excitatory and inhibitory balance in the visual cortex. While the findings are taken to suggest the existence of persistent alterations in Glx/GABA ratio and aperiodic EEG signals, the evidence supporting these claims is incomplete. Specifically, small sample sizes, lack of a specific control cohort, and other methodological limitations will likely restrict the usefulness of the work, with relevance limited to scientists working in this particular subfield.

As pointed out in the public reviews, there are only very few human models which allow for assessing the role of early experience on neural circuit development. While the prevalent research in permanent congenital blindness reveals the response and adaptation of the developing brain to an atypical situation (blindness), research in sight restoration addresses the question of whether and how atypical development can be remediated if typical experience (vision) is restored. The literature on the role of visual experience in the development of E/I balance in humans, assessed via Magnetic Resonance Spectroscopy (MRS), has been limited to a few studies on congenital permanent blindness. Thus, we assessed sight recovery individuals with a history of congenital blindness, as limited evidence from other researchers indicated that the visual cortex E/I ratio might differ compared to normally sighted controls.

Individuals with total bilateral congenital cataracts who remained untreated until later in life are extremely rare, particularly if only carefully diagnosed patients are included in a study sample. A sample size of 10 patients is, at the very least, typical of past studies in this population, even for exclusively behavioral assessments. In the present study, in addition to behavioral assessment as an indirect measure of sensitive periods, we investigated participants with two neuroimaging methods (Magnetic Resonance Spectroscopy and electroencephalography) to directly assess the neural correlates of sensitive periods in humans. The electroencephalography data allowed us to link the results of our small sample to findings documented in large cohorts of both, sight recovery individuals and permanently congenitally blind individuals. As pointed out in a recent editorial recommending an “exploration-then-estimation procedure,” (“Consideration of Sample Size in Neuroscience Studies,” 2020), exploratory studies like ours provide crucial direction and specific hypotheses for future work.

We included an age-matched sighted control group recruited from the same community, measured in the same scanner and laboratory, to assess whether early experience is necessary for a typical excitatory/inhibitory (E/I) ratio to emerge in adulthood. The present findings indicate that this is indeed the case. Based on these results, a possible question to answer in future work, with individuals who had developmental cataracts, is whether later visual deprivation causes similar effects. Note that even if visual deprivation at a later stage in life caused similar effects, the current results would not be invalidated; by contrast, they are essential to understand future work on late (permanent or transient) blindness.

Thus, we think that the present manuscript has far reaching implications for our understanding of the conditions under which E/I balance, a crucial characteristic of brain functioning, emerges in humans.

Finally, our manuscript is one of the first few studies which relates MRS neurotransmitter concentrations to parameters of EEG aperiodic activity. Since present research has been using aperiodic activity as a correlate of the E/I ratio, and partially of higher cognitive functions, we think that our manuscript additionally contributes to a better understanding of what might be measured with aperiodic neurophysiological activity.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this human neuroimaging and electrophysiology study, the authors aimed to characterize the effects of a period of visual deprivation in the sensitive period on excitatory and inhibitory balance in the visual cortex. They attempted to do so by comparing neurochemistry conditions ('eyes open', 'eyes closed') and resting state, and visually evoked EEG activity between ten congenital cataract patients with recovered sight (CC), and ten age-matched control participants (SC) with normal sight.

First, they used magnetic resonance spectroscopy to measure in vivo neurochemistry from two locations, the primary location of interest in the visual cortex, and a control location in the frontal cortex. Such voxels are used to provide a control for the spatial specificity of any effects because the single-voxel MRS method provides a single sampling location. Using MR-visible proxies of excitatory and inhibitory neurotransmission, Glx and GABA+ respectively, the authors report no group effects in GABA+ or Glx, no difference in the functional conditions 'eyes closed' and 'eyes open'. They found an effect of the group in the ratio of Glx/GABA+ and no similar effect in the control voxel location. They then performed multiple exploratory correlations between MRS measures and visual acuity, and reported a weak positive correlation between the 'eyes open' condition and visual acuity in CC participants.

The same participants then took part in an EEG experiment. The authors selected only two electrodes placed in the visual cortex for analysis and reported a group difference in an EEG index of neural activity, the aperiodic intercept, as well as the aperiodic slope, considered a proxy for cortical inhibition. They report an exploratory correlation between the aperiodic intercept and Glx in one out of three EEG conditions.

The authors report the difference in E/I ratio, and interpret the lower E/I ratio as representing an adaptation to visual deprivation, which would have initially caused a higher E/I ratio. Although intriguing, the strength of evidence in support of this view is not strong. Amongst the limitations are the low sample size, a critical control cohort that could provide evidence for a higher E/I ratio in CC patients without recovered sight for example, and lower data quality in the control voxel.

Strengths of study:

How sensitive period experience shapes the developing brain is an enduring and important question in neuroscience. This question has been particularly difficult to investigate in humans. The authors recruited a small number of sight-recovered participants with bilateral congenital cataracts to investigate the effect of sensitive period deprivation on the balance of excitation and inhibition in the visual brain using measures of brain chemistry and brain electrophysiology. The research is novel, and the paper was interesting and well-written.

Limitations:

(1.1) Low sample size. Ten for CC and ten for SC, and a further two SC participants were rejected due to a lack of frontal control voxel data. The sample size limits the statistical power of the dataset and increases the likelihood of effect inflation.

Applying strict criteria, we only included individuals who were born with no patterned vision in the CC group. The population of individuals who have remained untreated past infancy is small in India, despite a higher prevalence of childhood cataract than Germany. Indeed, from the original 11 CC and 11 SC participants tested, one participant each from the CC and SC group had to be rejected, as their data had been corrupted, resulting in 10 participants in each group.

It was a challenge to recruit participants from this rare group with no history of neurological diagnosis/intake of neuromodulatory medications, who were able and willing to undergo both MRS and EEG. For this study, data collection took more than 1.5 years.

We took care of the validity of our results with two measures; first, assessed not just MRS, but additionally, EEG measures of E/I ratio. The latter allowed us to link results to a larger population of CC individuals, that is, we replicated the results of a larger group of 38 individuals (Ossandón et al., 2023) in our sub-group.

Second, we included a control voxel. As predicted, all group effects were restricted to the occipital voxel.

(1.2) Lack of specific control cohort. The control cohort has normal vision. The control cohort is not specific enough to distinguish between people with sight loss due to different causes and patients with congenital cataracts with co-morbidities. Further data from more specific populations, such as patients whose cataracts have not been removed, with developmental cataracts, or congenitally blind participants, would greatly improve the interpretability of the main finding. The lack of a more specific control cohort is a major caveat that limits a conclusive interpretation of the results.

The existing work on visual deprivation and neurochemical changes, as assessed with MRS, has been limited to permanent congenital blindness. In fact, most of the studies on permanent blindness included only congenitally blind or early blind humans (Coullon et al., 2015; Weaver et al., 2013), or, in separate studies, only late-blind individuals (Bernabeu et al., 2009). Thus, accordingly, we started with the most “extreme” visual deprivation model, sight recovery after congenital blindness. If we had not observed any group difference compared to normally sighted controls, investigating other groups might have been trivial. Based on our results, subsequent studies in late blind individuals, and then individuals with developmental cataracts, can be planned with clear hypotheses.

(1.3) MRS data quality differences. Data quality in the control voxel appears worse than in the visual cortex voxel. The frontal cortex MRS spectrum shows far broader linewidth than the visual cortex (Supplementary Figures). Compared to the visual voxel, the frontal cortex voxel has less defined Glx and GABA+ peaks; lower GABA+ and Glx concentrations, lower NAA SNR values; lower NAA concentrations. If the data quality is a lot worse in the FC, then small effects may not be detectable.

Worse data quality in the frontal than the visual cortex has been repeatedly observed in the MRS literature, attributable to magnetic field distortions (Juchem & Graaf, 2017) resulting from the proximity of the region to the sinuses (recent example: (Rideaux et al., 2022)). Nevertheless, we chose the frontal control region rather than a parietal voxel, given the potential  neurochemical changes in multisensory regions of the parietal cortex due to blindness. Such reorganization would be less likely in frontal areas associated with higher cognitive functions. Further, prior MRS studies of the visual cortex have used the frontal cortex as a control region as well (Pitchaimuthu et al., 2017; Rideaux et al., 2022).

In the present study, we checked that the frontal cortex datasets for Glx and GABA+ concentrations were of sufficient quality: the fit error was below 8.31% in both groups (Supplementary Material S3). For reference, Mikkelsen et al. reported a mean GABA+ fit error of 6.24 +/- 1.95% from a posterior cingulate cortex voxel across 8 GE scanners, using the Gannet pipeline. No absolute cutoffs have been proposed for fit errors. However, MRS studies in special populations (I/E ratio assessed in narcolepsy (Gao et al., 2024), GABA concentration assessed in Autism Spectrum Disorder (Maier et al., 2022)) have used frontal cortex data with a fit error of <10% to identify differences between cohorts (Gao et al., 2024; Pitchaimuthu et al., 2017). Based on the literature, MRS data from the frontal voxel of the present study would have been of sufficient quality to uncover group differences.

In the revised manuscript, we will add the recently published MRS quality assessment form to the supplementary materials. Additionally, we would like to allude to our apriori prediction of group differences for the visual cortex, but not for the frontal cortex voxel.

(1.4) Because of the direction of the difference in E/I, the authors interpret their findings as representing signatures of sight improvement after surgery without further evidence, either within the study or from the literature. However, the literature suggests that plasticity and visual deprivation drive the E/I index up rather than down. Decreasing GABA+ is thought to facilitate experience-dependent remodelling. What evidence is there that cortical inhibition increases in response to a visual cortex that is over-sensitised due to congenital cataracts? Without further experimental or literature support this interpretation remains very speculative.

Indeed, higher inhibition was not predicted, which we attempt to reconcile in our discussion section. We base our discussion mainly on the non-human animal literature, which has shown evidence of homeostatic changes after prolonged visual deprivation in the adult brain (Barnes et al., 2015). It is also interesting to note that after monocular deprivation in adult humans, resting GABA+ levels decreased in the visual cortex (Lunghi et al., 2015). Assuming that after delayed sight restoration, adult neuroplasticity mechanisms must be employed, these studies would predict a “balancing” of the increased excitatory drive following sight restoration by a commensurate increase in inhibition (Keck et al., 2017). Additionally, the EEG results of the present study allowed for speculation regarding the underlying neural mechanisms of an altered E/I ratio. The aperiodic EEG activity suggested higher spontaneous spiking (increased intercept) and increased inhibition (steeper aperiodic slope between 1-20 Hz) in CC vs SC individuals (Ossandón et al., 2023).

In the revised manuscript, we will more clearly indicate that these speculations are based primarily on non-human animal work, due to the lack of human studies on the subject.

(1.5) Heterogeneity in the patient group. Congenital cataract (CC) patients experienced a variety of duration of visual impairment and were of different ages. They presented with co-morbidities (absorbed lens, strabismus, nystagmus). Strabismus has been associated with abnormalities in GABAergic inhibition in the visual cortex. The possible interactions with residual vision and confounds of co-morbidities are not experimentally controlled for in the correlations, and not discussed.

The goal of the present study was to assess whether we would observe changes in E/I ratio after restoring vision at all. We would not have included patients without nystagmus in the CC group of the present study, since it would have been unlikely that they experienced congenital patterned visual deprivation. Amongst diagnosticians, nystagmus or strabismus might not be considered genuine “comorbidities” that emerge in people with congenital cataracts. Rather, these are consequences of congenital visual deprivation, which we employed as diagnostic criteria. Similarly, absorbed lenses are clear signs that cataracts were congenital. As in other models of experience dependent brain development (e.g. the extant literature on congenital permanent blindness, including anophthalmic individuals (Coullon et al., 2015; Weaver et al., 2013), some uncertainty remains regarding whether the (remaining, in our case) abnormalities of the eye, or the blindness they caused, are the factors driving neural changes. In case of people with reversed congenital cataracts, at least the retina is considered to be intact, as they would otherwise not receive cataract removal surgery.

However, we consider it unlikely that strabismus caused the group differences, because the present study shows group differences in the Glx/GABA+ ratio at rest, regardless of eye opening or eye closure, for which strabismus would have caused distinct effects. By contrast, the link between GABA concentration and, for example, interocular suppression in strabismus, have so far been documented during visual stimulation (Mukerji et al., 2022; Sengpiel et al., 2006), and differed in direction depending on the amblyopic vs. non-amblyopic eye. Further, one MRS study did not find group differences in GABA concentration between the visual cortices of 16 amblyopic individuals and sighted controls (Mukerji et al., 2022), supporting that the differences in Glx/GABA+ concentration which we observed were driven by congenital deprivation, and not amblyopia-associated visual acuity or eye movement differences.  

In the revised manuscript, we will discuss the inclusion criteria in more detail, and the aforementioned reasons why our data remains interpretable.

(1.6) Multiple exploratory correlations were performed to relate MRS measures to visual acuity (shown in Supplementary Materials), and only specific ones were shown in the main document. The authors describe the analysis as exploratory in the 'Methods' section. Furthermore, the correlation between visual acuity and E/I metric is weak, and not corrected for multiple comparisons. The results should be presented as preliminary, as no strong conclusions can be made from them. They can provide a hypothesis to test in a future study.

In the revised manuscript, we will clearly indicate that the exploratory correlation analyses are reported to put forth hypotheses for future studies.

(1.7) P.16 Given the correlation of the aperiodic intercept with age ("Age negatively correlated with the aperiodic intercept across CC and SC individuals, that is, a flattening of the intercept was observed with age"), age needs to be controlled for in the correlation between neurochemistry and the aperiodic intercept. Glx has also been shown to negatively correlate with age.

The correlation between chronological age and aperiodic intercept was observed across groups, but the correlation between Glx and the intercept of the aperiodic EEG activity was seen only in the CC group, even though the SC group was matched for age. Thus, such a correlation was very unlikely to  be predominantly driven by an effect of chronological age.

In the revised manuscript, we will add the linear regressions with age as a covariate included below, for the relationship between aperiodic intercept and Glx concentration in the CC group. 

a. A linear regression was conducted within the CC group to predict the intercept during visual stimulation, based on age and visual cortex Glx concentration. The results of the regression analysis indicated that the model explained a significant proportion of the variance in the aperiodic intercept, 𝑅2\=0.82_, t_(2,7)=16.1_, 𝑝=0.0024._ Note that the coefficient for age was not significant, 𝛽=0.007, t(7)=0.82, 𝑝=0.439. The regression coefficients and their respective statistics are presented in Author response table 1.

Author response table 1.

Regression Analysis Summary for Predicting Aperiodic Intercept (Visual Stimulation) in the CC group

b. A linear regression was conducted to predict the intercept during eye opening at rest, based on age and visual cortex Glx concentration. The results of the regression analysis indicated that the model explained a significant proportion of the variance in the aperiodic intercept, 𝑅2\=0.842_, t_(2,7)=18.6,  𝑝=0.00159_._ Note that the coefficient for age was not significant, 𝛽=−0.005, t(7)=−0.90, 𝑝=0.400. The regression coefficients and their respective statistics are presented in Author response table 2.

Author response table 2.

Regression Analysis Summary for Predicting Aperiodic Intercept (Eyes Open) in the CC group

c. Given that the Glx coefficient is significant in both models and age does not significantly predict either outcome, it can be concluded that Glx independently predicts the intercept of the aperiodic intercept.

(1.8) Multiple exploratory correlations were performed to relate MRS to EEG measures (shown in Supplementary Materials), and only specific ones were shown in the main document. Given the multiple measures from the MRS, the correlations with the EEG measures were exploratory, as stated in the text, p.16, and in Figure 4. Yet the introduction said that there was a prior hypothesis "We further hypothesized that neurotransmitter changes would relate to changes in the slope and intercept of the EEG aperiodic activity in the same subjects." It would be great if the text could be revised for consistency and the analysis described as exploratory.

In the revised manuscript, we will improve the phrasing. We consider the correlation analyses as exploratory due to our sample size and the absence of prior work. However, we did hypothesize that both MRS and EEG markers would concurrently be altered in CC vs SC individuals.

(1.9) The analysis for the EEG needs to take more advantage of the available data. As far as I understand, only two electrodes were used, yet far more were available as seen in their previous study (Ossandon et al., 2023). The spatial specificity is not established. The authors could use the frontal cortex electrode (FP1, FP2) signals as a control for spatial specificity in the group effects, or even better, all available electrodes and correct for multiple comparisons. Furthermore, they could use the aperiodic intercept vs Glx in SC to evaluate the specificity of the correlation to CC.

The aperiodic intercept and slope did not differ between CC and SC individuals for Fp1 and Fp2, suggesting the spatial specificity of the results. In the revised manuscript, we will add this analysis to the supplementary material.

Author response image 1.

Aperiodic intercept (top) and slope (bottom) for congenital cataract-reversal (CC, red) and age-matched normally sighted control (SC, blue) individuals. Distributions of these parameters are displayed as violin plots for three conditions; at rest with eyes closed (EC), at rest with eyes open (EO) and during visual stimulation (LU). Aperiodic parameters were calculated across electrodes Fp1 and Fp2. Solid black lines indicate mean values, dotted black lines indicate median values. Coloured lines connect values of individual participants across conditions.

Further, Glx concentration in the visual cortex did not correlate with the aperiodic intercept in the SC group (Figure 4), suggesting that this relationship was indeed specific to the CC group.

The data from all electrodes has been analyzed and published in other studies as well (Pant et al., 2023; Ossandón et al., 2023).

Reviewer #2 (Public Review):

Summary:

The manuscript reports non-invasive measures of activity and neurochemical profiles of the visual cortex in congenitally blind patients who recovered vision through the surgical removal of bilateral dense cataracts. The declared aim of the study is to find out how restoring visual function after several months or years of complete blindness impacts the balance between excitation and inhibition in the visual cortex.

Strengths:

The findings are undoubtedly useful for the community, as they contribute towards characterising the many ways this special population differs from normally sighted individuals. The combination of MRS and EEG measures is a promising strategy to estimate a fundamental physiological parameter - the balance between excitation and inhibition in the visual cortex, which animal studies show to be heavily dependent upon early visual experience. Thus, the reported results pave the way for further studies, which may use a similar approach to evaluate more patients and control groups.

Weaknesses:

(2.1) The main issue is the lack of an appropriate comparison group or condition to delineate the effect of sight recovery (as opposed to the effect of congenital blindness). Few previous studies suggested an increased excitation/Inhibition ratio in the visual cortex of congenitally blind patients; the present study reports a decreased E/I ratio instead. The authors claim that this implies a change of E/I ratio following sight recovery. However, supporting this claim would require showing a shift of E/I after vs. before the sight-recovery surgery, or at least it would require comparing patients who did and did not undergo the sight-recovery surgery (as common in the field).

Longitudinal studies would indeed be the best way to test the hypothesis that the lower E/I ratio in the CC group observed by the present study is a consequence of sight restoration. However, longitudinal studies involving neuroimaging are an effortful challenge, particularly in research conducted outside of major developed countries and dedicated neuroimaging research facilities. Crucially, however, had CC and SC individuals, as well as permanently congenitally blind vs SC individuals (Coullon et al., 2015; Weaver et al., 2013), not differed on any neurochemical markers, such a longitudinal study might have been trivial. Thus, in order to justify and better tailor longitudinal studies, cross-sectional studies are an initial step.

(2.2) MR Spectroscopy shows a reduced GLX/GABA ratio in patients vs. sighted controls; however, this finding remains rather isolated, not corroborated by other observations. The difference between patients and controls only emerges for the GLX/GABA ratio, but there is no accompanying difference in either the GLX or the GABA concentrations. There is an attempt to relate the MRS data with acuity measurements and electrophysiological indices, but the explorative correlational analyses do not help to build a coherent picture. A bland correlation between GLX/GABA and visual impairment is reported, but this is specific to the patients' group (N=10) and would not hold across groups (the correlation is positive, predicting the lowest GLX/GABA ratio values for the sighted controls - the opposite of what is found). There is also a strong correlation between GLX concentrations and the EEG power at the lowest temporal frequencies. Although this relation is intriguing, it only holds for a very specific combination of parameters (of the many tested): only with eyes open, only in the patient group.

We interpret these findings differently, that is, in the context of experiments from non-human animals and the larger MRS literature.

Homeostatic control of E/I balance assumes that the ratio of excitation (reflected here by Glx) and inhibition (reflected here by GABA+) is regulated. Like prior work (Gao et al., 2024, 2024; Narayan et al., 2022; Perica et al., 2022; Steel et al., 2020; Takado et al., 2022; Takei et al., 2016), we assumed that the ratio of Glx/GABA+ is indicative of E/I balance rather than solely the individual neurotransmitter levels. One of the motivations for assessing the ratio vs the absolute concentration is that as per the underlying E/I balance hypothesis, a change in excitation would cause a concomitant change in inhibition, and vice versa, which has been shown in non-human animal work (Fang et al., 2021; Haider et al., 2006; Tao & Poo, 2005) and modeling research (Vreeswijk & Sompolinsky, 1996; Wu et al., 2022). Importantly, our interpretation of the lower E/I ratio is not just from the Glx/GABA+ ratio, but additionally, based on the steeper EEG aperiodic slope (1-20 Hz).  

As in the discussion section and response 1.4, we did not expect to see a lower Glx/GABA+ ratio in CC individuals. We discuss the possible reasons for the direction of the correlation with visual acuity and aperiodic offset during passive visual stimulation, and offer interpretations and (testable) hypotheses.

We interpret the direction of the  Glx/GABA+ correlation with visual acuity to imply that patients with highest (compensatory) balancing of the consequences of congenital blindness (hyperexcitation), in light of visual stimulation, are those who recover best. Note, the sighted control group was selected based on their “normal” vision. Thus, clinical visual acuity measures are not expected to sufficiently vary, nor have the resolution to show strong correlations with neurophysiological measures. By contrast, the CC group comprised patients highly varying in visual outcomes, and thus were ideal to investigate such correlations.

This holds for the correlation between Glx and the aperiodic intercept, as well. Previous work has suggested that the intercept of the aperiodic activity is associated with broadband spiking activity in neural circuits (Manning et al., 2009). Thus, an atypical increase of spiking activity during visual stimulation, as indirectly suggested by “old” non-human primate work on visual deprivation (Hyvärinen et al., 1981) might drive a correlation not observed in healthy populations.

In the revised manuscript, we will more clearly indicate in the discussion that these are possible post-hoc interpretations. We argue that given the lack of such studies in humans, it is all the more important that extant data be presented completely, even if the direction of the effects are not as expected.

(2.3) For these reasons, the reported findings do not allow us to draw firm conclusions on the relation between EEG parameters and E/I ratio or on the impact of early (vs. late) visual experience on the excitation/inhibition ratio of the human visual cortex.

Indeed, the correlations we have tested between the E/I ratio and EEG parameters were exploratory, and have been reported as such. The goal of our study was not to compare the effects of early vs. late visual experience. The goal was to study whether early visual experience is necessary for a typical E/I ratio in visual neural circuits. We provided clear evidence in favor of this hypothesis. Thus, the present results suggest the necessity of investigating the effects of late visual deprivation. In fact, such research is missing in permanent blindness as well.

Reviewer #3 (Public Review):

This manuscript examines the impact of congenital visual deprivation on the excitatory/inhibitory (E/I) ratio in the visual cortex using Magnetic Resonance Spectroscopy (MRS) and electroencephalography (EEG) in individuals whose sight was restored. Ten individuals with reversed congenital cataracts were compared to age-matched, normally sighted controls, assessing the cortical E/I balance and its interrelationship to visual acuity. The study reveals that the Glx/GABA ratio in the visual cortex and the intercept and aperiodic signal are significantly altered in those with a history of early visual deprivation, suggesting persistent neurophysiological changes despite visual restoration.

My expertise is in EEG (particularly in the decomposition of periodic and aperiodic activity) and statistical methods. I have several major concerns in terms of methodological and statistical approaches along with the (over)interpretation of the results. These major concerns are detailed below.

(3.1) Variability in visual deprivation:

- The document states a large variability in the duration of visual deprivation (probably also the age at restoration), with significant implications for the sensitivity period's impact on visual circuit development. The variability and its potential effects on the outcomes need thorough exploration and discussion.

We work with a rare, unique patient population, which makes it difficult to systematically assess the effects of different visual histories while maintaining stringent inclusion criteria such as complete patterned visual deprivation at birth. Regardless, we considered the large variance in age at surgery and time since surgery as supportive of our interpretation: group differences were found despite the large variance in duration of visual deprivation. Moreover, the existing variance was used to explore possible associations between behavior and neural measures, as well as neurochemical and EEG measures.

In the revised manuscript, we will detail the advantages and disadvantages of our CC sample, with respect to duration of congenital visual deprivation.

(3.2) Sample size:

- The small sample size is a major concern as it may not provide sufficient power to detect subtle effects and/or overestimate significant effects, which then tend not to generalize to new data. One of the biggest drivers of the replication crisis in neuroscience.

We address the small sample size in our discussion, and make clear that small sample sizes were due to the nature of investigations in special populations. It is worth noting that our EEG results fully align  with those of a larger sample of CC individuals (Ossandón et al., 2023), providing us confidence about their validity and reproducibility. Moreover, our MRS results and correlations of those with EEG parameters were spatially specific to occipital cortex measures, as predicted.

The main problem with the correlation analyses between MRS and EEG measures is that the sample size is simply too small to conduct such an analysis. Moreover, it is unclear from the methods section that this analysis was only conducted in the patient group (which the reviewer assumed from the plots), and not explained why this was done only in the patient group. I would highly recommend removing these correlation analyses.

We marked the correlation analyses as exploratory; note that we do not base most of our discussion on the results of these analyses. As indicated by Reviewer 1, reporting them allows for deriving more precise hypothesis for future studies. It has to be noted that we investigate an extremely rare population, tested outside of major developed economies and dedicated neuroimaging research facilities. In addition to being a rare patient group, these individuals come from poor communities. Therefore, we consider it justified to report these correlations as exploratory, providing direction for future research.

(3.3) Statistical concerns:

- The statistical analyses, particularly the correlations drawn from a small sample, may not provide reliable estimates (see https://www.sciencedirect.com/science/article/pii/S0092656613000858, which clearly describes this problem).

It would undoubtedly be better to have a larger sample size. We nonetheless think it is of value to the research community to publish this dataset, since 10 multimodal data sets from a carefully diagnosed, rare population, representing a human model for the effects of early experience on brain development, are quite a lot.  Sample sizes in prior neuroimaging studies in transient blindness have most often ranged from n = 1 to n = 10. They nevertheless provided valuable direction for future research, and integration of results across multiple studies provides scientific insights.  

Identifying possible group differences was the goal of our study, with the correlations being an exploratory analysis, which we have clearly indicated in the methods, results and discussion.

- Statistical analyses for the MRS: The authors should consider some additional permutation statistics, which are more suitable for small sample sizes. The current statistical model (2x2) design ANOVA is not ideal for such small sample sizes. Moreover, it is unclear why the condition (EO & EC) was chosen as a predictor and not the brain region (visual & frontal) or neurochemicals. Finally, the authors did not provide any information on the alpha level nor any information on correction for multiple comparisons (in the methods section). Finally, even if the groups are matched w.r.t. age, the time between surgery and measurement, the duration of visual deprivation, (and sex?), these should be included as covariates as it has been shown that these are highly related to the measurements of interest (especially for the EEG measurements) and the age range of the current study is large.

In our ANOVA models, the neurochemicals were the outcome variables, and the conditions were chosen as predictors based on prior work suggesting that Glx/GABA+ might vary with eye closure (Kurcyus et al., 2018). The study was designed based on a hypothesis of group differences localized to the occipital cortex, due to visual deprivation. The frontal cortex voxel was chosen to indicate whether these differences were spatially specific. Therefore, we conducted separate ANOVAs based on this study design.

In the revised manuscript, we will add permutation analyses for our outcomes, as well as multiple regression models investigating whether the variance in visual history might have driven these results. Note that in the supplementary materials (S6, S7), we have reported the correlations between visual history metrics and MRS/EEG outcomes.

The alpha level used for the ANOVA models specified in the methods section was 0.05. The alpha level for the exploratory analyses reported in the main manuscript was 0.008, after correcting for (6) multiple comparisons using the Bonferroni correction, also specified in the methods. Note that the p-values following correction are expressed as multiplied by 6, due to most readers assuming an alpha level of 0.05 (see response regarding large p-values).

We used a control group matched for age and sex. Moreover, the controls were recruited and tested in the same institutes, using the same setup. We feel that we followed the gold standards for recruiting a healthy control group for a patient group.

- EEG statistical analyses: The same critique as for the MRS statistical analyses applies to the EEG analysis. In addition: was the 2x3 ANOVA conducted for EO and EC independently? This seems to be inconsistent with the approach in the MRS analyses, in which the authors chose EO & EC as predictors in their 2x2 ANOVA.

The 2x3 ANOVA was not conducted independently for the eyes open/eyes closed condition, the ANOVA conducted on the EEG metrics was 2x3 because it had group (CC, SC) and condition (eyes open (EO), eyes closed (EC) and visual stimulation (LU)) as predictors.

- Figure 4: The authors report a p-value of >0.999 with a correlation coefficient of -0.42 with a sample size of 10 subjects. This can't be correct (it should be around: p = 0.22). All statistical analyses should be checked.

As specified in the methods and figure legend, the reported p values in Figure 4 have been corrected using the Bonferroni correction, and therefore multiplied by the number of comparisons, leading to the seemingly large values.

Additionally, to check all statistical analyses, we put the manuscript through an independent Statistics Check (Nuijten & Polanin, 2020) (https://michelenuijten.shinyapps.io/statcheck-web/) and will upload the consistency report with the revised supplementary material.

- Figure 2c. Eyes closed condition: The highest score of the *Glx/GABA ratio seems to be ~3.6. In subplot 2a, there seem to be 3 subjects that show a Glx/GABA ratio score > 3.6. How can this be explained? There is also a discrepancy for the eyes-closed condition.

The three subjects that show the Glx/GABA+ ratio > 3.6 in subplot 2a are in the SC group, whereas the correlations plotted in figure 2c are only for the CC group, where the highest score is indeed ~3.6.

(3.4) Interpretation of aperiodic signal:

- Several recent papers demonstrated that the aperiodic signal measured in EEG or ECoG is related to various important aspects such as age, skull thickness, electrode impedance, as well as cognition. Thus, currently, very little is known about the underlying effects which influence the aperiodic intercept and slope. The entire interpretation of the aperiodic slope as a proxy for E/I is based on a computational model and simulation (as described in the Gao et al. paper).

Apart from the modeling work from Gao et al., multiple papers which have also been cited which used ECoG, EEG and MEG and showed concomitant changes in aperiodic activity with pharmacological manipulation of the E/I ratio (Colombo et al., 2019; Molina et al., 2020; Muthukumaraswamy & Liley, 2018). Further, several prior studies have interpreted changes in the aperiodic slope as reflective of changes in the E/I ratio, including studies of developmental groups (Favaro et al., 2023; Hill et al., 2022; McSweeney et al., 2023; Schaworonkow & Voytek, 2021) as well as patient groups (Molina et al., 2020; Ostlund et al., 2021).

In the revised manuscript, we will cite those studies not already included in the introduction.

- Especially the aperiodic intercept is a very sensitive measure to many influences (e.g. skull thickness, electrode impedance...). As crucial results (correlation aperiodic intercept and MRS measures) are facing this problem, this needs to be reevaluated. It is safer to make statements on the aperiodic slope than intercept. In theory, some of the potentially confounding measures are available to the authors (e.g. skull thickness can be computed from T1w images; electrode impedances are usually acquired alongside the EEG data) and could be therefore controlled.

All electrophysiological measures indeed depend on parameters such as skull thickness and electrode impedance. As in the extant literature using neurophysiological measures to compare brain function between patient and control groups, we used a control group matched in age/ sex, recruited in the same region, tested with the same devices, and analyzed with the same analysis pipeline. For example, impedance was kept below 10 kOhm for all subjects. There is no evidence available suggesting that congenital cataracts are associated with changes in skull thickness that would cause the observed pattern of group results. Moreover, we cannot think of how any of the exploratory correlations between neurophysiological measures and MRS measures could be accounted for by a difference e.g. in skull thickness.

- The authors wrote: "Higher frequencies (such as 20-40 Hz) have been predominantly associated with local circuit activity and feedforward signaling (Bastos et al., 2018; Van Kerkoerle et al., 2014); the increased 20-40 Hz slope may therefore signal increased spontaneous spiking activity in local networks. We speculate that the steeper slope of the aperiodic activity for the lower frequency range (1-20 Hz) in CC individuals reflects the concomitant increase in inhibition." The authors confuse the interpretation of periodic and aperiodic signals. This section refers to the interpretation of the periodic signal (higher frequencies). This interpretation cannot simply be translated to the aperiodic signal (slope).

Prior work has not always separated the aperiodic and periodic components, making it unclear what might have driven these effects in our data. The interpretation of the higher frequency range was intended to contrast with the interpretations of lower frequency range, in order to speculate as to why the two aperiodic fits might go in differing directions. We will clarify our interpretation in the revised manuscript. Note that Ossandon et al. reported highly similar results (group differences for CC individuals and for permanently congenitally blind humans) for the aperiodic activity between 20-40 Hz and oscillatory activity in the gamma range. We will allude to these findings in the revised manuscript.

- The authors further wrote: We used the slope of the aperiodic (1/f) component of the EEG spectrum as an estimate of E/I ratio (Gao et al., 2017; Medel et al., 2020; Muthukumaraswamy & Liley, 2018). This is a highly speculative interpretation with very little empirical evidence. These papers were conducted with ECoG data (mostly in animals) and mostly under anesthesia. Thus, these studies only allow an indirect interpretation by what the 1/f slope in EEG measurements is actually influenced.

Note that Muthukumaraswamy et al. (2018) used different types of pharmacological manipulations and analyzed periodic and aperiodic MEG activity in addition to monkey ECoG (Medel et al., 2020) (now published as (Medel et al., 2023)) compared EEG activity in addition to ECoG data after propofol administration. The interpretation of our results are in line with a number of recent studies in developing (Hill et al., 2022; Schaworonkow & Voytek, 2021) and special populations using EEG. As mentioned above, several prior studies have used the slope of the 1/f component/aperiodic activity as an indirect measure of the E/I ratio (Favaro et al., 2023; Hill et al., 2022; McSweeney et al., 2023; Molina et al., 2020; Ostlund et al., 2021; Schaworonkow & Voytek, 2021), including studies using scalp-recorded EEG. We will make more clear in the introduction of the revised manuscript that this metric is indirect.

While a full understanding of aperiodic activity needs to be provided, some convergent ideas have emerged . We think that our results contribute to this enterprise, since our study is, to the best of our knowledge, the first which assessed MRS measured neurotransmitter levels and EEG aperiodic activity.

(3.5) Problems with EEG preprocessing and analysis:

- It seems that the authors did not identify bad channels nor address the line noise issue (even a problem if a low pass filter of below-the-line noise was applied).

As pointed out in the methods and Figure 1, we only analyzed data from two channels, O1 and O2, neither of which were rejected for any participant. Channel rejection was performed for the larger dataset, published elsewhere (Ossandón et al., 2023; Pant et al., 2023).

In both published works, we did not consider frequency ranges above 40 Hz to avoid any possible contamination with line noise. Here, we focused on activity between 0 and 20 Hz, definitely excluding line noise contaminations. The low pass filter (FIR, 1-45 Hz) guaranteed that any spill-over effects of line noise would be restricted to frequencies just below the upper cutoff frequency.

Additionally, a prior version of the analysis used the cleanline.m function to remove line noise before filtering, and the group differences remained stable. We will report this analysis in the supplementary version of the revised manuscript. Further, both groups were measured in the same lab, making line noise as an account for the observed group effects highly unlikely. Finally, any of the exploratory MRS-EEG correlations would be hard to explain if the EEG parameters would be contaminated with line noise.

- What was the percentage of segments that needed to be rejected due to the 120μV criteria? This should be reported specifically for EO & EC and controls and patients.

The mean percentage of 1 second segments rejected for each resting state condition is below. Mean percentage of 6.25 long segments rejected in each group for the visual stimulation condition are also included, and will be added to the revised manuscript:

Author response table 3.

- The authors downsampled the data to 60Hz to "to match the stimulation rate". What is the intention of this? Because the subsequent spectral analyses are conflated by this choice (see Nyquist theorem).

This data were collected as part of a study designed to evoke alpha activity with visual white-noise, which ranged in luminance with equal power at all frequencies from 1-60 Hz, restricted by the refresh rate of the monitor on which stimuli were presented (Pant et al., 2023). This paradigm and method was developed by VanRullen and colleagues (Schwenk et al., 2020; Vanrullen & MacDonald, 2012), wherein the analysis requires the same sampling rate between the presented frequencies and the EEG data. The downsampling function used here automatically applies an anti-aliasing filter (EEGLAB 2019) .

- "Subsequently, baseline removal was conducted by subtracting the mean activity across the length of an epoch from every data point." The actual baseline time segment should be specified.

The time segment was the length of the epoch, that is, 1 second for the resting state conditions and 6.25 seconds for the visual stimulation conditions. This will be explicitly stated in the revised manuscript.

- "We excluded the alpha range (8-14 Hz) for this fit to avoid biasing the results due to documented differences in alpha activity between CC and SC individuals (Bottari et al., 2016; Ossandón et al., 2023; Pant et al., 2023)." This does not really make sense, as the FOOOF algorithm first fits the 1/f slope, for which the alpha activity is not relevant.

We did not use the FOOOF algorithm/toolbox in this manuscript. As stated in the methods, we used a 1/f fit to the 1-20 Hz spectrum in the log-log space, and subtracted this fit from the original spectrum to obtain the corrected spectrum. Given the pronounced difference in alpha power between groups (Bottari et al., 2016; Ossandón et al., 2023; Pant et al., 2023), we were concerned it might drive differences in the exponent values.  Our analysis pipeline had been adapted from previous publications of our group and other labs (Ossandón et al., 2023; Voytek et al., 2015; Waschke et al., 2017).

We have conducted the analysis with and without the exclusion of the alpha range, as well as using the FOOOF toolbox both in the 1-20 Hz and 20-40 Hz ranges (Ossandón et al., 2023); The findings of a steeper slope in the 1-20 Hz range as well as lower alpha power in CC vs SC individuals remained stable. In Ossandón et al., the comparison between the piecewise fits and FOOOF fits led the authors to use the former as it outperformed the FOOOF algorithm for their data.

- The model fits of the 1/f fitting for EO, EC, and both participant groups should be reported.

In Figure 3 of the manuscript, we depicted the mean spectra and 1/f fits for each group. We will add the fit quality metrics and show individual subjects’ fits in the revised manuscript.

(3.6) Validity of GABA measurements and results:

- According the a newer study by the authors of the Gannet toolbox (https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.5076), the reliability and reproducibility of the gamma-aminobutyric acid (GABA) measurement can vary significantly depending on acquisition and modeling parameter. Thus, did the author address these challenges?

We took care of data quality while acquiring MRS data by ensuring appropriate voxel placement and linewidth prior to scanning. Acquisition as well as modeling parameters were constant for both groups, so they cannot have driven group differences.

The linked article compares the reproducibility of GABA measurement using Osprey, which was released in 2020 and uses linear combination modeling to fit the peak as opposed to Gannet’s simple peak fitting (Hupfeld et al., 2024). The study finds better test-retest reliability for Osprey compared to Gannet’s method.

As the present work was conceptualized in 2018, we used Gannet 3.0, which was the state-of-the-art edited spectral analysis toolbox at the time, and still is widely used. In the revised manuscript, we will include a supplementary section reanalyzing the main findings with Osprey.

- Furthermore, the authors wrote: "We confirmed the within-subject stability of metabolite quantification by testing a subset of the sighted controls (n=6) 2-4 weeks apart. Looking at the supplementary Figure 5 (which would be rather plotted as ICC or Blant-Altman plots), the within-subject stability compared to between-subject variability seems not to be great. Furthermore, I don't think such a small sample size qualifies for a rigorous assessment of stability.

Indeed, we did not intend to provide a rigorous assessment of within-subject stability. Rather, we aimed to confirm that data quality/concentration ratios did not systematically differ between the same subjects tested longitudinally; driven, for example, by scanner heating or time of day. As with the phantom testing, we attempted to give readers an idea of the quality of the data, as they were collected from a primarily clinical rather than a research site.

In the revised manuscript we will remove the statement regarding stability, and add the Blant-Altman plot.

- "Why might an enhanced inhibitory drive, as indicated by the lower Glx/GABA ratio" Is this interpretation really warranted, as the results of the group differences in the Glx/GABA ratio seem to be rather driven by a decreased Glx concentration in CC rather than an increased GABA (see Figure 2).

We used the Glx/GABA+ ratio as a measure, rather than individual Glx or GABA+ concentration, which did not significantly differ between groups. As detailed in Response 2.2, we think this metric aligns better with an underlying E/I balance hypothesis and has been used in many previous studies (Gao et al., 2024; Liu et al., 2015; Narayan et al., 2022; Perica et al., 2022).

Our interpretation of an enhanced inhibitory drive additionally comes from the combination of aperiodic EEG (1-20 Hz) and MRS measures, which, when considered together, are consistent with a decreased E/I ratio.

In the revised manuscript, we will rephrase this sentence accordingly. 

- Glx concentration predicted the aperiodic intercept in CC individuals' visual cortices during ambient and flickering visual stimulation. Why specifically investigate the Glx concentration, when the paper is about E/I ratio?

As stated in the methods, we exploratorily assessed the relationship between all MRS parameters (Glx, GABA+ and Glx/GABA+ ratio) with the aperiodic parameters (slope, offset), and corrected for multiple comparisons accordingly. We think this is a worthwhile analysis considering the rarity of the dataset/population (see 1.2, 1.6, 2.1 and reviewer 1’s comments about future hypotheses). We only report the Glx – aperiodic intercept correlation in the main manuscript as it survived correction for multiple comparisons.

(3.7) Interpretation of the correlation between MRS measurements and EEG aperiodic signal:

- The authors wrote: "The intercept of the aperiodic activity was highly correlated with the Glx concentration during rest with eyes open and during flickering stimulation (also see Supplementary Material S11). Based on the assumption that the aperiodic intercept reflects broadband firing (Manning et al., 2009; Winawer et al., 2013), this suggests that the Glx concentration might be related to broadband firing in CC individuals during active and passive visual stimulation." These results should not be interpreted (or with very caution) for several reasons (see also problem with influences on aperiodic intercept and small sample size). This is a result of the exploratory analyses of correlating every EEG parameter with every MRS parameter. This requires well-powered replication before any interpretation can be provided. Furthermore and importantly: why should this be specifically only in CC patients, but not in the SC control group?

We indicate clearly in all parts of the manuscript that these correlations are presented as exploratory. Further, we interpret the Glx-aperiodic offset correlation, and none of the others, as it survived the Bonferroni correction for multiple comparisons. We offer a hypothesis in the discussion section as to why such a correlation might exist in the CC but not the SC group (see response 2.2), and do not speculate further.

(3.8) Language and presentation:

- The manuscript requires language improvements and correction of numerous typos. Over-simplifications and unclear statements are present, which could mislead or confuse readers (see also interpretation of aperiodic signal).

In the revision, we will check that speculations are clearly marked and typos are removed.

- The authors state that "Together, the present results provide strong evidence for experience-dependent development of the E/I ratio in the human visual cortex, with consequences for behavior." The results of the study do not provide any strong evidence, because of the small sample size and exploratory analyses approach and not accounting for possible confounding factors.

We disagree with this statement and allude to convergent evidence of both MRS and neurophysiological measures. The latter link to corresponding results observed in a larger sample of CC individuals (Ossandón et al., 2023).

- "Our results imply a change in neurotransmitter concentrations as a consequence of *restoring* vision following congenital blindness." This is a speculative statement to infer a causal relationship on cross-sectional data.

As mentioned under 2.1, we conducted a cross-sectional study which might justify future longitudinal work. In order to advance science, new testable hypotheses were put forward at the end of a manuscript.

In the revised manuscript we will add “might imply” to better indicate the hypothetical character of this idea.

- In the limitation section, the authors wrote: "The sample size of the present study is relatively high for the rare population , but undoubtedly, overall, rather small." This sentence should be rewritten, as the study is plein underpowered. The further justification "We nevertheless think that our results are valid. Our findings neurochemically (Glx and GABA+ concentration), and anatomically (visual cortex) specific. The MRS parameters varied with parameters of the aperiodic EEG activity and visual acuity. The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) (Ossandón et al., 2023), and effects of chronological age were as expected from the literature." These statements do not provide any validation or justification of small samples. Furthermore, the current data set is a subset of an earlier published paper by the same authors "The EEG data sets reported here were part of data published earlier (Ossandón et al., 2023; Pant et al., 2023)." Thus, the statement "The group differences for the EEG assessments corresponded to those of a larger sample of CC individuals (n=38) " is a circular argument and should be avoided.

Our intention was not to justify having a small sample, but to justify why we think the results might be valid as they align with/replicate existing literature.

In the revised manuscript, we will add a figure showing that the EEG results of the 10 subjects considered here correspond to those of the 28 other subjects of Ossandon et al. We will adapt the text accordingly, clearly stating that the pattern of EEG results of the ten subjects reported here replicate those of the 28 additional subjects of Ossandon et al. (2023).

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Author response:

Reviewer #1 (Public review):

(1) Legionella effectors are often activated by binding to eukaryote-specific host factors, including actin. The authors should test the following: a) whether Lfat1 can fatty acylate small G-proteins in vitro; b) whether this activity is dependent on actin binding; and c) whether expression of the Y240A mutant in mammalian cells affects the fatty acylation of Rac3 (Figure 6B), or other small G-proteins.

We were not able to express and purify the full-length recombinant Lfat1 to perform fatty acylation of small GTPases in vitro. However, in cellulo overexpression of the Y240A mutant still retained ability to fatty acylate Rac3 and another small GTPase RheB (see Author response image 1 below). We postulate that under infection conditions, actin-binding might be required to fatty acylate certain GTPases due to the small amount of effector proteins that secreted into the host cell.

Author response image 1.

(2) It should be demonstrated that lysine residues on small G-proteins are indeed targeted by Lfat1. Ideally, the functional consequences of these modifications should also be investigated. For example, does fatty acylation of G-proteins affect GTPase activity or binding to downstream effectors?

We have mutated K178 on RheB and showed that this mutation abolished its fatty acylation by Lfat1 (see Author response image 2 below). We were not able to test if fatty acylation by Lfat1 affect downstream effector binding.

Author response image 2.

(3) Line 138: Can the authors clarify whether the Lfat1 ABD induces bundling of F-actin filaments or promotes actin oligomerization? Does the Lfat1 ABD form multimers that bring multiple filaments together? If Lfat1 induces actin oligomerization, this effect should be experimentally tested and reported. Additionally, the impact of Lfat1 binding on actin filament stability should be assessed. This is particularly important given the proposed use of the ABD as an actin probe.

The ABD domain does not form oligomer as evidenced by gel filtration profile of the ABD domain. However, we do see F-actin bundling in our in vitro -F-actin polymerization experiment when both actin and ABD are in high concentration (data not shown). Under low concentration of ABD, there is not aggregation/bundling effect of F-actin.

(4) Line 180: I think it's too premature to refer to the interaction as having "high specificity and affinity." We really don't know what else it's binding to.

We have revised the text and reworded the sentence by removing "high specificity and affinity."

(5) The authors should reconsider the color scheme used in the structural figures, particularly in Figures 2D and S4.

Not sure the comments on the color scheme of the structure figures.

(6) In Figure 3E, the WT curve fits the data poorly, possibly because the actin concentration exceeds the Kd of the interaction. It might fit better to a quadratic.

We have performed quadratic fitting and replaced Figure 3E.

(7) The authors propose that the individual helices of the Lfat1 ABD could be expressed on separate proteins and used to target multi-component biological complexes to F-actin by genetically fusing each component to a split alpha-helix. This is an intriguing idea, but it should be tested as a proof of concept to support its feasibility and potential utility.

It is a good suggestion. We plan to thoroughly test the feasibility of this idea as one of our future directions.

(7) The plot in Figure S2D appears cropped on the X-axis or was generated from a ~2× binned map rather than the deposited one (pixel size ~0.83 Å, plot suggests ~1.6 Å). The reported pixel size is inconsistent between the Methods and Table 1-please clarify whether 0.83 Å refers to super-resolution.

Yes, 0.83 Å is super-resolution. We have updated in the cryoEM table

Reviewer #2 (Public review):

Weaknesses:

(1) The authors should use biochemical reactions to analyze the KFAT of Llfat1 on one or two small GTPases shown to be modified by this effector in cellulo. Such reactions may allow them to determine the role of actin binding in its biochemical activity. This notion is particularly relevant in light of recent studies that actin is a co-factor for the activity of LnaB and Ceg14 (PMID: 39009586; PMID: 38776962; PMID: 40394005). In addition, the study should be discussed in the context of these recent findings on the role of actin in the activity of L. pneumophila effectors.

We have new data showed that Actin binding does not affect Lfat1 enzymatic activity. (see figure; response to Reviewer #1). We have added this new data as Figure S7 to the paper. Accordingly, we also revised the discussion by adding the following paragraph.

“The discovery of Lfat1 as an F-actin–binding lysine fatty acyl transferase raised the intriguing question of whether its enzymatic activity depends on F-actin binding. Recent studies have shown that other Legionella effectors, such as LnaB and Ceg14, use actin as a co-factor to regulate their activities. For instance, LnaB binds monomeric G-actin to enhance its phosphoryl-AMPylase activity toward phosphorylated residues, resulting in unique ADPylation modifications in host proteins (Fu et al, 2024; Wang et al, 2024). Similarly, Ceg14 is activated by host actin to convert ATP and dATP into adenosine and deoxyadenosine monophosphate, thereby modulating ATP levels in L. pneumophila–infected cells (He et al, 2025). However, this does not appear to be the case for Lfat1. We found that Lfat1 mutants defective in F-actin binding retained the ability to modify host small GTPases when expressed in cells (Figure S7). These findings suggest that, rather than serving as a co-factor, F-actin may serve to localize Lfat1 via its actin-binding domain (ABD), thereby confining its activity to regions enriched in F-actin and enabling spatial specificity in the modification of host targets.”

(2) The development of the ABD domain of Llfat1 as an F-actin domain is a nice extension of the biochemical and structural experiments. The authors need to compare the new probe to those currently commonly used ones, such as Lifeact, in labeling of the actin cytoskeleton structure.

We fully agree with the reviewer’s insightful suggestion. However, a direct comparison of the Lfat1 ABD domain with commonly used actin probes such as Lifeact, as well as evaluation of the split α-helix probe (as suggested by Reviewer #1), would require extensive and technically demanding experiments. These are important directions that we plan to pursue in future studies.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study reveals that TRPV1 signaling plays a key role in tympanic membrane (TM) healing by promoting macrophage recruitment and angiogenesis. Using a mouse TM perforation model, researchers found that blood-derived macrophages accumulated near the wound, driving angiogenesis and repair. TRPV1-expressing nerve fibers triggered neuroinflammatory responses, facilitating macrophage recruitment. Genetic Trpv1 mutation reduced macrophage infiltration, angiogenesis, and delayed healing. These findings suggest that targeting TRPV1 or stimulating sensory nerve fibers could enhance TM repair, improve blood flow, and prevent infections. This offers new therapeutic strategies for TM perforations and otitis media in clinical settings. This is an excellent and high-quality study that provides valuable insights into the mechanisms underlying TM wound healing.

Strengths:

The work is particularly important for elucidating the cellular and molecular processes involved in TM repair. However, there are several concerns about the current version.

We sincerely thank Reviewer #1 for their time and effort in evaluating and improving our study. Below, we are pleased to address the Reviewer's concerns point by point.

Weaknesses:

Major concerns

(1) The method of administration will be a critical factor when considering potential therapeutic strategies to promote TM healing. It would be beneficial if the authors could discuss possible delivery methods, such as topical application, transtympanic injection, or systemic administration, and their respective advantages and limitations for targeting TRPV1 signaling. For example, Dr. Kanemaru and his colleagues have proposed the use of Trafermin and Spongel to regenerate the eardrum.

We are grateful to the reviewer for raising this important point. While the present study primarily focuses on the mechanistic role of TRPV1 in TM repair, we agree that the mode of therapeutic delivery will be pivotal in translating these findings into clinical practice. In response, we will expand the discussion to explore possible delivery methods—such as topical application, transtympanic injection, and systemic routes—along with their respective benefits and challenges. We will also cite the work by Dr. Kanemaru and colleagues as an example of how local delivery systems may facilitate TM regeneration.

(2) The authors appear to have used surface imaging techniques to observe the TM. However, the TM consists of three distinct layers: the epithelial layer, the fibrous middle layer, and the inner mucosal layer. The authors should clarify whether the proposed mechanism involving TRPV1-mediated macrophage recruitment and angiogenesis is limited to the epithelial layer or if it extends to the deeper layers of the TM.

We apologize for any confusion caused by our previous description. In our study, we utilized Z-stack confocal imaging to capture the full thickness of the TM, as illustrated in Author response image 1 (reconstructed from the acquired Z-sections). This imaging technique allowed us to encompass all three layers of the TM entirely. Each sample was imaged using a 10X objective on an Olympus fluorescence microscope. Given the conical shape and size of the TM, we imaged it in four quadrants, acquiring approximately 30 optical sections (with a 3 µm step) per region. Each acquired images were projected and exported using FV10ASW 4.2 Viewer, then stitched together using Photoshop. The resulting Z-stack projections enabled us to visualize the distribution of macrophages, angiogenesis, and the localization of nerve fibers throughout the TM. We will include this detailed methodology in our revision to clarify any potential confusion.

Author response image 1.

Representative confocal images showing one quadrant of the TM collected from collected from CSR1F^EGFP bone marrow transplanted mouse at day 7 post-perforation. (A-B) 3D-rendered views from different angles reveal the close spatial relationship between CSF1R^EGFP cells (green) and blood vessels (red) within the TM. (C) Cross-sectional view highlights the depth-wise distribution of CSF1R^EGFP cells (green) and blood vessels (red) across the layered TM architecture. All images were processed using Imaris Viewer x64 (version 10.2.0).

Minor concerns

In Figure 8, the schematic illustration presents a coronal section of the TM. However, based on the data provided in the manuscript, it is unclear whether the authors directly obtained coronal images in their study. To enhance the clarity and impact of the schematic, it would be helpful to include representative images of coronal sections showing macrophage infiltration, angiogenesis, and nerve fiber distribution in the TM.

As noted above, we utilized Z-stack confocal imaging to capture the full thickness of the TM, enabling us to visualize structures across all three layers. This approach ensured that all layers were included in our analysis. Due to the thin and curved nature of the TM, traditional cross-sectional imaging often struggles to clearly depict the spatial relationships between macrophages, blood vessels, and nerve fibers, especially at low magnification as shown in Author response image 2. In response to the reviewer's suggestion, we will include representative coronal images in the revised manuscript to better illustrate the distribution of these structures at higher magnification.

Author response image 2.

Confocal images of eardrum cross-sections collected at day 1 (A), 3 (B), and 7 (C) post perforation to demonstrate the wound healing processes.

Reviewer #2 (Public review):

Summary:

This study examines the role of TRPV1 signaling in the recruitment of monocyte-derived macrophages and the promotion of angiogenesis during tympanic membrane (TM) wound healing. The authors use a combination of genetic mouse models, macrophage depletion, and transcriptomic approaches to suggest that neuronal TRPV1 activity contributes to macrophage-driven vascular responses necessary for tissue repair.

Strengths:

(1) The topic of neuroimmune interactions in tissue regeneration is of interest and underexplored in the context of the TM, which presents a unique model due to its anatomical features.

(2) The use of reporter mice and bone marrow chimeras allows for some dissection of immune cell origin.

(3) The authors incorporate transcriptomic data to contextualize inflammatory and angiogenic processes during wound healing.

We sincerely thank Reviewer #2 for their time and effort in improving our study and recognizing its strengths. Below, we are pleased to address the reviewer's concerns point by point.

Weaknesses:

(1) The primary claims of the manuscript are not convincingly supported by the evidence presented. Most of the data are correlative in nature, and no direct mechanistic experiments are included to establish causality between TRPV1 signaling and macrophage recruitment or function.

We appreciate Reviewer #2's perspective on the lack of molecular mechanisms linking TRPV1 signaling and macrophages. However, our data demonstrates that TRPV1 mutations significantly affect macrophage recruitment and angiogenesis. This initial study primarily focuses on the intriguing phenomenon of how sensory nerve fibers are involved in eardrum immunity and wound healing, an area that has not been clearly reported in the literature before. We believe that further research is necessary to explore this topic in greater depth.

(2) Functional validation of key molecular players (such as Tac1 or Spp1) is lacking, and their roles are inferred primarily from gene expression data rather than experimentally tested.

Although we have identified the TAC1 and SPP1 signals as potentially important for TM wound healing for the first time, we agree with the Reviewer's view regarding the lack of molecular mechanisms explored in this study. We have not yet tested the downstream signaling pathways, but we plan to investigate them in a series of future studies. As this is an early report, we will continue to explore these signals and their potential clinical applications based on our initial findings moving forward.

(3) The reuse of publicly available scRNA-seq data is not sufficiently integrated or extended to yield new biological insights, and it remains largely descriptive.

We appreciate Reviewer #2 for highlighting this point. Leveraging publicly available scRNA-seq databases and established analysis pipelines not only saves time and resources—my lab recently collected macrophages from the eardrums of postnatal P15 mice, with each trial requiring 20 eardrums from 10 animals to obtain a sufficient number of cells—but also allows researchers to build on previous work and focus on new biological questions without the need to repeat experiments. A prior study conducted by Dr. Tward and his team utilized scRNA-seq data to make initial discoveries related to eardrum wound healing, primarily focusing on epithelial cells rather than macrophages. We are building on their raw data to uncover new biological insights regarding macrophages, even though we have not yet tested the unidentified signals, which we believe will be valuable to our peers.

(4) The macrophage depletion model (CX3CR1CreER; iDTR) lacks specificity, and possible off-target or systemic effects are not addressed.

We agree with reviewer #2, although macrophage depletion model used in our study is a standard and well-used animal model (Shi, Hua et al. 2018), which has been used by many other laboratories, it is important to note that any macrophage depletion model may have potential issues. We will discuss this in our revision.

(5) Several interpretations of the data appear overstated, particularly regarding the necessity of TRPV1 for monocyte recruitment and wound healing.

We thank the reviewer for pointing this out. We will revise our manuscript where it is overstated accordingly.

(6) Overall, the study appears to apply known concepts - namely, TRPV1-mediated neurogenic inflammation and macrophage-driven angiogenesis - to a new anatomical site without providing new mechanistic insight or advancing the field substantially.

Although our study may not seem highly innovative at first glance, it reveals a previously unknown role of the TRPV1 pain signaling pathway in promoting eardrum healing for the first time. This healing process includes the recruitment of monocyte-derived macrophages and the formation of new blood vessels (angiogenesis). While this process has been documented in other organs, most research on macrophage-driven angiogenesis has been conducted using in vitro models, with very few studies demonstrating this process in vivo. Our findings could lead to new translational opportunities, especially considering that tympanic membrane perforation, along with damage-induced otitis media and conductive hearing loss, are common clinical issues affecting millions of people worldwide. Targeting TRPV1 signaling could enhance tympanic membrane immunity, improve blood circulation, promote the repair of damaged tympanic membranes, and ultimately prevent middle ear infections—an idea that has not been previously proposed.

Overall:

While the study addresses an interesting topic, the current version does not provide sufficiently strong or novel evidence to support its major conclusions. Additional mechanistic experiments and more rigorous validation would be necessary to substantiate the proposed model and clarify the relevance of the findings beyond this specific tissue context.

We greatly thank the two reviewers for their helpful critiques to improve our study. We especially thank the Section Editors for their insightful and constructive comments on this initial study.

References:

Shi, J., L. Hua, D. Harmer, P. Li and G. Ren (2018). "Cre Driver Mice Targeting Macrophages." Methods Mol Biol 1784: 263-275.

Author response:

Reviewer #1 (Public review):

Summary:

This article investigates the origin of movement slowdown in weightlessness by testing two possible hypotheses: the first is based on a strategic and conservative slowdown, presented as a scaling of the motion kinematics without altering its profile, while the second is based on the hypothesis of a misestimation of effective mass by the brain due to an alteration of gravity-dependent sensory inputs, which alters the kinematics following a controller parameterization error.

Strengths:

The article convincingly demonstrates that trajectories are affected in 0g conditions, as in previous work. It is interesting, and the results appear robust. However, I have two major reservations about the current version of the manuscript that prevent me from endorsing the conclusion in its current form.

Weaknesses:

(1) First, the hypothesis of a strategic and conservative slow down implicitly assumes a similar cost function, which cannot be guaranteed, tested, or verified. For example, previous work has suggested that changing the ratio between the state and control weight matrices produced an alteration in movement kinematics similar to that presented here, without changing the estimated mass parameter (Crevecoeur et al., 2010, J Neurophysiol, 104 (3), 1301-1313). Thus, the hypothesis of conservative slowing cannot be rejected. Such a strategy could vary with effective mass (thus showing a statistical effect), but the possibility that the data reflect a combination of both mechanisms (strategic slowing and mass misestimation) remains open.

We test whether changing the ratio between the state and control weight matrices can generate the observed effect. As shown in Author response image 1 and Author response image 2, the cost function change cannot produce a reduced peak velocity/acceleration and their timing advance simultaneously, but a mass estimation change can. In other words, using mass underestimation alone can explain the two key findings, amplitude reduction and timing advance. Yes, we cannot exclude the possibility of a change in cost function on top of the mass underestimation, but the principle of Occam’s Razor would support to adhering to a simple explanation, i.e., using body mass underestimation to explain the key findings. We will include our exploration on possible changes in cost function in the revision (in the Supplemental Materials).

Author response image 1.

Simulation using an altered cost function with α = 3.0. Panels A, B, and E show simulated position, velocity, and acceleration profiles, respectively, for the three movement directions. Solid lines correspond to pre- and post-exposure conditions, while dashed lines represent the in-flight condition. Panels C and D display the peak velocity and its timing across the three phases (Pre, In, Post), and Panels F and G show the corresponding peak acceleration and its timing. Note, varying the cost function, while leading to reduced peak velocity/acceleration, leads to an erroneous prediction of delayed timing of peak velocity/acceleration.

Author response image 2.

Simulation results using a cost function with α = 0.3. The format is the same as in Author response image 1. Note, this ten-fold decrease in α, while finally getting the timing of peak velocity/acceleration right (advanced or reduced), leads to an erroneous prediction of increased peak velocity/acceleration.

(2) The main strength of the article is the presence of directional effects expected under the hypothesis of mass estimation error. However, the article lacks a clear demonstration of such an effect: indeed, although there appears to be a significant effect of direction, I was not sure that this effect matched the model's predictions. A directional effect is not sufficient because the model makes clear quantitative predictions about how this effect should vary across directions. In the absence of a quantitative match between the model and the data, the authors' claims regarding the role of misestimating the effective mass remain unsupported.

Our paper does not aim to quantitatively reproduce human reaching movements in microgravity. We will make this more clearly in the revision.

(1) The model is a simplification of the actual situation. For example, the model simulates an ideal case of moving a point mass (effective mass) without friction and without considering Coriolis and centripetal torques, while the actual situation is that people move their finger across a touch screen. The two-link arm model assumes planar movements, but our participants move their hand on a table top without vertical support to constrain their movement in 2D.

(2) Our study merely uses well-established (though simplified) models to qualitatively predict the overall behavioral patterns if mass underestimation is at play. For this purpose, the results are well in line with models’ qualitative predictions: we indeed confirm that key kinematic features (peak velocity and acceleration) follow the same ranking order of movement direction conditions as predicted.

(3) Using model simulation to qualitatively predict human behavioral patterns is a common practice in motor control studies, prominent examples including the papers on optimal feedback control (Todorov, 2004 and 2005) and movement vigor (Shadmehr et al., 2016). In fact, our model was inspired by the model in the latter paper.

Citations:

Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience, 7(9), 907.

Todorov, E. (2005). Stochastic optimal control and estimation methods adapted to the noise characteristics of the sensorimotor system. Neural Computation, 17(5), 1084–1108.

Shadmehr, R., Huang, H. J., & Ahmed, A. A. (2016). A Representation of Effort in Decision-Making and Motor Control. Current Biology: CB, 26(14), 1929–1934.

In general, both the hypotheses of slowing motion (out of caution) and misestimating mass have been put forward in the past, and the added value of this article lies in demonstrating that the effect depended on direction. However, (1) a conservative strategy with a different cost function can also explain the data, and (2) the quantitative match between the directional effect and the model's predictions has not been established.

Specific points:

(1) I noted a lack of presentation of raw kinematic traces, which would be necessary to convince me that the directional effect was related to effective mass as stated.

We are happy to include exemplary speed and acceleration trajectories. One example subject’s detailed trajectories are shown below and will be included in the revision. The reduced and advanced velocity/acceleration peaks are visible in typical trials.

Author response image 3.

Hand speed profiles (upper panels), hand acceleration profiles (middle panels) and speed profiles of the primary submovements (lower panels) towards different directions from an example participant.

(2) The presentation and justification of the model require substantial improvement; the reason for their presence in the supplementary material is unclear, as there is space to present the modelling work in detail in the main text. Regarding the model, some choices require justification: for example, why did the authors ignore the nonlinear Coriolis and centripetal terms?

Response: In brief, our simulations show that Coriolis and centripetal forces, despite having some directional anisotropy, only have small effects on predicted kinematics (see our responses to Reviewer 2). We will move descriptions of the model into the main text with more justifications for using a simple model.

(3) The increase in the proportion of trials with subcomponents is interesting, but the explanatory power of this observation is limited, as the initial percentage was already quite high (from 60-70% during the initial study to 70-85% in flight). This suggests that the potential effect of effective mass only explains a small increase in a trend already present in the initial study. A more critical assessment of this result is warranted.

Response: Indeed, the percentage of submovements only increases slightly, but the more important change is that the IPI (the inter-peak interval between submovements) also increases at the same time. Moreover, it is the effect of IPI that significantly predicts the duration increase in our linear mixed model. We will highlight this fact in our revision to avoid confusion.

Reviewer #2 (Public review):

This study explores the underlying causes of the generalized movement slowness observed in astronauts in weightlessness compared to their performance on Earth. The authors argue that this movement slowness stems from an underestimation of mass rather than a deliberate reduction in speed for enhanced stability and safety.

Overall, this is a fascinating and well-written work. The kinematic analysis is thorough and comprehensive. The design of the study is solid, the collected dataset is rare, and the model tends to add confidence to the proposed conclusions. That being said, I have several comments that could be addressed to consolidate interpretations and improve clarity.

Main comments:

(1) Mass underestimation

a) While this interpretation is supported by data and analyses, it is not clear whether this gives a complete picture of the underlying phenomena. The two hypotheses (i.e., mass underestimation vs deliberate speed reduction) can only be distinguished in terms of velocity/acceleration patterns, which should display specific changes during the flight with a mass underestimation. The experimental data generally shows the expected changes but for the 45{degree sign} condition, no changes are observed during flight compared to the pre- and post-phases (Figure 4). In Figure 5E, only a change in the primary submovement peak velocity is observed for 45{degree sign}, but this finding relies on a more involved decomposition procedure. It suggests that there is something specific about 45{degree sign} (beyond its low effective mass). In such planar movements, 45{degree sign} often corresponds to a movement which is close to single-joint, whereas 90{degree sign} and 135{degree sign} involve multi-joint movements. If so, the increased proportion of submovements in 90{degree sign} and 135{degree sign} could indicate that participants had more difficulties in coordinating multi-joint movements during flight. Besides inertia, Coriolis and centripetal effects may be non-negligible in such fast planar reaching (Hollerbach & Flash, Biol Cyber, 1982) and, interestingly, they would also be affected by a mass underestimation (thus, this is not necessarily incompatible with the author's view; yet predicting the effects of a mass underestimation on Coriolis/centripetal torques would require a two-link arm model). Overall, I found the discrepancy between the 45{degree sign} direction and the other directions under-exploited in the current version of the article. In sum, could the corrective submovements be due to a misestimation of Coriolis/centripetal torques in the multi-joint dynamics (caused specifically -or not- by a mass underestimation)?

We agree that the effect of mass underestimation is less in the 45° direction than the other two directions, possibly related to its reliance on single-joint (elbow) as opposed to two-joints (elbow and shoulder) movements. Plus, movement correction using one joint is probably easier (as also suggested by another reviewer), this possibility will be further discussed in the revision. However, we find that our model simplification (excluding Coriolis and centripetal torques) does not affect our main conclusions at all. First, we performed a simple simulation and found that, under the current optimal hand trajectory, incorporating Coriolis and centripetal torques has only a limited impact on the resulting joint torques (see simulations in Author response image 4). One reason is that we used smaller movements than Hallerbach & Flash did. In addition, we applied an optimal feedback control model to a more realistic 2-joint arm configuration. Despite its simplicity, this model produced a speed profile consistent with our current predictions and made similar predictions regarding the effects of mass underestimation (Author response image 5). We will provide a more realistic 2-joint arm model muscle dynamics in the revision to improve the simulation further, but the message will be same: including or excluding Coriolis and centripetal torques will not affect the theoretical predictions about mass underestimation. Second, as the reviewer correctly pointed out, the mass (and its underestimation) also affects these two torque terms, thus its effect on kinematic measures is not affected much even with the full model.

Author response image 4.

Joint angles and joint torque of shoulder and elbow with simulated trajectories towards different directions. A. Shoulder (green) and elbow (blue) angles change with time for the 45° movement direction. B. Components of joint interaction torques at the shoulder. Solid line: net torque at the shoulder; dotted line: shoulder inertia torque; dashed line: shoulder Coriolis and centripetal torque. C. Same plot as B for the elbow joint. D–F. Coriolis and centripetal components in the full 360° workspace, beyond three movement directions (45°, 90°, and 135°). D. Net torque. E. Inertial torque. F. Combined Coriolis and centripetal torque. Note the polar plots of Coriolis/centripetal torques (F) have a scale that is two magnitudes smaller than that of inertial torque in our simulation. All torques were simulated with the optimal movement duration. Torques were squared and integrated over each trajectory.

Author response image 5.

Comparison between simulation results from the full model with the addition of Coriolis/centripetal torques (left) and the simplified model (right). The position profiles (top) and the corresponding speed profiles low) are shown. Solid lines are for normal mass estimation and dashed lines for mass underestimation in microgravity. The three colors represent three movement directions (dark red: 45°, red: 90°, yellow: 135°). The full model used a 2-link arm model without realistic muscle dynamics yet (will include in the formal revision) thus the speed profile is not smooth. Importantly, the full model also predict the same effect of mass underestimation, i.e., reduced peak velocity/acceleration and their timing advance.

b) Additionally, since the taikonauts are tested after 2 or 3 weeks in flight, one could also assume that neuromuscular deconditioning explains (at least in part) the general decrease in movement speed. Can the authors explain how to rule out this alternative interpretation? For instance, weaker muscles could account for slower movements within a classical time-effort trade-off (as more neural effort would be needed to generate a similar amount of muscle force, thereby suggesting a purposive slowing down of movement). Therefore, could the observed results (slowing down + more submovements) be explained by some neuromuscular deconditioning combined with a difficulty in coordinating multi-joint movements in weightlessness (due to a misestimation or Coriolis/centripetal torques) provide an alternative explanation for the results?

Response: Neuromuscular deconditioning is indeed a space or microgravity effect; thanks for bringing this up as we omitted the discussion of its possible contribution in the initial submission. However, muscle weakness is less for upper-limb muscles than for postural and lower-limb muscles (Tesch et al., 2005). The handgrip strength decreases 5% to 15% after several months (Moosavi et al., 2021); shoulder and elbow muscles atrophy, though not directly measured, was estimated to be minimal (Shen et al., 2017). The muscle weakness is unlikely to play a major role here since our reaching task involves small movements (~12cm) with joint torques of a magnitude of ~2N·m. Coriolis/centripetal torques does not affect the putative mass effect (as shown above simulations). The reviewer suggests that their poor coordination in microgravity might contribute to slowing down + more submovements. Poor coordination is an umbrella term for any motor control problems, and it can explain any microgravity effect. The feedforward control changes caused by mass underestimation can also be viewed as poor coordination. If we limit it as the coordination of the two joints or coordinating Coriolis/centripetal torques, we should expect to see some trajectory curvature changes in microgravity. However, we further analyzed our reaching trajectories and found no sign of curvature increase in our large collection of reaching movements. We probably have the largest dataset of reaching movements collected in microgravity thus far, given that we had 12 taikonauts and each of them performed about 480 to 840 reaching trials during their spaceflight. We believe the probability of Type II error is quite low here. We will include descriptive statistics of these new analyses in our revision.

Citation: Tesch, P. A., Berg, H. E., Bring, D., Evans, H. J., & LeBlanc, A. D. (2005). Effects of 17-day spaceflight on knee extensor muscle function and size. European journal of applied physiology, 93(4), 463-468.

Moosavi, D., Wolovsky, D., Depompeis, A., Uher, D., Lennington, D., Bodden, R., & Garber, C. E. (2021). The effects of spaceflight microgravity on the musculoskeletal system of humans and animals, with an emphasis on exercise as a countermeasure: A systematic scoping review. Physiological Research, 70(2), 119.

Shen, H., Lim, C., Schwartz, A. G., Andreev-Andrievskiy, A., Deymier, A. C., & Thomopoulos, S. (2017). Effects of spaceflight on the muscles of the murine shoulder. The FASEB Journal, 31(12), 5466.

(2) Modelling

a) The model description should be improved as it is currently a mix of discrete time and continuous time formulations. Moreover, an infinite-horizon cost function is used, but I thought the authors used a finite-horizon formulation with the prefixed duration provided by the movement utility maximization framework of Shadmehr et al. (Curr Biol, 2016). Furthermore, was the mass underestimation reflected both in the utility model and the optimal control model? If so, did the authors really compute the feedback control gain with the underestimated mass but simulate the system with the real mass? This is important because the mass appears both in the utility framework and in the LQ framework. Given the current interpretations, the feedforward command is assumed to be erroneous, and the feedback command would allow for motor corrections. Therefore, it could be clarified whether the feedback command also misestimates the mass or not, which may affect its efficiency. For instance, if both feedforward and feedback motor commands are based on wrong internal models (e.g., due to the mass underestimation), one may wonder how the astronauts would execute accurate goal-directed movements.

b) The model seems to be deterministic in its current form (no motor and sensory noise). Since the framework developed by Todorov (2005) is used, sensorimotor noise could have been readily considered. One could also assume that motor and sensory noise increase in microgravity, and the model could inform on how microgravity affects the number of submovements or endpoint variance due to sensorimotor noise changes, for instance.

c) Finally, how does the model distinguish the feedforward and feedback components of the motor command that are discussed in the paper, given that the model only yields a feedback control law? Does 'feedforward' refer to the motor plan here (i.e., the prefixed duration and arguably the precomputed feedback gain)?

We appreciate these very helpful suggestions about our model presentation. Indeed, our initial submission did not give detailed model descriptions in the main text, due to text limits for early submissions. We actually used a finite-horizon framework throughout, with a pre-specified duration derived from the utility model. In the revision, we will make that point clear, and we will also revise the Methods section to explicitly distinguish feedforward vs. feedback components, clarify the use of mass underestimation in both utility and control models, and update the equations accordingly.

(3) Brevity of movements and speed-accuracy trade-off

The tested movements are much faster (average duration approx. 350 ms) than similar self-paced movements that have been studied in other works (e.g., Wang et al., J Neurophysiology, 2016; Berret et al., PLOS Comp Biol, 2021, where movements can last about 900-1000 ms). This is consistent with the instructions to reach quickly and accurately, in line with a speed-accuracy trade-off. Was this instruction given to highlight the inertial effects related to the arm's anisotropy? One may however, wonder if the same results would hold for slower self-paced movements (are they also with reduced speed compared to Earth performance?). Moreover, a few other important questions might need to be addressed for completeness: how to ensure that astronauts did remember this instruction during the flight? (could the control group move faster because they better remembered the instruction?). Did the taikonauts perform the experiment on their own during the flight, or did one taikonaut assume the role of the experimenter?

Thanks for highlighting the brevity of movements in our experiment. Our intention in emphasizing fast movements is to rigorously test whether movement is indeed slowed down in microgravity. The observed prolonged movement duration clearly shows that microgravity affects people’s movement duration, even when they are pushed to move fast. The second reason for using fast movement is to highlight that feedforward control is affected in microgravity. Mass underestimation specifically affects feedforward control in the first place. Slow movement would inevitably have online corrections that might obscure the effect of mass underestimation. Note that movement slowing is not only observed in our speed-emphasized reaching task, but also in whole-arm pointing in other astronauts studies (Berger, 1997; Sangals, 1999), which have been quoted in our paper. We thus believe these findings are generalizable.

Regarding the consistency of instructions: all our experiments conducted in the Tiangong space station were monitored in real time by experimenters in the Control Center located in Beijing. The task instructions were presented on the initial display of the data acquisition application and ample reading time was allowed. In fact, all the pre-, in-, and post-flight test sessions were administered by the same group of experimenters with the same instruction. It is common that astronauts serve both as participants and experimenters at the same time. And, they were well trained for this type of role on the ground. Note that we had multiple pre-flight test sessions to familiarize them with the task. All these rigorous measures were in place to obtain high-quality data. We will include these experimental details and the rationales for emphasizing fast movements in the revision.

Citations:

Berger, M., Mescheriakov, S., Molokanova, E., Lechner-Steinleitner, S., Seguer, N., & Kozlovskaya, I. (1997). Pointing arm movements in short- and long-term spaceflights. Aviation, Space, and Environmental Medicine, 68(9), 781–787.

Sangals, J., Heuer, H., Manzey, D., & Lorenz, B. (1999). Changed visuomotor transformations during and after prolonged microgravity. Experimental Brain Research. Experimentelle Hirnforschung. Experimentation Cerebrale, 129(3), 378–390.

(4) No learning effect

This is a surprising effect, as mentioned by the authors. Other studies conducted in microgravity have indeed revealed an optimal adaptation of motor patterns in a few dozen trials (e.g., Gaveau et al., eLife, 2016). Perhaps the difference is again related to single-joint versus multi-joint movements. This should be better discussed given the impact of this claim. Typically, why would a "sensory bias of bodily property" persist in microgravity and be a "fundamental constraint of the sensorimotor system"?

We believe the differences between our study and Gaveau et al.’s study cannot be simply attributed to single-joint versus multi-joint movements. One of the most salient differences is that their adaptation is about incorporating microgravity in control for minimizing effort, while our adaptation is about rightfully perceiving body mass. We will elaborate on possible reasons for the lack of learning in the light of this previous study.

We can elaborate on “sensory bias” and “fundamental constraint of the sensorimotor system”. If an inertial change is perceived (like an extra weight attached to the forearm, as in previous motor adaptation studies), people can adapt their reaching in tens of trials. In this case, sensory cues are veridical as they correctly inform about the inertial perturbation. However, in microgravity, reduced gravitational pull and proprioceptive inputs constantly inform the controller that the body mass is less than its actual magnitude. In other words, sensory cues in space are misleading for estimating body mass. The resulting sensory bias prevents the sensorimotor system from correctly adapt. Our statement was too brief in the initial submission; we will expand it in the revision.

Reviewer #3 (Public review):

Summary:

The authors describe an interesting study of arm movements carried out in weightlessness after a prolonged exposure to the so-called microgravity conditions of orbital spaceflight. Subjects performed radial point-to-point motions of the fingertip on a touch pad. The authors note a reduction in movement speed in weightlessness, which they hypothesize could be due to either an overall strategy of lowering movement speed to better accommodate the instability of the body in weightlessness or an underestimation of body mass. They conclude for the latter, mainly based on two effects. One, slowing in weightlessness is greater for movement directions with higher effective mass at the end effector of the arm. Two, they present evidence for an increased number of corrective submovements in weightlessness. They contend that this provides conclusive evidence to accept the hypothesis of an underestimation of body mass.

Strengths:

In my opinion, the study provides a valuable contribution, the theoretical aspects are well presented through simulations, the statistical analyses are meticulous, the applicable literature is comprehensively considered and cited, and the manuscript is well written.

Weaknesses:

Nevertheless, I am of the opinion that the interpretation of the observations leaves room for other possible explanations of the observed phenomenon, thus weakening the strength of the arguments.

First, I would like to point out an apparent (at least to me) divergence between the predictions and the observed data. Figures 1 and S1 show that the difference between predicted values for the 3 movement directions is almost linear, with predictions for 90º midway between predictions for 45º and 135º. The effective mass at 90º appears to be much closer to that of 45º than to that of 135º (Figure S1A). But the data shown in Figure 2 and Figure 3 indicate that movements at 90º and 135º are grouped together in terms of reaction time, movement duration, and peak acceleration, while both differ significantly from those values for movements at 45º.

Furthermore, in Figure 4, the change in peak acceleration time and relative time to peak acceleration between 1g and 0g appears to be greater for 90º than for 135º, which appears to me to be at least superficially in contradiction with the predictions from Figure S1. If the effective mass is the key parameter, wouldn't one expect as much difference between 90º and 135º as between 90º and 45º? It is true that peak speed (Figure 3B) and peak speed time (Figure 4B) appear to follow the ordering according to effective mass, but is there a mathematical explanation as to why the ordering is respected for velocity but not acceleration? These inconsistencies weaken the author's conclusions and should be addressed.

Indeed, the model predicts an almost equal separation between 45° and 90° and between 90° and 135°, while the data indicate that the spacing between 45° and 90° is much smaller than between 90° and 135°. We do not regard the divergence as evidence undermining our main conclusion since 1) the model is a simplification of the actual situation. For example, the model simulates an ideal case of moving a point mass (effective mass) without friction and without considering Coriolis and centripetal torques. 2) Our study does not make quantitative predictions of all the key kinematic measures; that will require model fitting and parameter estimation; instead, our study uses well-established (though simplified) models to qualitatively predict the overall behavioral pattern we would observe. For this purpose, our results are well in line with our expectations: though we did not find equal spacing between direction conditions, we do confirm that the key kinematic properties (Figure 2 and Figure 3 as questioned) follow the same ranking order of directions as predicted.

We thank the reviewer for pointing out the apparent discrepancy between model simulation and observed data. We will elaborate on the reasons behind the discrepancy in the revision.

Then, to strengthen the conclusions, I feel that the following points would need to be addressed:

(1) The authors model the movement control through equations that derive the input control variable in terms of the force acting on the hand and treat the arm as a second-order low-pass filter (Equation 13). Underestimation of the mass in the computation of a feedforward command would lead to a lower-than-expected displacement to that command. But it is not clear if and how the authors account for a potential modification of the time constants of the 2nd order system. The CNS does not effectuate movements with pure torque generators. Muscles have elastic properties that depend on their tonic excitation level, reflex feedback, and other parameters. Indeed, Fisk et al.* showed variations of movement characteristics consistent with lower muscle tone, lower bandwidth, and lower damping ratio in 0g compared to 1g. Could the variations in the response to the initial feedforward command be explained by a misrepresentation of the limbs' damping and natural frequency, leading to greater uncertainty about the consequences of the initial command? This would still be an argument for unadapted feedforward control of the movement, leading to the need for more corrective movements. But it would not necessarily reflect an underestimation of body mass.

*Fisk, J. O. H. N., Lackner, J. R., & DiZio, P. A. U. L. (1993). Gravitoinertial force level influences arm movement control. Journal of neurophysiology, 69(2), 504-511.

We agree that muscle properties, tonic excitation level, proprioception-mediated reflexes all contribute to reaching control. Fisk et al. (1993) study indeed showed that arm movement kinematics change, possibly owing to lower muscle tone and/or damping. However, reduced muscle damping and reduced spindle activity are more likely to affect feedback-based movements. Like in Fisk et al.’s study, people performed continuous arm movements with eyes closed; thus their movements largely relied on proprioceptive control. Our major findings are about the feedforward control, i.e., the reduced and “advanced” peak velocity/acceleration in discrete and ballistic reaching movements. Note that the peak acceleration happens as early as approximately 90-100ms into the movements, clearly showing that feedforward control is affected -- a different effect from Fisk et al’s findings. It is unlikely that people “advanced” their peak velocity/acceleration because they feel the need for more later corrective movements. Thus, underestimation of body mass remains the most plausible explanation.

(2) The movements were measured by having the subjects slide their finger on the surface of a touch screen. In weightlessness, the implications of this contact are expected to be quite different than those on the ground. In weightlessness, the taikonauts would need to actively press downward to maintain contact with the screen, while on Earth, gravity will do the work. The tangential forces that resist movement due to friction might therefore be different in 0g. This could be particularly relevant given that the effect of friction would interact with the limb in a direction-dependent fashion, given the anisotropy of the equivalent mass at the fingertip evoked by the authors. Is there some way to discount or control for these potential effects?

We agree that friction might play a role here, but normal interaction with a touch screen typically involves friction between 0.1 and 0.5N (e.g., Ayyildiz et al., 2018). We believe that the directional variation is even smaller than 0.1N. It is very small compared to the force used to accelerate the arm for the reaching movement (10-15N). Thus, friction anisotropy is unlikely to explain our data.

Citation: Ayyildiz M, Scaraggi M, Sirin O, Basdogan C, Persson BNJ. Contact mechanics between the human finger and a touchscreen under electroadhesion. Proc Natl Acad Sci U S A. 2018 Dec 11;115(50):12668-12673.

(3) The carefully crafted modelling of the limb neglects, nevertheless, the potential instability of the base of the arm. While the taikonauts were able to use their left arm to stabilize their bodies, it is not clear to what extent active stabilization with the contralateral limb can reproduce the stability of the human body seated in a chair in Earth gravity. Unintended motion of the shoulder could account for a smaller-than-expected displacement of the hand in response to the initial feedforward command and/or greater propensity for errors (with a greater need for corrective submovements) in 0g. The direction of movement with respect to the anchoring point could lead to the dependence of the observed effects on movement direction. Could this be tested in some way, e.g., by testing subjects on the ground while standing on an unstable base of support or sitting on a swing, with the same requirement to stabilize the torso using the contralateral arm?

Body stabilization is always a challenge for human movement studies in space. We minimized its potential confounding effects by using left-hand grasping and foot straps for postural support throughout the experiment. We would argue shoulder stability is an unlikely explanation because unexpected shoulder instability should not affect the feedforward (early) part of the ballistic reaching movement: the reduced peak acceleration and its early peak were observed at about 90-100ms after movement initiation. This effect is too early to be explained by an expected stability issue.

The arguments for an underestimation of body mass would be strengthened if the authors could address these points in some way.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this study from Zhu and colleagues, a clear role for MED26 in mouse and human erythropoiesis is demonstrated that is also mapped to amino acids 88-480 of the human protein. The authors also show the unique expression of MED26 in later-stage erythropoiesis and propose transcriptional pausing and condensate formation mechanisms for MED26's role in promoting erythropoiesis. Despite the author's introductory claim that many questions regarding Pol II pausing in mammalian development remain unanswered, the importance of transcriptional pausing in erythropoiesis has actually already been demonstrated (Martell-Smart, et al. 2023, PMID: 37586368, which the authors notably did not cite in this manuscript). Here, the novelty and strength of this study is MED26 and its unique expression kinetics during erythroid development.

Strengths:

The widespread characterization of kinetics of mediator complex component expression throughout the erythropoietic timeline is excellent and shows the interesting divergence of MED26 expression pattern from many other mediator complex components. The genetic evidence in conditional knockout mice for erythropoiesis requiring MED26 is outstanding. These are completely new models from the investigators and are an impressive amount of work to have both EpoR-driven deletion and inducible deletion. The effect on red cell number is strong in both. The genetic over-expression experiments are also quite impressive, especially the investigators' structure-function mapping in primary cells. Overall the data is quite convincing regarding the genetic requirement for MED26. The authors should be commended for demonstrating this in multiple rigorous ways.

Thank you for your positive feedback.

Weaknesses:

(1) The authors state that MED26 was nominated for study based on RNA-seq analysis of a prior published dataset. They do not however display any of that RNA-seq analysis with regards to Mediator complex subunits. While they do a good job showing protein-level analysis during erythropoiesis for several subunits, the RNA-seq analysis would allow them to show the developmental expression dynamics of all subunit members.

Thank you for this helpful suggestion. While we did not originally nominate MED26 based on RNA-seq analysis, we have analyzed the transcript levels of Mediator complex subunits in our RNA-seq data across different stages of erythroid differentiation (Author response image 1). The results indicate that most Mediator subunits, including MED26, display decreased RNA expression over the course of differentiation, with the exception of MED25, as reported previously (Pope et al., Mol Cell Biol 2013. PMID: 23459945).

Notably, our study is based on initial observations at the protein level, where we found that, unlike most other Mediator subunits that are downregulated during erythropoiesis, MED26 remains relatively abundant. Protein expression levels more directly reflect the combined influences of transcription, translation and degradation processes within cells, and are likely more closely related to biological functions in this context. It is possible that post-transcriptional regulation (such as m6A-mediated improvement of translational efficiency) or post-translational modifications (like escape from ubiquitination) could contribute to the sustained levels of MED26 protein, and this will be an interesting direction for future investigation.

Author response image 1.

Relative RNA expression of Mediator complex subunits during erythropoiesis in human CD34+ erythroid cultures. Different differentiation stages from HSPCs to late erythroblasts were identified using CD71 and CD235a markers, progressing sequentially as CD71-CD235a-, CD71+CD235a-, CD71+CD235a+, and CD71-CD235a+. Expression levels were presented as TPM (transcripts per million).

(2) The authors use an EpoR Cre for red cell-specific MED26 deletion. However, other studies have now shown that the EpoR Cre can also lead to recombination in the macrophage lineage, which clouds some of the in vivo conclusions for erythroid specificity. That being said, the in vitro erythropoiesis experiments here are convincing that there is a major erythroid-intrinsic effect.

Thank you for this insightful comment. We recognize that EpoR-Cre can drive recombination in both erythroid and macrophage lineages (Zhang et al., Blood 2021, PMID: 34098576). However, EpoR-Cre remains the most widely used Cre for studying erythroid lineage effects in the hematopoietic community. Numerous studies have employed EpoR-Cre for erythroid-specific gene knockout models (Pang et al, Mol Cell Biol 2021, PMID: 22566683; Santana-Codina et al., Haematologica 2019, PMID: 30630985; Xu et al., Science 2013, PMID: 21998251.).

While a GYPA (CD235a)-Cre model with erythroid specificity has recently been developed (https://www.sciencedirect.com/science/article/pii/S0006497121029074), it has not yet been officially published. We look forward to utilizing the GYPA-Cre model for future studies. As you noted, our in vivo mouse model and primary human CD34+ erythroid differentiation system both demonstrate that MED26 is essential for erythropoiesis, suggesting that the regulatory effects of MED26 in our study are predominantly erythroid-intrinsic.

(3) Te donor chimerism assessment of mice transplanted with MED26 knockout cells is a bit troubling. First, there are no staining controls shown and the full gating strategy is not shown. Furthermore, the authors use the CD45.1/CD45.2 system to differentiate between donor and recipient cells in erythroblasts. However, CD45 is not expressed from the CD235a+ stage of erythropoiesis onwards, so it is unclear how the authors are detecting essentially zero CD45-negative cells in the erythroblast compartment. This is quite odd and raises questions about the results. That being said, the red cell indices in the mice are the much more convincing data.

Thank you for your careful and thorough feedback. We have now included negative staining controls (Author response image 2A, top). We agree that CD45 is typically not expressed in erythroid precursors in normal development. Prior studies have characterized BFU-E and CFU-E stages as c-Kit+CD45+Ter119−CD71low and c-Kit+CD45−Ter119−CD71high cells in fetal liver (Katiyar et al, Cells 2023, PMID: 37174702).

However, our observations indicate that erythroid surface markers differ during hematopoiesis reconstitution following bone marrow transplantation.  We found that nearly all nucleated erythroid progenitors/precursors (Ter119+Hoechst+) express CD45 after hematopoiesis reconstitution (Author response image 2A, bottom).

To validate our assay, we performed next-generation sequencing by first mixing mouse CD45.1 and CD45.2 total bone marrow cells at a 1:2 ratio. We then isolated nucleated erythroid progenitors/precursors (Ter119+Hoechst+) by FACS and sequenced the CD45 gene locus by targeted sequencing. The resulting CD45 allele distribution matched our initial mixing ratio, confirming the accuracy of our approach (Author response image 2B).

Moreover, a recent study supports that reconstituted erythroid progenitors can indeed be distinguished by CD45 expression following bone marrow transplantation (He et al., Nature Aging 2024, PMID: 38632351. Extended Data Fig. 8). 

In conclusion, our data indicate that newly formed erythroid progenitors/precursors post-transplant express CD45, enabling us to identify nucleated erythroid progenitors/precursors by Ter119+Hoechst+ and determine their origin using CD45.1 and CD45.2 markers.

Author response image 2.

Representative flow cytometry gating strategy of erythroid chimerism following mouse bone marrow transplantation. A. Gating strategy used in the erythroid chimerism assay. B. Targeted sequencing result of Ter119+Hoechst+ cells isolated by FACS. The cell sample was pre-mixed with 1/3 CD45.2 and 2/3 CD45.1 bone marrow cells. Ptprc is the gene locus for CD45.

(4) The authors make heavy use of defining "erythroid gene" sets and "non-erythroid gene" sets, but it is unclear what those lists of genes actually are. This makes it hard to assess any claims made about erythroid and non-erythroid genes.

Thank you for this helpful suggestion. We defined "erythroid genes" and "non-erythroid genes" based on RNA-seq data from Ludwig et al. (Cell Reports 2019. PMID: 31189107. Figure 2 and Table S1). Genes downregulated from stages k1 to k5 are classified as “non-erythroid genes,” while genes upregulated from stages k6 to k7 are classified as “erythroid genes.” We will add this description in the revised manuscript.

(5) Overall the data regarding condensate formation is difficult to interpret and is the weakest part of this paper. It is also unclear how studies of in vitro condensate formation or studies in 293T or K562 cells can truly relate to highly specialized erythroid biology. This does not detract from the major findings regarding genetic requirements of MED26 in erythropoiesis.

Thank you for the rigorous feedback. Assessing the condensate properties of MED26 protein in primary CD34+ erythroid cells or mouse models is indeed challenging. As is common in many condensate studies, we used in vitro assays and cellular assays in HEK293T and K562 cells to examine the biophysical properties (Figure S7), condensation formation capacity (Figure 5C and Figure S7C), key phase-separation regions of MED26 protein (Figure S6), and recruitment of pausing factors (Figure 6A-B) in live cells. We then conducted functional assays to demonstrate that the phase-separation region of MED26 can promote erythroid differentiation similarly to the full-length protein in the CD34+ system and K562 cells (Figure 5A). Specifically, overexpressing the MED26 phase-separation domain accelerates erythropoiesis in primary human erythroid culture, while deleting the Intrinsically Disordered Region (IDR) impairs MED26’s ability to form condensates and recruit PAF1 in K562 cells.

In summary, we used HEK293T cells to study the biochemical and biophysical properties of MED26, and the primary CD34+ differentiation system to examine its developmental roles. Our findings support the conclusion that MED26-associated condensate formation promotes erythropoiesis.

(6) For many figures, there are some panels where conclusions are drawn, but no statistical quantification of whether a difference is significant or not.

Thank you for your thorough feedback. We have checked all figures for statistical quantification and added the relevant statistical analysis methods to the corresponding figure legends (Figure 2L and Figure S4C) to clarify the significance of the observed differences. The updated information will be incorporated into the revised manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhu et al describes a novel role for MED26, a subunit of the Mediator complex, in erythroid development. The authors have discovered that MED26 promotes transcriptional pausing of RNA Pol II, by recruiting pausing-related factors.

Strengths:

This is a well-executed study. The authors have employed a range of cutting-edge and appropriate techniques to generate their data, including: CUT&Tag to profile chromatin changes and mediator complex distribution; nuclear run-on sequencing (PRO-seq) to study Pol II dynamics; knockout mice to determine the phenotype of MED26 perturbation in vivo; an ex vivo erythroid differentiation system to perform additional, important, biochemical and perturbation experiments; immunoprecipitation mass spectrometry (IP-MS); and the "optoDroplet" assay to study phase-separation and molecular condensates.

This is a real highlight of the study. The authors have managed to generate a comprehensive picture by employing these multiple techniques. In doing so, they have also managed to provide greater molecular insight into the workings of the MEDIATOR complex, an important multi-protein complex that plays an important role in a range of biological contexts. The insights the authors have uncovered for different subunits in erythropoiesis will very likely have ramifications in many other settings, in both healthy biology and disease contexts.

Thank you for your thoughtful summary and encouraging feedback.

Weaknesses:

There are almost no discernible weaknesses in the techniques used, nor the interpretation of the data. The IP-MS data was generated in HEK293 cells when it could have been performed in the human CD34+ HSPC system that they employed to generate a number of the other data. This would have been a more natural setting and would have enabled a more like-for-like comparison with the other data.

Thank you for your positive feedback and insightful suggestions. We will perform validation of the immunoprecipitation results in CD34+ derived erythroid cells to further confirm our findings.

Reviewer #3 (Public review):

Summary:

The authors aim to explore whether other subunits besides MED1 exert specific functions during the process of terminal erythropoiesis with global gene repression, and finally they demonstrated that MED26-enriched condensates drive erythropoiesis through modulating transcription pausing.

Strengths:

Through both in vitro and in vivo models, the authors showed that while MED1 and MED26 co-occupy a plethora of genes important for cell survival and proliferation at the HSPC stage, MED26 preferentially marks erythroid genes and recruits pausing-related factors for cell fate specification. Gradually, MED26 becomes the dominant factor in shaping the composition of transcription condensates and transforms the chromatin towards a repressive yet permissive state, achieving global transcription repression in erythropoiesis.

Thank you for your positive summary and feedback.

Weaknesses:

In the in vitro model, the author only used CD34+ cell-derived erythropoiesis as the validation, which is relatively simple, and more in vitro erythropoiesis models need to be used to strengthen the conclusion.

Thank you for your thoughtful suggestions. We have shown that MED26 promotes erythropoiesis using the primary human CD34+ differentiation system (Figure 2 K-M and Figure S4) and have demonstrated its essential role in erythropoiesis through multiple mouse models (Figure 2A-G and Figure S1-3). Together, these in vitro and in vivo results support our conclusion that MED26 regulates erythropoiesis. However, we are open to further validating our findings with additional in vitro erythropoiesis models, such as iPSC or HUDEP erythroid differentiation systems.

Author Response

Reviewer #1 (Public Review):

[...] Genes expressed in the same direction in lowland individuals facing hypoxia (the plastic state) as what is found in the colonised state are defined as adaptative, while genes with the opposite expression pattern were labelled as maladaptive, using the assumption that the colonised state must represent the result of natural selection. Furthermore, genes could be classified as representing reversion plasticity when the expression pattern differed between the plasticity and colonised states and as reinforcement when they were in the same direction (for example more expressed in the plastic state and the colonised state than in the ancestral state). They found that more genes had a plastic expression pattern that was labelled as maladaptive than adaptive. Therefore, some of the genes have an expression pattern in accordance with what would be predicted based on the plasticity-first hypothesis, while others do not.

Thank you for a precise summary of our work. We appreciate the very encouraging comments recognizing the value of our work. We have addressed concerns from the reviewer in greater detail below.

Q1. As pointed out by the authors themselves, the fact that temperature was not included as a variable, which would make the experimental design much more complex, misses the opportunity to more accurately reflect the environmental conditions that the colonizer individuals face at high altitude. Also pointed out by the authors, the acclimation experiment in hypoxia lasted 4 weeks. It is possible that longer term effects would be identifiable in gene expression in the lowland individuals facing hypoxia on a longer time scale. Furthermore, a sample size of 3 or 4 individuals per group depending on the tissue for wild individuals may miss some of the natural variation present in these populations. Stating that they have a n=7 for the plastic stage and n= 14 for the ancestral and colonized stages refers to the total number of tissue samples and not the number of individuals, according to supplementary table 1.

We shared the same concerns as the reviewer. This is partly because it is quite challenging to bring wild birds into captivity to conduct the hypoxia acclimation experiments. We had to work hard to perform acclimation experiments by taking lowland sparrows in a hypoxic condition for a month. We indeed have recognized the similar set of limitations as the review pointed out and have discussed the limitations in the study, i.e., considering hypoxic condition alone, short time acclimation period, etc. Regarding sample sizes, we have collected cardiac muscle from nine individuals (three individuals for each stage) and flight muscle from 12 individuals (four individuals for each stage). We have clarified this in Supplementary Table 1.

Q2. Finally, I could not find a statement indicating that the lowland individuals placed in hypoxia (plastic stage) were from the same population as the lowland individuals for which transcriptomic data was already available, used as the "ancestral state" group (which themselves seem to come from 3 populations Qinghuangdao, Beijing, and Tianjin, according to supplementary table 2) nor if they were sampled in the same time of year (pre reproduction, during breeding, after, or if they were juveniles, proportion of males or females, etc). These two aspects could affect both gene expression (through neutral or adaptive genetic variation among lowland populations that can affect gene expression, or environmental effects other than hypoxia that differ in these populations' environments or because of their sexes or age). This could potentially also affect the FST analysis done by the authors, which they use to claim that strong selective pressure acted on the expression level of some of the genes in the colonised group.

The reviewer asked how individual tree sparrows used in the transcriptomic analyses were collected. The individuals used for the hypoxia acclimation experiment and represented the ancestral lowland population were collected from the same locality (Beijing) and at the same season (i.e., pre-breeding) of the year. They are all adults and weight approximately 18g. We have clarified this in the Supplementary Table S1 and Methods. We did not distinguish males from females (both sexes look similar) under the assumption that both sexes respond similarly to hypoxia acclimation in their cardiac and flight muscle gene expression.

The Supplementary Table 2 lists the individuals that were used for sequence analyses. These individuals were only used for sequence comparisons but not for the transcriptomic analyses. The population genetic structure analyzed in a previously published study showed that there is no clear genetic divergence within the lowland population (i.e., individuals collected from Beijing, Tianjing and Qinhuangdao) or the highland population (i.e., Gangcha and Qinghai Lake). In addition, there was no clear genetic divergence between the highland and lowland populations (Qu et al. 2020).

Author response image 1.

Population genetic structure of the Eurasian Tree Sparrow (Passer montanus). The genetic structure generated using FRAPPE. The colors in each column represent the contribution from each subcluster (Qu et al. 2020). Yellow, highland population; blue, lowland population.

Q4. Impact of the work There has been work showing that populations adapted to high altitude environments show changes in their hypoxia response that differs from the short-term acclimation response of lowland population of the same species. For example, in humans, see Erzurum et al. 2007 and Peng et al. 2017, where they show that the hypoxia response cascade, which starts with the gene HIF (Hypoxia-Inducible Factor) and includes the EPO gene, which codes for erythropoietin, which in turns activates the production of red blood cell, is LESS activated in high altitude individuals compared to the activation level in lowland individuals (which gives it its name). The present work adds to this body of knowledge showing that the short-term response to hypoxia and the long term one can affect different pathways and that acclimation/plasticity does not always predict what physiological traits will evolve in populations that colonize these environments over many generations and additional selection pressure (UV exposure, temperature, nutrient availability). Altogether, this work provides new information on the evolution of reaction norms of genes associated with the physiological response to one of the main environmental variables that affects almost all animals, oxygen availability. It also provides an interesting model system to study this type of question further in a natural population of homeotherms.

Erzurum, S. C., S. Ghosh, A. J. Janocha, W. Xu, S. Bauer, N. S. Bryan, J. Tejero et al. "Higher blood flow and circulating NO products offset high-altitude hypoxia among Tibetans." Proceedings of the National Academy of Sciences 104, no. 45 (2007): 17593-17598. Peng, Y., C. Cui, Y. He, Ouzhuluobu, H. Zhang, D. Yang, Q. Zhang, Bianbazhuoma, L. Yang, Y. He, et al. 2017. Down-regulation of EPAS1 transcription and genetic adaptation of Tibetans to high-altitude hypoxia. Molecular biology and evolution 34:818-830.

Thank you for highlighting the potential novelty of our work in light of the big field. We found it very interesting to discuss our results (from a bird species) together with similar findings from humans. In the revised version of manuscript, we have discussed short-term acclimation response and long-term adaptive evolution to a high-elevation environment, as well as how our work provides understanding of the relative roles of short-term plasticity and long-term adaptation. We appreciate the two important work pointed out by the reviewer and we have also cited them in the revised version of manuscript.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response. The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

Thank you for a precise summary of our work and constructive comments to improve this study. We have addressed your concerns in greater detail below.

Q1. The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

We appreciate the critical comments from the reviewer. The reviewer raised the concerns about the batch effect from birds collected from the previous study and this study. There is an important detail we didn’t describe in the previous version. All tissues from hypoxia acclimated birds and highland and lowland birds have been collected at the same time (i.e., Qu et al. 2020). RNA library construction and sequencing of these samples were also conducted at the same time, although only the transcriptomic data of lowland and highland tree sparrows were included in Qu et al. (2020). The data from acclimated birds have not been published before.

In the revised version of manuscript, we also compared log-transformed transcript per million (TPM) across all genes and determined the most conserved genes (i.e., coefficient of variance ≤  0.3 and average TPM ≥ 1 for each sample) for the flight and cardiac muscles, respectively (Hao et al. 2023). We compared the median expression levels of these conserved genes and found no difference among the lowland, hypoxia-exposed lowland, and highland tree sparrows (Wilcoxon signed-rank test, P<0.05). As these results suggested little batch effect on the transcriptomic data, we used TPM values to calculate gene expression level and intensity. This methodological detail has been further clarified in the Methods and we also provided a new supplementary Figure (Figure S5) to show the comparative results.

Author response image 2.

The median expression levels of the conserved genes (i.e., coefficient of variance ≤ 0.3 and average TPM ≥ 1 for each sample) did not differ among the lowland, hypoxia-exposed lowland, and highland tree sparrows (Wilcoxon signed-rank test, P<0.05).

The reviewer also raised the issue of sample size. We certainly would have liked to have more individuals in the study, but this was not possible due to the logistical problem of keeping wild bird in a common garden experiment for a long time. We have acknowledged this in the manuscript. In order to mitigate this we have tested the hypothesis of plasticity following by genetic change using two different tissues (cardiac and flight muscles) and two different datasets (co-expressed gene-set and muscle-associated gene-set). As all these analyses show similar results, they indicate that the main conclusion drawn from this study is robust.

Q2. Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

Thank you for the critical comments. There are two questions raised we should like to address them separately. The first concern centered on the issue of arbitrarily chosen thresholds. In our manuscript, we used a range of thresholds, i.e., 50%, 100%, 150% and 200% of change in the gene expression levels of the ancestral lowland tree sparrow to detect genes with reinforcement and reversion plasticity. By this design we wanted to explore the magnitudes of gene expression plasticity (i.e., Ho & Zhang 2018), and whether strength of selection (i.e., genetic variation) changes with the magnitude of gene expression plasticity (i.e., Campbell-Staton et al. 2021).

As the reviewer pointed out, we have now realized that this threshold selection is arbitrarily. We have thus implemented two other categorization schemes to test the robustness of the observation of unequal proportions of genes with reinforcement and reversion plasticity. Specifically, we used a parametric bootstrap procedure as described in Ho & Zhang (2019), which aimed to identify genes resulting from genuine differences rather than random sampling errors. Bootstrap results suggested that genes exhibiting reversing plasticity significantly outnumber those exhibiting reinforcing plasticity, suggesting that our inference of an excess of genes with reversion plasticity is robust to random sampling errors. We have added these analyses to the revised version of manuscript, and provided results in the Figure 2d and Figure 3d.

Author response image 3.

Figure 2a (left) and Figure 2b (right). Frequencies of genes with reinforcement and reversion plasticity (>50%) and their subsets that acquire strong support in the parametric bootstrap analyses (≥ 950/1000).

In addition, we adapted a bin scheme (i.e., 20%, 40% and 60% bin settings along the spectrum of the reinforcement/reversion plasticity). These analyses based on different categorization schemes revealed similar results, and suggested that our inference of an excess of genes with reversion plasticity is robust. We have provided these results in the Supplementary Figure S2 and S4.

Author response image 4.

(A) and Figure S4 (B). Frequencies of genes with reinforcement and reversion plasticity in the flight and cardiac muscle. (A) For genes identified by WGCNA, all comparisons show that there are more genes showing reversion plasticity than those showing reinforcement plasticity for both the flight and cardiac msucles. (B) For genes that associated with muscle phentoypes, all comparisons show that there are more genes showing reversion plasticity than those showing reinforcement plasticity for the flight muscle, while more than 50% of comparisons support an excess of genes with reversion plasticity for the cardiac muscle. Two-tailed binomial test, NS, non-significant; , P < 0.05; , P < 0.01; **, P < 0.001.

The second issue that the reviewer raised is that the plastic changes simply have more space to reverse direction rather than having any biological reason to do so. While a causal reason why there are more genes with expression levels being reversed than those with expression levels being reinforced at the late stages is still contentious, increasingly many studies show that genes expression plasticity at the early stage may be functionally maladapted to novel environment that the species have recently colonized (i.e., lizard, Campbell-Staton et al. 2021; Escherichia coli, yeast, guppies, chickens and babblers, Ho and Zhang 2018; Ho et al. 2020; Kuo et al. 2023). Our comparisons based on the two genesets that are associated with muscle phenotypes corroborated with these previous studies and showed that initial gene expression plasticity may be nonadaptive to the novel environments (i.e., Ghalambor et al. 2015; Ho & Zhang 2018; Ho et al. 2020; Kuo et al. 2023; Campbell-Staton et al. 2021).

Q3. The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

The reviewer raised an issue that the correlation between plastic change and evolved divergence is an artifact because of the definition of adaptive versus maladaptive changes, for example, Figure 3d. We agree with the reviewer that the correlation analysis is circular because the definition of adaptive and maladaptive plasticity depends on the direction of plastic change matched or opposed that of the colonized tree sparrows. We have thus removed previous Figure 3d-e and related texts from the revised version of manuscript. Meanwhile, we have changed Figure 3a to further clarify the schematic framework.

eLife Assessment

This study presents a fundamental discovery of how cerebellar climbing fibers modulate plastic changes in the somatosensory cortex by identifying both the responsible cortical circuit and the anatomical pathways. The evidence supporting the conclusions is convincing and well supported by modern neuroscience methodologies. Overall, this work represents a significant contribution that will be of broad interest to neuroscientists, especially those studying the long-distance cerebellar influence on non-motor brain functions.

Reviewer #1 (Public review):

Summary:

Silbaugh, Koster, and Hansel investigated how the cerebellar climbing fiber (CF) signals influence neuronal activity and plasticity in mouse primary somatosensory (S1) cortex. They found that optogenetic activation of CFs in the cerebellum modulates responses of cortical neurons to whisker stimulation in a cell-type-specific manner and suppresses potentiation of layer 2/3 pyramidal neurons induced by repeated whisker stimulation. This suppression of plasticity by CF activation is mediated through modulation of VIP- and SST-positive interneurons. Using transsynaptic tracing and chemogenetic approaches, the authors identified a pathway from the cerebellum through the zona incerta and the thalamic posterior medial (POm) nucleus to the S1 cortex, which underlies this functional modulation.

Strengths:

This study employed a combination of modern neuroscientific techniques, including two-photon imaging, opto- and chemo-genetic approaches, and transsynaptic tracing. The experiments were thoroughly conducted, and the results were clearly and systematically described. The interplay between the cerebellum and other brain regions - and its functional implications - is one of the major topics in this field. This study provides solid evidence for an instructive role of the cerebellum in experience-dependent plasticity in the S1 cortex.

Weaknesses:

There may be some methodological limitations, and the physiological relevance of the CF-induced plasticity modulation in the S1 cortex remains unclear. In particular, it has not been elucidated how CF activity influences the firing patterns of downstream neurons along the pathway to the S1 cortex during stimulation.

(1) Optogenetic stimulation may have activated a large population of CFs synchronously, potentially leading to strong suppression followed by massive activation in numerous cerebellar nuclear (CN) neurons. Given that there is no quantitative estimation of the stimulated area or number of activated CFs, observed effects are difficult to interpret directly. The authors should at least provide the basic stimulation parameters (coordinates of stim location, power density, spot size, estimated number of Purkinje cells included, etc.).

(2) There are CF collaterals directly innervating CN (PMID:10982464). Therefore, antidromic spikes induced by optogenetic stimulation may directly activate CN neurons. On the other hand, a previous study reported that CN neurons exhibit only weak responses to CF collateral inputs (PMID: 27047344). The authors should discuss these possibilities and the potential influence of CF collaterals on the interpretation of the results.

(3) The rationale behind the plasticity induction protocol for RWS+CF (50 ms light pulses at 1 Hz during 5 min of RWS, with a 45 ms delay relative to the onset of whisker stimulation) is unclear.

a) The authors state that 1 Hz was chosen to match the spontaneous CF firing rate (line 107); however, they also introduced a delay to mimic the CF response to whisker stimulation (line 108). This is confusing, and requires further clarification, specifically, whether the protocol was designed to reproduce spontaneous or sensory-evoked CF activity.

b) Was the timing of delivering light pulses constant or random? Given the stochastic nature of CF firing, randomly timed light pulses with an average rate of 1Hz would be more physiologically relevant. At the very least, the authors should provide a clear explanation of how the stimulation timing was implemented.

(4) CF activation modulates inhibitory interneurons in the S1 cortex (Figure 2): responses of interneurons in S1 to whisker stimulation were enhanced upon CF coactivation (Figure 2C), and these neurons were predominantly SST- and PV-positive interneurons (Figure 2H, I). In contrast, VIP-positive neurons were suppressed only in the late time window of 650-850 ms (Figure 2G). If the authors' hypothesis-that the activity of VIP neurons regulates SST- and PV-neuron activity during RWS+CF-is correct, then the activity of SST- and PV-neurons should also be increased during this late time window. The authors should clarify whether such temporal dynamics were observed or could be inferred from their data.

(5) Transsynaptic tracing from CN nicely identified zona incerta (ZI) neurons and their axon terminals in both POm and S1 (Figure 6 and Figure S7).

a) Which part of the CN (medial, interposed, or lateral) is involved in this pathway is unclear.

b) Were the electrophysiological properties of these ZI neurons consistent with those of PV neurons?

c) There appears to be a considerable number of axons of these ZI neurons projecting to the S1 cortex (Figure S7C). Would it be possible to estimate the relative density of axons projecting to the POm versus those projecting to S1? In addition, the authors should discuss the potential functional role of this direct pathway from the ZI to the S1 cortex.

Reviewer #2 (Public review):

Summary:

The authors examined long-distance influence of climbing fiber (CF) signaling in the somatosensory cortex by manipulating whiskers through stimulation. Also, they examined CF signaling using two-photon imaging and mapped projections from the cerebellum to the somatosensory cortex using transsynaptic tracing. As a final manipulation, they used chemogenetics to perturb parvalbumin-positive neurons in the zona incerta and recorded from climbing fibers.

Strengths:

There are several strengths to this paper. The recordings were carefully performed, and AAVs used were selective and specific for the cell types and pathways being analyzed. In addition, the authors used multiple approaches that support climbing fiber pathways to distal regions of the brain. This work will impact the field and describes nice methods to target difficult-to-reach brain regions, such as the inferior olive.

Weaknesses:

There are some details in the methods that could be explained further. The discussion was very short and could connect the findings in a broader way.

Reviewer #3 (Public review):

Summary:

The authors developed an interesting novel paradigm to probe the effects of cerebellar climbing fiber activation on short-term adaptation of somatosensory neocortical activity during repetitive whisker stimulation. Normally, RWS potentiated whisker responses in pyramidal cells and weakly suppressed them in interneurons, lasting for at least 1h. Crusii Optogenetic climbing fiber activation during RWS reduced or inverted these adaptive changes. This effect was generally mimicked or blocked with chemogenetic SST or VIP activation/suppression as predicted based on their "sign" in the circuit.

Strengths:

The central finding about CF modulation of S1 response adaptation is interesting, important, and convincing, and provides a jumping-off point for the field to start to think carefully about cerebellar modulation of neocortical plasticity.

Weaknesses:

The SST and VIP results appeared slightly weaker statistically, but I do not personally think this detracts from the importance of the initial finding (if there are multiple underlying mechanisms, modulating one may reproduce only a fraction of the effect size). I found the suggestion that zona incerta may be responsible for the cerebellar effects on S1 to be a more speculative result (it is not so easy with existing technology to effectively modulate this type of polysynaptic pathway), but this may be an interesting topic for the authors to follow up on in more detail in the future.

eLife Assessment

This valuable manuscript presents findings supported by solid data to identify a surprising glia-exclusive function for betapix in vascular integrity and angiogenesis. The manuscript also describes the optimisation of a modified CRISPR-based Zwitch approach to generate conditional knockouts in zebrafish

Reviewer #1 (Public review):

The manuscript by Chiu et al describes the modification of the Zwitch strategy to efficiently generate conditional knockouts of zebrafish betapix. They leverage this system to identify a surprising glia-exclusive function of betapix in mediating vascular integrity and angiogenesis. Betapix has been previously associated with vascular integrity and angiogenesis in zebrafish, and betapix function in glia has also been proposed. However, this study identifies glial betapix in vascular stability and angiogenesis for the first time.

The study derives its strength from the modified CRISPR-based Zwitch approach to identify the specific role of glial betapix (and not neuronal, mural or endothelial). Using RNA-in situ hybridisation and analysis of scRNA-Seq data, they also identify delayed maturation of neurons and glia and implicate a reduction in stathmin levels in the glial knockouts in mediating vascular homeostasis and angiogenesis. The study also implicates a betapix-zfhx3/4-vegfa axis in mediating cerebral angiogenesis.

There is both technical (the generation of conditional KOs) and knowledge-related (the exclusive role of glial betapix in vascular stability/angiogenesis) novelty in this work that is going to benefit the community significantly.

However, the study has the following major weaknesses:

(1) The lack of glia-specific rescue of betapix in the global KOs/mutants prevents the study from making a compelling case for the unexpected glial-specific function in vascular development and stability.

(2) Given the known splice-isoform specific function of betapix in haemorrhaging (Liu et al, 2007), at least an expression profile of the isoforms in glia at the relevant timepoints would have further underscored betapix function.

(3) Direct evidence of the status of endothelial cell proliferation/survival deficits, if any, in the glial betapix KOs would have provided a key mechanistic handle. It becomes all the more relevant as Liu et al, 2012 have demonstrated reduced proliferation of endothelial cells in bbh fish and linked it to deficits in angiogenesis.

Author response:

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

Reviewer #1 (Public review):

The manuscript by Chiu et al describes the modification of the Zwitch strategy to efficiently generate conditional knockouts of zebrafish betapix. They leverage this system to identify a surprising glia-exclusive function of betapix in mediating vascular integrity and angiogenesis. Betapix has been previously associated with vascular integrity and angiogenesis in zebrafish, and betapix function in glia has also been proposed. However, this study identifies glial betapix in vascular stability and angiogenesis for the first time.

The study derives its strength from the modified CRISPR-based Zwitch approach to identify the specific role of glial betapix (and not neuronal, mural, or endothelial). Using RNA-in situ hybridization and analysis of scRNA-Seq data, they also identify delayed maturation of neurons and glia and implicate a reduction in stathmin levels in the glial knockouts in mediating vascular homeostasis and angiogenesis. The study also implicates a betapix-zfhx3/4-vegfa axis in mediating cerebral angiogenesis.

There is both technical (the generation of conditional KOs) and knowledge-related (the exclusive role of glial betapix in vascular stability/angiogenesis) novelty in this work that is going to benefit the community significantly.

While the text is well written, it often elides details of experiments and relies on implicit understanding on the part of the reader. Similarly, the figure legends are laconic and often fail to provide all the relevant details.

Thanks for this reviewer on his/her overall supports on our manuscript. We have now revised the manuscript text and figure legends making them to have all relevant details as much as we can. 

Specific comments:

(1) While the evidence from cKO's implicating glial betapix in vascular stability/angiogenesis is exciting, glia-specific rescue of betapix in the global KOs/mutants (like those performed for stathmin) would be necessary to make a water-tight case for glial betapix.

We fully agree with the reviewer that it would be ideal to examine glia-specific rescue of betaPix in its global KOs. At the same time, it is difficult to achieve optimal transient expression of betaPix by injecting plasmid clone of gfap:betaPix while it takes long time to establish stable transgenic line gfap:betaPix for rescuing mutant phenotypes. We would like to pursue this line of researches in the future.

(2) Splice variants of betapix have been shown to have differential roles in haemorrhaging (Liu, 2007). What are the major glial isoforms, and are there specific splice variants in the glial that contribute to the phenotypes described?

We agree that it would be important to address whether any specific splice variants in glia contribute to betaPix mutant phenotypes. Previous studies have shown that the isoform a of betaPix is ubiquitously expressed across various tissues, while isoforms b, c, and d are predominantly expressed in the nervous system. In mice, the expression level of isoform betaPix-d is essential for the neurite outgrowth and migration. In the nervous system, we have not assessed glial specific betaPix isoforms directly. Our current data cannot rule out whether specific isoform is involved in its function in glial responses. The Zwitch cassette of betaPix resides on intron 5, thus disrupting all transcripts when Cre is activated. However, we are fully aware of the potential of identifying glial betaPix isoform with direct downstream targets. Further studies to dissect their roles in cerebral vascular development and diseases are part of our future plans.

(3) Liu et al, 2012 demonstrated reduced proliferation of endothelial cells in bbh fish and linked it to deficits in angiogenesis. Are there proliferation/survival defects in endothelial cells in the glial KOs?

We thank the reviewer for highlighting endothelial cell phenotypes in betaPix mutants. We are aware of endothelial cells might directly link to the mutant defects in angiogenesis. We assessed and quantified endothelial migration by measuring the length of developing central arteries, but we did not examine endothelial cell proliferation/survival defects in glial KOs. In our scRNA-seq analysis, the proportion of endothelial cells reduced among betaPix deficiency, indicating that endothelial cell proliferation/survival might decrease in mutants. In this endothelial cell cluster, we found disrupted transcriptional landscape in a set of angiogenic associated genes (Figure 6M). While these analysis highlights altered angiogenic transcriptome profile in endothelial cells of betaPix knockouts, we acknowledge that our study does not directly address proliferation/survival phenotypes in endothelial cells, which warrants future investigations on the role of betaPix in regulating glia-endothelial cell interaction.  

Reviewer #2 (Public review):

Summary:

Using a genetic model of beta-pix conditional trap, the authors are able to regulate the spatio-temporal depletion of beta-pix, a gene with an established role in maintaining vascular integrity (shown elsewhere). This study provides strong in vivo evidence that glial beta-pix is essential to the development of the blood-brain barrier and maintaining vascular integrity. Using genetic and biochemical approaches, the authors show that PAK1 and Stathmins are in the same signaling axis as beta-pix, and act downstream to it, potentially regulating cytoskeletal remodeling and controlling glial migration. How exactly the glial-specific (beta-pix driven-) signaling influences angiogenesis or vascular integrity is not clear.

Strengths:

(1) Developing a conditional gene-trap genetic model which allows for tracking knockin reporter driven by endogenous promoter, plus allowing for knocking down genes. This genetic model enabled the authors to address the relevant scientific questions they were interested in, i.e., a) track expression of beta-pix gene, b) deletion of beta-pix gene in a cell-specific manner.

(2) The study reveals the glial-specific role of beta-pix, which was unknown earlier. This opens up avenues for further research. (For instance, how do such (multiple) cell-specific signaling converge onto endothelial cells which build the central artery and maintain the blood-brain barriers?)

We thank this reviewer for his/her overall supports on our work.

Weaknesses:

Major:

(1) The study clearly establishes a role of beta-pix in glial cells, which regulates the length of the central artery and keeps the hemorrhages under control. Nevertheless, it is not clear how this is accomplished.

(a) Is this phenotype (hemorrhage) a result of the direct interaction of glial cells and the adjacent endothelial cells? If direct, is the communication established through junctions or through secreted molecules?

Thanks for this critical question. We attempted to address this issue by performing live imaging using light-sheet confocal microscopy, but failed to achieve sub-cellular resolution. We don’t have data to address this critical issue that warrants future investigations. 

(b) The authors do not exclude the possibility that the effects observed on endothelial cells (quantified as length of central artery) could be secondary to the phenotype observed with deletion of glial beta-pix. For instance, can glial beta-pix regulate angiogenic factors secreted by peri-vascular cells, which consequently regulate the length of the central artery or vascular integrity?

Thank the reviewer for this critical point. While we found the major defects of endothelial cell migration quantified by the central artery length, could not rule out the participation of signals from other peri-vascular cells. We fully agree that it will be important to address the cell-type specific relationship by angiogenic factors. Of note, degradation of extracellular matrix and focal adhesion is critical for the hemorrhagic phenotypes of bbh mutants. In a previous published study in our group, we found that suppressing the globally induced MEK/ERK/MMP9 signaling in bbh mutants significantly decreases hemorrhages. Accordingly, we edited a paragraph in the Discussion section on pages 24-25. We plan to continue investigating whether the complex interactions in the perivascular space contribute to vascular integrity disruption, as well as the cross-talks among different cell types during vascular development in these mutants. We believe that our model of glial specific betaPix function will guide us to further study cellular interactions in the follow-up studies.

(c) The pictorial summary of the findings (Figure 7) does not include Zfhx or Vegfa. The data do not provide clarity on how these molecules contribute (directly or indirectly) to endothelial cell integrity. Vegfaa is expressed in the central artery, but the expression of the receptor in these endothelial cells is not shown. Similarly, all other experimental analyses for Zfhx and Vegfa expression were performed in glial cells. More experimental evidence is necessary to show the regulation of angiogenesis (of endothelial cells) by glial beta-pix. Is the Vegfaa receptor present on central arteries, and how does glial depletion of beta-pix affect its expression or response of central artery endothelial cells (both pertaining to angiogenesis and vascular integrity).

Thank this reviewer for pointing out this critical issue. We have now revised the pictorial summary including Zfhx or Vegfa information in Figure 7. The key receptors of VEGF-A ligand are VEGFR-1 and VEGFR-2. In zebrafish, expression of Vegfr-2, as known as kdrl, is well-documented at endothelial cells including the hindbrain central arteries. We fully agree that it would indeed be of great value to assess changes of kdrl expression pattern after betaPix deficiency in vivo. It warrants future investigations to address how the VEGFA-VEGFR2 signaling in endothelial cells is altered in betaPix mutants.

(2) Microtubule stabilization via glial beta-pix, claimed in Figure 5M, is unclear. Magnified images for h-betapix OE and h-stmn-1 glial cells are absent. Is this migration regulated by beta-pix through its GEF activity for Cdc42/Rac?

We have now revised Figure 5M to include magnified images for h-betaPIX and h-STMN1 overexpression groups. It has been shown that there is a positive feedback loop of microtubule regulation consisting of Rac1-Pak1-Stathmin at the cell edge (Zeitz and Kierfeld, 2014 Biophys J.). Previous studies have shown betaPix activates Rac1 through its GEF activity and also regulates the activity of Pak1 via direct binding. As reported by Kwon et al., betaPix-d isoform promotes neurite outgrowth via the PAK-dependent inactivation of Stathmin1. In this work, we did not assess binding activity of betaPix to Rac1 or Pak1. Nevertheless, our data on the rescue experiments via IPA-3 suggest that betaPix deficiency impaired migration through Pak1 signaling. 

(3) Hemorrhages are caused by compromised vascular integrity, which was not measured (either qualitatively or quantitatively) throughout the manuscript. The authors do measure the length of the central artery in several gene deletion models (2I, 3C. 5F/J, 6G/K), which is indicative of artery growth/ angiogenesis. How (if at all) defects in angiogenesis are an indication of hemorrhage should be explained or established. Do these angiogenic growth defects translate into junctional defects at later developmental time points? Formation and maintenance of endothelial cell junctions within the hemorrhaging arteries should be assessed in fish with deleted beta-pix from astrocytes.

We appreciate the reviewer’s point and agree that this is a key aspect we need to clarify. To address junctional defects in our model, we re-examined the scRNA-seq data and found mild downregulation of junction protein claudin-5a (cldn5a) levels in the transcriptome analysis of the endothelial cluster (Author response image 1). We agree in principle that single cell RNA sequencing findings should be validated by immunostaining. While we did not measure junctional defects directly in this work, we have previously reported comparable tight junction protein zonula occludens-1 (ZO1) expression between siblings and bbh mutants (Yang et al., 2017 Dis Model Mech). In zebrafish, functionally characterized blood brain barrier (BBB) is only identified after 3 dpf. The lack of mature BBB might be due to the immature status of barrier signature at this developmental stage. Hemorrhage phenotype occurred around 40 hpf, and hematomas would be almost completely absorbed at later stage since most mutants recover and survive to adulthood. Thus future studies are needed to address the junctional characteristics on the cellular and molecular level in later developmental stages of betaPix mutants.   

Author response image 1.

Violin plots showing cdh5, cldn5a, cldn5b and oclna expression levels in endothelial sub-cluster. ctrl, control siblings; ko, betaPix knockouts (CRISPR mutants); 1d or 2d, 1 or 2 days post fertilization.

(4) More information is required about the quality control steps for 10X sequencing (Figure 4, number of cells, reads, etc.). What steps were taken to validate the data quality? The EC groups, 1 and 2-days post-KO are not visible in 4C. One appreciates that the progenitor group is affected the most 2 days post-KO. But since the effects are expected to be on the endothelial cell group as well (which is shown in in vivo data), an extensive analysis should be done on the EC group (like markers for junctional integrity, angiogenesis, mesenchymal interaction, etc.). Are Stathmins limited to glial cells? Are there indicators for angiogenic responses in endothelial cells?

Thank the reviewer for these critical suggestions. The detailed statements about the quality control steps for 10X sequencing are now provided in the Materials and Methods section. We validate the data quality through multiple steps, including verification of the number of viable cells used in experiment, assessment of peak shapes and fragment sizes of scRNA-seq libraries, confirmation of sufficient cell counts and sequencing reads for data analyses, and implementation of stringent filtering steps to exclude low-quality cells. Stathmins expressions as shown in Violin plots in Figure 4E and stmn1a, stmn1b and stmn4l expressions in UMAP plots in Figure S6C. These expressions are not limited to glial cells but distributed more widely among zebrafish tissues. We would like to point out that despite the small amount, the endothelial cell clusters are presented in Figure 4C with color brown. The proportions of EC groups split by four sample are visualized in Figure S6B and shown significant reduction among betaPix knockouts at 2 dpf, which had similar trend as glial progenitors. In addition, gene ontology analysis identified a set of down-regulated angiogenic genes expression in endothelial cluster (Figure 6M). We realize our interpretation of endothelial cell phenotypes was not sufficiently clear in this work and have now added sentences to the manuscript text on pages 16-17. As noted above, future studies are needed to address how glial betaPix regulates endothelial cell and BBB function. 

Reviewing Editor Comments:

comments on your manuscript. Addressing comments 1-3 from Reviewer 1 and comment 1 and its subparts from Reviewer 2 (major weaknesses) will significantly improve the manuscript by reinforcing the cell autonomous requirement of betaPix and also gain mechanistic insights. In addition, extensive proofreading and editing of the text, as well as changes to the figure, figure legends, and the discussion as indicated by both reviewers, will improve the readability and clarity of this manuscript.

Thanks for Reviewing Editor on his/her supports on this manuscript. As noted above, we are trying to address the reviewers’ comments using the data we obtained in this work, as well as our plans for future investigations. We have now made extensive proofreading and editing of manuscript text and figure legends for improving the readability and clarity of this manuscript.

Reviewer #1 (Recommendations for the authors):

(1) The Discussion is written like an introduction with very little engagement with the data generated in the manuscript. The role of betapix-Pak-stathmin and betapix-zfhx3/4-vegfaa is barely discussed and contextualised vis-à-vis the current knowledge in the field.

We appreciate the reviewer’s critical comments regarding the Discussion section. We have now revised the manuscript text on pages 20-23 to address the role of betapix-Pak-stathmin and betapix-zfhx3/4-vegfaa axis with contributions from this work.

(2) Line 145: "light sheet microscopy" - explain that this was only for experiments involving fluorescence. Currently, it reads as if the data presented in Figures 1D and E are also obtained via light sheet microscopy. E.g., the paragraph starting on line 139 does not say what line was imaged (and what it labels) to reach the conclusions reached. This detail is not there even in the associated figure legend. Similarly, line 153 discusses radial glia, but there is no indication that these were labelled using Tg (GFAP:GFP) except in the figure annotation. There are various instances of such omissions throughout the text, and they should be remedied to indicate what each line is and what it labels, at least in the first instance.

Thank the reviewer for their thoughtful points. In this revised version, we have incorporated more statements of the objectives and methodologies in the text in pages 8-9. We hope that the revised manuscript can better present the data with clarifying methodologies and materials used in this work. 

(3) Figure 1E legend: What is the haemorrhage percentage? Is it the number of embryos per experiment showing hemorrhage? Indicate in the text. In the right panel, what is the number of embryos used? Please ensure all numbers (number of embryos, experiments, etc) used to plot any data in the set of figures in the entire manuscript are clearly indicated.

Thank the reviewer for the suggestion. In this revised version, we have incorporated more detailed statements in figures and figure legends in the manuscript to show the numbers of embryos used.

(4) The Discussion section suddenly introduces the blood-brain barrier and extensively discusses it. However, while cerebral haemorrhage can disrupt the BBB and exacerbate the effects of the haemorrhage, this manuscript does not suggest that a weakened BBB is the cause of haemorrhages in betapix mutants. More likely, betapix stabilises and maintains vascular integrity, and loss of this function causes haemorrhaging and subsequent disruption of the BBB. The glial function noted in this study is likely to be distinct from the glial function in BBB development and maintenance. The authors do not show any direct evidence for the latter. These should be shortened, and only relevant aspects facilitating contextualisation of data generated in this manuscript should be retained.

We have now revised the Discussion section to reduce the introduction of blood-brain barrier and add statements according to the suggestions from both reviewers. We hope that the revisions provide a more relevant and balanced discussion.

(5) Is the scratch assay in Figure 5 controlled for differences in cell proliferation among the different manipulations?

We plated the same numbers of cells and cultured them in the same condition. Before conducted scratch assay we replaced medium with serum-free culture medium to reduce the effect from cell proliferation among the different manipulation groups. 

(6) In the glioblastoma experiments involving betapix KD, does stathmin RNA/protein decrease? What about Ser 16 phosphorylation (as shown for neurons in Kwon et al, 2020)?

STMN1 RNA was down-regulated by betaPIX deficiency, which was rescued by betaPIX overexpression in glial cells (Author response image 2). These results are similar to those from in vivo analysis (Figure 5A, 5B and S7A). We agree with the reviewer that it would been ideal to examine Ser 16 phosphorylation of Stathmin in our models. However, we believe that our data have established Stathmins function downstream to betaPix.

Author response image 2.

qRT-PCR analysis showing that betaPIX over-expression (betaPix OE) rescued STMN1 expression in betaPIX siRNA knockdown (betaPix KD) in U251 cells. Data are presented in mean ± SEM; one-way ANOVA analysis with Dunnett's test, individual P values mentioned in the figure

(7) How was the rescue of betapix in glioblastoma cells with siRNA-mediated betapix knockdown performed? Is this by betapix-resistant cDNA? Further, no information about isoforms of betapix (both for siRNA-mediated KD and rescue) or stathmin is provided.

As similar to our Zwitch method that disrupting all betaPix transcripts in vivo, the knockdown of human betaPIX were designed to target conserved region of all transcripts in glioblastoma cell lines. And the rescue human betaPIX were obtained from the U251 cDNA library, ideally all isoforms enriched in the glioblastoma cell line would be isolated. The missing details are now provided in the Materials and Methods section, page 26. 

(8) It is unclear what the authors' thoughts are on the decrease in stathmin observed and the functional outcome of this decrease. The Discussion could benefit from this.

Thanks. We have now incorporated a new paragraph in the Discussion section at pages 21-22 addressing that down-regulated expression of Stathmins is associated with functional outcome of this decrease.

(9) Zfhx4 mRNA injection is performed on bbh and betapixKO (is this a global or glial KO?) and found to rescue haemorrhaging. While vegfaa mRNA increases, it is formally possible that the rescue is not due to the increase in vegfaa (or that vegfaa is sufficient). Injection of vegfaa mRNA could address this issue.

Zfhx4 mRNA injection was performed on bbh mutants and global betapix knockouts (crispr mutants). To avoid confusion, we have now included a sentence highlighting global knockout mutants used for this rescue experiment. For the second part, we acknowledge that this study cannot definitively prove the necessity of increased vegfaa levels in the rescue experiment. However, our data established Zhfx3/4 as novel downstream effectors to betaPix in cerebral vessel development. And these effects might partly be linked to angiogenic responses regulated by Zhfx3/4. In this revised version, we carefully proposed that Vegfaa signals act downstream of betaPix-Zfhx3/4 axis and highlighted the weakness of our manuscript on not fully investigating sufficiency of Vegfaa in the Discussion section at page 24. We intend to pursue more extensive analysis in our follow-up studies.

(10) A significant part of the manuscript looks at angiogenesis/vascularisation, however, the title of the paper only reflects vessel integrity (which can be distinct from angiogenesis).

Thanks. We have now changed the title to: Glial betaPix is essential for blood vessel development in the zebrafish brain

(11) Line 366: The BBB abbreviation is used without indicating the full form. Perhaps this can be introduced in the preceding sentence.

We have now edited the following sentence: “The maturation hallmark of central nervous system (CNS) vasculature is acquisition of blood brain barrier (BBB) properties, establishing a stable environment ...” in lines 386-387, Discussion section.

(12) Line 371: "rupture" and not "rapture".

We thank the reviewer for pointing out the spelling error, and have now made this correction. 

(13) Line 416: "is enriched" instead of "enriches"?

We have now edited as: “...end feet that is enriched with aquaporin-4 ...” in line 411, page 19. 

(14) The sentence in lines 121-123 should be simplified.

We have now revised this sentence as the following: “A previous work has shown that bubblehead (bbh^fn40a) mutant has a global reduction in betaPix transcripts, and bbh^m292 mutant has a hypomorphic mutation in betaPix, thus establishing that betaPix is responsible for bubblehead mutant phenotypes [10]”. 

(15) No mention in the text of what o-dianisine labels.

We have now edited the following sentence: “By using o-dianisidine staining to label hemoglobins, we found severe brain hemorrhages ...” in lines 131-133.

(16) Line 165: Sentence requires improvement. Perhaps "Vascularisation of the central arteries in the zebrafish hindbrain ...".

We have now edited this sentence as: “Vascularisation of the central arteries in the zebrafish hindbrain starts at 29 hpf.” in this revised version (line 176). 

(17) Line 184: Why is "hematopoiesis" mentioned? The genesis of blood cells is not tested anywhere in the manuscript.

Thanks. We have now edited this statement as: “IPA-3 treatment had no effect on heamorrhage induction in betaPix^ct/ct control siblings.” 

(18) Line 222-223: Improve "increasing trends". Perhaps "increased relative proportions". Clarify "progenitors" means neuronal and glial progenitors.

We have now edited this statement: “we found that most neuronal clusters increased relative proportions ...” in this revised version.

(19) Line 232-233: "arrow indicates" - perhaps "indicated by the arrow"? Also, the arrow indicating gfap needs to be mentioned in the Figure S6A legend. Cannot understand what is meant by "as of its enriched gfap".

We have now edited in the text as: “Figure S6A, indicated by the arrow”, and added “Box area and arrow highlighting gfap expressions.” in Figure S6 legend. To avoid confusion, we have revised "as of its enriched gfap" sentence as the following: “We next focused on the progenitor cluster owing to the enriched gfap expression and the significantly reduced numbers of cells in this cluster by betaPix deficiency.”

(20) Line 239 - 240: While the sentence says "... revealed three major categories:", well, more than 3 are mentioned subsequently.

To avoid possible confusion in the text, we have now removed the sub-category examples and presented the data as: “three major categories: epigenetic remodeling, microtubule organizations and neurotransmitter secretion/transportation (Figure 4D).” 

(21) Line 252: Stathmins negatively regulate microtubule stability. Why are they referred to as "microtubule polymerization genes stathmins"?

We are thankful to the reviewer for pointing out this error, and we have now made correction in the text as “microtubule-destabilizing protein Stathmins”.

(22) Line 262-265: The citation used to indicate concurrence with mouse data is disingenuous. That study did not show a reduction in stathmin levels upon betapix loss. Rather, it showed an increase in Ser16 phosphorylation on stathmin, which reduces stathmin's microtubule destabilising function. Please elaborate on the difference between the two studies.

We completely agree with the reviewer’s statement that in the cited article, increased Ser16 phosphorylation on stathmin reduces its microtubule destabilising function. While that study did not show a reduction in Stathmin levels, others have shown that transcriptionally downregulated Stathmins are associated with the impaired neuronal and glial development. We have now revised the Discussion section by adding a new paragraph to address the disrupted homeostasis of Stathmins in these previous studies and their possible association with our data. We hope that these changes we made can clarify this issue. 

(23) Line 310: While ZFHX3 levels are reduced in betapix mutants and KD in glioblastomas, were ZFHX3 and 4 up- or downregulated in the scRNA-Seq data?

Thanks for this critical point. Indeed, our results showed that ZFHX3 and 4 down-regulated in the glial progenitor cluster in the scRNA-Seq data (Figure S8A) in betaPix knockouts and the FACS-sorted glia cells (Figure S8B). 

(24) Line 317: "... betaPix acts upstream to Zfhx3/4-VEGFA signaling in regulating angiogenesis ...". While this is established later, the data at the time of this sentence does not warrant this claim.

We agree with the reviewer’s statement and restated this sentence in the following way: “Zfhx3/4 might act as downstream effector of betaPix.”

Reviewer #2 (Recommendations for the authors):

(1) The images shown in 2E/H, 3B, 6F/J can use a schematic that helps readers to understand what to expect or look for. Splitting up the channels may also help in visualizing the vasculature clearly.

Thank the reviewer for these suggestions. In this revised version, we have included schematic diagrams in the figures and incorporated more detailed statements in the legends.

(2) Many times, arrows are pointing to structures (2E/H, 3B), but are not explained clearly (neither in the text nor in the legends). In 3B, the arrow is pointing to a negative space.

(3) Legends are minimalistic and do not provide much information. The reader is left to interpret the data on their own.

We apologize for not explaining the figures in enough details. In this revised version, we have now incorporated more detailed statements in the figure legends and have adjusted arrows in all figures.

(4) The text needs heavy proofreading. For example:

(a) Line 208- the title does not seem appropriate since the following text does not discuss Stathmins at all, which comes later.

We agree with the reviewer’s statement and restated the title in the following way: “Single-cell transcriptome profiling reveals that gfap-positive progenitors were affected in betaPix knockouts.”

(b) There is no mention of Figure 7 throughout the text.

(c) Figure 7 does not include Zfhx or Vegfaa.

Thank the reviewer for pointing out these errors. We have now revised Figure 7 and incorporated it to corresponding paragraphs in the Discussion section. 

(5) The discussion seems incoherent in its current state.

We have now revised the Discussion section according to the suggestions from both reviewers. We hope these revisions adequately address your concerns.

(6) Please include some of the following points, if possible, in the discussion.

(a) How is GEF activity of Rac/Cdc42 expected to be affected in beta-pix KO fishes?

(b) What are the possible different ways the angiogenic pathways merge onto endothelial cells? Or do the authors imagine this process to be entirely driven by glial cells (directly)?

We would like to thank the reviewer for his/her invaluable suggestions. We have now revised the Discussion section and hope that these changes can provide better and more balanced discussion. Since we have no data directly related to GEF activity of Rac/Cdc42 that might be affected in betaPix mutants, as well as have very limited data showing how glial betaPix regulates cerebral endothelial cells and BBB function, we would like to have the Discussion focused on the CRISPR-induced KI and cKO technologies, glial betaPix function and brain hemorrhage, and the putative role of betaPix-Zfhx3/4-VEGF function in central artery development. 

References:

Daub, H., Gevaert, K., Vandekerckhove, J., Sobel, A., and Hall, A. (2001). Rac/Cdc42 and p65PAK regulate the microtubule-destabilizing protein stathmin through phosphorylation at serine 16. J Biol Chem 276, 1677-1680. 10.1074/jbc.C000635200.

Kim S, Park H, Kang J, Choi S, Sadra A, Huh SO. β-PIX-d, a Member of the ARHGEF7 Guanine Nucleotide Exchange Factor Family, Activates Rac1 and Induces Neuritogenesis in Primary Cortical Neurons. Exp Neurobiol. 2024;33(5):215-224. doi:10.5607/en24026

Kwon Y, Jeon YW, Kwon M, Cho Y, Park D, Shin JE. βPix-d promotes tubulin acetylation and neurite outgrowth through a PAK/Stathmin1 signaling pathway [published correction appears in PLoS One. 2020 May 13;15(5):e0233327. doi: 10.1371/journal.pone.0233327.]. PLoS One. 2020;15(4):e0230814. Published 2020 Apr 6. doi:10.1371/journal.pone.0230814

Kwon Y, Lee SJ, Shin YK, Choi JS, Park D, Shin JE. Loss of neuronal βPix isoforms impairs neuronal morphology in the hippocampus and causes behavioral defects. Anim Cells Syst (Seoul). 2025;29(1):57-71. Published 2025 Jan 8. doi:10.1080/19768354.2024.2448999

Wittmann, T., Bokoch, G.M., and Waterman-Storer, C.M. (2004). Regulation of microtubule destabilizing activity of Op18/stathmin downstream of Rac1. J Biol Chem 279, 6196-6203.10.1074/jbc.M307261200.

Zeitz, M., and Kierfeld, J. (2014). Feedback mechanism for microtubule length regulation by stathmin gradients. Biophys J 107, 2860-2871.10.1016/j.bpj.2014.10.056.

eLife Assessment

This paper addresses the significant question of quantifying epistasis patterns, which affect the predictability of evolution, by reanalyzing a recently published combinatorial deep mutational scan experiment. The findings are that epistasis is fluid, i.e. strongly background dependent, but that fitness effects of mutations are predictable based on the wild-type phenotype. However, these potentially interesting claims are inadequately supported by the analysis, because measurement noise is not accounted for, arbitrary cutoffs are used, and global nonlinearities are not sufficiently considered. If the results continue to hold after these major improvements in the analysis, they should be of interest to all biologists working in the field of fitness landscapes.

Reviewer #1 (Public review):

This paper describes a number of patterns of epistasis in a large fitness landscape dataset recently published by Papkou et al. The paper is motivated by an important goal in the field of evolutionary biology to understand the statistical structure of epistasis in protein fitness landscapes, and it capitalizes on the unique opportunities presented by this new dataset to address this problem.

The paper reports some interesting previously unobserved patterns that may have implications for our understanding of fitness landscapes and protein evolution. In particular, Figure 5 is very intriguing. However, I have two major concerns detailed below. First, I found the paper rather descriptive (it makes little attempt to gain deeper insights into the origins of the observed patterns) and unfocused (it reports what appears to be a disjointed collection of various statistics without a clear narrative. Second, I have concerns with the statistical rigor of the work.

(1) I think Figures 5 and 7 are the main, most interesting, and novel results of the paper. However, I don't think that the statement "Only a small fraction of mutations exhibit global epistasis" accurately describes what we see in Figure 5. To me, the most striking feature of this figure is that the effects of most mutations at all sites appear to be a mixture of three patterns. The most interesting pattern noted by the authors is of course the "strong" global epistasis, i.e., when the effect of a mutation is highly negatively correlated with the fitness of the background genotype. The second pattern is a "weak" global epistasis, where the correlation with background fitness is much weaker or non-existent. The third pattern is the vertically spread-out cluster at low-fitness backgrounds, i.e., a mutation has a wide range of mostly positive effects that are clearly not correlated with fitness. What is very interesting to me is that all background genotypes fall into these three groups with respect to almost every mutation, but the proportions of the three groups are different for different mutations. In contrast to the authors' statement, it seems to me that almost all mutations display strong global epistasis in at least a subset of backgrounds. A clear example is C>A mutation at site 3.

1a. I think the authors ought to try to dissect these patterns and investigate them separately rather than lumping them all together and declaring that global epistasis is rare. For example, I would like to know whether those backgrounds in which mutations exhibit strong global epistasis are the same for all mutations or whether they are mutation- or perhaps position-specific. Both answers could be potentially very interesting, either pointing to some specific site-site interactions or, alternatively, suggesting that the statistical patterns are conserved despite variation in the underlying interactions.

1b. Another rather remarkable feature of this plot is that the slopes of the strong global epistasis patterns seem to be very similar across mutations. Is this the case? Is there anything special about this slope? For example, does this slope simply reflect the fact that a given mutation becomes essentially lethal (i.e., produces the same minimal fitness) in a certain set of background genotypes?

1c. Finally, how consistent are these patterns with some null expectations? Specifically, would one expect the same distribution of global epistasis slopes on an uncorrelated landscape? Are the pivot points unusually clustered relative to an expectation on an uncorrelated landscape?

1d. The shapes of the DFE shown in Figure 7 are also quite interesting, particularly the bimodal nature of the DFE in high-fitness (HF) backgrounds. I think this bimodality must be a reflection of the clustering of mutation-background combinations mentioned above. I think the authors ought to draw this connection explicitly. Do all HF backgrounds have a bimodal DFE? What mutations occupy the "moving" peak?

1e. In several figures, the authors compare the patterns for HF and low-fitness (LF) genotypes. In some cases, there are some stark differences between these two groups, most notably in the shape of the DFE (Figure 7B, C). But there is no discussion about what could underlie these differences. Why are the statistics of epistasis different for HF and LF genotypes? Can the authors at least speculate about possible reasons? Why do HF and LF genotypes have qualitatively different DFEs? I actually don't quite understand why the transition between bimodal DFE in Figure 7B and unimodal DFE in Figure 7C is so abrupt. Is there something biologically special about the threshold that separates LF and HF genotypes? My understanding was that this was just a statistical cutoff. Perhaps the authors can plot the DFEs for all backgrounds on the same plot and just draw a line that separates HF and LF backgrounds so that the reader can better see whether the DFE shape changes gradually or abruptly.

1f. The analysis of the synonymous mutations is also interesting. However I think a few additional analyses are necessary to clarify what is happening here. I would like to know the extent to which synonymous mutations are more often neutral compared to non-synonymous ones. Then, synonymous pairs interact in the same way as non-synonymous pair (i.e., plot Figure 1 for synonymous pairs)? Do synonymous or non-synonymous mutations that are neutral exhibit less epistasis than non-neutral ones? Finally, do non-synonymous mutations alter epistasis among other mutations more often than synonymous mutations do? What about synonymous-neutral versus synonymous-non-neutral. Basically, I'd like to understand the extent to which a mutation that is neutral in a given background is more or less likely to alter epistasis between other mutations than a non-neutral mutation in the same background.

(2) I have two related methodological concerns. First, in several analyses, the authors employ thresholds that appear to be arbitrary. And second, I did not see any account of measurement errors. For example, the authors chose the 0.05 threshold to distinguish between epistasis and no epistasis, but why this particular threshold was chosen is not justified. Another example: is whether the product s12 × (s1 + s2) is greater or smaller than zero for any given mutation is uncertain due to measurement errors. Presumably, how to classify each pair of mutations should depend on the precision with which the fitness of mutants is measured. These thresholds could well be different across mutants. We know, for example, that low-fitness mutants typically have noisier fitness estimates than high-fitness mutants. I think the authors should use a statistically rigorous procedure to categorize mutations and their epistatic interactions. I think it is very important to address this issue. I got very concerned about it when I saw on LL 383-388 that synonymous stop codon mutations appear to modulate epistasis among other mutations. This seems very strange to me and makes me quite worried that this is a result of noise in LF genotypes.

Reviewer #2 (Public review):

Significance:

This paper reanalyzes an experimental fitness landscape generated by Papkou et al., who assayed the fitness of all possible combinations of 4 nucleotide states at 9 sites in the E. coli DHFR gene, which confers antibiotic resistance. The 9 nucleotide sites make up 3 amino acid sites in the protein, of which one was shown to be the primary determinant of fitness by Papkou et al. This paper sought to assess whether pairwise epistatic interactions differ among genetic backgrounds at other sites and whether there are major patterns in any such differences. They use a "double mutant cycle" approach to quantify pairwise epistasis, where the epistatic interaction between two mutations is the difference between the measured fitness of the double-mutant and its predicted fitness in the absence of epistasis (which equals the sum of individual effects of each mutation observed in the single mutants relative to the reference genotype). The paper claims that epistasis is "fluid," because pairwise epistatic effects often differs depending on the genetic state at the other site. It also claims that this fluidity is "binary," because pairwise effects depend strongly on the state at nucleotide positions 5 and 6 but weakly on those at other sites. Finally, they compare the distribution of fitness effects (DFE) of single mutations for starting genotypes with similar fitness and find that despite the apparent "fluidity" of interactions this distribution is well-predicted by the fitness of the starting genotype.

The paper addresses an important question for genetics and evolution: how complex and unpredictable are the effects and interactions among mutations in a protein? Epistasis can make the phenotype hard to predict from the genotype and also affect the evolutionary navigability of a genotype landscape. Whether pairwise epistatic interactions depend on genetic background - that is, whether there are important high-order interactions -- is important because interactions of order greater than pairwise would make phenotypes especially idiosyncratic and difficult to predict from the genotype (or by extrapolating from experimentally measured phenotypes of genotypes randomly sampled from the huge space of possible genotypes). Another interesting question is the sparsity of such high-order interactions: if they exist but mostly depend on a small number of identifiable sequence sites in the background, then this would drastically reduce the complexity and idiosyncrasy relative to a landscape on which "fluidity" involves interactions among groups of all sites in the protein. A number of papers in the recent literature have addressed the topics of high-order epistasis and sparsity and have come to conflicting conclusions. This paper contributes to that body of literature with a case study of one published experimental dataset of high quality. The findings are therefore potentially significant if convincingly supported.

Validity:

In my judgment, the major conclusions of this paper are not well supported by the data. There are three major problems with the analysis.

(1) Lack of statistical tests. The authors conclude that pairwise interactions differ among backgrounds, but no statistical analysis is provided to establish that the observed differences are statistically significant, rather than being attributable to error and noise in the assay measurements. It has been established previously that the methods the authors use to estimate high-order interactions can result in inflated inferences of epistasis because of the propagation of measurement noise (see PMID 31527666 and 39261454). Error propagation can be extreme because first-order mutation effects are calculated as the difference between the measured phenotype of a single-mutant variant and the reference genotype; pairwise effects are then calculated as the difference between the measured phenotype of a double mutant and the sum of the differences described above for the single mutants. This paper claims fluidity when this latter difference itself differs when assessed in two different backgrounds. At each step of these calculations, measurement noise propagates. Because no statistical analysis is provided to evaluate whether these observed differences are greater than expected because of propagated error, the paper has not convincingly established or quantified "fluidity" in epistatic effects.

(2) Arbitrary cutoffs. Many of the analyses involve assigning pairwise interactions into discrete categories, based on the magnitude and direction of the difference between the predicted and observed phenotypes for a pairwise mutant. For example, the authors categorize as a positive pairwise interaction if the apparent deviation of phenotype from prediction is >0.05, negative if the deviation is <-0.05, and no interaction if the deviation is between these cutoffs. Fluidity is diagnosed when the category for a pairwise interaction differs among backgrounds. These cutoffs are essentially arbitrary, and the effects are assigned to categories without assessing statistical significance. For example, an interaction of 0.06 in one background and 0.04 in another would be classified as fluid, but it is very plausible that such a difference would arise due to error alone. The frequency of epistatic interactions in each category as claimed in the paper, as well as the extent of fluidity across backgrounds, could therefore be systematically overestimated or underestimated, affecting the major conclusions of the study.

(3) Global nonlinearities. The analyses do not consider the fact that apparent fluidity could be attributable to the fact that fitness measurements are bounded by a minimum (the fitness of cells carrying proteins in which DHFR is essentially nonfunctional) and a maximum (the fitness of cells in which some biological factor other than DHFR function is limiting for fitness). The data are clearly bounded; the original Papkou et al. paper states that 93% of genotypes are at the low-fitness limit at which deleterious effects no longer influence fitness. Because of this bounding, mutations that are strongly deleterious to DHFR function will therefore have an apparently smaller effect when introduced in combination with other deleterious mutations, leading to apparent epistatic interactions; moreover, these apparent interactions will have different magnitudes if they are introduced into backgrounds that themselves differ in DHFR function/fitness, leading to apparent "fluidity" of these interactions. This is a well-established issue in the literature (see PMIDs 30037990, 28100592, 39261454). It is therefore important to adjust for these global nonlinearities before assessing interactions, but the authors have not done this.

This global nonlinearity could explain much of the fluidity claimed in this paper. It could explain the observation that epistasis does not seem to depend as much on genetic background for low-fitness backgrounds, and the latter is constant (Figure 2B and 2C): these patterns would arise simply because the effects of deleterious mutations are all epistatically masked in backgrounds that are already near the fitness minimum. It would also explain the observations in Figure 7. For background genotypes with relatively high fitness, there are two distinct peaks of fitness effects, which likely correspond to neutral mutations and deleterious mutations that bring fitness to the lower bound of measurement; as the fitness of the background declines, the deleterious mutations have a smaller effect, so the two peaks draw closer to each other, and in the lowest-fitness backgrounds, they collapse into a single unimodal distribution in which all mutations are approximately neutral (with the distribution reflecting only noise).<br /> Global nonlinearity could also explain the apparent "binary" nature of epistasis. Sites 4 and 5 change the second amino acid, and the Papkou paper shows that only 3 amino acid states (C, D, and E) are compatible with function; all others abolish function and yield lower-bound fitness, while mutations at other sites have much weaker effects. The apparent binary nature of epistasis in Figure 5 corresponds to these effects given the nonlinearity of the fitness assay. Most mutations are close to neutral irrespective of the fitness of the background into which they are introduced: these are the "non-epistatic" mutations in the binary scheme. For the mutations at sites 4 and 5 that abolish one of the beneficial mutations, however, these have a strong background-dependence: they are very deleterious when introduced into a high-fitness background but their impact shrinks as they are introduced into backgrounds with progressively lower fitness. The apparent "binary" nature of global epistasis is likely to be a simple artifact of bounding and the bimodal distribution of functional effects: neutral mutations are insensitive to background, while the magnitude of the fitness effect of deleterious mutations declines with background fitness because they are masked by the lower bound. The authors' statement is that "global epistasis often does not hold." This is not established. A more plausible conclusion is that global epistasis imposed by the phenotype limits affects all mutations, but it does so in a nonlinear fashion.

In conclusion, most of the major claims in the paper could be artifactual. Much of the claimed pairwise epistasis could be caused by measurement noise, the use of arbitrary cutoffs, and the lack of adjustment for global nonlinearity. Much of the fluidity or higher-order epistasis could be attributable to the same issues. And the apparently binary nature of global epistasis is also the expected result of this nonlinearity.

Reviewer #3 (Public review):

Summary:

The authors have studied a previously published large dataset on the fitness landscape of a 9 base-pair region of the folA gene. The objective of the paper is to understand various aspects of epistasis in this system, which the authors have achieved through detailed and computationally expensive exploration of the landscape. The authors describe epistasis in this system as "fluid", meaning that it depends sensitively on the genetic background, thereby reducing the predictability of evolution at the genetic level. However, the study also finds two robust patterns. The first is the existence of a "pivot point" for a majority of mutations, which is a fixed growth rate at which the effect of mutations switches from beneficial to deleterious (consistent with a previous study on the topic). The second is the observation that the distribution of fitness effects (DFE) of mutations is predicted quite well by the fitness of the genotype, especially for high-fitness genotypes. While the work does not offer a synthesis of the multitude of reported results, the information provided here raises interesting questions for future studies in this field.

Strengths:

A major strength of the study is its detailed and multifaceted approach, which has helped the authors tease out a number of interesting epistatic properties. The study makes a timely contribution by focusing on topical issues like the prevalence of global epistasis, the existence of pivot points, and the dependence of DFE on the background genotype and its fitness. The methodology is presented in a largely transparent manner, which makes it easy to interpret and evaluate the results.

The authors have classified pairwise epistasis into six types and found that the type of epistasis changes depending on background mutations. Switches happen more frequently for mutations at functionally important sites. Interestingly, the authors find that even synonymous mutations in stop codons can alter the epistatic interaction between mutations in other codons. Consistent with these observations of "fluidity", the study reports limited instances of global epistasis (which predicts a simple linear relationship between the size of a mutational effect and the fitness of the genetic background in which it occurs). Overall, the work presents some evidence for the genetic context-dependent nature of epistasis in this system.

Weaknesses:

Despite the wealth of information provided by the study, there are some shortcomings of the paper which must be mentioned.

(1) In the Significance Statement, the authors say that the "fluid" nature of epistasis is a previously unknown property. This is not accurate. What the authors describe as "fluidity" is essentially the prevalence of certain forms of higher-order epistasis (i.e., epistasis beyond pairwise mutational interactions). The existence of higher-order epistasis is a well-known feature of many landscapes. For example, in an early work, (Szendro et. al., J. Stat. Mech., 2013), the presence of a significant degree of higher-order epistasis was reported for a number of empirical fitness landscapes. Likewise, (Weinreich et. al., Curr. Opin. Genet. Dev., 2013) analysed several fitness landscapes and found that higher-order epistatic terms were on average larger than the pairwise term in nearly all cases. They further showed that ignoring higher-order epistasis leads to a significant overestimate of accessible evolutionary paths. The literature on higher-order epistasis has grown substantially since these early works. Any future versions of the present preprint will benefit from a more thorough contextual discussion of the literature on higher-order epistasis.

(2) In the paper, the term 'sign epistasis' is used in a way that is different from its well-established meaning. (Pairwise) sign epistasis, in its standard usage, is said to occur when the effect of a mutation switches from beneficial to deleterious (or vice versa) when a mutation occurs at a different locus. The authors require a stronger condition, namely that the sum of the individual effects of two mutations should have the opposite sign from their joint effect. This is a sufficient condition for sign epistasis, but not a necessary one. The property studied by the authors is important in its own right, but it is not equivalent to sign epistasis.

(3) The authors have looked for global epistasis in all 108 (9x12) mutations, out of which only 16 showed a correlation of R^2 > 0.4. 14 out of these 16 mutations were in the functionally important nucleotide positions. Based on this, the authors conclude that global epistasis is rare in this landscape, and further, that mutations in this landscape can be classified into one of two binary states - those that exhibit global epistasis (a small minority) and those that do not (the majority). I suspect, however, that a biologically significant binary classification based on these data may be premature. Unsurprisingly, mutational effects are stronger at the functional sites as seen in Figure 5 and Figure 2, which means that even if global epistasis is present for all mutations, a statistical signal will be more easily detected for the functionally important sites. Indeed, the authors show that the means of DFEs decrease linearly with background fitness, which hints at the possibility that a weak global epistatic effect may be present (though hard to detect) in the individual mutations. Given the high importance of the phenomenon of global epistasis, it pays to be cautious in interpreting these results.

(4) The study reports that synonymous mutations frequently change the nature of epistasis between mutations in other codons. However, it is unclear whether this should be surprising, because, as the authors have already noted, synonymous mutations can have an impact on cellular functions. The reader may wonder if the synonymous mutations that cause changes in epistatic interactions in a certain background also tend to be non-neutral in that background. Unfortunately, the fitness effect of synonymous mutations has not been reported in the paper.

(5) The authors find that DFEs of high-fitness genotypes tend to depend only on fitness and not on genetic composition. This is an intriguing observation, but unfortunately, the authors do not provide any possible explanation or connect it to theoretical literature. I am reminded of work by (Agarwala and Fisher, Theor. Popul. Biol., 2019) as well as (Reddy and Desai, eLife, 2023) where conditions under which the DFE depends only on the fitness have been derived. Any discussion of possible connections to these works could be a useful addition.

Author response:

Thank you for sharing a detailed review of our manuscript titled, Variations and predictability of epistasis on an intragenic fitness landscape. We have now carefully gone through the reviewers’ and the editor’s comments and have the following preliminary responses.

(1) Measurement noise in the folA fitness landscape. All three reviewers and the editors raise the important matter of incorporating measurement noise in the fitness landscape. The paper by Papkou and coworkers makes the fitness measurements of the landscape in six independent repeats. They show that the fitness data is highly correlated in each repeat, and use the weighted mean of the repeats to report their results. They do not study how measurement noise influences their findings. The results by Papkou and coworkers were our starting point, and hence, we built on the landscape properties reported in their study. As a result, we also analyse our results working with the same mean of the six independent measurements.

The main result of the work by Papkou and coworkers is that largest subgraph in the landscape has 514 fitness peaks. 

We revisit this result by quantifying how measurement noise changes this number. By doing this, we note the subgraph contains only 127 peaks which are statistically significant. We define a sequence as a peak when its corresponding fitness is greater than all its one-distance neighbours with a p-value < 0.05. This shows that, as pointed out in the reviews, incorporating noise in the landscape results significantly changes how we view the landscape – a facet not included in Papkou et al and the current version of our manuscript. 

Not incorporating measurement noise means that the entire landscape has 4055 peaks. When measurement noise is included in the analysis, this number reduces to 137, out of which 136 are high fitness backgrounds (functional). 

In the revised version of our manuscript, we will incorporate measurement noise in our analysis. Through this, we will also address the concern regarding the use of an arbitrary cut-off to study “fluid” epistasis. However, we note that arbitrary cut-offs to define DFEs have been recently used (Sane et al., PNAS, 2023).

We also note that previous work with large scale landscapes (Wu et al, eLife, 2016) also reported a fitness landscape with a single experiment, with no repeats. 

(2) Global nonlinearities and higher-order leading to fluid epistasis. Attempts at building models for higher-order epistasis from empirical data have largely been confined to landscapes of a limited data size. For example, Sailer & Harms, Genetics, 2017 propose models for higher-order epistasis from seven empirical data sets, each with less than a 100 data points. Another recent attempt (Park et al, Nat Comm, 2024) proposes rule for protein structure-function with 20 fitness landscapes. In this study, only one landscape which used fitness as a phenotype had ~160000 data points (of which only 42% were included for analysis). All other data sets which used fitness as a phenotype contained less than 10000 data points. While these statistical proposals of how higher-order epistasis operates exist, none of them are reliant of large scale, exhaustive network, like the one proposed by Papkou and coworkers.  

In the edited manuscript, we will replace our arbitrary cut-off with results of statistical tests carried out based on measurement noise. 

Global non-linearities shape evolutionary responses. We would like to emphasize that the goal of this work to study and understand how these global non-linearities result in patterns on a large fitness landscape by presenting the sum total of these fundamental factors in shaping statistical patterns. 

While we understand that we may not have sufficiently explained the effects of global non-linearities on our results, we do not agree with the reviewer’s conclusion that our results are artifacts of these non-linearities. We will expand on the role of these nonlinearities on the patterns that we observe (like, fitness being bounded, as pointed out by reviewer 2, or differential impact of a mutation in functional vs. non-functional variants).

We also speculate that changing our arbitrary cut-off (selection coefficient of 0.05) to measurement noise will not alter our results qualitatively. 

The question we address in our work is, therefore, how does the nature of epistasis change with genetic background over a large, exhaustive landscape. The nature of epistasis between two mutations is analysed in all 4⁷ backgrounds. The causative agents for the change in epistasis will be context-dependent, depending on the precise nature of the two mutations and the background. For instance, a certain background might simply introduce a Stop codon in the sequence. Notwithstanding these precise, local mechanistic explanations, we seek to answer how epistasis changes statistically in a sequence. Investigating statistical patterns which explain switch in nature of epistasis in deep, exhaustive landscapes is a long-term goal of this research.

(3) Last, in our revised manuscript, we will address the reviewers’ other minor comments on the various aspects of the manuscript.

eLife Assessment

This valuable study introduces the peptidisc-TPP approach as a promising solution to challenges in membrane proteomics, enabling thermal proteome profiling in a detergent-free system. The concept is innovative and holds significant potential, and the demonstration of its utility and validation is solid. The method presents a strong foundation for broader applications in identifying physiologically and pharmacologically relevant membrane protein-ligand interactions.

Reviewer #1 (Public review):

Summary:

The idea is appealing, but the authors have not sufficiently demonstrated the utility of this approach.

Strengths:

Novelty of the approach, potential implications for discovering novel interactions

Comments on revisions:

The authors have adequately addressed most of my concerns in this improved version of the manuscript

Reviewer #2 (Public review):

Summary:

The membrane mimetic thermal proteome profiling (MM-TPP) presented by Jandu et al. promises a useful way to minimize the interference of detergents in efficient mass spectrometry analysis of membrane proteins. Thermal proteome profiling is a mass spectrometric method that measures binding of a drug to different proteins in a cell lysate by monitoring thermal stabilization of the proteins because of the interaction with the ligands that are being studied. This method has been underexplored for membrane proteome because of the inefficient mass spectrometric detection of membrane proteins and because of the interference from detergents that are used often for membrane protein solubilization.

Strengths:

In this report the binding of ligands to membrane protein targets has been monitored in crude membrane lysates or tissue homogenates exalting the efficacy of the method to detect both intended and off-target binding events in a complex physiologically relevant sample setting. The manuscript is lucidly written and the data presented seems clear. Kudos to the authors. This methodology shows immense potential for identifying membrane protein binders (small-molecule or protein) in a near-native environment, and as a result promises to be a great tool for drug discovery campaigns.

Weaknesses:

While this is a solid report and a promising tool for analyzing membrane protein drug interactions in a detergent-free environment, it is crucial to bear in mind that the process of reconstitution begins with detergent solubilization of the proteome and does not completely circumvent structural perturbations invoked by detergents.

Author response:

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

Reviewer #1 (Public Review):

Summary: 

The idea is appealing, but the authors have not sufficiently demonstrated the utility of this approach.

Strengths: 

Novelty of the approach, potential impli=cations for discovering novel interactions

Weaknesses:

The Duong had introduced their highly elegant peptidisc approach several years ago. In this present work, they combine it with thermal proteome profiling (TPP) and attempt to demonstrate the utility of this combination for identifying novel membrane protein-ligand interactions.

While I find this idea intriguing, and the approach potentially useful, I do not feel that the authors had sufficiently demonstrated the utility of this approach. My main concern is that no novel interactions are identified and validated. For the presentation of any new methodology, I think this is quite necessary. In addition, except for MsbA, no orthogonal methods are used to support the conclusions, and the authors rely entirely on quantifying rather small differences in abundances using either iBAQ or LFQ.

We thank the reviewer for their thoughtful comments. In this revision, we have experimentally addressed the reviewer’s concerns in three ways:

(1) To demonstrate the utility of our MM-TPP method over the detergent-based TPP workflow (termed DB-TPP), we performed a side-by-side comparison using ATP–VO₄ at 51 °C (Figure 3B and Figure 4A). From the DB-TPP dataset, 7.4% of all identified proteins were annotated as ATP-binding, while 6.4% of proteins differentially stabilized were annotated as ATP-binding. In contrast, in the MM-TPP dataset, 9.3% of all identified proteins were annotated as ATP-binding proteins, while 17% of proteins differentially stabilized were annotated as ATP-binding. The lack of enrichment in the detergent-based approach indicates that the observed differences are likely stochastic, rather than a result of specific ATP–VO₄-mediated stabilization as found with MM-TPP. For instance, several key proteins—BCS1, P2RY6, SLC27A2, ABCB1, ABCC2, and ABCC9— found differentially stabilized using the MM-TPP method showed no such pattern in the DB-TPP dataset. This divergence strongly supports the specificity and utility of our Peptidisc approach. 

(2) To demonstrate that MM-TPP can resolve not only the broader effects of ATP–VO₄ but also specific ligand–protein interactions, we employed 2-methylthio-ADP (2-MeS-ADP), a selective agonist of the P2RY12 receptor [PMID: 24784220]. In that case, we observed clear thermal stabilization of P2RY12, with more than 6-fold increase in stability at both 51 °C and 57 °C (–log₁₀ p > 5.97; Figure 4B and Figure S4). Notably, no other proteins—including the structurally related but non-responsive P2RY6 receptor- showed comparable stabilization fold change at these temperatures.

(3) To further probe the reproducibility of the method, we performed an independent MMTPP evaluation with ATP–VO₄ at 51 °C using data-independent acquisition (DIA), in contrast to the data-dependent acquisition (DDA) approach used in the initial study (Figure S5). Overall, 7.8% of all identified proteins were annotated as ATP-binding, and as before, this proportion increased to 17% among proteins with log₂ fold changes greater than 0.5. Specifically, BCS1 and SLC27A2 exhibited strong stabilization (log₂ fold change > 1), while P2RY6, ABCB11, ABCC2, and ABCG2 showed moderate stabilization (log₂ fold changes between 0.5 and 1), and consistent with previous results, P2RX4 was destabilized, with a log₂ fold change below –1. These findings support the consistency and reproducibility of the method across distinct data acquisition methods.

My main concern is that no novel interactions are identified and validated. For the presentation of any new methodology, I think this is quite necessary.  

The primary objective of our study is to establish and benchmark the MM-TPP workflow using known targets, rather than to discover novel ligand–protein interactions. Identifying new binders requires extensive screening and downstream validations, which we believe is beyond the scope of this methodological report. Instead, our study highlights the sensitivity and reliability of the MM-TPP approach by demonstrating consistent and reproducible results with well-characterized interactions.

We respectfully disagree with the notion that introducing a new methodology must necessarily include the discovery of novel interactions. For instance, Martinez Molina et al. [PMID: 23828940] introduced the cellular thermal shift assay (CETSA) by validating established targets such as MetAP2 with TNP-470 and CDK2 with AZD-5438, without identifying novel protein–ligand pairs. Similarly, Kalxdorf et al. [PMID: 33398190] published their cell-surface thermal proteome profiling (CS-TPP) using Ouabain to stabilize the Na⁺/K⁺-ATPase pump in K562 cells, and SB431542 to stabilize its canonical target JAG1. In fact, when these methods revealed additional stabilizations, these were not validated but instead interpreted through reasoning grounded in the literature. For instance, they attributed the SB431542-induced stabilization of MCT1 to its reported role in cell migration and tumor invasiveness, and explained that SLC1A2 stabilization is related to the disruption of Na⁺/K⁺-ATPase activity by Ouabain. In the same way, our interpretation of ATP-VO₄–mediated stabilization of Mao-B is justified by predictive AlphaFold-3 rather than direct orthogonal assays, which are beyond the scope of our methodological presentation. 

Collectively, the influential studies cited above have set methodological precedents by prioritizing validation and proof-of-concept over merely finding uncharacterized binders. In the same spirit, our work is centred on establishing MM-TPP as a robust platform for probing membrane protein–ligand interactions in a water-soluble format. The discovery of novel binders remains an exciting future direction—one that will build upon the methodological foundation laid by the present study.

In addition, except for MsbA, no orthogonal methods are used to support the conclusions, and the authors rely entirely on quantifying rather small differences in abundances using either iBAQ or LFQ.

We deliberately began this study with our model protein, MsbA, examined under both native and overexpressed conditions, to establish an adequation between MMTPP (Figure 2D) and biochemical stability assays (Figure 2A). This validation has provided us with the foundation to confidently extend MM-TPP to the mouse organ proteome. To demonstrate the validity of our workflow, we have used ATP-VO₄ because it has expected targets. 

We note that orthogonal validation often requires overproduction and purification of the candidate proteins, including suitable antibodies, which is a true challenge for membrane proteins. Here, we demonstrate that MM-TPP can detect ligand-induced thermal shifts directly in native membrane preparations, without requiring protein overproduction or purification. We also emphasize several influential studies in TPP, including Martinez Molina et al. (PMID: 23828940) and Fang et al. (PMID: 34188175), which focused primarily on establishing and benchmarking the methodology, rather than on extensive orthogonal validation. In the same spirit, our study prioritizes methodological development, and accordingly, several orthogonal validations are now included in this revision.

[...] and the authors rely entirely on quantifying rather small differences in abundances using either iBAQ or LFQ.

To clarify, all analyses on ligand-induced stabilization or destabilization were carried out using LFQ values. The sole exception is on Figure 2B, where we used iBAQ values to depict the relative abundance of proteins within a single sample; this to show MsbA's relative level within the E. coli peptidisc library.

Respectfully, we disagree with the assertion that we are “quantifying rather small differences in abundances using either iBAQ or LFQ.” We were able to clearly distinguish between stabilizations driven by specific ligands binding to their targets versus those caused by non-specific ligands with broader activity. This is further confirmed by comparing 2-MeS-ADP, a selective ligand for P2RY12, with ATP-VO₄, a highly promiscuous ligand, and AMP-PNP, which exhibits intermediate breadth. When tested in triplicate at 51 °C, 2-MeS-ADP significantly altered the thermal stability of 27 proteins,  AMP-PNP 44 proteins, and ATP-VO₄ 230 proteins, consistent with the expectation that broader ligands stabilize more proteins nonspecifically. Importantly, 2-MeS-ADP produced markedly stronger stabilization of its intended target, P2RY12 (–log₁₀p = 9.32), than the top stabilized proteins for ATP–VO₄ (DNAJB3, –log₁₀p = 5.87) or AMP-PNP (FTH1, p = 5.34). Moreover, 2-MeS-ADP did not significantly stabilize proteins that were consistently stabilized by the broad ligands, such as SLC27A2, which was strongly stabilized by both ATP-VO₄ and AMP-PNP (–log₁₀ p>2.5). Together, these findings demonstrate that MMTPP can robustly distinguish between broad-spectrum and target-specific ligands, with selective ligands inducing stronger and more physiologically meaningful stabilization at their intended targets compared to promiscuous ligands.

Finally, we emphasize that our findings are not marginal, but meet quantitative and statistical rigor consistent with best practices in proteomics. We apply dual thresholds combining effect size (|log₂FC| ≥ 1, i.e., at least a two-fold change) with statistical significance (FDR-adjusted p ≤ 0.05)—criteria commonly used in proteomics methodology studies (e.g., PMID: 24942700, 38724498). Moreover, the stabilization and destabilization events we report are reproducible across biological replicates (n = 3), consistent across adjacent temperatures for most targets, and technically robust across acquisition modes (DDA vs. DIA). Taken together, these results reflect statistically valid and biologically meaningful effects, fully aligned with standards set by prior published proteomics studies.

Furthermore, the reported changes in abundances are solely based on iBAQ or LFQ analysis. This must be supported by a more quantitative approach such as SILAC or labeled peptides. In summary, I think this story requires a stronger and broader demonstration of the ability of peptidisc-TPP to identify novel physiologically/pharmacologically relevant interactions.

With respect to labeling strategies, we deliberately avoided using TMT due to concerns about both cost and potential data quality issues. Some recent studies have documented the drawbacks of TMT in contexts directly relevant to our work. For example, a benchmarking study of LiP-MS workflows showed that although TMT increased proteome depth and reduced technical variance, it was less accurate in identifying true drug–protein interactions and produced weaker dose–response correlations compared with label-free DIA approaches [PMID: 40089063]. More broadly, technical reviews have highlighted that isobaric tagging is intrinsically prone to ratio compression and reporterion interference due to co-isolation and co-fragmentation of peptides, which flatten measured fold-changes and obscure biologically meaningful differences [PMID: 22580419, 22036744]. In terms of SILAC, the technique requires metabolic incorporation of heavy amino acids, which is feasible in cultured cells but not in physiologically relevant tissues such as the liver organ used here. SILAC mouse models exist, but they are expensive and time-consuming [PMID: 18662549, 21909926]. We are not a mouse lab, and introducing liver organ SILAC labeling in our workflow is beyond the scope of these revisions. We also note that several hallmark TPP studies have been successfully carried out using label-free quantification [PMID: 25278616, 26379230, 33398190, 23828940], establishing this as an accepted and widely applied approach in the field. 

To further support our conclusions, we added controls showing that detergent solubilization of mouse liver membranes followed by SP4 cleanup fails to detect ATP-VO₄– mediated stabilization of ATP-binding proteins, underscoring the necessity of Peptidisc reconstitution for capturing ligand-induced thermal stabilization. We also present new data demonstrating selective stabilization of the P2Y12 receptor by its agonist 2-MeS-ADP, providing orthogonal, receptor-specific validation within the MM-TPP framework. Finally, an orthogonal DIA acquisition on separate replicates confirmed robust ATP-vanadate stabilization of ATP-binding proteins, including BCS1l and SLC27A2. Together, these additions reinforce that the observed stabilizations are genuine, physiologically relevant ligand–protein interactions and highlight the unique advantage of the Peptidisc-based workflow in capturing such events.

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Reviewer #2 (Public Review):

Summary:

The membrane mimetic thermal proteome profiling (MM-TPP) presented by Jandu et al. seems to be a useful way to minimize the interference of detergents in efficient mass spectrometry analysis of membrane proteins. Thermal proteome profiling is a mass spectrometric method that measures binding of a drug to different proteins in a cell lysate by monitoring thermal stabilization of the proteins because of the interaction with the ligands that are being studied. This method has been underexplored for membrane proteome because of the inefficient mass spectrometric detection of membrane proteins and because of the interference from detergents that are used often for membrane protein solubilization.

Strengths:

In this report the binding of ligands to membrane protein targets has been monitored in crude membrane lysates or tissue homogenates exalting the efficacy of the method to detect both intended and off-target binding events in a complex physiologically relevant sample setting.

The manuscript is lucidly written and the data presented seems clear. The only insignificant grammatical error I found was that the 'P' in the word peptidisc is not capitalized in the beginning of the methods section "MM-TPP profiling on membrane proteomes". The clear writing made it easy to understand and evaluate what has been presented. Kudos to the authors.

Weaknesses:

While this is a solid report and a promising tool for analyzing membrane protein drug interactions, addressing some of the minor caveats listed below could make it much more impactful.

The authors claim that MM-TPP is done by "completely circumventing structural perturbations invoked by detergents[1] ". This may not be entirely accurate, because before reconstitution of the membrane proteins in peptidisc, the membrane fractions are solubilized by 1% DDM. The solubilization and following centrifugation step lasts at least for 45 min. It is less likely that all the structural perturbations caused by DDM to various membrane proteins and their transient interactions become completely reversed or rescued by peptidisc reconstitution.

We thank the reviewer for this insightful comment. In response, we have revised the sentence and expanded the discussion to clarify that the Peptidisc provides a complementary approach to detergent-based preparations for studying membrane proteins, preserving native lipid–protein interactions and stabilization effects that may be diminished in detergent.

To further address the structural perturbations invoked by detergents, and as already detailed to our response to Reviewer 1, we have compared the thermal profile of the Peptidisc library to the mouse liver membranes solubilized with 1% DDM, after incubation with ATP–VO₄ at 51 °C (Figure 4A). The results with the detergent extract revealed random patterns of stabilization and destabilization, with only 6.4% of differentially stabilized proteins being ATP-binding—comparable to the 7.4% observed in the background. In contrast, in the Peptidisc library, 17% of differentially stabilized proteins were ATP-binding, compared to 9.3% in the background. Thus, while Peptidisc reconstitution does not fully avoid initial detergent exposure, these findings underscore the importance of implementing Peptidisc in the TPP workflow when dealing with membrane proteins.

In the introduction, the authors make statements such as "..it is widely acknowledged that even mild detergents can disrupt protein structures and activities, leading to challenges in accurately identifying drug targets.." and "[peptidisc] libraries are instrumental in capturing and stabilizing IMPs in their functional states while preserving their interactomes and lipid allosteric modulators...'. These need to be rephrased, as it has been shown by countless studies that even with membrane protein suspended in micelles robust ligand binding assays and binding kinetics have been performed leading to physiologically relevant conclusions and identification of protein-protein and protein-ligand interactions.

We thank the reviewer for this valuable feedback and fully agree with the point raised. In response, we have revised the Introduction and conclusion to moderate the language concerning the limitations of detergent use. We now explicitly acknowledge that numerous studies have successfully used detergent micelles for ligand-binding assays and kinetic analyses, yielding physiologically relevant insights into both protein–protein and protein–ligand interactions [e.g., PMID: 22004748, 26440106, 31776188].

At the same time, we clarify that the Peptidisc method offers a complementary advantage, particularly in the context of thermal proteome profiling (TPP), which involves mass spectrometry workflows that are incompatible with detergents. In this setting, Peptidiscs facilitate the detection of ligand-binding events that may be more difficult to observe in detergent micelles.

We have reframed our discussion accordingly to present Peptidiscs not as a replacement for detergent-based methods, but rather as a complementary tool that broadens the available methodological landscape for studying membrane protein interactions.

If the method involves detergent solubilization, for example using 1% DDM, it is a bit disingenuous to argue that 'interactomes and lipid allosteric modulators' characterized by lowaffinity interactions will remain intact or can be rescued upon detergent removal. Authors should discuss this or at least highlight the primary caveat of the peptidisc method of membrane protein reconstitution - which is that it begins with detergent solubilization of the proteome and does not completely circumvent structural perturbations invoked by detergents.

We would like to clarify that, in our current workflow, ligand incubation occurs after reconstitution into Peptidiscs. As such, the method is designed to circumvent the negative effects of detergent during the critical steps involving low-affinity interactions.

That said, we fully acknowledge that Peptidisc reconstitution begins with detergent solubilization (e.g., 1% DDM), and we have revised the conclusion to explicitly state this important caveat. As the reviewer correctly points out, this initial step may introduce some structural perturbations or result in the loss of weakly associated lipid modulators.

However, reconstitution into Peptidiscs rapidly restores a detergent-free environment for membrane proteins, which has been shown in our previous studies [PMID: 38577106, 38232390, 31736482, 31364989] to mitigate these effects. Specifically, we have demonstrated that time-limited DDM exposure, followed by Peptidisc reconstitution, minimizes membrane protein delipidation, enhances thermal stability, retains functionality, and preserves multi-protein assemblies.

It would also be important to test detergents that are even milder than 1% DDM and ones which are harsher than 1% DDM to show that this method of reconstitution can indeed rescue the perturbations to the structure and interactions of the membrane protein done by detergents during solubilization step. 

We selected 1% DDM based on our previous work [PMID: 37295717, 39313981,38232390], where it consistently enabled robust and reproducible solubilization for Peptidisc reconstitution. We agree that comparing milder detergents (e.g., LMNG) and harsher ones (e.g., SDC) would provide valuable insights into how detergent strength influences structural perturbations, and how effectively these can be mitigated by Peptidisc reconstitution. Preliminary data (not shown) from mouse liver membranes indicate broadly similar proteomic profiles following solubilization with DDM, LMNG, and SDC, although potential differences in functional activity or ligand binding remain to be investigated.

Based on the methods provided, it appears that the final amount of detergent in peptidisc membrane protein library was 0.008%, which is ~150 uM. The CMC of DDM depending on the amount of NaCl could be between 120-170 uM.

While we cannot entirely rule out the presence of residual DDM (0.008%) in the raw library, its free concentration may be lower than initially estimated. This is related to the formation of mixed micelles with the amphipathic peptide scaffold, which is supplied in excess during reconstitution. These mixed micelles are subsequently removed during the ultrafiltration step. Furthermore, in related work using His-tagged Peptidiscs [PMID: 32364744], we purified the library by nickel-affinity chromatography following a 5× dilution into a detergent-free buffer. Although this purification step reduced the number of soluble proteins, the same membrane proteins were retained, suggesting that any residual detergent does not significantly interfere with Peptidisc reconstitution. Supporting this, our MM-TPP assays on purified libraries (data not shown) consistently demonstrated stabilization of ATP-binding proteins (e.g., SLC27A2, DNAJB3), indicating that the observed ligand–protein interactions result from successful incorporation into Peptidiscs.

Perhaps, to completely circumvent the perturbations from detergents other methods of detergentfree solubilization such as using SMA polymers and SMALP reconstitution could be explored for a comparison. Moreover, a comparison of the peptidisc reconstitution with detergent-free extraction strategies, such as SMA copolymers, could lend more strength to the presented method.

We agree that detergent-free methods such as SMA polymers hold promise for membrane protein solubilization. However, in preliminary single-replicate experiments using SMA2000 at 51 °C in the presence of ATP–VO₄ (data not shown), we observed broad, non-specific stabilization effects. Of the 2,287 quantified proteins, 9.3% were annotated as ATP-binding, yet 9.9% of the 101 proteins showing a log₂ fold change >1 or <–1 were ATPbinding, indicating no meaningful enrichment. Given this lack of specificity and the limited dataset, we chose not to pursue further SMA experiments and have not included them here. However, in a recent study (https://doi.org/10.1101/2025.08.25.672181), we directly compared Peptidisc, SMA, and nanodiscs for liver membrane proteome profiling. In that work, Peptidisc outperformed both SMA and nanodiscs in detecting membrane protein dysregulation between healthy and diseased liver. By extension, we expect Peptidisc to offer superior sensitivity and specificity for detecting ligand-induced stabilization events, such as those observed here with ATP–vanadate.

Cross-verification of the identified interactions, and subsequent stabilization or destabilizations, should be demonstrated by other in vitro methods of thermal stability and ligand binding analysis using purified protein to support the efficacy of the MM-TPP method. An example cross-verification using SDS-PAGE, of the well-studied MsbA, is shown in Figure 2. In a similar fashion, other discussed targets such as, BCS1L, P2RX4, DgkA, Mao-B, and some un-annotated IMPs shown in supplementary figure 3 that display substantial stabilization or destabilization should be cross-verified.

We appreciate this suggestion and note that a similar point was raised in R1’s comment “In addition, except for MsbA, no orthogonal methods are used to support the conclusions, and the authors rely entirely on quantifying rather small differences in abundances using either iBAQ or LFQ.” We have developed a detailed response to R1 on this matter, which equally applies here. 

Cited Reference:

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Reviewer #1 (Recommendations for the authors):

“The authors use iBAC or LFQ to compare across samples. This inconsistency is puzzling. As far as I know, LFQ should always be used when comparing across samples”

As mentioned above, we use iBAQ only in Fig. 2B to illustrate within-sample relative abundance; all comparative analyses elsewhere use LFQ. We have updated the Fig. 2B legend to state this explicitly.

We used iBAQ Fig. 2B as it provides a notion of protein abundance within a sample, normalizing the summed peptide intensities by the number of theoretically observable peptides. This normalization facilitates comparisons between proteins within the same sample, offering a clearer understanding of their relative molar proportions [PMID: 33452728]. LFQ, by contrast, is optimized for comparing the same protein across different samples. It achieves this by performing delayed normalization to reduce run-to-run variability and by applying maximal peptide ratio extraction, which integrates pairwise peptide intensity ratios across all samples to build a consistent protein-level quantification matrix [PMID: 24942700]. These features make LFQ more robust to missing values and technical variation, thereby enabling accurate detection of relative abundance changes in the same protein under different experimental conditions. This distinction is well supported by the proteomics literature: Smits et al. [PMID: 23066101] used iBAQ specifically to determine the relative abundance of proteins within one sample, whereas LFQ was applied for comparative analyses between conditions.

“[Regarding Figure 2A] Why does the control also contain ATP-vanadate? Also, I am not aware of a commercially available chemical "ATP-VO4". I assume this is a mistake”

The control condition in Figure 2A was mislabeled, and the figure has been corrected to remove this discrepancy. In our experiments, ATP and orthovanadate (VO₄) were added together, and for simplicity this was annotated as “ATP-VO₄.” 

“[Regarding Figure 2B] What is the fold change in MsbA iBAQ values? It seems that the differences are quite small, and as such require a more quantitative approach than iBAQ (e.g SILAC or some other internal standard). In addition, what information does this panel add relative to 2C”

The figure has been updated to clarify that the values shown are log₂transformed iBAQ intensities. Figures 2B and 2C are complementary: Figure 2B shows that in the control sample, MsbA’s peptide abundance decreases with temperatures (51, 56, and 61 °C) relative to the remaining bulk proteins. Figure 2C shows the specific thermal profiles of MsbA in control and ATP–vanadate conditions. To make this clearer, we have added a sentence to the Results section explaining the specific role of Figure 2B.

Together, these panels indicate that the method can identify ligand-induced stabilization even for proteins whose abundance decreases faster than the bulk during the TPP assay. We have provided the rationale for not using SILAC or TMT labeling in our public response.

“[Regarding Figure 2C] Although not mentioned in the legend, I assume this is iBAQ quantification, which as mentioned above isn't accurate enough for such small differences. In addition, I find this data confusing: why is MsbA more stable at the lower temperatures in the absence of ATP-vanadate? The smoothed-line representation is misleading, certainly given the low number of data points”

The data presented represent LFQ values for MsbA, and we have updated the figure legend to clearly indicate this. Additionally, as suggested, we have removed the smoothing line to more accurately reflect the data. Regarding the reviewer’s concern about stability at lower temperatures, we note that MsbA exhibits comparable abundance at 38 °C and 46 °C under both conditions, with overlapping error bars. We therefore interpret these data as indicating no significant difference in stability at the lower temperatures, with ligand-dependent stabilization becoming apparent only at elevated temperatures. We do not exclude the possibility that MsbA stability at these temperatures is affected by the conformational dynamics of this ABC transporter upon ATP binding and hydrolysis.

“[Regarding Figure 3A] is this raw LFQ data? Why did the authors suddenly change from iBAQ to LFQ? I find this inconsistency puzzling”

To clarify, all analyses of protein stabilization or destabilization presented in the manuscript are based on LFQ values. The only instance where iBAQ was used is Figure 2B, where it served to illustrate the relative peptide abundance of MsbA within the same sample. We have revised the figure legends and text to make this distinction explicit and ensure consistency in presentation.

“[Regarding Figure 3B] The non-specific ATP-dependent stabilization increases the likelihood of false positive hits. This limitation is not mentioned by the authors. I think it is important to show other small molecules, in addition to ATP. The authors suggest that their approach is highly relevant for drug screening. Therefore, a good choice is to test an effect of a known stabilizing drug (eg VX-809 and CFTR)”

We thank the reviewer for this suggestion. As noted in the manuscript (results and discussion sections), ATP is a natural hydrotrope and is therefore expected to induce broad, non-specific stabilization effects, a phenomenon also observed in previous proteome-wide studies, which demonstrated ATP’s widespread influence on cytosolic protein solubility and thermal stability (PMID: 30858367). To demonstrate that MM-TPP can resolve specific ligand–protein interactions beyond these global ATP effects, we tested 2-methylthio-ADP (2-MeS-ADP), a selective agonist of P2RY12 (PMID: 14755328). In these experiments, we observed robust and reproducible stabilization of P2RY12 at both 51°C and 57°C, with no consistent stabilization of unrelated proteins across temperatures. This provides direct evidence that our workflow can distinguish specific from non-specific ligand-induced effects. We selected 2-MeS-ADP due to its structural stability and receptor higher-affinity over ADP, allowing us to extend our existing workflow while testing a receptor-specific interaction. We agree that extending this approach to clinically relevant small-molecule drugs, such as VX-809 with CFTR, would further underscore the pharmacological potential of MM-TPP, and we have now noted this as an important avenue for future studies.

“X axis of Figure 3B: Log 2 fold difference of what? iBAQ? LFQ? Similar ambiguity regarding the Y axis of 3E. What peptide? And why the constant changes in estimating abundances?”

We thank the reviewer for pointing out these inaccuracies in the figure annotations. As mentioned above, all analyses (except Figure 2B) are based on LFQ values. We have revised the figure legends and text to make this clear.

In Figure 3E, “peptide intensity” refers to log2 LFQ peptide intensities derived from the BCS1L protein, as indicated in the figure caption. 

“The authors suggest that P2RY6 and P2RY12 are stabilized by ADP, the hydrolysis product of ATP. Currently, the support for this suggestion is highly indirect. To support this claim, the authors need to directly show the effect of ADP. In reference to the alpha fold results shown in Figure 4D, the authors state that "Collectively, these data highlight the ability of MM-TPP to detect the side effects of parent compounds, an important consideration for drug development". To support this claim, it is necessary to show that Mao-B is indeed best stabilized with ADP or AMP, rather than ATP.”

In this revision, we chose not to test ADP directly, as it is a broadly binding, relatively weak ligand that would likely stabilize many proteins without revealing clear target-specific effects. Since we had already evaluated ATP-VO₄, a similarly broad, non-specific ligand, additional testing with ADP would provide limited additional insight. Instead, we prioritized 2-methylthio-ADP, a selective agonist of P2RY12, to more effectively demonstrate the specificity of MM-TPP. With this ligand, we observed clear and reproducible stabilization of P2RY12, underscoring the ability of MM-TPP to resolve receptor–ligand interactions beyond ATP’s broad hydrotropic effects. Importantly, and as expected, we did not observe stabilization of the related purinergic receptor P2RY6, further supporting the specificity of the observed effect.

We have also revised the AlphaFold-related statement in Figure 4D to adopt a more cautious tone: “Collectively, these data suggest that MM-TPP may detect potential side effects of parent compounds, an important consideration for drug development.” In this context, we use AlphaFold not as a validation tool, but rather as a structural aid to help rationalize why certain off-target proteins (e.g., ATP with Mao-B) exhibit stabilization.

Reviewer #2 (Recommendations for the authors):

“In the main text, it will be useful to include the unique peptides table of at least the targets discussed in the manuscript. For example, in presence of AMP-PNP at 51oC P2RY6 shows 4-6 peptides in all n=3 positive & negative ionization modes. But, for P2RY12 only 1-3 peptides were observed. Depending on the sequence length and the relative abundance in the cell of a protein of interest, the number of peptides observed could vary a lot per protein. Given the unique peptide abundance reported in the supplementary file, for various proteins in different conditions, it appears the threshold of observation of two unique peptides for a protein to be analyzed seems less stringent.”

By applying a filter requiring at least two unique peptides in at least one replicate, we exclude, on average, 15–20% of the total identified proteins. We consider this a reasonable level of stringency that balances confidence in protein identification with the retention of relevant data. This threshold was selected because it aligns with established LC-MS/MS data analysis practices (PMID: 32591519, 33188197, 26524241), and we have included these references in the Methods section to justify our approach. We have included in this revision a Supplemental Table 2 showing the unique peptide counts for proteins highlighted in this study.  

“It appears that the time of heat treatment for peptidisc library subjected to MM-TPP profiling was chosen as 3 min based on the results presented in Supplementary Figure 1A, especially the loss of MsbA observed in 1% DDM after 3 min heat perturbation. However, when reconstituted in peptidisc there seems to be no loss in MsbA even after 12 mins at 45oC. So, perhaps a longer heat treatment would be a more efficient perturbation.”

Previous studies indicate that heat exposure of 3–5 minutes is optimal for visualizing protein denaturation (PMID: 23828940, 32133759). We have added a statement to the Results section to justify our choice of heat exposure. Although MsbA remains stable at 45 °C for extended periods, higher temperatures allow for more effective perturbation to reveal destabilization. Supplementary Figure 1A specifically illustrates MsbA instability in detergent environments.

“Some of the stabilized temperatures listed in Table 1 are a bit confusing. For example, ABCC3 and ABCG2. In the case of ABCC3 stabilization was observed at 51oC and 60oC, but 56oC is not mentioned. In the same way, 51oC is not mentioned for ABCG2. You would expect protein to be stabilized at 56oC if it is stabilized at both 51oC and 60oC. So, it is unclear if the stabilizations were not monitored for these proteins at the missing temperatures in the table or if no peptides could be recorded at these temperatures as in the case of P2RX4 at 60oC in Figure 4C.”

Both scenarios are represented in our data. For some proteins, like ABCG2, sufficient peptide coverage was achieved, but no stabilization was observed at intermediate temperatures (e.g., 56 °C), likely because the perturbation was not strong enough to reveal an effect. In other cases, such as ABCC3 at 56 °C or P2RX4 at 60 °C, the proteins were not detected due to insufficient peptide identifications at those temperatures, which explains their omission from the table. 

“In Figure 4C, it is perplexing to note that despite n = 3 there were no peptide fragments detected for P2RX4 at 60oC in presence of ATP-VO4, but they were detected in presence of AMP-PNP. It will be useful to learn authors explanation for this, especially because both of these ligands destabilize P2RX4. In Figure 4B, it would have been great to see the effect of ADP too, to corroborate the theory that ATP metabolites could impact the thermal stability.”

In Figure 4C, the absence of P2RX4 peptide detection at 60 °C with ATP–VO₄ mirrors variability observed in the corresponding control (n = 6). Specifically, neither the control nor ATP–VO₄ produced unique peptides for P2RX4 at 60 °C in that replicate, whereas peptides were detected at 60 °C in other replicates for both the control and AMPPNP, and at 64 °C for ATP–VO₄, the controls, and AMP-PNP. Such missing values are a natural feature of MS-based proteomics and can arise from multiple technical factors, including inconsistent heating, incomplete digestion, stochastic MS injection, or interference from Peptidisc peptides. We therefore interpret the absence of peptides in this replicate as a technical artifact rather than evidence against protein destabilization. Importantly, the overall dataset consistently shows that both ATP–VO₄ and AMP-PNP destabilize P2RX4, supporting their characterization as broad, non-specific ligands with off-target effects.

Because ATP and ADP belong to the same class of broadly binding, non-specific ligands, additional testing with ADP would not provide meaningful mechanistic insight. Instead, we chose to test 2-methylthio-ADP, a selective P2RY12 agonist. This experiment revealed robust, reproducible stabilization of P2RY12, without consistent effects on unrelated proteins at 51 °C and 57 °C, thereby demonstrating the ability of MM-TPP to detect specific receptor–ligand interactions.

Finally, we note that P2RX4 is not a primary target of ATP–VO₄ or AMP-PNP. Consequently, the observed destabilization of P2RX4 is expected to be less pronounced than the strong, physiologically consistent stabilization of ABC transporters by ATP–VO₄, as shown in Figure 3D, where the majority of ABC transporters are thermally stabilized across all tested temperatures.

“As per Figure 4, P2Y receptors P2RY6 and P2RY12 both showed great thermal stability in presence of ATP-VO4 despite their preference for ADP. The authors argue this could be because of ATP metabolism, and binding of the resultant ADP to the P2RY6. If P2RX4 prefers ATP and not the metabolized product ADP that apparently is available, ideally you should not see a change in stability. A stark destabilization would indicate interaction of some sorts. P2X receptors are activated by ATP and are not naturally activated by AMP-PNP. So, destabilization of P2RX4 upon binding to ATP that can activate P2X receptors is conceivable. However, destabilization both in presence of ATP-VO4 and AMP-PNP is unclear. It is perhaps useful to test effect of ADP using this method, and maybe even compare some antagonists such as TNPATP.”

In this study, we did not directly test ADP, as we had already demonstrated that MM-TPP detects stabilization by broad-binding ligands such as ATP–VO₄. Instead, we focused on a more selective ligand, 2-MeS-ADP, a specific agonist of P2RY12 [PMID: 14755328]. Here, we observed robust and reproducible stabilization of P2RY12 at 51 °C and 57 °C, while P2RY6 showed no significant changes, and no other proteins were consistently stabilized (Figure 4B, S4). This confirms that MM-TPP can distinguish specific ligand–receptor interactions from broader ATP-induced effects. To further explore the assay’s nuance and sensitivity, testing additional nucleotide ligands—including antagonists like TNP-ATP or ATPγS—would provide valuable insights, and we have identified this as an important future direction.

eLife Assessment

This valuable study reports the physiological function of a putative transmembrane UDP-N-acetylglucosamine transporter called SLC35G3 in spermatogenesis. The conclusion that SLC35G3 is a new and essential factor for male fertility in mice and probably in humans is supported by convincing data. This study will be of interest to reproductive biologists and physicians working on male infertility.

Reviewer #2 (Public review):

Summary:

This study characterized the function of SLC35G3, a putative transmembrane UDP-N-acetylglucosamine transporter, in spermatogenesis. They showed that SLC35G3 is testis-specific and expressed in round spermatids. Slc35g3-null males were sterile but females were fertile. Slc35g3-null males produced normal sperm count but sperm showed subtle head morphology. Sperm from Slc35g3-null males have defects in uterotubal junction passage, ZP binding, and oocyte fusion. Loss of SLC35G3 causes abnormal processing and glycosylation of a number sperm proteins in testis and sperm. They demonstrated that SLC35G3 functions as a UDP-GlcNAc transporter in cell lines. Two human SLC35G3 variants impaired its transporter activity, implicating these variants in human infertility.

Strengths:

This study is thorough. The mutant phenotype is strong and interesting. The major conclusions are supported by the data. This study demonstrated SLC35G3 as a new and essential factor for male fertility in mice, which is likely conserved in humans.

Weaknesses:

Some data interpretations needed to be revised. These have been adequately addressed in the revised manuscript.

Author response:

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

Reviewer #1 (Public review):

Summary:

In the present manuscript, Mashiko and colleagues describe a novel phenotype associated with deficient SLC35G3, a testis-specific sugar transporter that is important in glycosylation of key proteins in sperm function. The study characterizes a knockout mouse for this gene and the multifaceted male infertility that ensues. The manuscript is well-written and describes novel physiology through a broad set of appropriate assays.

Strengths:

Robust analysis with detailed functional and molecular assays

Weaknesses:

(1) The abstract references reported mutations in human SLC35G3, but this is not discussed or correlated to the murine findings to a sufficient degree in the manuscript. The HEK293T experiments are reasonable and add value, but a more detailed discussion of the clinical phenotype of the known mutations in this gene and whether they are recapitulated in this study (or not) would be beneficial.

Since no patients have been identified, our experiment was conducted to investigate the activity of the mutation found in humans.

(2) Can the authors expand on how this mutation causes such a wide array of phenotypic defects? I am surprised there is a morphological defect, a fertilization defect, and a transit defect. Do the authors believe all of these are present in humans as well?

Thank you for your comment. There are many glycoprotein-coding genes that influence sperm head morphology, fertilization defect, and transit defect have been identified in knockout mouse studies, and most of these are conserved in humans. Therefore, we believe that glycan modification by SLC35G3 is also involved in the regulation of human sperm. 

Reviewer #2 (Public review):

Summary:

This study characterized the function of SLC35G3, a putative transmembrane UDP-N-acetylglucosamine transporter, in spermatogenesis. They showed that SLC35G3 is testis-specific and expressed in round spermatids. Slc35g3-null males were sterile, but females were fertile. Slc35g3-null males produced a normal sperm count, but sperm showed subtle head morphology. Sperm from Slc35g3-null males have defects in uterotubal junction passage, ZP binding, and oocyte fusion. Loss of SLC35G3 causes abnormal processing and glycosylation of a number of sperm proteins in the testis and sperm. They demonstrated that SLC35G3 functions as a UDP-GlcNAc transporter in cell lines. Two human SLC35G3 variants impaired their transporter activity, implicating these variants in human infertility.

Strengths:

This study is thorough. The mutant phenotype is strong and interesting. The major conclusions are supported by the data. This study demonstrated SLC35G3 as a new and essential factor for male fertility in mice, which is likely conserved in humans.

Weaknesses:

Some data interpretations need to be revised.

Thank you for comments. We revised interpretations.

Reviewer #1 (Recommendations for the authors):

(1) The introduction could be structured more efficiently. Much of what is discussed in the first paragraph appears to be redundant to the second paragraph (or perhaps unrelated to the present manuscript).

In the Introduction, we described the process of glycoprotein formation, 1) quality control or nascent glycoproteins in the ER and its relations importance in sperm fertilizing ability, 2) glycan maturation in the Golgi apparatus and its importance in sperm fertilizing ability, and 3) the supply of nucleotide sugars as the basis of these processes. 

We would like to retain this structure in the revised manuscript and appreciate your understanding.

(2) Given the significant difference in morphology between murine and human sperm, can the authors comment on whether these findings are directly translatable to humans?

Thank you for your comment. There are significant differences in sperm morphology between mice and humans, but many glycoprotein-coding genes that influence sperm head morphology have been identified in knockout mouse studies, and most of these are conserved in humans. Therefore, we believe that glycan modification by SLC35G3 is also involved in the regulation of human sperm head morphology. Observing sperm samples from individuals with SLC35G3 mutations is the most direct approach to verify this point and is considered an important goal for future research. The following text has been added to clarify the point:

New Line 338; While these proteins are also found in humans, it is still too early to infer the importance of SLC35G3 in the morphogenesis of human sperm heads. Observing sperm samples from individuals with SLC35G3 mutations would be the most direct approach to address this, and we consider it an important objective for future studies.

(3) Line 194 - while the inability to pass the UTJ may indeed be a component of this infertility phenotype, I would argue that a complete lack of ability to fertilize (even with IVF but not ICSI) suggests that the primary defect is elsewhere. This statement should be removed, and the topic of these two separate mechanisms should be compared/contrasted in the discussion.

We agree that this is an overstatement, so we changed it;

New line 187; Thus, the defective UTJ migration is one of the primary causes of Slc35g3-/- male infertility. 

We believe the current statement in the discussion can stay as it is. 

Line 379; We reaffirmed that glycosylation-related genes specific to the testis play a crucial role in the synthesis, quality control, and function of glycoproteins on sperm, which are essential for male fertility through their interactions with eggs and the female reproductive system.

(4) Did the authors consider performing TEM to assess the sperm ultrastructure and the acrosome?

Since morphological abnormalities were evident even at the macro level, TEM was not performed in this study. In the future, we plan to use immune-TEM against affected/non-affected glycoproteins when the antibodies become available.

(5) I would argue that Figure 3 should not be labeled as "essential", given the abnormal sperm head morphology compared to humans, the relatively modest difference between the groups on PCA, and more broadly speaking, the relatively poor correlation with morphology and human male infertility. While globozoospermia is clearly an exception, the data in this figure may not translate to human sperm and/or may not be clinically relevant even if it does.

Indeed, other KO spermatozoa with similar morphological features are known to cause a reduction in litter size but do not result in complete infertility. As discussed in line 1, this head shape is not essential for fertilization. Reviewer 2 also pointed out that the phrase "Slc35g3 is essential for sperm head formation" is too strong; therefore, we would like to revise Fig3 title to "Slc35g3 is involved in the regulation of sperm head morphology."

(6) Have the authors generated slc35b4 KO mice?

No, we did not. Since Slc35b4 is expressed throughout the body, a straight knockout may affect other organs or developmental processes. To investigate its role specifically in the testis, it will be necessary to generate a conditional knockout (cKO) model. As this requires considerable cost, time, and labor, we would like to leave it for future investigation.

Reviewer #2 (Recommendations for the authors):

(1) Lines 122-123: "it is prominently expressed in the testis, beginning 21 days postpartum (Figure 1B), suggesting expression from the secondary spermatocyte stage to the round spermatid stage in mice." Day 21 indicates the first appearance of round spermatids, but not secondary spermatocytes. Please change to the following: ...suggesting that its expression begins in round spermatids in mice.

I agree with your comment and have revised the text accordingly (New line 114).

(2) Figure 1E: What germ cells are they? The type of germ cells needs to be labelled on the image. Double staining with a germ cell marker would be helpful to distinguish germ cells from testicular somatic cells.

Thank you for your comment. We replaced the Figure 1E as follows.

To distinguish germ cells from testicular somatic cells, we used the germ cell marker TRA98 antibody. Furthermore, based on the nuclear and GM130 staining pattern, we consider that the Golgi apparatus of round spermatids is labeled.

(3) Figure 2C: The most abundant WB band is between 20 and 25 kD and is non-specific. Does the arrow point to the expected SLC35G3 band? There are two minor bands above the main non-specific band. Are both bands specific to SLC35G3? Given the strong non-specific band on WB, how specific is the immunofluorescence signal produced by this antibody? These need to be explained and discussed.

The arrow pointed to the expected size (35kDa).

We thought that these non-specific bands could be due to blood contamination, so we retried with testicular germ cells. We confirmed that non-specific bands disappeared in the subsequent Western blot analysis. The specificity of the immunofluorescence signal is supported by its complete absence in the KO, as shown in the Supplementary Figures. We have decided to include this improved dataset. Thank you for your comment, which helped us improve the data.

Author response image 1.

(4) Line 184: "Slc35g3-/--derived sperm have defects in ZP binding and oolemma fusion ability, but genomic integrity is intact." Producing viable offspring does not necessarily mean that genomic integrity is intact. Suggestion: Slc35g3-/--derived sperm have defects in ZP binding and oolemma fusion ability but produce viable offspring. Likewise, the Figure S9 caption also needs to be changed.

Thank you for your constructive comment. We have revised the text as you suggested.

(5) Figure 3. "Slc35g3 is essential for sperm head formation". This statement is too strong. It is not essential for sperm head formation. The sperm head is still formed, but shows subtle deformation.

Thank you for your suggestion. We changed as follows:

FIg.3; ”Slc35g3 is involved in the regulation of sperm head morphology.”

(6) Lines 204-205: Figure 6B: "Interestingly, some bands of sperm acrosome-associated 1 (SPACA1; 26) disappeared in Slc35g3-/- testis lysates." I don't see the absence of SPACA1 bands in -/- testis. This needs to be clearly labeled with arrows. On the contrary, the bands are stronger in Slc35g3-/- testis lysates.

Thank you for your comment. After carefully considering your comments, we concluded that using "disappeared" is indeed inappropriate. We would like to revise the sentence as follows: New line 197; "Interestingly, SPACA1 (Sperm Acrosome Associated 1; 26) exhibited a subtle difference in banding pattern in the Slc35g3-/- testis lysate."

eLife Assessment

This study reports important negative results, showing that genetically removing the RNA-binding protein PTBP1 in astrocytes is insufficient to convert them into neurons, thereby challenging previous claims in the field. It also offers a compelling analysis of PTBP1's role in regulating astrocyte-specific splicing. The evidence is strong, as the experiments are technically sound, carefully controlled, and supported by both imaging and transcriptomic analyses.

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNA-binding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated - using fluorescence imaging and bulk and single-cell RNA-sequencing - whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to the broad readership of eLife.

Original weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However - assuming that the coverage plots are CPM-normalized - the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

Point 1 has been successfully addressed in the revision by providing relevant references/discussion. Points 2-4 were addressed by including additional data/analyses.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhang and colleagues describes a study that investigated if deletion of PTBP1 in adult astrocytes in mice led to an astrocyte-to-neuron conversion. The study revisited the hypothesis that reduced PTBP1 expression reprogrammed astrocytes to neurons. More than 10 studies have been published on this subject, with contradicting results. Half of the studies supported the hypothesis while the other half did not. The question being addressed is an important one because if the hypothesis is correct, it can lead to exciting therapeutic applications for treating neurodegenerative diseases such as Parkinson's disease.

In this study, Zhang and colleagues conducted a conditional mouse knockout study to address the question. They used the Cre-LoxP system to specifically delete PTBP1 in adult astrocytes. Through a series of carefully controlled experiments including cell lineage tracing, the authors found no evidence for the astrocyte-to-neuron conversion.

The authors then carried out a key experiment that none of previous studies on the subject did: investigating alternative splicing pattern changes in PTBP1-depleted cells using RNA-seq analysis. The idea is to compare the splicing pattern change caused by PTBP1 deletion in astrocytes to what occurs during neurodevelopment. This is an important experiment that will help illuminate if the astrocyte-to-neuron transition occurred in the system. The result was consistent with that of the cell staining experiments: no significant transition being detected.

These experiments demonstrate that, in this experiment setting, PTBT1 deletion in adult astrocytes did not convert the cells to neurons.

Strengths:

This is a well-designed, elegantly conducted, and clearly described study that addresses an important question. The conclusions provide important information to the field.<br /> To this reviewer, this study provided convincing and solid experimental evidence to support the authors' conclusions.

My concerns in the previous review have been addressed satisfactorily.

Author response:

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

Reviewer #1 (Public review):

Summary:

Zhang et al. used a conditional knockout mouse model to re-examine the role of the RNAbinding protein PTBP1 in the transdifferentiation of astroglial cells into neurons. Several earlier studies reported that PTBP1 knockdown can efficiently induce the transdifferentiation of rodent glial cells into neurons, suggesting potential therapeutic applications for neurodegenerative diseases. However, these findings have been contested by subsequent studies, which in turn have been challenged by more recent publications. In their current work, Zhang et al. deleted exon 2 of the Ptbp1 gene using an astrocyte-specific, tamoxifen-inducible Cre line and investigated, using fluorescence imaging and bulk and single-cell RNA-sequencing, whether this manipulation promotes the transdifferentiation of astrocytes into neurons across various brain regions. The data strongly indicate that genetic ablation of PTBP1 is not sufficient to drive efficient conversion of astrocytes into neurons. Interestingly, while PTBP1 loss alters splicing patterns in numerous genes, these changes do not shift the astroglial transcriptome toward a neuronal profile.

Strengths:

Although this is not the first report of PTBP1 ablation in mouse astrocytes in vivo, this study utilizes a distinct knockout strategy and provides novel insights into PTBP1-regulated splicing events in astrocytes. The manuscript is well written, and the experiments are technically sound and properly controlled. I believe this study will be of considerable interest to a broad readership.

Weaknesses:

(1) The primary point that needs to be addressed is a better understanding of the effect of exon 2 deletion on PTBP1 expression. Figure 4D shows successful deletion of exon 2 in knockout astrocytes. However, assuming that the coverage plots are CPM-normalized, the overall PTBP1 mRNA expression level appears unchanged. Figure 6A further supports this observation. This is surprising, as one would expect that the loss of exon 2 would shift the open reading frame and trigger nonsense-mediated decay of the PTBP1 transcript. Given this uncertainty, the authors should confirm the successful elimination of PTBP1 protein in cKO astrocytes using an orthogonal approach, such as Western blotting, in addition to immunofluorescence. They should also discuss possible reasons why PTBP1 mRNA abundance is not detectably affected by the frameshift.

We thank the reviewer for raising this important point. Indeed, the deletion of exon 2 introduces a frameshift that is predicted to disrupt the PTBP1 open reading frame and trigger nonsensemediated decay (NMD). While our CPM-normalized coverage plots (Figure 4D) and gene-level expression analysis (Figure 6A) suggest that PTBP1 mRNA levels remain largely unchanged in cKO astrocytes, we acknowledge that this observation is counterintuitive and merits further clarification.

We suspect that the process of brain tissue dissociation and FACS sorting for bulk or single cell RNA-seq may enrich for nucleic material and thus dilute the NMD signal, which occurs in the cytoplasm. Alternatively, the transcripts (like other genes) may escape NMD for unknown mechanisms. Although a frameshift is a strong indicator for triggering NMD, it does not guarantee NMD will occur in every case. (lines 346-353)

Regarding the validation of PTBP1 protein depletion in cKO astrocytes by Western blotting, we acknowledge that orthogonal approaches to confirm PTBP1 elimination would address uncertainty around the effect of exon 2 deletion on PTBP1 expression. The low cell yield of cKO astrocytes vis FACS poses a significant burden on obtaining sufficient samples for immunoblotting detection of PTBP1 depletion. On average 3-5 adult animals per genotype (with three different alleles) are needed for each biological replicate. The manuscript contains PTBP1 immunofluorescence staining of brain slides to demonstrate PTBP1 deletion (Figures 1-2, Figure 3 supplement 1). Our characterization of this Ptbp1 deletion allele in other contexts show the loss of full length PTBP1 proteins in ESCs using Western blotting (PMID: 30496473). Furthermore, germline homozygous mutant mice do not survive beyond embryonic day 6, supporting that it is a loss of function allele.

(2) The authors should analyze PTBP1 expression in WT and cKO substantia nigra samples shown in Figure 3 or justify why this analysis is not necessary.

We thank the reviewer for pointing out this important question. Although we are using an astrocyte-specific PTBP1 knockout (KO) mouse model, which is designed to delete PTBP1 in all the astrocyte throughout mouse brain, and although we have systematically verified PTBP1 elimination in different mouse brain regions (cortex and striatum) at multiple time points (from 4w to 12w after tamoxifen administration), we agree that it remains necessary and important to demonstrate whether the observed lack of astrocyte-to-neuron conversion is indeed associated with sufficient PTBP1 depletion.

We have analyzed the PTBP1 expression in the substantia nigra, as we did in the cortex and striatum. We added a new figure (Figure 3-figure supplement 1) to show the results. We found in cKO samples, tdT+ cells lack PTBP1 immunostaining, and there is no overlapping of NeuN+ and tdT+ signals. These results show effective PTBP1 depletion in the substantia nigra, similar to that observed in the cortex and striatum. (line 221-224)

(3) Lines 236-238 and Figure 4E: The authors report an enrichment of CU-rich sequences near PTBP1-regulated exons. To better compare this with previous studies on position-specific splicing regulation by PTBP1, it would be helpful to assess whether the position of such motifs differs between PTBP1-activated and PTBP1-repressed exons.

We thank the reviewer for this insightful comment. We agree that assessing the positional distribution of CU-rich motifs between PTBP1-activated and PTBP1-repressed exons would provide valuable insight into the position-specific regulatory mechanisms of PTBP1. In response, we have performed separate motif enrichment analyses for PTBP1-activated and PTBP1-repressed exons and examined whether their positional patterns differ (Figure 4–figure supplement 2).

Our analysis revealed that CU-rich motifs were significantly enriched in the upstream introns of both activated and repressed exons by PTBP1 loss, with higher enrichment observed in repressed exons (Enrichment ratio = 2.14, q = 9.00×10-5) compared to activated exons (Enrichment ratio = 1.72, q = 7.75×10-5) (Figure 4–figure supplement 2B–C). In contrast, no CU-rich motifs were found downstream of activated exons (Figure 4–figure supplement 2D), while a weak, non-significant enrichment was observed downstream of repressed exons (Enrichment ratio = 1.21, q = 0.225; Figure 4–figure supplement 2E). These results do not necessarily fully fit with a couple of earlier PTBP1 CLIP studies showing differential PTBP1 binding for repressed vs activated exons but are more in line with the Black Lab study (PMID: 24499931) that PTBP1 binds upstream introns of both repressed and activated exons. Either case, PTBP1 affects a diverse set of alternative exons and likely involves diverse contextdependent binding patterns (lines 244-257).

(4) The analyses in Figure 5 and its supplement strongly suggest that the splicing changes in PTBP1-depleted astrocytes are distinct from those occurring during neuronal differentiation. However, the authors should ensure that these comparisons are not confounded by transcriptome-wide differences in gene expression levels between astrocytes and developing neurons. One way to address this concern would be to compare the new PTBP1 cKO data with publicly available RNA-seq datasets of astrocytes induced to transdifferentiate into neurons using proneural transcription factors (e.g., PMID: 38956165).

We would like to express our gratitude for the thoughtful feedback. We agree that transcriptome-wide differences in gene expression between astrocytes and developing neurons could confound the interpretation of splicing differences. To address this concern, we have incorporated publicly available RNA-seq datasets from studies in which astrocytes are reprogrammed into neurons using proneural transcription factors, Ngn2 or PmutNgn2 (PMID: 38956165).

The results of principal component analysis (PCA) for splicing profiles revealed that the in vivo splicing profiles from this study and the in vitro splicing profiles from PMID 38956165 are well separated on PC1 and PC2. While Ngn2/PmutNgn2-induced neurons and control astrocytes started to show distinction on PC3 (and to some degree on PC4), Ptbp1 cKO samples remained tightly grouped with control astrocytes and showed no directional shift toward the neuronal cluster (Figure 5–figure supplement 2B). These findings further support the conclusion that PTBP1 depletion in mature astrocytes does not induce a neuronal-like splicing program, even when compared against neurons derived from the astrocyte lineage (lines 306318).

The pairwise correlation analysis of percent spliced in between Ptbp1 cKO, control astrocytes, and induced neurons confirmed that Ptbp1 cKO astrocytes are highly similar to control astrocytes (ρ = 0.81) and clearly distinct from induced neurons (ρ = 0.62) (Figure 5– figure supplement 2C), reinforcing the notion that PTBP1 loss alone is insufficient to drive a neuronal-like splicing transition (lines 319-336).

Consistent with the analysis for splicing profiles, PCA for gene expression profiles showed that control and Ptbp1 cKO astrocytes clustered tightly together and no directional shift toward the neuronal cluster while Ngn2/PmutNgn2-induced neurons and control astrocytes were distributed across a broader range (Figure 6–figure supplement 1A–B). Correlation analysis further supported this result, with a strong similarity between Ptbp1 cKO and control astrocytes (ρ = 0.97), and low similarity between Ptbp1 cKO astrocytes and induced neurons (ρ = 0.27) (Figure 6–figure supplement 1C). These findings indicate that, even with PTBP1 loss, cKO astrocytes retain a transcriptional profile very distinct from that of neurons, underscoring that Ptbp1 deficiency alone does not induce astrocyte-to-neuron reprogramming at the transcriptomic level (lines 366-373).

Reviewer #2 (Public review):

Summary:

The manuscript by Zhang and colleagues describes a study that investigated whether the deletion of PTBP1 in adult astrocytes in mice led to an astrocyte-to-neuron conversion. The study revisited the hypothesis that reduced PTBP1 expression reprogrammed astrocytes to neurons. More than 10 studies have been published on this subject, with contradicting results. Half of the studies supported the hypothesis while the other half did not. The question being addressed is an important one because if the hypothesis is correct, it can lead to exciting therapeutic applications for treating neurodegenerative diseases such as Parkinson's disease.

In this study, Zhang and colleagues conducted a conditional mouse knockout study to address the question. They used the Cre-LoxP system to specifically delete PTBP1 in adult astrocytes. Through a series of carefully controlled experiments, including cell lineage tracing, the authors found no evidence for the astrocyte-to-neuron conversion.

The authors then carried out a key experiment that none of the previous studies on the subject did: investigating alternative splicing pattern changes in PTBP1-depleted cells using RNA-seq analysis. The idea is to compare the splicing pattern change caused by PTBP1 deletion in astrocytes to what occurs during neurodevelopment. This is an important experiment that will help illuminate whether the astrocyte-to-neuron transition occurred in the system. The result was consistent with that of the cell staining experiments: no significant transition was detected.

These experiments demonstrate that, in this experimental setting, PTBT1 deletion in adult astrocytes did not convert the cells to neurons.

Strengths:

This is a well-designed, elegantly conducted, and clearly described study that addresses an important question. The conclusions provide important information to the field.

To this reviewer, this study provided convincing and solid experimental evidence to support the authors' conclusions.

Weaknesses:

The Discussion in this manuscript is short and can be expanded. Can the authors speculate what led to the contradictory results in the published studies? The current study, in combination with the study published in Cell in 2021 by Wang and colleagues, suggests that observed difference is not caused by the difference of knockdown vs. knockout. Is it possible that other glial cell types are responsible for the transition? If so, what cells? Oligodendrocytes?

We are grateful for the reviewer’s careful reading and valuable suggestions. We have expanded the Discussion to include discussion of possible origins of glial cells responsible for neuronal transition. (lines 441-461)

Reviewer #1 (Recommendations for the authors):

(1) Throughout the text and figures, it is customary to write loxP with a capital "P".

We have capitalized “P” in loxP throughout the text and figures.

(2) It would be helpful to indicate the brain regions analyzed above the images in Figure 1B-C, Figure 2A-B, Figure 1 - Supplement 3, and Figure 2 - Supplement 2, as was done in Figure 1 - Supplement 1.

The labels indicating brain regions of corresponding images have been added to the figures. 

(3) The arrowheads in Figure 1C, Figure 2B, Figure 3, and several supplemental panels are nearly equilateral triangles, making their direction difficult to discern. Consider using a more slender or indented design (e.g., ➤).

We have replaced triangular arrowheads with indented arrowheads in the figures. 

(4) Lines 181-209: This section should be revised, given that the striatum is not a midbrain structure.

We have revised this section to reflect our analysis of the striatum as a brain region of the nigrostriatal pathway rather than a midbrain structure. 

Reviewer #2 (Recommendations for the authors):

In Supplemental Figure 1, the two open triangles are almost indistinguishable. It would be better if the colors of these open triangles were changed so that it is easier to tell what's what. There is not enough contrast between white and yellow.

We have changed the open triangle arrowheads to solid yellow and violet arrowheads to improve contrast between labels.

eLife Assessment

This computational study examines how neurons in the songbird premotor nucleus HVC might generate the precise, sparse burst sequences that drive adult song. The findings would be useful for understanding how intrinsic conductances and HVC microcircuitry may produce neural sequences, but the work is incomplete because of arbitrary network assumptions, insufficient consideration of biological details such as how silent gaps in song sequences are represented, and failure to incorporate interactions with auditory and brainstem inputs. As a result, the study offers limited advance and only a modest conceptual advance over prior models.

Reviewer #2 (Public review):

Summary:

In this paper, the authors use numerical simulations to try to understand better a major experimental discovery in songbird neuroscience from 2002 by Richard Hahnloser and collaborators. The 2002 paper found that a certain class of projection neurons in the premotor nucleus HVC of adult male zebra finch songbirds, the neurons that project to another premotor nucleus RA, fired sparsely (once per song motif) and precisely (to about 1 ms accuracy) during singing.

The experimental discovery is important to understand since it initially suggested that the sparsely firing RA-projecting neurons acted as a simple clock that was localized to HVC and that controlled all details of the temporal hierarchy of singing: notes, syllables, gaps, and motifs. Later experiments suggested that the initial interpretation might be incomplete: that the temporal structure of adult male zebra finch songs instead emerged in a more complicated and distributed way, still not well understood, from the interaction of HVC with multiple other nuclei, including auditory and brainstem areas. So at least two major questions remain unanswered more than two decades after the 2002 experiment: What is the neurobiological mechanism that produces the sparse precise bursting: is it a local circuit in HVC or is it some combination of external input to HVC and local circuitry? And how is the sparse precise bursting in HVC related to a songbird's vocalizations?

The authors only investigate part of the first question, whether the mechanism for sparse precise bursts is local to HVC. They do so indirectly, by using conductance-based Hodgkin-Huxley-like equations to simulate the spiking dynamics of a simplified network that includes three known major classes of HVC neurons and such that all neurons within a class are assumed to be identical. A strength of the calculations is that the authors include known biophysically deduced details of the different conductances of the three majors classes of HVC neurons, and they take into account what is known, based on sparse paired recordings in slices, about how the three classes connect to one another. One weakness of the paper is that the authors make arbitrary and not-well-motivated assumptions about the network geometry, and they do not use the flexibility of their simulations to study how their results depend on their network assumptions. A second weakness is that they ignore many known experimental details such as projections into HVC from other nuclei, dendritic computations (the somas and dendrites are treated by the authors as point-like isopotential objects), the role of neuromodulators, and known heterogeneity of the interneurons. These weaknesses make it difficult for readers to know the relevance of the simulations for experiments and for advancing theoretical understanding.

Strengths:

The authors use conductance-based Hodgkin-Huxley-like equations to simulate spiking activity in a network of neurons intended to model more accurately songbird nucleus HVC of adult male zebra finches. Spiking models are much closer to experiments than models based on firing rates or on 2-state neurons.

The authors include information deduced from modeling experimental current-clamp data such as the types and properties of conductances. They also take into account how neurons in one class connect to neurons in other classes via excitatory or inhibitory synapses, based on sparse paired recordings in slices by other researchers.

The authors obtain some new results of modest interest such as how changes in the maximum conductances of four key channels (e.g., A-type K+ currents or Ca-dependent K+ currents) influence the structure and propagation of bursts, while simultaneously being able to mimic accurately current-clamp voltage measurements.

Weaknesses:

One weakness of this paper is the lack of a clearly stated, interesting, and relevant scientific question to try to answer. The authors do not discuss adequately in their introduction what questions have recent experimental and theoretical work failed to explain adequately concerning HVC neural dynamics and its role in producing vocalizations. The authors do not discuss adequately why they chose the approach of their paper and how their results address some of these questions.

For example, the authors need to explain in more detail how their calculations relate to the works of Daou et al, J. Neurophys. 2013 (which already fitted spiking models to neuronal data and identified certain conductances), to Jin et al J. Comput. Neurosci. 2007 (which already discussed how to get bursts using some experimental details), and to the rather similar paper by E. Armstrong and H. Abarbanel, J. Neurophys 2016, which already postulated and studied sequences of microcircuits in HVC. This last paper is not even cited by the authors.

The authors' main achievement is to show that simulations of a certain simplified and idealized network of spiking neurons, that includes some experimental details but ignores many others, can match some experimental results like current-clamp-derived voltage time series for the three classes of HVC neurons (although this was already reported in earlier work by Daou and collaborators in 2013), and simultaneously the robust propagation of bursts with properties similar to those observed in experiments. The authors also present results about how certain neuronal details and burst propagation change when certain key maximum conductances are varied.

But these are weak conclusions for two reasons. First, the authors did not do enough calculations to allow the reader to understand how many parameters were needed to obtain these fits and whether simpler circuits, say with fewer parameters and simpler network topology, could do just as well. Second, many previous researchers have demonstrated robust burst propagation in a variety of feed-forward models. So what is new and important about the authors' results compared to the previous computational papers?

Also missing is a discussion, or at least an acknowledgement, of the fact that not all of the fine experimental details of undershoots, latencies, spike structure, spike accommodation, etc may be relevant for understanding vocalization. While it is nice to know that some model can match these experimental details and produce realistic bursts, that does not mean that all of these details are relevant for the function of producing precise vocalizations. Scientific insights in biology often require exploring which of the many observed details can be ignored, and especially identifying the few that are essential for answering some questions. As one example, if HVC-X neurons are completely removed from the authors' model, does one still get robust and reasonable burst propagation of HVC-RA neurons? While part of nucleus HVC acts as a premotor circuit that drives nucleus RA, part of HVC is also related to learning. It is not clear that HVC-X neurons, which carry out some unknown calculation and transmit information to area X in a learning pathway, are relevant for burst production and propagation of HVC-RA neurons, and so relevant for vocalization. Simulations provide a convenient and direct way to explore questions of this kind.

One key question to answer is whether the bursting of HVC-RA projection neurons is based on a mechanism local to HVC or is some combination of external driving (say from auditory nuclei) and local circuitry. The authors do not contribute to answering this question because they ignore external driving and assume that the mechanism is some kind of intrinsic feed-forward circuit, which they put in by hand in a rather arbitrary and poorly justified way, by assuming the existence of small microcircuits consisting of a few HVC-RA, HVC-X, and HVC-I neurons that somehow correspond to "sub-syllabic segments". To my knowledge, experiments do not suggest the existence of such microcircuits nor does theory suggest the need for such microcircuits.

Another weakness of this paper is an unsatisfactory discussion of how the model was obtained, validated, and simulated. The authors should state as clearly as possible, in one location such as an appendix, what is the total number of independent parameters for the entire network and how parameter values were deduced from data or assigned by hand. With enough parameters and variables, many details can be fit arbitrarily accurately so researchers have to be careful to avoid overfitting. If parameter values were obtained by fitting to data, the authors should state clearly what was the fitting algorithm (some iterative nonlinear method, whose results can depend on the initial choice of parameters), what was the error function used for fitting (sum of least squares?), and what data were used for the fitting.

The authors should also state clearly what is the dynamical state of the network, the vector of quantities that evolve over time. (What is the dimension of that vector, which is also the number of ordinary differential equations that have to be integrated?) The authors do not mention what initial state was used to start the numerical integrations, whether transient dynamics were observed and what were their properties, or how the results depend on the choice of initial state. The authors do not discuss how they determined that their model was programmed correctly (it is difficult to avoid typing errors when writing several pages or more of a code in any language) or how they determined the accuracy of the numerical integration method beyond fitting to experimental data, say by varying the time step size over some range or by comparing two different integration algorithms.

Also disappointing is that the authors do not make any predictions to test, except rather weak ones such as that varying a maximum conductance sufficiently (which might be possible by using dynamic clamps) might cause burst propagation to stop or change its properties. Based on their results, the authors do not make suggestions for further experiments or calculations, but they should.

Comments on revised version:

The second version, unfortunately, did not address most of the substantive comments so that, while some parts of the discussion were expanded, most of the serious scientific weaknesses mentioned in the first round of review remain. The revised preprint is not a substantive improvement over the first.

Author response:

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

Reviewer #1 (Public review):

Summary:

The paper presents a model for sequence generation in the zebra finch HVC, which adheres to cellular properties measured experimentally. However, the model is fine-tuned and exhibits limited robustness to noise inherent in the inhibitory interneurons within the HVC, as well as to fluctuations in connectivity between neurons. Although the proposed microcircuits are introduced as units for sub-syllabic segments (SSS), the backbone of the network remains a feedforward chain of HVC_RA neurons, similar to previous models.

Strengths:

The model incorporates all three of the major types of HVC neurons. The ion channels used and their kinetics are based on experimental measurements. The connection patterns of the neurons are also constrained by the experiments.

Weaknesses:

The model is described as consisting of micro-circuits corresponding to SSS. This presentation gives the impression that the model's structure is distinct from previous models, which connected HVC_RA neurons in feedforward chain networks (Jin et al 2007, Li & Greenside, 2006; Long et al 2010; Egger et al 2020). However, the authors implement single HVC_RA neurons into chain networks within each micro-circuit and then connect the end of the chain to the start of the chain in the subsequent micro-circuit. Thus, the HVC_RA neuron in their model forms a single-neuron chain. This structure is essentially a simplified version of earlier models.

In the model of the paper, the chain network drives the HVC_I and HVC_X neurons. The role of the micro-circuits is more significant in organizing the connections: specifically, from HVC_RA neurons to HVC_I neurons, and from HVC_I neurons to both HVC_X and HVC_RA neurons.

We thank Reviewer 1 for their thoughtful comments.

While the reviewer is correct about the fact that the propagation of sequential activity in this model is primarily carried by HVC_RA neurons in a feed-forward manner, we need to emphasize that this is true only if there is no intrinsic or synaptic perturbation to the HVC network. For example, we showed in Figures 10 and 12 how altering the intrinsic properties of HVC_X neurons or for interneurons disrupts sequence propagation. In other words, while HVC_RA neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics. Moreover, all existing models that describe premotor sequence generation in the HVC either assume a distributed model (Elmaleh et al., 2021) that dictates that local HVC circuitry is not sufficient to advance the sequence but rather depends upon moment to-moment feedback through Uva (Hamaguchi et al., 2016), or assume models that rely on intrinsic connections within HVC to propagate sequential activity. In the latter case, some models assume that HVC is composed of multiple discrete subnetworks that encode individual song elements (Glaze & Troyer, 2013; Long & Fee, 2008; Wang et al., 2008), but lacks the local connectivity to link the subnetworks, while other models assume that HVC may have sufficient information in its intrinsic connections to form a single continuous network sequence (Long et al. 2010). The HVC model we present extends the concept of a feedforward network by incorporating additional neuronal classes that influence the propagation of activity (interneurons and HVC_X neurons). We have shown that any disturbance of the intrinsic or synaptic conductances of these latter neurons will disrupt activity in the circuit even when HVC_RA neurons properties are maintained. 

In regard to the similarities between our model and earlier models, several aspects of our model distinguish it from prior work. In short, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties. We tuned the intrinsic and the synaptic properties bases on the traces collected by Daou et al. (2013) and Mooney and Prather (2005) as shown in Figure 3. The three classes of model neurons incorporated to our network as well as the synaptic currents that connect them are based on Hodgkin- Huxley formalisms that contain ion channels and synaptic currents which had been pharmacologically identified. This is an advancement over prior models that primarily focused on the role of synaptic interactions or external inputs. The model is based on feedforward chain of microcircuits that encode for the different sub-syllabic segments and that interact with each other through structured feedback inhibition, defining an ordered sequence of cell firing. Moreover, while several models highlight the critical role of inhibitory interneurons in shaping the timing and propagation of bursts of activity in HVC_RA neurons, our work offers an intricate and comprehensive model that help understand this critical role played by inhibition in shaping song dynamics and ensuring sequence propagation.

How useful is this concept of micro-circuits? HVC neurons fire continuously even during the silent gaps. There are no SSS during these silent gaps.

Regarding the concern about the usefulness of the 'microcircuit' concept in our study, we appreciate the comment and we are glad to clarify its relevance in our network. While we acknowledge that HVC_RA neurons interconnect microcircuits, our model's dynamics are still best described within the framework of microcircuitry particularly due to the firing behavior of HVC_X neurons and interneurons. Here, we are referring to microcircuits in a more functional sense, rather than rigid, isolated spatial divisions (Cannon et al. 2015), and we now make this clear on page 21. A microcircuit in our model reflects the local rules that govern the interaction between all HVC neuron classes within the broader network, and that are essential for proper activity propagation. For example, HVC_INT neurons belonging to any microcircuit burst densely and at times other than the moments when the corresponding encoded SSS is being “sung”. What makes a particular interneuron belong to this microcircuit or the other is merely the fact that it cannot inhibit HVC_RA neurons that are housed in the microcircuit it belongs to. In particular, if HVC_INT inhibits HVC_RA in the same microcircuit, some of the HVC_RA bursts in the microcircuit might be silenced by the dense and strong HVC_INT inhibition breaking the chain of activity again. Similarly, HVC_X neurons were selected to be housed within microcircuits due to the following reason: if an HVC_X neuron belonging to microcircuit i sends excitatory input to an HVC_INT neuron in microcircuit j, and that interneuron happens to select an HVC_RA neuron from microcircuit i, then the propagation of sequential activity will halt, and we’ll be in a scenario similar to what was described earlier for HVC_INT neurons inhibiting HVC_RA neurons in the same microcircuit.

We agree that there are no sub-syllabic segments described during the silent gaps and we thank the reviewer to pointing this out. Although silent gaps are integral to the overall process of song production, we have not elaborated on them in this model due to the lack of a clear, biophysically grounded representation for the gaps themselves at the level of HVC. Our primary focus has been on modeling the active, syllable-producing phases of the song, where the HVC network’s sequential dynamics are critical for song. However, one can think the encoding of silent gaps via similar mechanisms that encode SSSs, where each gap is encoded by similar microcircuits comprised of the three classes of HVC neurons (let’s call them GAP rather than SSS) that are active only during the silent gaps. In this case, the propagation of sequential activity is carried throughout the GAPs from the last SSS of the previous syllable to the first SSS of the subsequent syllable. This is no described more clearly on page 22 of the manuscript.

A significant issue of the current model is that the HVC_RA to HVC_RA connections require fine-tuning, with the network functioning only within a narrow range of g_AMPA (Figure 2B). Similarly, the connections from HVC_I neurons to HVC_RA neurons also require fine-tuning. This sensitivity arises because the somatic properties of HVC_RA neurons are insufficient to produce the stereotypical bursts of spikes observed in recordings from singing birds, as demonstrated in previous studies (Jin et al 2007; Long et al 2010). In these previous works, to address this limitation, a dendritic spike mechanism was introduced to generate an intrinsic bursting capability, which is absent in the somatic compartment of HVC_RA neurons. This dendritic mechanism significantly enhances the robustness of the chain network, eliminating the need to fine-tune any synaptic conductances, including those from HVC_I neurons (Long et al 2010). Why is it important that the model should NOT be sensitive to the connection strengths?

We thank the reviewer for the comment. While mathematical models designed for highly complex nonlinear biological processes tangentially touch the biological realism, the current network as is right now is the first biologically realistic-enough network model designed for HVC that explains sequence propagation. We do not include dendritic processes in our network although that increases the realistic dynamics for various reasons. 1) The ion channels we integrated into the somatic compartment are known pharmacologically (Daou et al. 2013), but we don’t know about the dendritic compartment’s intrinsic properties of HVC neurons and the cocktail of ion channels that are expressed there. 2) We are able to generate realistic bursting in HVC_RA neurons despite the single compartment, and the main emphasis in this network is on the interactions between excitation and inhibition, the effects of ion channels in modulating sequence propagation, etc … 3) The network model already incorporates thousands of ODEs that govern the dynamics of each of the HVC neurons, so we did not want to add more complexity to the network especially that we don’t know the biophysical properties of the dendritic compartments.

Therefore, our present focus is on somatic dynamics and the interaction between HVC_RA and HVC_INT neurons, but we acknowledge the importance of these processes in enhancing network resiliency. Although we agree that adding dendritic processes improves robustness, we still think that somatic processes alone can offer insightful information on the sequential dynamics of the HVC network. While the network should be robust across a wide range of parameters, it is also essential that certain parameters are designed to filter out weaker signals, ensuring that only reliable, precise patterns of activity propagate. Hence, we specifically chose to make the HVC_RA-to-HVC_RA excitatory connections more sensitive (narrow range of values) such that only strong, precise and meaningful stimuli can propagate through the network representing the high stereotypy and precision seen in song production.

First, the firing of HVC_I neurons is highly noisy and unreliable. HVC_I neurons fire spontaneous, random spikes under baseline conditions. During singing, their spike timing is imprecise and can vary significantly from trial to trial, with spikes appearing or disappearing across different trials. As a result, their inputs to HVC_RA neurons are inherently noisy. If the model relies on precisely tuned inputs from HVC_I neurons, the natural fluctuations in HVC_I firing would render the model non-functional. The authors should incorporate noisy HVC_I neurons into their model to evaluate whether this noise would render the model non-functional.

We acknowledge that under baseline and singing settings, interneurons fire in an extremely noisy and inaccurate manner, although they exhibit time locked episodes in their activity (Hahnloser et al 2002, Kozhinikov and Fee 2007). In order to mimic the biological variability of these neurons, our model does, in fact, include a stochastic current to reflect the intrinsic noise and random variations in interneuron firing shown in vivo (and we highlight this in the Methods). However, to make sure the network is resilient to this randomness in interneuron firing, introduced a stochastic input current of the form I_noise (t)= σ.ξ(t) where ξ(t) is a Gaussian white noise with zero mean and unit variance, and σ is the noise amplitude. This stochastic drive was introduced to every model neuron and it mimics the fluctuations in synaptic input arising from random presynaptic activity and background noise. For values of σ within 1-5% of the mean synaptic conductance, the stochastic current has no effect on network propagation. For larger values of σ, the desired network activity was disrupted or halted. We now talk about this on page 22 of the manuscript.  

Second, Kosche et al. (2015) demonstrated that reducing inhibition by suppressing HVC_I neuron activity makes HVC_RA firing less sparse but does not compromise the temporal precision of the bursts. In this experiment, the local application of gabazine should have severely disrupted HVC_I activity. However, it did not affect the timing precision of HVC_RA neuron firing, emphasizing the robustness of the HVC timing circuit. This robustness is inconsistent with the predictions of the current model, which depends on finely tuned inputs and should, therefore, be vulnerable to such disruptions.

We thank the reviewer for the comment. The differences between the Kosche et al. (2015) findings and the predictions of our model arise from differences in the aspect of HVC function we are modeling. Our model is more sensitive to inhibition, which is a designed mechanism for achieving precise song patterning. This is a modeling simplification we adopted to capture specific characteristics of HVC function. Hence, Kosche et al. (2015) findings do not invalidate the approach of our model, but highlights that HVC likely operates with several, redundant mechanisms that overall ensure temporal precision. 

Third, the reliance on fine-tuning of HVC_RA connections becomes problematic if the model is scaled up to include groups of HVC_RA neurons forming a chain network, rather than the single HVC_RA neurons used in the current work. With groups of HVC_RA neurons, the summation of presynaptic inputs to each HVC_RA neuron would need to be precisely maintained for the model to function. However, experimental evidence shows that the HVC circuit remains functional despite perturbations, such as a few degrees of cooling, micro-lesions, or turnover of HVC_RA neurons. Such robustness cannot be accounted for by a model that depends on finely tuned connections, as seen in the current implementation.

Our model of individual HVC_RA neurons and as stated previously is reductive model that focuses on understanding the mechanisms that govern sequential neural activity. We agree that scaling the model to include many of HVC_RA neurons poses challenges, specifically concerning the summation of presynaptic inputs. However, our model can still be adapted to a larger network without requiring the level of fine-tuning currently needed. In fact, the current fine-tuning of synaptic connections in the model is a reflection of fundamental network mechanisms rather than a limitation when scaling to a larger network. Besides, one important feature of this neural network is redundancy. Even if some neurons or synaptic connections are impaired, other neurons or pathways can compensate for these changes, allowing the activity propagation to remain intact.

The authors examined how altering the channel properties of neurons affects the activity in their model. While this approach is valid, many of the observed effects may stem from the delicate balancing required in their model for proper function. In the current model, HVC_X neurons burst as a result of rebound activity driven by the I_H current. Rebound bursts mediated by the I_H current typically require a highly hyperpolarized membrane potential. However, this mechanism would fail if the reversal potential of inhibition is higher than the required level of hyperpolarization. Furthermore, Mooney (2000) demonstrated that depolarizing the membrane potential of HVC_X neurons did not prevent bursts of these neurons during forward playback of the bird's own song, suggesting that these bursts (at least under anesthesia, which may be a different state altogether) are not necessarily caused by rebound activity. This discrepancy should be addressed or considered in the model.

In our HVC network model, one goal with HVC_X neurons is to generate bursts in their underlying neuron population. Since HVC_X neurons in our model receive only inhibitory inputs from interneurons, we rely on inhibition followed by rebound bursts orchestrated by the I_H and the I_CaT currents to achieve this goal. The interplay between the T-type Ca⁺⁺ current and the H current in our model is fundamental to generate their corresponding bursts, as they are sufficient for producing the desired behavior in the network. Due to this interplay, we do not need significant inhibition to generate rebound bursts, because the T-type Ca₊₊ current’s conductance can be stronger leading to robust rebound bursting even when the degree of inhibition is not very strong. This is now highlighted on page 42 in the revised version.

Some figures contain direct copies of figures from published papers. It is perhaps a better practice to replace them with schematics if possible.

We wanted on purpose to keep the results shown in Mooney and Prather (2005) to be shown as is, in order to compare them with our model simulations highlighting the degree of resemblance. We believe that creating schematics of the Mooney and Prather (2005) results will not have the same impact, similarly creating a schematic for Hahnloser et al (2002) results won’t help much. However, if the reviewer still believes that we should do that, we’re happy to do it.

Reviewer #2 (Public review):

Summary:

In this paper, the authors use numerical simulations to try to understand better a major experimental discovery in songbird neuroscience from 2002 by Richard Hahnloser and collaborators. The 2002 paper found that a certain class of projection neurons in the premotor nucleus HVC of adult male zebra finch songbirds, the neurons that project to another premotor nucleus RA, fired sparsely (once per song motif) and precisely (to about 1 ms accuracy) during singing.

The experimental discovery is important to understand since it initially suggested that the sparsely firing RA-projecting neurons acted as a simple clock that was localized to HVC and that controlled all details of the temporal hierarchy of singing: notes, syllables, gaps, and motifs. Later experiments suggested that the initial interpretation might be incomplete: that the temporal structure of adult male zebra finch songs instead emerged in a more complicated and distributed way, still not well understood, from the interaction of HVC with multiple other nuclei, including auditory and brainstem areas. So at least two major questions remain unanswered more than two decades after the 2002 experiment: What is the neurobiological mechanism that produces the sparse precise bursting: is it a local circuit in HVC or is it some combination of external input to HVC and local circuitry? And how is the sparse precise bursting in HVC related to a songbird's vocalizations? The authors only investigate part of the first question, whether the mechanism for sparse precise bursts is local to HVC. They do so indirectly, by using conductance-based Hodgkin-Huxley-like equations to simulate the spiking dynamics of a simplified network that includes three known major classes of HVC neurons and such that all neurons within a class are assumed to be identical. A strength of the calculations is that the authors include known biophysically deduced details of the different conductances of the three major classes of HVC neurons, and they take into account what is known, based on sparse paired recordings in slices, about how the three classes connect to one another. One weakness of the paper is that the authors make arbitrary and not well-motivated assumptions about the network geometry, and they do not use the flexibility of their simulations to study how their results depend on their network assumptions. A second weakness is that they ignore many known experimental details such as projections into HVC from other nuclei, dendritic computations (the somas and dendrites are treated by the authors as point-like isopotential objects), the role of neuromodulators, and known heterogeneity of the interneurons. These weaknesses make it difficult for readers to know the relevance of the simulations for experiments and for advancing theoretical understanding.

Strengths:

The authors use conductance-based Hodgkin-Huxley-like equations to simulate spiking activity in a network of neurons intended to model more accurately songbird nucleus HVC of adult male zebra finches. Spiking models are much closer to experiments than models based on firing rates or on 2-state neurons.

The authors include information deduced from modeling experimental current-clamp data such as the types and properties of conductances. They also take into account how neurons in one class connect to neurons in other classes via excitatory or inhibitory synapses, based on sparse paired recordings in slices by other researchers. The authors obtain some new results of modest interest such as how changes in the maximum conductances of four key channels (e.g., A-type K+ currents or Ca-dependent K+ currents) influence the structure and propagation of bursts, while simultaneously being able to mimic accurately current-clamp voltage measurements.

Weaknesses:

One weakness of this paper is the lack of a clearly stated, interesting, and relevant scientific question to try to answer. In the introduction, the authors do not discuss adequately which questions recent experimental and theoretical work have failed to explain adequately, concerning HVC neural dynamics and its role in producing vocalizations. The authors do not discuss adequately why they chose the approach of their paper and how their results address some of these questions.

For example, the authors need to explain in more detail how their calculations relate to the works of Daou et al, J. Neurophys. 2013 (which already fitted spiking models to neuronal data and identified certain conductances), to Jin et al J. Comput. Neurosci. 2007 (which already discussed how to get bursts using some experimental details), and to the rather similar paper by E. Armstrong and H. Abarbanel, J. Neurophys 2016, which already postulated and studied sequences of microcircuits in HVC. This last paper is not even cited by the authors.

We thank the reviewer for this valuable comment, and we agree that we did not clarify enough throughout the paper the utility of our model or how it advanced our understanding of the HVC dynamics and circuitry. To that end, we revised several places of the manuscript and made sure to cite and highlight the relevance and relatedness of the mentioned papers.

In short, and as mentioned to Reviewer 1, while several models of how sequence is generated within HVC have been proposed (Cannon et al., 2015; Drew & Abbott, 2003; Egger et al., 2020; Elmaleh et al., 2021; Galvis et al., 2018; Gibb et al., 2009a, 2009b; Hamaguchi et al., 2016; Jin, 2009; Long & Fee, 2008; Markowitz et al., 2015; Jin et al., 2007), all the models proposed either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. These models do not capture the complex details of spike morphology, do not include the right ionic currents, do not incorporate all classes of HVC neurons, or do not generate realistic firing patterns as seen in vivo. Our model is the first biophysically realistic model that incorporates all classes of HVC neurons and their intrinsic properties. 

No existing hypothesis had been challenged with our model, rather; our model is a distillation of the various models that’s been proposed for the HVC network. We go over this in detail in the Discussion. We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

The authors' main achievement is to show that simulations of a certain simplified and idealized network of spiking neurons, which includes some experimental details but ignores many others, match some experimental results like current-clamp-derived voltage time series for the three classes of HVC neurons (although this was already reported in earlier work by Daou and collaborators in 2013), and simultaneously the robust propagation of bursts with properties similar to those observed in experiments. The authors also present results about how certain neuronal details and burst propagation change when certain key maximum conductances are varied. However, these are weak conclusions for two reasons. First, the authors did not do enough calculations to allow the reader to understand how many parameters were needed to obtain these fits and whether simpler circuits, say with fewer parameters and simpler network topology, could do just as well. Second, many previous researchers have demonstrated robust burst propagation in a variety of feed-forward models. So what is new and important about the authors' results compared to the previous computational papers?

A major novelty of our work is the incorporation of experimental data with detailed network models. While earlier works have established robust burst propagation, our model uses realistic ion channel kinetics and feedback inhibition not only to reproduce experimental neural activity patterns but also to suggest prospective mechanisms for song sequence production in the most biophysical way possible. This aspect that distinguishes our work from other feed-forward models. We go over this in detail in the Discussion. However, the reviewer is right regarding the details of the calculations conducted for the fits, we will make sure to highlight this in the Methods and throughout the manuscript with more details.

We believe that the network model we developed provide a step forward in describing the biophysics of HVC circuitry, and may throw a new light on certain dynamics in the mammalian brain, particularly the motor cortex and the hippocampus regions where precisely-timed sequential activity is crucial. We suggest that temporally-precise sequential activity may be a manifestation of neural networks comprised of chain of microcircuits, each containing pools of excitatory and inhibitory neurons, with local interplay among neurons of the same microcircuit and global interplays across the various microcircuits, and with structured inhibition as well as intrinsic properties synchronizing the neuronal pools and stabilizing timing within a firing sequence.

Also missing is a discussion, or at least an acknowledgment, of the fact that not all of the fine experimental details of undershoots, latencies, spike structure, spike accommodation, etc may be relevant for understanding vocalization. While it is nice to know that some models can match these experimental details and produce realistic bursts, that does not mean that all of these details are relevant for the function of producing precise vocalizations. Scientific insights in biology often require exploring which of the many observed details can be ignored and especially identifying the few that are essential for answering some questions. As one example, if HVC-X neurons are completely removed from the authors' model, does one still get robust and reasonable burst propagation of HVC-RA neurons? While part of the nucleus HVC acts as a premotor circuit that drives the nucleus RA, part of HVC is also related to learning. It is not clear that HVC-X neurons, which carry out some unknown calculation and transmit information to area X in a learning pathway, are relevant for burst production and propagation of HVCRA neurons, and so relevant for vocalization. Simulations provide a convenient and direct way to explore questions of this kind.

One key question to answer is whether the bursting of HVC-RA projection neurons is based on a mechanism local to HVC or is some combination of external driving (say from auditory nuclei) and local circuitry. The authors do not contribute to answering this question because they ignore external driving and assume that the mechanism is some kind of intrinsic feed-forward circuit, which they put in by hand in a rather arbitrary and poorly justified way, by assuming the existence of small microcircuits consisting of a few HVC-RA, HVC-X, and HVC-I neurons that somehow correspond to "sub-syllabic segments". To my knowledge, experiments do not suggest the existence of such microcircuits nor does theory suggest the need for such microcircuits. 

Recent results showed a tight correlation between the intrinsic properties of neurons and features of song (Daou and Margoliash 2020, Medina and Margoliash 2024), where adult birds that exhibit similar songs tend to have similar intrinsic properties. While this is relevant, we acknowledge that not all details may be necessary for every aspect of vocalization, and future models could simplify concentrate on core dynamics and exclude certain features while still providing insights into the primary mechanisms.

The question of whether HVC_X neurons are relevant for burst propagation given that our model includes these neurons as part of the network for completeness, the reviewer is correct, the propagation of sequential activity in this model is primarily carried by HVC_RA neurons in a feed-forward manner, but only if there is no perturbation to the HVC network. For example, we have shown how altering the intrinsic properties of HVC_X neurons or for interneurons disrupts sequence propagation. In other words, while HVC neurons are the key forces to carry the chain forward, the interplay between excitation and inhibition in our network as well as the intrinsic parameters for all classes of HVC neurons are equally important forces in carrying the chain of activity forward. Thus, the stability of activity propagation necessary for song production depend on a finely balanced network of HVC neurons, with all classes contributing to the overall dynamics.

We agree with the reviewer however that a potential drawback of our model is that its sole focus is on local excitatory connectivity within the HVC (Kornfeld et al., 2017; Long et al., 2010), while HVC neurons receive afferent excitatory connections (Akutagawa & Konishi, 2010; Nottebohm et al., 1982) that plays significant roles in their local dynamics. For example, the excitatory inputs that HVC neurons receive from Uvaeformis may be crucial in initiating (Andalman et al., 2011; Danish et al., 2017; Galvis et al., 2018) or sustaining (Hamaguchi et al., 2016) the sequential activity. While we acknowledge this limitation, our main contribution in this work is the biophysical insights onto how the patterning activity in HVC is largely shaped by the intrinsic properties of the individual neurons as well as the synaptic properties where excitation and inhibition play a major role in enabling neurons to generate their characteristic bursts during singing. This is true and holds irrespective of whether an external drive is injected onto the microcircuits or not. We elaborated on this further in the revised version in the Discussion.

Another weakness of this paper is an unsatisfactory discussion of how the model was obtained, validated, and simulated. The authors should state as clearly as possible, in one location such as an appendix, what is the total number of independent parameters for the entire network and how parameter values were deduced from data or assigned by hand. With enough parameters and variables, many details can be fit arbitrarily accurately so researchers have to be careful to avoid overfitting. If parameter values were obtained by fitting to data, the authors should state clearly what the fitting algorithm was (some iterative nonlinear method, whose results can depend on the initial choice of parameters), what the error function used for fitting (sum of least squares?) was, and what data were used for the fitting.

The authors should also state clearly the dynamical state of the network, the vector of quantities that evolve over time. (What is the dimension of that vector, which is also the number of ordinary differential equations that have to be integrated?) The authors do not mention what initial state was used to start the numerical integrations, whether transient dynamics were observed and what were their properties, or how the results depended on the choice of the initial state. The authors do not discuss how they determined that their model was programmed correctly (it is difficult to avoid typing errors when writing several pages or more of a code in any language) or how they determined the accuracy of the numerical integration method beyond fitting to experimental data, say by varying the time step size over some range or by comparing two different integration algorithms.

We thank the reviewer again. The fitting process in our model occurred only at the first stage where the synaptic parameters were fit to the Mooney and Prather as well as the Kosche results. There was no data shared and we merely looked at the figures in those papers and checked the amplitude of the elicited currents, the magnitudes of DC-evoked excitations etc … and we replicated that in our model. While this is suboptimal, it was better for us to start with it rather than simply using equations for synaptic currents from the literature for other types of neurons (that are not even HVC’s or in the songbird) and integrate them into our network model. The number of ODEs that govern the dynamics of every model neuron is listed on page 10 of the manuscript as well as in the Appendix.  Moreover, we highlighted the details of this fitting process in the revised version.

Also disappointing is that the authors do not make any predictions to test, except rather weak ones such as that varying a maximum conductance sufficiently (which might be possible by using dynamic clamps) might cause burst propagation to stop or change its properties. Based on their results, the authors do not make suggestions for further experiments or calculations, but they should.

We agree that making experimental testable predictions is crucial for the advancement of the model. Our predictions include testing whether eradication of a class of neurons such as HVC_X neurons disrupts activity propagation which can be done through targeted neuron elimination. This also can be done through preventing rebound bursting in HVC_X by pharmacologically blocking the I_H channels. Others include down regulation of certain ion channels (pharmacologically done through ion blockers) and testing which current is fundamental for song production (and there a plenty of test based our results, like the SK current, the T-type Ca²⁺ current, the A-type K⁺ current, etc…). We incorporated these into the Discussion of the revised manuscript to better demonstrate the model's applicability and to guide future research directions.

Main issues:

(1) Parameters are overly fine-tuned and often do not match known biology to generate chains. This fine-tuning does not reveal fundamental insights.

(1a) Specific conductances (e.g. AMPA) are finely tweaked to generate bursts, in part due to a lack of a dendritic mechanism for burst generation. A dendritic mechanism likely reflects the true biology of HVC neurons.

We acknowledge that the model does not include active dendritic processes and we do not regard this as a limitation. In fact, our present approach, although simplified, is intended to focus on somatic mechanisms to identify minimal conditions required for stable sequential propagation. We know HVC_RA neurons possess thin, spiny dendrites which can contribute to burst initiation and shaping. Future models that include such nonlinear dendritic mechanisms would likely reduce the need for fine tuning of specific conductances at the soma and consequently better match the known biology of HVC_RA neurons. 

In text: “While our simplified, somatically driven architecture enables better exploration of mechanisms for sequence propagation, future extensions of the model will incorporate dendritic compartments to more accurately reflect the intrinsic bursting mechanisms observed in HVC_RA neurons.”

(1b) In this paper, microcircuits are simulated and then concatenated to make the HVC chain, resulting in no representations during silent gaps. This is out of touch with the known HVC function. There is no anatomical nor functional evidence for microcircuits of the kind discussed in this paper or in the earlier and rather similar paper by Eve Armstrong and Henry Abarbanel (J. Neurophy 2016). One can write a large number of papers in which one makes arbitrary unconstrained guesses of network structure in HVC and, unless they reveal some novel principle or surprising detail, they are all going to be weak.

Although the model is composed of sequentially activated microcircuits, the gaps between each microcircuit’s output do not represent complete silence in the network. During these periods, other neurons such as those in other microcircuits may still exhibit bursting activity. Thus, what may appear as a 'silent gap' from the perspective of a given output microcircuit is, in fact, part of the ongoing background dynamics of the larger HVC neuron network. We fully acknowledge the reviewer's point that there is no direct anatomical or physiological evidence supporting the presence of microcircuits with this structure in HVC. Our intention was not to propose the existence of such a physical model but to use it as a computational simplification to make precise sequential bursting activity feasible given the biologically realistic neuronal dynamics used. Hence, our use of 'microcircuits' refers to a modeling construct rather than a structural hypothesis. Even if the network topology is hypothetical, we still believe that the temporal structuring suggested allows us to generate specific predictions for future work about burst timing and neuronal connections.

(1c) HVC interneuron discharge in the author's model is overly precise; addressing the observation that these neurons can exhibit noisy discharge. Real HVC interneurons are noisy. This issue is critical: All reviewers strongly recommend that the authors should, at the minimum in a revision, focus on incorporating HVC-I noise in their model.

We agree that capturing the variability in interneuron bursting is critical for biological realism. In our model, HVC interneurons receive stochastic background current that introduces variability in their firing patterns as observed in vivo. This variability is seen in our simulations and produces more biologically realistic dynamics while maintaining sequence propagation. We clarify this implementation in the Methods section. 

(1d) Address the finding that Kosche et al show that even with reduced inhibition, HVCra neuronal timing is preserved; it is the burst pattern that is affected.

The differences between the Kosche et al. (2015) findings and the predictions of our model arise from differences in the aspect of HVC function we are modeling. Our model is more sensitive to inhibition, which is a designed mechanism for achieving precise song patterning. This is a modeling simplification we adopted to capture specific characteristics of HVC function. 

We acknowledged this point in the discussion: “While findings of Kosche et al. (2015) emphasize the robustness of the HVC timing circuit to inhibition, our model is more sensitive to inhibition, highlighting that HVC likely operates with several, redundant mechanisms that overall ensure temporal precision.”

(1e) The real HVC is robust to microlesions, cooling, and HVCra neuron turnover. The model in this paper relies on precise HVCra connectivity and is not robust.

Although our model is grounded in the biologically observed behavior of HVC neurons in vivo, we don’t claim that it fully captures the resilience seen in the HVC network. Instead, we see this as a simplified framework that helps us explore the basic principles of sequential activity. In the future, adding features like recurrent excitation, synaptic plasticity, or homeostatic mechanisms could make the model more robust.

(1f) There is unclear motivation for Ih-driven HVCx bursting, given past findings from the Mooney group.

Daou et al (2013) noticed that the observed in HVC_X and HVC_INT neurons in response to hyperpolarizing current pulses (Dutar et al. 1998; Kubota and Saito 1991; Kubota and Taniguchi 1998) was completely abolished after the application of the drug ZD 7288 in all of the neurons tested indicating that the sag in these HVC neurons is due to the hyperpolarization-activated inward current (I_h). in addition, the sag and the rebound seen in these two neuron groups were larger as for larger hyperpolarization current pulses.

(1g) The initial conditions of the network and its activity under those conditions, as well as the possible reliance on external inputs, are not defined.

In our model, network activity is initiated through a brief, stochastic excitatory input to a small HVC_RA neuron of one microcircuit. This drive represents a simplified version of external input from upstream brain regions known to project to HVC, such as nuclei in the high vocal center's auditory pathways such as Nif and Uva. Modeling the activity of these upstream regions and their influence on HVC dynamics is an ongoing research work to be published in the future.

(1h) It has been known from the time of Hodgkin and Huxley how to include temperature dependences for neuronal dynamics so another suggestion is for the authors to add such dependences for the three classes of neurons and see if their simulation causes burst frequencies to speed up or slow down as T is varied.

We added this as limitation to the discussion section: “Our model was run at a fixed physiological temperature, but it's well known going all the way back to Hodgkin and Huxley that both ion channel activity and synaptic dynamics can change with temperature. In future work, adding temperature scaling (like Q10 factors) could help us explore how burst timing and sequence speed change with temperature changes, and how neural activity in HVC would/would not preserve its precision under different physiological conditions.”

(2) The scope of the paper and its objectives must be clearly defined. Defining the scope and providing caveats for what is not considered will help the reader contextualize this study with other work.

(2a) The paper does not consider the role of external inputs to HVC, which are very likely important for the capacity of the HVC chain to tile the entire song, including silent gaps.

The role of afferent input to HVC particularly from nuclei such as Uva and Nif is critical in shaping the timing and initiation of HVC sequences throughout the song, including silent intervals. In fact, external inputs are likely involved in more than just triggering sequences, they may also influence the continuity of activity across motifs. However, in this study, we chose to focus on the intrinsic dynamics of HVC as a step toward understanding the internal mechanisms required for generating temporally precise sequences and for this reason, we used a simplified external input only to initiate activity in the chain.

(2b) The paper does not consider important dendritic mechanisms that almost certainly facilitate the all-or-none bursting behavior of HVC projection neurons. the authors need to mention and discuss that current-clamped neuronal response - in which an electrode is inserted into the soma and then a constant current-step is applied - bypasses dendritic structure and dendritic processing and so is an incomplete way to characterize a neuron's properties. In particular, claiming to fit current-clamp data accurately and then claiming that one now has a biophysically accurate network model, as the authors do, is greatly misleading.

While we addressed this is 1a, we do not suggest that our model is a fully accurate biophysical representation of HVC network. Instead, we see it as a simplified framework that helps reveal how much of HVC’s sequential activity can be explained by somatic properties and synaptic interactions alone. However, additional biological mechanisms, like dendritic processing, are likely to play an important role and should be explored in future work.

(2c) The introduction does not provide a clear motivation for the paper - what hypotheses are being tested? What is at stake in the model outcomes? It is not inherently informative to take a known biological representation and fine-tune a limited model to replicate that representation.

We explicitly added the hypotheses to the revised introduction.

(2d) There have been several published modeling efforts applied to the HVC chain (Seung, Fee, Long, Greenside, Jin, Margoliash, Abarbanel). These and others need to be introduced adequately, and it needs to be crystal clear what, if anything, the present study is adding to the canon.

While several influential models have explored how HVC might generate sequences ranging from synfire chains to recurrent dynamics or externally driven sequences (e.g., Seung, Fee, Long, Greenside, Jin, Abarbanel, and others), these models could not capture the detailed dynamics observed in vivo. Our aim was to bridge a gap in the modeling literature by exploring how far biophysically grounded intrinsic properties and experimentally supported synaptic connections that are local to the HVC can alone produce temporally precise sequences. We have proven that these mechanisms are sufficient to generate these sequences, although some missing components (such as dendritic mechanisms or external inputs) might be needed to fully capture the complexity and robustness of HVC function.

(2e) The authors mention learning prominently in the abstract, summary, and introduction but this paper has nothing to do with learning. Most or all mentions of learning should be deleted since they are misleading.

We appreciate the reviewer’s observation however our intent by referencing learning was not to suggest that our model directly simulates learning processes, but rather to place HVC function within the broader context of song learning and production, where temporal sequencing plays a fundamental role. Yet, repeated references to learning may be misleading given that our current model does not incorporate plasticity, synaptic modification, or developmental changes. Hence, we have carefully revised the manuscript to rephrase mentions of learning unless directly relevant to context. 

(3) Using the model for hypothesis generation and prediction of experimental results.

(3a) The utility of a model is to provide conceptual insight into how or why the real HVC functions as it does, or to predict outcomes in yet-to-be conducted experiments to help motivate future studies. This paper does not adequately achieve these goals.

We revised the Discussion of the manuscript to better emphasize potential contributions and point out many experiments that could validate or challenge the model’s predictions. These include dynamic clamp or ion channel blockers targeting A-type K⁺ in HVC_RA neurons to assess their impact on burst precision, optogenetic disruption of inhibitory interneurons to observe changes in burst timing and sequence propagation, pharmacological modulation of I_h or I_CaT in HVC_X and interneurons etc. 

(3b) Additionally, it can be interesting to conduct an experiment on an existing model; for example, what happens to the HVCra chain in your model if you delete the HVCx neurons? What happens if you block NMDA receptors? Such an approach in a modeling paper can help motivate hypotheses and endow the paper with a sense of purpose.

We agree that running targeted experiments to test our computational model such as removing an HVC neuron population or blocking a synaptic receptor can be a powerful way to generate new ideas and guide future experiments. While we didn’t include these specific tests in the current study, the model is well suited for this kind of exploration. For instance, removing interneurons could help us better understand their role in shaping the timing of HVC_RA bursts. These are great directions for future experiments, and we now highlight this in the discussion as a way the model could be used to guide experiments.

(4) Changes to the paper's organization may improve clarity.

(4a) Nearly all equations should be moved to an Appendix so that the main part of the paper can focus on the science: assumptions made, details of simulations, conclusions obtained, and their significance. The authors present many equations without discussion which weakens the paper.

Equations moved to appendix.

(4b) There are many grammatical errors, e.g., verbs do not match the subject in terms of being single or plural. The authors need to run their manuscript through a grammar checker.

Done.

(4c) Many of the figures are poorly designed and should be substantially modified. E.g. in Figure 1B, too many colors are used, making it hard to grasp what is being plotted and the colors are not needed. Figures 1C and 1D are entire figures taken from other papers, and there is no way a reader will be able to see or appreciate all the details when this figure is published on a single page. Figure 2 uses colors for dots that are almost identical, and the colors could be avoided by using different symbols. Figure 5 fills an entire page but most of the figure conveys no information, there is no need to show the same details for all 120 neurons, just show the top 1/3 of this figure; the same for Figure 7, a lot of unnecessary information is being included. Figure 10, the bottom time series of spikes should be replaced with a time series of rates, cannot extract useful information.

Adjusted as requested. 

(4d) Table 1 is long and largely uninteresting, and should be moved to an appendix.

Table 1 moved to appendix.

(4e) Many sentences are not carefully written, which greatly weakens the paper. As one typical example, the first sentence in the Discussion section "In this study, we have designed a neural network model that describes [sic] zebra finch song production in the HVC." This is inaccurate, the model does not describe song production, it just explores some properties of one nucleus involved with song production. Just one or few sentences like this is ok but there are so many sentences of this kind that the reader loses faith in the authors.

Thank you for raising this point, we revised the manuscript to improve the precision of the writing. We replaced the first sentence of the discussion with this: "In this study, we developed a biophysically realistic neural network model to explore how intrinsic neuronal properties and local connectivity within the songbird nucleus HVC may support the generation of temporally precise activity sequences associated with zebra finch song."

eLife Assessment

This is a valuable analysis of STORM data that characterizes the clustering of active zones in retinogeniculate terminals across ages and in the absence of retinal waves. The design makes it possible to relate fixed time point structural data to a known outcome of activity-dependent remodeling. The latest revision has tempered the causal claims made in previous versions. The result provides solid structural support for the hypotheses regarding how activity influences the clustering of these synapses.

Joint Public Review:

Summary:

The authors previously published a study of RGC boutons in the dLGN in developing wild-type mice and developing mutant mice with disrupted spontaneous activity. In the current manuscript, they have broken down their analysis of RGC boutons according to the number of Homer/Bassoon puncta associated with each vGlut3 cluster.

The authors find that, in the first post-natal week, RGC boutons with multiple active zones (mAZs) are about a third as common as boutons with a single active zone (sAZ). The size of the vGluT2 cluster associated with each bouton was proportional to the number of active zones present in each bouton. Within the author's ability to estimate these values (n=3 per group, 95% of results expected to be within ~2.5 standard deviations), these results are consistent across groups: 1) dominant eye vs. non-dominant eye, 2) wild-type mice vs. mice with activity blocked, and at 3) ages P2, P4, and P8. The authors also found that mAZs and sAZs also have roughly the same number (about 1.5) of sAZs clustered around them (within 1.5 um).

There has been much discussion with the reviewers through multiple versions of this paper. of how to interpret these findings. Based on a large number of tests for statistical significance, the authors interpreted the presence of a statistical significance difference as evidence that "Eye-specific active zone clustering underlies synaptic competition in the developing visual system (title of previous version of manuscript)". The reviewers have focused on the small effect size as indicating that the small differences observed are not informative regarding this biological question. The authors have now tempered this interpretation.

Strengths:

The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model for activity dependent synaptic remodeling.

Reviewing Editor's comment on the latest revision (without sending the paper back to the individual reviewers):

In their latest revision, the authors have moderated earlier causal claims, incorporated additional statistical controls, and largely maintained their original interpretation of the data. While these changes address some prior concerns, the underlying issues remain. The previous review emphasized that the reported effect sizes were small and therefore hard to link to biological relevance. The authors argue that the effect sizes are large. Given the lack of a biological argument for this effect size, this point is really semantic. We would like to point out that the effect size measurement the authors used is likely a standard effect size calculation (the difference between groups is divided by the standard deviation of the groups). With only three experiments and irregular variance, it is likely that their estimates of standard deviation-and therefore effect size-are unreliable. Overall, the revisions improve presentation but do not substantively resolve the difficulty in drawing strong conclusions from the data set raised earlier.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary

The authors previously published a study of RGC boutons in the dLGN in developing wild-type mice and developing mutant mice with disrupted spontaneous activity. In the current manuscript, they have broken down their analysis of RGC boutons according to the number of Homer/Bassoon puncta associated with each vGlut3 cluster.

The authors find that, in the first post-natal week, RGC boutons with multiple active zones (mAZs) are about a third as common as boutons with a single active zone (sAZ). The size of the vGluT2 cluster associated with each bouton was proportional to the number of active zones present in each bouton. Within the author's ability to estimate these values (n=3 per group, 95% of results expected to be within ~2.5 standard deviations), these results are consistent across groups: 1) dominant eye vs. nondominant eye, 2) wild-type mice vs. mice with activity blocked, and at 3) ages P2, P4, and P8. The authors also found that mAZs and sAZs also have roughly the same number (about 1.5) of sAZs clustered around them (within 1.5 um).

However, the authors do not interpret this consistency between groups as evidence that active zone clustering is not a specific marker or driver of activity dependent synaptic segregation. Rather, the authors perform a large number of tests for statistical significance and cite the presence or absence of statistical significance as evidence that "Eye-specific active zone clustering underlies synaptic competition in the developing visual system (title)". I don't believe this conclusion is supported by the evidence.

We have revised the title to be descriptive: "Eye-specific differences in active zone addition during synaptic competition in the developing visual system." While our correlative approach does not establish direct causality, our findings provide important structural evidence that complements existing functional studies of activity-dependent synaptic refinement. We have carefully revised the text throughout to avoid causal language, focusing instead on the developmental patterns we observe.

Strengths

The source dataset is high resolution data showing the colocalization of multiple synaptic proteins across development. Added to this data is labeling that distinguishes axons from the right eye from axons from the left eye. The first order analysis of this data showing changes in synapse density and in the occurrence of multi-active zone synapses is useful information about the development of an important model for activity dependent synaptic remodeling.

Weaknesses

In my previous review I argued that it was not possible to determine, from their analysis, whether the differences they were reporting between groups was important to the biology of the system. The authors have made some changes to their statistics (paired t-tests) and use some less derived measures of clustering. However, they still fail to present a meaningfully quantitative argument that the observed group differences are important. The authors base most of their claims on small differences between groups. There are two big problems with this practice. First, the differences between groups appear too small to be biologically important. Second, the differences between groups that are used as evidence for how the biology works are generally smaller than the precision of the author's sampling. That is, the differences are as likely to be false positives as true positives.

(1) Effect size. The title claims: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". Such a claim might be supported if the authors found that mAZs are only found in dominant-eye RGCs and that eye-specific segregation doesn't begin until some threshold of mAZ frequency is reached. Instead, the behavior of mAZs is roughly the same across all conditions. For example, the clear trend in Figure 4C and D is that measures of clustering between mAZ and sAZ are as similar as could reasonably be expected by the experimental design. However, some of the comparisons of very similar values produced p-values < 0.05. The authors use this fact to argue that the negligible differences between mAZ and sAZs explain the development of the dramatic differences in the distribution of ipsilateral and contralateral RGCs.

We have changed the title to avoid implying a causal relationship between clustering and eye-specific segregation. Our key findings in Figures 4C and 4D demonstrate effect sizes >2.0 with high statistical power (Supplemental Table S2). While the absolute magnitude of differences is modest (5-7%), these high effect sizes combined with low inter-animal variability demonstrate consistent, reproducible biological phenomena. During development, small differences during critical periods can have profound downstream consequences for synaptic refinement outcomes.

We acknowledge that significance in Figure 4 arises due to low variance between biological replicates rather than large mean differences. We have revised the text to describe these as "slight" differences and that "WT mice show a tendency toward forming more synapses near mAZ inputs," reflecting appropriate caution in our interpretation while maintaining the statistical robustness of our findings.

(2) Sample size. Performing a large number of significance tests and comparing pvalues is not hypothesis testing and is not descriptive science. At best, with large sample sizes and controls for multiple tests, this approach could be considered exploratory. With n=3 for each group, many comparisons of many derived measures, among many groups, and no control for multiple testing, this approach constitutes a random result generator.

The authors argue that n=3 is a large sample size for the type of high resolution / large volume data being used. It is true that many electron microscopy studies with n=1 are used to reveal the patterns of organization that are possible within an individual. However, such studies cannot control individual variation and are, therefore, not appropriate for identifying subtle differences between groups.

In response to previous critiques along these lines, the authors argue they have dealt with this issue by limiting their analysis to within-individual paired comparisons. There are several problems with their thinking in this approach. The main problem is that they did not change the logic of their arguments, only which direction they pointed the t-tests. Instead of claiming that two groups are different because p < 0.05, they say that two groups are different because one produced p < 0.05 and the other produced p > 0.05. These arguments are not statistically valid or biologically meaningful.

We have implemented rigorous statistical controls, applying false discovery rate (FDR) correction using the Benjamini-Hochberg method (α = 0.05) within each experimental condition (age × genotype combination). This correction strategy treats each condition as addressing a distinct experimental question: “What synaptic properties differ between left eye and right eye inputs in this specific developmental stage and genotype?” The approach appropriately controls for multiple testing while preserving power to detect biologically meaningful differences. We applied FDR correction separately to the ~20-34 measurements (varying by age and genotype) within each of the six experimental conditions, resulting in condition-specific adjusted p-values reported in updated Supplemental Table S2. This correction confirmed the robustness of our key findings. We do not base conclusions solely on comparing p-values across conditions. Our interpretations focus on effect sizes, confidence intervals, and consistent patterns within each condition, with statistical significance providing supporting evidence rather than the primary basis for biological conclusions.

To the best of my understanding, the results are consistent with the following model:

RGCs form mAZs at large boutons (known)

About a quarter of week-one RGC boutons are mAZs (new observation)

Vesicle clustering is proportional to active zone number (~new observation)

RGC synapse density increases during the first post-week (known)

Blocking activity reduces synapse density (known)

Contralateral eye RGCs for more and larger synapses in the lateral dLGN (known)

While mAZ formation is known in adult and juvenile dLGN, the formation of mAZ boutons during eye-specific competition represents new information with important functional implications. Synapses with multiple release sites should be stronger than single-active-zone synapses, suggesting a structural correlate for competitive advantage during refinement.

We demonstrate distinct developmental patterns for sAZ versus mAZ contacts during the first postnatal week. Multi-active zone density favors the dominant eye, while single active-zone synapse density from the competing eye increases from P2-P4 to match dominant-eye levels. This reveals that newly formed synapses from the competing eye predominantly contain single release sites, marking P4-P8 as a critical window for understanding molecular mechanisms driving synaptic elimination.

Our results show that altered retinal activity patterns (β2KO mice) reduce synapse density during eye-specific competition. We relied on β2 knockout mice, which retain retinal waves and spontaneous spike activity but with disrupted patterns and output levels compared to controls. We make no claims about complete activity blockade. Previous studies using different activity manipulations (epibatidine, TTX) have examined terminal morphology, but effects on synapse density during competition remain largely unknown. Achieving complete retinal activity blockade is technically challenging, making it of interest to revisit the role of activity using more precise manipulations to control spike output and relative timing.

With n=3 and effect sizes smaller than 1 standard deviation, a statistically significant result is about as likely to be a false positive as a true positive.

A true-positive statistically significant result does is not evidence of a meaningful deviation from a biological model.

Our conclusions are based on results with effect sizes substantially larger than 1. Key findings demonstrate effect sizes exceeding 2.0. These large effect sizes, combined with rigorous FDR correction and low inter-animal variability, provide evidence against false positive results. During critical developmental periods, consistent structural differences, even those modest in absolute magnitude, can reflect important regulatory mechanisms that influence refinement outcomes. All statistical results, effect sizes, and power analyses are reported in Supplementary Tables S2, with confidence intervals in Supplementary Table S3. We have revised the text in several places where small differences are presented to reflect appropriate caution in our interpretation.

Providing plots that show the number of active zones present in boutons across these various conditions is useful. However, I could find no compelling deviation from the above default predictions that would influence how I see the role of mAZs in activity dependent eye-specific segregation.

Below are critiques of most of the claims of the manuscript.

Claim (abstract): individual retinogeniculate boutons begin forming multiple nearby presynaptic active zones during the first postnatal week.

Confirmed by data.

Claim (abstract): the dominant-eye forms more numerous mAZ contacts,

Misleading: The dominant-eye (by definition) forms more contacts than the nondominant eye. That includes mAZ.

While the dominant eye forms more total contacts, the pattern depends critically on contact type and developmental stage. The dominant eye forms more mAZ contacts across all ages (Figures 2 and S1). However, for sAZ contacts, the two eyes form similar numbers at P4, with the non-dominant eye showing increased sAZ formation during this critical period. This differential pattern by synapse type represents an important aspect of how synaptic competition unfolds structurally.

Claim (abstract): At the height of competition, the non-dominant-eye projection adds many single active zone (sAZ) synapses

Weak: While the individual observation is strong, it is a surprising deviation based on a single n=3 experiment in a study that performed twelve such experiments (six ages, mutant/wildtype, sAZ/mAZ)

The difference in eye-specific sAZ formation at P2 and P8 had effect sizes of ~5.3 and ~2.7 respectively (after FDR correction the difference was still significant at P2 and trending at P8). At P4, no effect was observed by paired T-test and the 5/95% confidence intervals ranged from -0.021-0.008 synapses/m³. The consistency of this pattern across P2 and P8, combined with the large effect sizes, supports the reliability of this developmental finding. We report all effect sizes and power test analyses in Supplemental Table S2, and confidence intervals in Supplemental Table S3. 

Claim (abstract): Together, these findings reveal eye-specific differences in release site addition during synaptic competition in circuits essential for visual perception and behavior.

False: This claim is unambiguously false. The above findings, even if true, do not argue for any functional significance to active zone clustering.

Our phrasing “circuits essential for visual perception and behavior” referred to the general importance of binocular organization in the retinogeniculate system for visual processing and we did not intend to claim direct functional significance of our structural data. For clarity we have deleted the latter part of this sentence. In lines 35-37, the abstract now reads “Together, these findings reveal eye-specific differences in release site addition that correlate with axonal refinement outcomes during retinogeniculate refinement.”

Claim (line 84): "At the peak of synaptic competition midway through the first postnatal week, the non-dominant-eye formed numerous sAZ inputs, equalizing the global synapse density between the two eyes"

Weak: At one of twelve measures (age, bouton type, genotype) performed with 3 mice each, one density measure was about twice as high as expected.

The difference in eye-specific sAZ formation at P2 and P8 had effect sizes of ~5.3 and ~2.7 respectively (after FDR correction the difference was still significant at P2 and trending at P8). At P4, no effect was observed by paired T-test and the 5/95% confidence intervals ranged from -0.021-0.008 synapses/m³. The consistency of this pattern across P2 and P8, combined with the large effect sizes, supports the reliability of this developmental finding. We report all effect sizes and power test analyses in Supplemental Table S2, and confidence intervals in Supplemental Table S3. 

Claim (line 172): "In WT mice, both mAZ (Fig. 3A, left) and sAZ (Fig. 3B, left) inputs showed significant eye-specific volume differences at each age."

Questionable: There appears to be a trend, but the size and consistency is unclear.

Claim (line 175): "the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left)."

Cherry picking. Twelve differences were measured with an n of 3, 3 each time. The biggest difference of the group was cited. No analysis is provided for the range of uncertainty about this measure (2.5 standard deviations) as an individual sample or as one of twelve comparisons.

Claim (line 174): "In the middle of eye-specific competition at P4 in WT mice, the median VGluT2 cluster volume in dominant-eye mAZ inputs was 3.72 fold larger than that of non-dominant-eye inputs (Fig. 3A, left). In contrast, β2KO mice showed a smaller 1.1 fold difference at the same age (Fig. 3A, right panel). For sAZ synapses at P4, the magnitudes of eye-specific differences in VGluT2 volume were smaller: 1.35-fold in WT (Fig. 3B, left) and 0.41-fold in β2KO mice (Fig. 3B, right). Thus, both mAZ and sAZ input size favors the dominant eye, with larger eye-specific differences seen in WT mice (see Table S3)."

No way to judge the reliability of the analysis and trivial conclusion: To analyze effect size the authors choose the median value of three measures (whatever the middle value is). They then make four comparisons at the time point where they observed the biggest difference in favor of their hypothesis. There is no way to determine how much we should trust these numbers besides spending time with the mislabeled scatter plots. The authors then claim that this analysis provides evidence that there is a difference in vGluT2 cluster volume between dominant and non-dominant RGCs and that that difference is activity dependent. The conclusion that dominant axons have bigger boutons and that mutants that lack the property that would drive segregation would show less of a difference is very consistent with the literature. Moreover, there is no context provided about what 1.35 or 1.1 fold difference means for the biology of the system.

We focused on P4 for biological reasons rather than post-hoc selection. P4 represents the established peak of synaptic competition when eye-specific synapse densities are globally equivalent. This is a timepoint consistently highlighted throughout our manuscript and supported by previous literature. We have modified our presentation from fold changes to measured eye-specific differences in volume (mean ± standard error) and added confidence intervals in Supplemental Table S3. The effect sizes for eye-specific differences in VGluT2 volume at P4 are robust: ~2.3 and ~1.5 for mAZ and sAZ measurements in WT mice, and ~2.5 and ~1.8 in β2KO mice, with all analyses well-powered (Supplemental Table S2).

We were unable to identify any mislabeled scatter plots and believe all figures are correctly labeled. While dominant-eye advantage in bouton size is consistent with previous literature, our study provides the first detailed analysis of how this develops specifically during the critical period of competition, with distinct patterns for single versus multi-active zone contacts. Our data show that dominant-eye inputs have larger vesicle pools that scale with active zone number. While this suggests enhanced transmission capacity, we make no direct physiological claims based on structural data alone.

Claim (189): "This shows that vesicle docking at release sites favors the dominant-eye as we previously reported but is similar for like eye type inputs regardless of AZ number."

Contradicts core claim of manuscript: Consistent with previous literature, there is an activity dependent relative increase in vGlut2 clustering of dominant eye RGCs. The new information is that that activity dependence is more or less the same in sAZ and mAZ. The only plausible alternative is that vGlut2 scaling only increases in mAZ which would be consistent with the claims of their paper. That is not what they found. To the extent that the analysis presented in this manuscript tests a hypothesis, this is it. The claim of the title has been refuted by figure 3.

We report the volume of docked vesicle signal (VGluT2) nearby each active zone, finding this is greater for dominant-eye synapses. Within each eye-specific synapse population, vesicle signal per active zone is similar regardless of whether these are part of single- or multi-active zone contacts. This is consistent with a modular program of active zone assembly and maintenance: core molecular programs facilitate docking at each AZ similarly regardless of how many AZs are nearby. 

This finding does not contradict our main conclusions but rather provides insight into how synaptic advantages are structured. The dominant eye's advantage may arise in part from forming more multi-AZ contacts (which have proportionally more docked vesicles) rather than from enhanced vesicle loading per individual active zone. This organization may reflect how developmental competition operates through contact number and active zone addition rather than fundamental changes to individual release site properties.

We have changed the title to be descriptive rather than mechanistic.

Claim (line 235): "For the non-dominant eye projection, however, clustered mAZ inputs outnumbered clustered sAZ inputs at P4 (Fig. 4C, bottom left panel), the age when this eye adds sAZ synapses (Fig. 2C)."

Misleading: The overwhelming trend across 24 comparisons is that the sAZ clustering looks like mAZ clustering. That is the objective and unambiguous result. Among these 24 underpowered tests (n=3), there were a few p-values < 0.05. The authors base their interpretation of cell behavior on these crossings.

In Figures 4C and 4D we report significant results with high effect sizes (effect sizes all greater than 2; see Supplemental Table S2). The mean differences are modest (5-7%) and significance arises due to low variance between biological replicates. We acknowledge that clustering patterns are generally similar between mAZ and sAZ inputs across most conditions. We have revised the text to describe these as “slight” differences and that “WT mice show a tendency toward forming more synapses near mAZ inputs”, reflecting appropriate caution in our interpretation while noting the statistical consistency of these patterns.

Claim (line 328): "The failure to add synapses reduced synaptic clustering and more inputs formed in isolation in the mutants compared to controls."

Trivially true: Density was lower in mutant.

We have rewritten the sentence for clarity: “The failure to add synapses could explain the observation that synaptic clustering was reduced and more inputs formed in isolation in the mutants compared to controls.”

Claim (line 332): "While our findings support a role for spontaneous retinal activity in presynaptic release site addition and clustering..."

Not meaningfully supported by evidence: I could not find meaningful differences between WT and mutant beside the already known dramatic difference in synapse density.

We have changed the sentence to avoid overinterpreting the results. The new sentence in lines 415-417 reads: “While our results highlight developmental changes in presynaptic release site addition and clustering, activity-dependent postsynaptic mechanisms also influence input refinement at later stages.”

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhang and Speer examine changes in the spatial organization of synaptic proteins during eye specific segregation, a developmental period when axons from the two eyes initially mingle and gradually segregate into eye-specific regions of the dorsal lateral geniculate. The authors use STORM microscopy and immunostain presynaptic (VGluT2, Bassoon) and postsynaptic (Homer) proteins to identify synaptic release sites. Activity-dependent changes of this spatial organization are identified by comparing the β2KO mice to WT mice. They describe two types of synapses based on Bassoon clustering: the multiple active zone (mAZ) synapse and single active zone (sAZ) synapse. In this revision, the authors have added EM data to support the idea that mAZ synapses represent boutons with multiple release sites. They have also reanalyzed their data set with different statistical approaches.

Strengths:

The data presented is of good quality and provides an unprecedented view at high resolution of the presynaptic components of the retinogeniculate synapse during active developmental remodeling. This approach offers an advance to the previous mouse EM studies of this synapse because of the CTB label allows identification of the eye from which the presynaptic terminal arises.

Weaknesses:

While the interpretation of this data set is much more grounded in this second revised submission, some of the authors' conclusions/statements still lack convincing supporting evidence. In particular, the data does not support the title: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". The data show that there are fewer synapses made for both contra- and ipsi- inputs in the β2KO mice-- this fact alone can account for the differences in clustering. There is no evidence linking clustering to synaptic competition. Moreover, the findings of differences in AZ# or distance between AZs that the authors report are quite small and it is not clear whether they are functionally meaningful.

We thank the reviewer for their helpful suggestions that improved the manuscript in this revision. We have changed the title to remove the reference to “clustering” and to avoid implying any causal relationships. The new title is descriptive: “Eye-specific differences in active zone addition during synaptic competition in the developing visual system”.

To further address the reviewers comments, we have removed the remaining references to activity-dependent effects on synaptic development (line 36, line 96, line 415). We have also modified the text in lines 411-413 to state that “The failure to add synapses could explain the observation that synaptic clustering was reduced and more inputs formed in isolation in the mutants compared to controls.”

We have also updated our presentation of results for Figure 4 to ensure that we do not causally link clustering to synaptic competition. In Figures 4C and 4D we report significant results with high effect sizes (effect sizes all greater than 2; see Supplemental Table S2). The mean differences are modest (5-7%) and significance arises due to low variance between biological replicates. We acknowledge that clustering patterns are generally similar between mAZ and sAZ inputs across most conditions. We have revised the text to describe these as “slight” differences and that “WT mice show a tendency toward forming more synapses near mAZ inputs”, reflecting appropriate caution in our interpretation while noting the statistical consistency of these patterns.

Reviewer #3 (Public review):

This study is a follow-up to a recent study of synaptic development based on a powerful data set that combines anterograde labeling, immunofluorescence labeling of synaptic proteins, and STORM imaging (Cell Reports, 2023). Specifically, they use anti-Vglut2 label to determine the size of the presynaptic structure (which they describe as the vesicle pool size), anti-Bassoon to label active zones with the resolution to count them, and anti-Homer to identify postsynaptic densities. Their previous study compared the detailed synaptic structure across the development of synapses made with contraprojecting vs. ipsi-projecting RGCs and compared this developmental profile with a mouse model with reduced retinal waves. In this study, they produce a new detailed analysis on the same data set in which they classify synapses into "multi-active zone" vs. "single-active zone" synapses and assess the number and spacing of these synapses. The authors use measurements to make conclusions about the role of retinal waves in the generation of same-eye synaptic clusters. The authors interpret these results as providing insight into how neural activity drives synapse maturation, the strength of their conclusions is not directly tested by their analysis.

Strengths:

This is a fantastic data set for describing the structural details of synapse development in a part of the brain undergoing activity-dependent synaptic rearrangements. The fact that they can differentiate the eye of origin is what makes this data set unique over previous structural work. The addition of example images from the EM dataset provides confidence in their categorization scheme.

Weaknesses:

Though the descriptions of single vs multi-active zone synapses are important and represent a significant advance, the authors continue to make unsupported conclusions regarding the biological processes driving these changes. Although this revision includes additional information about the populations tested and the tests conducted, the authors do not address the issue raised by previous reviews. Specifically, they provide no assessment of what effect size represents a biologically meaningful result. For example, a more appropriate title is "The distribution of eye-specific single vs multiactive zone is altered in mice with reduced spontaneous activity" rather than concluding that this difference in clustering is somehow related to synaptic competition. Of course, the authors are free to speculate, but many of the conclusions of the paper are not supported by their results.

We appreciate the reviewer’s helpful critique. We have changed the title to be descriptive and avoid implying causal relationships. 

We have applied false discovery rate (FDR) correction using the Benjamini-Hochberg method with α = 0.05 within each experimental condition (age × genotype combination). The FDR correction treats each condition as addressing a distinct experimental question: 'What synaptic properties differ between left eye and right eye inputs in this specific developmental stage and genotype?'

This correction strategy is appropriate because: 1) we focus our statistical comparisons within each age/genotype; 2) each age-genotype combination represents a separate biological context where different synaptic properties between eye-of-origin may be relevant; and 3) this approach controls for multiple testing within each experimental question while maintaining statistical power to detect meaningful biological differences.

We applied FDR correction separately to the ~20-34 measurements (varying with age and genotype) within each of the six experimental conditions (P2-WT, P2-ß2, P4-WT, P4-ß2, P8-WT, P8-ß2), resulting in condition-specific adjusted p-values. These are reported in the updated Supplemental Table S2. Figures have been also been updated to reflect the FDR-adjusted values. Selected between-genotype comparisons are presented descriptively using 5/95% confidence intervals. This correction confirmed the robustness of our key findings.

With regard to the biological significance of effect sizes, our key findings demonstrate effect sizes >2.0, indicating robust effects. During critical developmental periods, consistent structural differences, even those modest in absolute magnitude, can reflect important regulatory mechanisms that influence refinement outcomes. The differences in synaptic organization we observe occur during the first postnatal week when eyespecific competition is active, suggesting these patterns may be relevant to understanding how structural advantages emerge during synaptic refinement.

Reviewer #1 (Recommendations for the authors):

I have tried to understand the analysis and biology of this manuscript as best I can. I believe the analytical approach taken is not reliable and I have explained why in my public comments. I don't believe this manuscript is unique in taking this approach. I have recently published a paper on how common this approach is and why it doesn't work. I don't want to give the impression that the problem with the analysis was that it was not computationally sophisticated enough or that you did not jump through a specific statistical hoop. If I strip out the arguments that depend on misinterpretations of p-values and -instead- look at the scatterplots, I come up with a very different view of the data than what is described in the paper.

The information in the plots could be translated into a rigorous statistical analysis of estimated differences between groups given the uncertainties of the experimental design. I don't really think that analysis would be useful. I think it would have been enough to publish the plots and report your estimates of the number of active zones in RGCs during development. I don't see evidence of an additional effect.

We appreciate the reviewer’s helpful comments throughout the review process. Mean active zone numbers per mAZ contact are presented in Figure S2D/E. We look forward to further technical and computational advances that will help us increase our data acquisition throughput and sample sizes when designing future studies. 

Reviewer #2 (Recommendations for the authors):

The authors should modify the title and other text to be more consistent with the data. There is no evidence that active zone clustering has any direct relationship to synaptic competition.

We appreciate the reviewer’s helpful suggestions to ensure appropriate language around causal effects. We have modified the title to accurately reflect the results: "Eyespecific differences in active zone addition during synaptic competition in the developing visual system." We have revised the text in the abstract, introduction, and results section for Figures 4 to be consistent with the data and not imply causality of synapse clustering on segregation phenotypes.

Reviewer #3 (Recommendations for the authors):

Change the title.

We appreciate the reviewer’s feedback throughout the review process. We have modified the title to accurately reflect the results: "Eye-specific differences in active zone addition during synaptic competition in the developing visual system."

eLife Assessment

This important work advances our understanding of NMDAR diversity in the brain by providing evidence into the subunit arrangement, architecture, and activation mechanism of GluN1-N2-N3A tri-NMDAR. However, the evidence supporting the conclusions provides incomplete proof for the presence and functional properties of this NMDA receptor subtype. The work will be of broad interest to neuroscientists and biophysicists.

Reviewer #1 (Public review):

Summary:

The previous evidence for NMDARs containing N1, N2, and N3 subunits (t-NMDARs) was weak. All previous results could be explained by mixtures of di-heteromeric receptors. The authors here set out to identify t-NMDARs both in vitro and in the brain.

Strengths:

The single-channel recording is quite convincing because the authors could reproduce previous results in their system, but could also then add new observations. It is quite hard (if not impossible) to obtain the N1-N2A-N3A result at 100 µM Glu/Gly from a mixture, because the N1-N2A diheteromer has such a high open probability. Therefore, any idea that this might be, in fact, two receptors (GluN1-N2A and GluN1-N3A) is trivially falsified. The authors might prefer to make this argument based on the reduction of open probability, which cannot be achieved from a mixture masquerading as a single channel.

With regard to crosslinker usage in brain tissue, these are very impressive attempts, which I applaud. The fluorescence images of the brain sections look convincing. But the bands corresponding to N2-N3 crosslinked subunits from neurons or the brain are faint. I would want more information to be convinced that these faint bands come from GluN2-N3 dimers.

Weaknesses:

In the first part of the paper, where the CryoEM structure is determined, it's not really clear to me the extent to which Fab binding might bias the position of the ATDs (and even then the arrangement of each subunit within the whole complex). Then, much later at the end of the results, there is a structural analysis that claims to be integrative (Figure 7) but does not obviously rely on any other data than the structures, but does mention this point about the Fabs. The results could be rearranged to make these points clearer.

I have my biggest doubts about the crosslinking of native receptors. For the biochemistry from neurons or brain tissue, this is a very ambitious idea that has been hard to execute over the past 15-20 years. The authors use AzF for the obvious reason that this was done before in NMDARs. The constructs that have been assembled are neat. But AzF is a really bad crosslinker. The authors attribute the weak bands to subunit mobility, but the minor abundance is more likely due to the strong constraints on AzF crosslinking and its unsuitable photochemistry in general (very easily activated with room light, for example).

There is no information at all given about the wavelength, intensity, duration of UV exposure, and how, for example, the right exposure was determined. How were the samples protected in between?

Reviewer #2 (Public review):

Summary:

The authors purified and solved by cryo-EM a structure of tri-heteromeric GluN1/GluN2A/GluN3A NMDA receptors, whose existence has long been contentious. Using patch-clamp electrophysiology on GluN1/GluN2/GluN3A NMDARs reconstituted into liposomes, they characterized the function of this NMDAR subtype. Finally, thanks to site-targeted crosslinking using unnatural amino acid incorporation, they show that the GluN2A subunit can crosslink with the GluN3A subunit in a cellular context, both in recombinant systems (HEK cells) and neuronal cultures and in vivo.

Strengths:

The NMDAR GluN3 subunit is a glycine-binding subunit that was long thought to assemble into GluN1/GluN2/GluN3 tri-heteromeric receptors during development, acting as a brake for synaptic development. However, several studies based on single subunit counting (Ulbrich et al., PNAS 2008) and ex vivo/in vivo electrophysiology have challenged the existence of these tri-heteromers (see Bossi, Pizzamiglio et al., Trends Neurosci. 2023). A large part of the controversy stems from the difficulty in isolating the tri-heteromeric population from their di-heteromeric counterparts, which led to a lack of knowledge on the biophysical and pharmacological properties of putative GluN1/GluN2/GluN3 receptors. To counteract this problem, the authors used a two-step purification method - first with a strep-tag attached to the GluN3 subunit, then with a His tag attached to the GluN2 subunit - to isolate GluN1/GluN2/GluN3 tri-heteromers from GluN1/GluN2A and GluN1/GluN3 di-heteromers, and they did observe these entities in Western blot and FSEC. They solved a cryo-EM structure of this NMDAR subtype using specific FAbs to identify the GluN1 and GluN2A subunits, showing an asymmetrical, splayed architecture. Then, they reconstituted the purified receptors in lipid vesicles to perform single-channel electrophysiological recordings. Finally, in order to validate the tri-heteromeric arrangement in a cellular system, they performed photocrosslinking experiments between the GluN2A and GluN3 subunits. For this purpose, a photoactivatable unnatural amino acid (AzF) was incorporated at the bottom of GluN2A NTD, a region embedded within the receptor complex that is predicted to be in close proximity to the GluN3 subunit. This is an elegant approach to validate the existence of GluN1/GluN2/GluN3 tri-hets, since at the chosen AzF incorporation position, crosslinking between GluN2A and GluN3 is more likely to reflect interaction of subunits within the same receptor complex than between two receptors. They show crosslinking between GluN2A and GluN3 in the presence of AzF and UV light, but not if UV light or AzF were not provided, suggesting that GluN2A and GluN3 can indeed be incorporated in the same complex. In a further attempt to demonstrate the physiological relevance of these tri-heteromers, they performed the same crosslinking experiments in cultured neurons and even native brain samples. While unnatural amino acid incorporation is now a well-established technique in vitro, such an approach is very difficult to implement in vivo. The technical effort put into the validation of the presence of these tri-heteromers in vivo should thus be commended.

Overall, all the strategies used by this paper to prove the existence of GluN1/GluN2/GluN3 tri-heteromers, and investigate their structure and function, are well-thought-out and very elegant. But the current data do not fully support the conclusions of the paper.

Weaknesses:

All the experiments aiming at proving the existence of GluN1/GluN2/GluN3 tri-heteromers rely on the purification of these receptors from whole cell extracts. There is therefore no proof that these receptors are expressed at the membrane and are functional. This is a limitation that has been overlooked and should be discussed in the manuscript. In addition, in the current manuscript state, each demonstration suffers from caveats that do not allow for a firm conclusion about the existence and the properties of this receptor subtype.

(1) In Cryo-EM images of GluN1/GluN2A/GluN3A receptors, the GluN3 subunit is identified as the subunit having no Fab bound to it. How can the authors be sure that this is indeed the GluN3A subunit and not a GluN2A subunit that has not bound the Fab? Does the GluN3A subunit carry features that would allow distinguishing it independently of Fab binding? In addition, it is surprising that the authors did not incubate the tri-heteromers with a Fab against GluN3A, since Extended Figure 3 shows that such a Fab is available.

(2) Whether the single-channel recordings reflect the activity of GluN1/GluN2/GluN3 tri-heteromers is not convincing. Indeed, currents from liposomes containing these tri-heteromers have two conductance levels that correspond to the conductances of the corresponding di-heteromers. There is therefore a need for additional proof that the measured currents do not reflect a mixture of currents from N1/2A di-heteromers on one side, and N1/3A di-heteromers on the other side. What is the purity of the N1/3A sample? Indeed, given the high open probability and high conductance of N1/2A tri-heteromers, even a small fraction of them could significantly contribute to the single-channel currents. Additionally, although the authors show no current induced by 3uM glycine alone on proteoliposomes with the N1/2A/3A prep (no stats provided, though), given the sharp dependence of N1/3A currents on glycine concentration, this control alone cannot rule out the presence of contaminant N1/3A dihets in the preparation.

Finally, pharmacological characterization of these tri-heteromers is lacking. In vivo, the presence of tri-heteromeric GluN1/GluN2/GluN3 tri-heteromers was inferred from recordings of NMDARs activated by glutamate but with low magnesium sensitivity. What is the effect of magnesium on N1/2A/3A currents? Does APV, the classical NMDAR antagonist acting at the glutamate site, inhibit the tri-heteromers? What is the effect of CGP-78608, which inhibits GluN1/GluN2 NMDARs but potentiates GluN1/GluN3 NMDARs? Such pharmacological characterization is critical to validate that the measured currents are indeed carried by a tri-heteromeric population, and would also be very important to identify such tri-heteromers in native tissues.

(3) Validation of GluN1/GluN2/GluN3 tri-heteromer expression by photocrosslinking: The mixture of constructions used (full-length or CTD-truncated constructs, with or without tags) is confusing, and it is difficult to track the correct molecular weight of the different constructs. In Figure 6, the band corresponding to a putative GluN3/GluN2A dimer is very weak. In addition, given the differences in molecular weights between the GluN2 subunits and GluN3, we would expect the band corresponding to a GluN2A/GluN2B to migrate differently from the GluN2A/GluN3 dimer, but all high molecular weight bands seem to be a the same level in the blot. Finally, in the source data, the blots display additional bands that were not dismissed by the authors without justification. In short, better clarification of the constructs and more careful interpretation of the blots are necessary to support the conclusions claimed by the authors.

eLife Assessment

This important study sought to investigate the role that early childhood malaria exposure plays in the development of antibody responses to unrelated pathogens and vaccine-derived antigens in Kenyan children. In this natural experiment, the authors compare antibody levels among children who have been exposed to different levels of malaria transmission by using protein microarray technology. Although the findings are of importance, the evidence remains incomplete, and the analysis would benefit from a more in-depth evaluation of potential confounders. With the appropriate analysis, the findings will be of great interest for global health, immunology, and vaccine development.

Reviewer #1 (Public review):

Summary:

The study shows that childhood malaria can weaken the antibody response to other vaccines and infections. This suggests that early exposure to P. falciparum may have a long-lasting effect on immunity, with implications for vaccine efficacy in endemic areas.

Strengths:

This study stands out for its longitudinal design, the use of robust immunological techniques, and the comparison between areas with different levels of malaria exposure. Its findings reveal that early malaria can weaken the response to childhood vaccines, with important implications for public health in endemic regions.

Weaknesses:

One of the study's main limitations is the lack of functional data confirming the clinical impact of the low antibody levels. Furthermore, although multiple immune responses were measured, other important components, such as cellular immunity, were not assessed. Furthermore, the results may not be generalizable to other regions.

Reviewer #2 (Public review):

Summary:

The authors investigated whether early-life malaria exposure has long-term effects on immune responses to unrelated antigens. They leveraged a natural experiment in coastal Kenya where two adjacent communities (Junju and Ngerenya) experienced divergent malaria transmission patterns after 2004. Using 15 years of longitudinal data from 123 children with weekly malaria surveillance and annual serological sampling, they measured antibody responses to multiple pathogens using a protein microarray technology and ELISA.

Strengths:

(1) Extensive longitudinal data collection with weekly malaria surveillance, enabling precise exposure classification.

(2) Use of a natural experiment design that allows for causal inference about malaria's immunological effects.

(3) Broad panel of antigens tested, demonstrating generalized rather than antigen-specific effects.

(4) Within-cohort analysis in Ngerenya controls for geographic and environmental factors.

(5) Validation of key findings using both serologic microarray and ELISA.

(6) Important public health implications for vaccine strategies in malaria-endemic regions.

Weaknesses:

(1) Lack of participants' characteristics (socio-economic, nutritional, physical).

(2) Somewhat limited sample size (longitudinal analysis of 123 children total), with further subdivision reducing statistical power for some analyses.

(3) Potential confounding by unmeasured socioeconomic, nutritional, or environmental factors between communities.

(4) Lack of ability to determine the direction of the associations found between malaria exposure and other IgG levels to unrelated pathogens.

(5) Despite good longitudinal data, the main analysis was conducted as a cross-sectional analysis at age 10 for many comparisons, which limits the understanding of temporal dynamics.

(6) Statistical analysis is limited to univariable comparisons without consideration for confounders or adjusting for multiple comparisons.

(7) No mechanistic understanding of how early malaria exposure creates lasting immunosuppression.

(8) No understanding of the clinical Implications of the reduced IgG levels observed in the area with high malaria exposure.

Assessment of Claims:

The data appear to support the authors' primary claims, but the strength of the evidence is limited, and the results should be interpreted with caution. Together with the currently available evidence of P. falciparum's impact on the host's immune function, this natural experiment design provides further evidence for a relationship between early malaria exposure and reduced antibody responses. The within-Ngerenya analysis controls for geographic factors and thus enhances the quality of the evidence; however, it still fails to account for the physical, nutritional, and socio-economic factors that may have driven the observed changes. Additionally, the mechanism underlying this effect remains unclear, and the clinical significance of reduced antibody levels is not established.

Impact and Utility:

This work has fundamental implications for understanding vaccine effectiveness in malaria-endemic regions and may contribute to informing vaccination strategies. The findings, if strengthened, would suggest that children in areas of high malaria transmission may require modified immunization approaches. The dataset provides a valuable resource for future studies of malaria's immunological legacy.

Context:

This study builds on prior work showing acute immunosuppressive effects of malaria but uniquely attempts to demonstrate the durability of these effects years after exposure. The natural experiment design addresses limitations of previous observational studies by providing a more controlled comparison.

eLife Assessment

This important work combines theoretical analysis with precise experimental perturbation to demonstrate that the Wnt signaling pathway is characterized by anti-resonance, or a suppression of pathway output at intermediate activation frequencies. The authors identify an anti-resonance behavior, with compelling evidence from optogenetic stimulation in multiple cell types, alongside modeling results that corroborate the phenomenon. While the demonstration of this phenomenon has yet to be extended to fully physiological situations, its clear existence within optogenetically stimulated systems shows that it is likely a significant factor that contributes to the behavior of this central signaling pathway.

Reviewer #1 (Public review):

Summary:

This report demonstrates that the gene expression output of the Wnt pathway, when controlled precisely by a synthetic light-based input, depends substantially on the frequency of stimulation. The particular frequency-dependent trend that is observed - anti-resonance, a suppression of target gene expression at intermediate frequencies given a constant duty cycle - is a novel aspect that has not been clearly shown before for this or other signaling pathways. The paper provides both clear experimental evidence of the phenomenon with engineered cellular systems and a model-based analysis of how the pairing of rate constants in pathway activation/deactivation could result in such a trend.

Strengths:

This report couples in vitro experimental data with an abstracted mathematical model. Both of these approaches appear to be technically sound and to provide consistent and strong support for the main conclusion. The experimental data are particularly clear, and the demonstration that Brachyury expression is subject to anti-resonance in ESCs is particularly compelling. The modeling approach is reasonably scaled for the system at the level of detail that is needed in this case, and the hidden variable analysis provides some insight into how the anti-resonance works.

Weaknesses:

(1) The anti-resonance phenomenon has not been demonstrated using physiological Wnt ligands; however, I view this as only a minor weakness for an initial report of the phenomenon. The potential significance of the phenomenon for Wnt outweighs the amount of effort it would take to carry the demonstration further - testing different frequencies/duty cycles at the level of ligand stimulus using microfluidics could get quite involved, and would likely take quite some time. Adding some more discussion about how the time scales of ligand-receptor binding could play into the reduced model would further ameliorate this issue.

(2) While the model is fully consistent with the data, it has not been validated using experimental manipulations to establish that the mechanisms of the cell system and the model are the same. There may be some ways to make such modifications, for example, using a proteasome inhibitor. An alternative would be to more explicitly mention the need to validate the model's mechanism with experiments.

(3) I think the manuscript misses an opportunity to discuss the potential of the phenomenon in other pathways. The hedgehog pathway, for example, involves GSK3-mediated partial proteolysis of a transcription factor, which could conceivably be subject to similar behaviors, and there are certainly other examples as well.

(4) Some aspects of the modeling and hidden variable analysis are not optimally presented in the main text, although when considered together with the Supplemental Data, there are no significant deficiencies.

Reviewer #2 (Public review):

Summary:

By combining optogenetics with theoretical modelling, the authors identify an anti-resonance behavior in the WnT signaling pathway. This behavior is manifested as a minimal response at a certain stimulation frequency. Using an abstracted hidden variable model, the authors explain their findings by a competition of timescales. Furthermore, they experimentally show that this anti-resonance influences the cell fate decision involved in human gastrulation.

Strengths:

(1) This interdisciplinary study combines precise optogenetic manipulation with advanced modelling.

(2) The results are directly tested in two different systems: HEK293T cells and H9 human embryonic stem cells.

(3) The model is implemented based on previous literature and has two levels of detail: i) a detailed biochemical model and ii) an abstract model with a hidden parameter.

Weaknesses:

(1) While the experiments provide both single-cell data and population data, the model only considers population data.

(2) Although the model captures the experimental data for TopFlash very well, the beta-Cat curves (Figure 2B) are only described qualitatively. This discrepancy is not discussed.

Overall Assessment:

The authors convincingly identified an anti-resonance behavior in a signaling pathway that is involved in cell fate decisions. The focus on a dynamic signal and the identification of such a behavior is important. I believe that the model approach of abstracting a complicated pathway with a hidden variable is an important tool to obtain an intuitive understanding of complicated dependencies in biology. Such a combination of precise ontogenetic manipulation with effective models will provide a new perspective on causal dependencies in signaling pathways and should not be limited only to the system that the authors study.

eLife Assessment

This fundamental study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides compelling evidence demonstrating reliable longitudinal identification of neurons across the second postnatal week in mice. The study should be of interest to development neuroscientists engaged in population-level recordings using two-photon imaging.

Reviewer #1 (Public review):

Summary:

This manuscript presents a compelling and innovative approach that combines Track2p neuronal tracking with advanced analytical methods to investigate early postnatal brain development. The work provides a powerful framework for exploring complex developmental processes such as the emergence of sensory representations, cognitive functions, and activity-dependent circuit formation. By enabling the tracking of the same neurons over extended developmental periods, this methodology sets the stage for mechanistic insights that were previously inaccessible.

Strengths:

(1) Innovative Methodology:

The integration of Track2p with longitudinal calcium imaging offers a unique capability to follow individual neurons across critical developmental windows.

(2) High Conceptual Impact:

The manuscript outlines a clear path for using this approach to study foundational developmental questions, such as how early neuronal activity shapes later functional properties and network assembly.

(3) Future Experimental Potential:

The authors convincingly argue for the feasibility of extending this tracking into adulthood and combining it with targeted manipulations, which could significantly advance our understanding of causality in developmental processes.

(4) Broad Applicability:

The proposed framework can be adapted to a wide range of experimental designs and questions, making it a valuable resource for the field.

Weaknesses:

None major. The manuscript is conceptually strong and methodologically sound. Future studies will need to address potential technical limitations of long-term tracking, but this does not detract from the current work's significance and clarity of vision

Comments on revisions:

I have no further requests. I think this is an excellent manuscript

Reviewer #2 (Public review):

Summary:

The manuscript by Majnik and colleagues introduces "Track2p", a new tool designed to track neurons across imaging sessions of two-photon calcium imaging in developing mice. The method addresses the challenge of tracking cells in the growing brain of developing mice. The authors showed that "Track2p" successfully tracks hundreds of neurons in the barrel cortex across multiple days during the second postnatal week. This enabled identification of the emergence of behavioral state modulation and desynchronization of spontaneous network activity around postnatal day 11.

Strengths

The authors have satisfactorily addressed the majority of our questions and comments, and the revisions substantially improve the manuscript. The expansion of Track2p to accept general NumPy array inputs makes the tool more accessible to researchers using different analysis pipelines. While the absence of benchmarking standards remains a limitation across the field, the release of the ground-truth dataset is an important step forward that will allow other researchers to evaluate and compare algorithms.

Minor point

(1) The authors tested the robustness of the algorithm across non-consecutive days. As expected, performance drops significantly under these conditions. We agree that this limitation reflects biological constraints due to brain growth rather than shortcomings of the algorithm itself. This is relevant for researchers planning to use Track2p for longitudinal imaging or benchmarking new algorithms, and we recommend including some of this information in the Supplementary Information along with a brief discussion.

Comments on revisions:

We acknowledge the extended documentation for using Track2p and converting between Suite2p outputs and NumPy arrays. This addition is of great utility. We would also suggest further expanding the documentation for the NumPy array implementation, as we ran into some errors when testing this feature using NumPy arrays generated from deltaF traces, TIFF FOVs, and Cellpose masks.

Reviewer #3 (Public review):

Summary:

In this manuscript Majnik et al. developed a computational algorithm to track individual developing interneurons in the rodent cortex at postnatal stages. Considerable development in cortical networks takes place during the first postnatal weeks, however, tools to study them longitudinally at a single cell level are scarce. This paper provides a valuable approach to study both single cell dynamics across days and state-drive network changes. The authors used Gad67Cre mice together with virally introduced TdTom to track interneurons based on their anatomical location in the FOV and AAVSynGCaMP8m to follow their activity across the second postnatal week, a period during which the cortex is known to undergo marked decorrelation in spontaneous activity. Using Track2P, the authors show feasibility to track populations of neurons in the same mice capturing with their analysis previously described developmental decorrelation and uncovering stable representations of neuronal activity, coincident with the onset of spontaneous active movement. The quality of the imaging data is compelling, and the computational analysis is thorough, providing a widely applicable tool for the analysis of emerging neuronal activity in the cortex. Below are some points for the authors to consider.

Major points

The authors use a viral approach to label cortical interneurons. It is unclear how Track2P will perform in dense networks of excitatory cells using GCaMP transgenic mice.

The authors used 20 neurons to generate a ground truth data set. The rational for this sample size is unclear. Figure 1 indicates capability to track ~728 neurons. A larger ground truth data set will increase the robustness of the conclusions.

It is unclear how movement was scored in the analysis shown in Fig 5A. Was the time that the mouse spent moving scored after visual inspection of the videos? Were whisker and muscle twitches scored as movement or was movement quantified as amount of time in which the treadmill was displaced?

The rational for binning the data analysis in early P11 is unclear. As the authors acknowledged, it is likely that the decoder captured active states from P11 onwards. Because active whisking begins around P14, it is unlikely to drive this change in network dynamics at P11. Does pupil dilation in the pups change during locomotor and resting states? Does the arousal state of the pups abruptly change at P11?

Comments on revisions:

The authors have addressed carefully all my comments. This is an interesting paper.

Author response:

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

Reviewer #1 (Public review):

We thank the reviewer for very enthusiastic and supportive comments on our manuscript. 

Summary:

This manuscript presents a compelling and innovative approach that combines Track2p neuronal tracking with advanced analytical methods to investigate early postnatal brain development. The work provides a powerful framework for exploring complex developmental processes such as the emergence of sensory representations, cognitive functions, and activity-dependent circuit formation. By enabling the tracking of the same neurons over extended developmental periods, this methodology sets the stage for mechanistic insights that were previously inaccessible.

Strengths:

(1) Innovative Methodology:

The integration of Track2p with longitudinal calcium imaging offers a unique capability to follow individual neurons across critical developmental windows.

(2) High Conceptual Impact:

The manuscript outlines a clear path for using this approach to study foundational developmental questions, such as how early neuronal activity shapes later functional properties and network assembly.

(3) Future Experimental Potential:

The authors convincingly argue for the feasibility of extending this tracking into adulthood and combining it with targeted manipulations, which could significantly advance our understanding of causality in developmental processes.

(4) Broad Applicability:

The proposed framework can be adapted to a wide range of experimental designs and questions, making it a valuable resource for the field.

Weaknesses:

No major weaknesses were identified by this reviewer. The manuscript is conceptually strong and methodologically sound. Future studies will need to address potential technical limitations of long-term tracking, but this does not detract from the current work's significance and clarity of vision.

Reviewer #2 (Public review):

Summary:

The manuscript by Majnik and colleagues introduces "Track2p", a new tool designed to track neurons across imaging sessions of two-photon calcium imaging in developing mice. The method addresses the challenge of tracking cells in the growing brain of developing mice. The authors showed that "Track2p" successfully tracks hundreds of neurons in the barrel cortex across multiple days during the second postnatal week. This enabled the identification of the emergence of behavioral state modulation and desynchronization of spontaneous network activity around postnatal day 11.

Strengths:

The manuscript is well written, and the analysis pipeline is clearly described. Moreover, the dataset used for validation is of high quality, considering the technical challenges associated with longitudinal two-photon recordings in mouse pups. The authors provide a convincing comparison of both manual annotation and "CellReg" to demonstrate the tracking performance of "Track2p". Applying this tracking algorithm, Majnik and colleagues characterized hallmark developmental changes in spontaneous network activity, highlighting the impact of longitudinal imaging approaches in developmental neuroscience. Additionally, the code is available on GitHub, along with helpful documentation, which will facilitate accessibility and usability by other researchers.

Weaknesses:

(1) The main critique of the "Track2p" package is that, in its current implementation, it is dependent on the outputs of "Suite2p". This limits adoption by researchers who use alternative pipelines or custom code. One potential solution would be to generalize the accepted inputs beyond the fixed format of "Suite2p", for instance, by accepting NumPy arrays (e.g., ROIs, deltaF/F traces, images, etc.) from files generated by other software. Otherwise, the tool may remain more of a useful add-on to "Suite2p" (see https://github.com/MouseLand/suite2p/issues/933) rather than a fully standalone tool.

We thank the reviewer for this excellent suggestion. 

We have now implemented this feature, where Track2p is now compatible with ‘raw’ NumPy arrays for the three types of inputs. For more information, please check the updated documentation: https://track2p.github.io/run_inputs_and_parameters.html#raw-npy-arrays. We have also tested this feature using a custom segmentation and trace extraction pipeline using Cellpose for segmentation.

(2) Further benchmarking would strengthen the validation of "Track2p", particularly against "CaIMaN" (Giovannucci et al., eLife, 2019), which is widely used in the field and implements a distinct registration approach.

This reviewer suggested  further benchmarking of Track2P.  Ideally, we would want to benchmark Track2p against the current state-of-the-art method. However, the field currently lacks consensus on which algorithm performs best, with multiple methods available including CaIMaN, SCOUT (Johnston et al. 2022), ROICaT (Nguyen et al. 2023), ROIMatchPub (recommended by Suite2p documentation and recently used by Hasegawa et al. 2024), and custom pipelines such as those described by Sun et al. 2025. The absence of systematic benchmarking studies—particularly for custom tracking pipelines—makes it impossible to identify the current state-of-the-art for comparison with Track2p. While comparing Track2p against all available methods would provide comprehensive evaluation, such an analysis falls beyond the scope of this paper.

We selected CellReg for our primary comparison because it has been validated under similar experimental conditions—specifically, 2-photon calcium imaging in developing hippocampus between P17-P25 (Wang et al. 2024)—making it the most relevant benchmark for our developmental neocortex dataset.

That said, to support further benchmarking in mouse neocortex (P8-P14), we will publicly release our ground truth tracking dataset.

(3) The authors might also consider evaluating performance using non-consecutive recordings (e.g., alternate days or only three time points across the week) to demonstrate utility in other experimental designs.

Thank you for your suggestion. We have performed a similar analysis prior to submission, but we decided against including it in the final manuscript, to keep the evaluation brief and to not confuse the reader with too many different evaluation methods. We have included the results inAuthor response images 1 and 2 below.

To evaluate performance in experimental designs with larger time spans between recordings (>1 day) we performed additional evaluation of tracking from P8 to each of the consecutive days while omitting the intermediate days (e. g. P8 to P9, P8 to P10 … P8 to P14). The performance for the three mice from the manuscript is shown below:

Author response image 1.

As expected with increasing time difference between the two recordings the performance drops significantly (dropping to effectively zero for 2 out of 3 mice). This could also explain why CellReg struggles to track cells across all days, since it takes P8 as a reference and attempts to register all consecutive days to that time point before matching, instead of performing registration and matching in consecutive pairs of recordings (P8-P9, P9-P10 … P13-P14) as we do.

Finally for one of the three mice we also performed an additional test where we asked how adding an additional recording day might rescue the P8-P14 tracking performance. This corresponds to the comment from the reviewer, answering the question if we can only perform three days of recording which additional day would give the best tracking performance. 

Author response image 2.

As can be seen from the plot, adding the P10 or P11 recording shows the most significant improvement to the tracking performance, however the performance is still significantly lower than when including all days (see Fig. 4). This test suggests that including a day that is slightly skewed to earlier ages might improve the performance more than simply choosing the middle day between the two extremes. This would also be consistent with the qualitative observation that the FOV seems to show more drastic day-to-day changes at earlier ages in our recording conditions.

Reviewer #3 (Public review):

Summary:

In this manuscript, Majnik et al. developed a computational algorithm to track individual developing interneurons in the rodent cortex at postnatal stages. Considerable development in cortical networks takes place during the first postnatal weeks; however, tools to study them longitudinally at a single-cell level are scarce. This paper provides a valuable approach to study both single-cell dynamics across days and state-driven network changes. The authors used Gad67Cre mice together with virally introduced TdTom to track interneurons based on their anatomical location in the FOV and AAVSynGCaMP8m to follow their activity across the second postnatal week, a period during which the cortex is known to undergo marked decorrelation in spontaneous activity. Using Track2P, the authors show the feasibility of tracking populations of neurons in the same mice, capturing with their analysis previously described developmental decorrelation and uncovering stable representations of neuronal activity, coincident with the onset of spontaneous active movement. The quality of the imaging data is compelling, and the computational analysis is thorough, providing a widely applicable tool for the analysis of emerging neuronal activity in the cortex. Below are some points for the authors to consider.

We thank the reviewer for a constructive and positive evaluation of our MS. 

Major points:

(1) The authors used 20 neurons to generate a ground truth dataset. The rationale for this sample size is unclear. Figure 1 indicates the capability to track ~728 neurons. A larger ground truth data set will increase the robustness of the conclusions.

We think this was a misunderstanding of our ground truth dataset analysis which included 192 and not 20 neurons. Indeed, as explained in the methods section, since manually tracking all cells would require prohibitive amounts of time, we decided to generate sparse manual annotations, only tracking a subset of all cells from the first recording day onwards. To do this, we took the first recording (s0), and we defined a grid 64 equidistant points over the FOV and, for each point, identified the closest ROI in terms of euclidean distance from the median pixel of the ROI (see Fig. S3A). We then manually tracked these 64 ROIs across subsequent days. Only neurons that were detected and tracked across all sessions were taken into account and referred to as our ground truth dataset (‘GT’ in Fig. 4). This was done for 3 mice, hence 3X64 neurons and not 20 were used to generate our GT dataset. 

(2) It is unclear how movement was scored in the analysis shown in Figure 5A. Was the time that the mouse spent moving scored after visual inspection of the videos? Were whisker and muscle twitches scored as movement, or was movement quantified as the amount of time during which the treadmill was displaced?

Movement was scored using a ‘motion energy’ metric as in Stringer et al. 2019 (V1) or Inácio et al. 2025 (S1). This metric takes each two consecutive frames of the videography recordings and computes the difference between them by summing up the square of pixelwise differences between the two images. We made the appropriate changes in the manuscript to further clarify this in the main text and methods in order to avoid confusion.

Since this metric quantifies global movements, it is inherently biased to whole-body movements causing more significant changes in pixel values around the whole FOV of the camera. Slight twitches of a single limb, or the whisker pad would thus contribute much less to this metric, since these are usually slight displacements in a small region of the camera FOV. Additionally, comparing neural activity across all time points (using correlation or R²) also favours movements that last longer (such as wake movements / prolonged periods of high arousal) since each time point is treated equally.

As we suggested in the discussion, in further analysis it would be interesting to look at the link between twitches and neural activity, but this would likely require extensive manual scoring. We could then treat movements not as continuous across all time-points, but instead using event-based analysis for example peri-movement time histograms for different types of movements at different ages, which is however outside of the scope of this study.

(3) The rationale for binning the data analysis in early P11 is unclear. As the authors acknowledged, it is likely that the decoder captured active states from P11 onwards. Because active whisking begins around P14, it is unlikely to drive this change in network dynamics at P11. Does pupil dilation in the pups change during locomotor and resting states? Does the arousal state of the pups abruptly change at P11?

We agree that P11 does not match any change in mouse behavior that we have been able to capture. However, arousal state in mice does change around postnatal day 11. This period marks a transition from immature, fragmented states to more organized and regulated sleep-wake patterns, along with increasing influence from neuromodulatory and sensory systems. All of these changes have been recently reviewed in Wu et al. 2024 (see also Martini et al. 2021). In addition, in the developing somatosensory system, before postnatal day 11 (P11), wake-related movements (reafference) are actively gated and blocked by the external cuneate nucleus (ECN, Tiriac et al. 2016 and all excellent recent work from the Blumberg lab). This gating prevents sensory feedback from wake movements from reaching the cortex, ensuring that only sleep-related twitches drive neural responses. However, around P11, this gating mechanism abruptly lifts, enabling sensory signals from wake movements to influence cortical processing—signaling a dramatic developmental shift from Wu et al. 2024

Reviewer #1 (Recommendations for the authors):

This manuscript represents a significant advancement in the field of developmental neuroscience, offering a powerful and elegant framework for longitudinal cellular tracking using the Track2p method combined with robust analytical approaches. The authors convincingly demonstrate that this integrated methodology provides an invaluable template for investigating complex developmental processes, including the emergence of sensory representations and higher cognitive functions.

A major strength of this work is its emphasis on the power of longitudinal imaging to illuminate activity-dependent development. By tracking the same neurons over time, the authors open up new possibilities to uncover how early activity patterns shape later functional outcomes and the organization of neuronal assemblies-insights that would be inaccessible using conventional cross-sectional designs.

Importantly, the manuscript highlights the potential for this approach to be extended even further, enabling continuous tracking into adulthood and thus offering an unprecedented window into long-term developmental trajectories. The authors also underscore the exciting opportunity to incorporate targeted perturbation experiments, allowing researchers to causally link early circuit dynamics to later outcomes.

Given the increasing recognition that early postnatal alterations can underlie the etiology of various neurodevelopmental disorders, this work is especially timely. The methods and perspectives presented here are poised to catalyze a new generation of developmental studies that can reveal mechanistic underpinnings of both typical and atypical brain development.

In summary, this is a technically impressive and conceptually forward-looking study that sets the stage for transformative advances in developmental neuroscience.

Thank you for the thoughtful feedback—it's greatly appreciated!

Reviewer #2 (Recommendations for the authors):

Minor points:

(1) Figure 1. Consider merging or moving to Supplemental, as its rationale is well described in the text.

We would like to retain the current figure as we believe it provides an effective visual illustration of our rationale that will capture readers' attention and could serve as a valuable reference for others seeking to justify longitudinal tracking of the developing brain. We hope the reviewer will understand our decision.

(2) Some axis labels and panels are difficult to read due to small font sizes (e.g. smaller panels in Figures 5-7).

Modified, thanks 

(3) Supplementary Figures. The order of appearance in the main text is occasionally inconsistent.

This was modified, thanks

(4) Line 132. Add a reference to the registration toolbox used (elastix). A brief description of the affine transformation would also be helpful, either here or in the Methods section (p. 27).

We have added reference to Ntatsis et al. 2023 and described affine transformation in the main text (lines 133-135): 

Firstly, we estimate the spatial transformation between s0 and s1 using affine image registration (i.e. allowing shifting, rotation, scaling and shearing, see Fig. 2B, the transformation is denoted as T).

(5) Lines 147-151. If this method is adapted from another work, please cite the source.

Computing the intersection over union of two ROIs for tracking is a widely established and intuitive method used across numerous studies, representing standard practice rather than requiring specific citation. We have however included the reference to the paper describing the algorithm we use to solve the linear sum assignment problem used for matching neurons across a pair of consecutive days (Crouse 2016).

(6) Line 218. "classical" or automatic?

We meant “classical” in the sense of widely used. 

(7) Lines 220-231. Did the authors find significant variability of successfully tracked neurons across mice? While the data for successfully tracked cells is reported (Figure 5B), the proportions are not. Could differences in neuron dropout across days and mice affect the analysis of neuronal activity statistics?

We thank the reviewer for raising this important point. We computed the fraction of successfully tracked cells in our dataset and found substantial variability:

Cells detected on day 0: [607, 1849, 2190, 1988, 1316, 2138] 

Proportion successfully tracked: [0.47, 0.20, 0.36, 0.37, 0.41, 0.19]

Notably, the number of cells detected on the first day varies considerably (607–2138 cells). There appears to be a trend whereby datasets with fewer initially detected cells show higher tracking success rates, potentially because only highly active cells are identified in these cases.

To draw more definitive conclusions about the proportion of active cells and tracking dropout rates, we would require activity-independent cell detection methods (such as Cellpose applied to isosbestic 830 nm fluorescence, or ideally a pan-neuronal marker in a separate channel, e.g., tdTomato). We have incorporated the tracking success proportions into the revised manuscript.

(8) Line 260. Please briefly explain, here or in the Methods, the rationale for using data from only 3 mice (rather than all 6) for evaluating tracking performance.

We used three mice for this analysis due to the labor-intensive nature of manually annotating 64 ROIs across several days. Given the time constraints of this manual process, we determined that three subjects would provide adequate data to reliably assess tracking performance.

(9) Line 277. Consider clarifying or rephrasing the phrase "across progressively shorter time intervals"? Do you mean across consecutive days?

This has been rephrased as follows: 

Additionally, to assess tracking performance over time, we quantified the proportion of reconstructed ground truth tracks over progressively longer time intervals (first two days, first three days etc. ‘Prop. correct’ in Fig. 4C-F, see Methods). This allowed us to understand how tracking accuracy depends on the number of successive sessions, as well as at which time points the algorithm might fail to successfully track cells.

(10) Line 306. "we also provide additional resources and documentation". Please add a reference or link.

Done, thanks

Track2p  

(11) Lines 342-344. Specify that the raster plots refer to one example mouse, not the entire sample.

Done, thanks.

(12) Lines 996-1002. Please confirm whether only successfully tracked neurons were used to compute the Pearson correlations between all pairs.

Yes of course, this only applies to tracked neurons as it is impossible to compute this for non-tracked pairs.

(13) Line 1003. Add a reference to scikit-learn.

Reference was added to: 

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 

(14) Typos.Correct spacing between numeric values and units.

We did not find many typos regarding spacing between the numerical value and the unit symbol (degrees and percent should not be spaced right?).

Reviewer #3 (Recommendations for the authors):

The font size in many of the figures is too small. For example, it is difficult to follow individual ROIs in Figure S3.

Figure font size has been increased, thanks. In Figure S3 there might have been a misunderstanding, since the three FOV images do not correspond to the FOV of the same mouse across three days but rather to the first recording for each of the three mice used in evaluation (the ROIs can thus not be followed across images since they correspond to a different mouse). To avoid confusion we have labelled each of the FOV images with the corresponding mouse identifier (same as in Fig. 4 and 5).

eLife Assessment

This is a valuable study that explores the role of the conserved transcription factor POU4-2 in the maintenance, regeneration, and function of planarian mechanosensory neurons. The authors present convincing evidence provided by gene expression and functional studies to demonstrate that POU4-2 is required for the maintenance and regeneration of mechanosensory neurons and mechanosensory function in planarians. Furthermore, the authors identify conserved genes associated with human auditory and rheosensory neurons as potential targets of this transcription factor.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors explore the role of the conserved transcription factor POU4-2 in planarian maintenance and regeneration of mechanosensory neurons. The authors explore the role of this transcription factor and identify potential targets of this transcription factor. Importantly, many genes discovered in this work are deeply conserved, with roles in mechanosensation and hearing, indicating that planarians may be a useful model with which to study the roles of these key molecules. This work is important within the field of regenerative neurobiology, but also impactful for those studying evolution of the machinery that is important for human hearing.

Strengths:

The paper is rigorous and thorough, with convincing support for the conclusions of the work.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the role of the transcription factor Smed-pou4-2 in the maintenance, regeneration and function of mechanosensory neurons in the freshwater planarian Schmidtea mediterranea. First, they characterize the expression of pou4-2 in mechanosensory neurons during both homeostasis and regeneration, and examine how its expression is affected by the knockdown of soxB1, 2, a previously identified transcription factor essential for the maintenance and regeneration of these neurons. Second, the authors assess whether pou4-2 is functionally required for the maintenance and regeneration of mechanosensory neurons.

Strengths:

The study provides some new insights into the regulatory role of pou4-2 in the differentiation, maintenance, and regeneration of ciliated mechanosensory neurons in planarians.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Public review): 

Summary: 

In this manuscript, the authors explore the role of the conserved transcription factor POU4-2 in planarian maintenance and regeneration of mechanosensory neurons. The authors explore the role of this transcription factor and identify potential targets of this transcription factor. Importantly, many genes discovered in this work are deeply conserved, with roles in mechanosensation and hearing, indicating that planarians may be a useful model with which to study the roles of these key molecules. This work is important within the field of regenerative neurobiology, but also impactful for those studying the evolution of the machinery that is important for human hearing. 

Strengths: 

The paper is rigorous and thorough, with convincing support for the conclusions of the work. 

Weaknesses: 

Weaknesses are relatively minor and could be addressed with additional experiments or changes in writing.

Reviewer #2 (Public review): 

Summary: 

In this manuscript, the authors investigate the role of the transcription factor Smed-pou4-2 in the maintenance, regeneration, and function of mechanosensory neurons in the freshwater planarian Schmidtea mediterranea. First, they characterize the expression of pou4-2 in mechanosensory neurons during both homeostasis and regeneration, and examine how its expression is affected by the knockdown of soxB1, 2, a previously identified transcription factor essential for the maintenance and regeneration of these neurons. Second, the authors assess whether pou4-2 is functionally required for the maintenance and regeneration of mechanosensory neurons. 

Strengths: 

The study provides some new insights into the regulatory role of pou4-2 in the differentiation, maintenance, and regeneration of ciliated mechanosensory neurons in planarians. 

Weaknesses: 

The overall scope is relatively limited. The manuscript lacks clear organization, and many of the conclusions would benefit from additional experiments and more rigorous quantification to enhance their strength and impact. 

Reviewing Editor Comments: 

(1) Quantification of pou4-2(+) cells that express (or do not express) hmcn-1-L and/or pkd1L-2(-) is a common suggestion amongst reviewers. It is recognized that Ross et al. (2018) showed that pkd1L-2 and hmcn-1L expression is detected in separate cells by double FISH, and the analysis presented in Supplementary Figure S3 is helpful in showing that some cells expressing pou4-2 (magenta) are not labeled by the combined signal of pkd1L-2 and hmcn-1-L riboprobes (green). However, I am not sure that we can conclude that pkd1L-2 and hmcn-1-L are effectively detected when riboprobes are combined in the analysis. Therefore, quantification of labeled cells as proposed by Reviewers 1 and 2 would help.

Combining riboprobes is a standard approach in the field, and we chose this method as a direct way to determine which cells lack expression of both genes. We agree that providing the raw quantification data would be helpful for readers, and we included this data in Supplementary File S7; the file contains the quantification information for this dFISH experiment represented in Supplementary Figure 3.

(2) It may be helpful to comment on changes (or lack of changes) in atoh gene RNA levels in RNAseq analyses of pou4-2 animals. As mentioned by one of the reviewers, in situs that don't show signal are inconclusive in this regard. 

We fully agree with both reviewers. Two of the planarian atonal homologs are difficult to detect and produce background signals, which we attempted and previously reported in Cowles et al. Development (2013). We conceived performing reciprocal RNAi/in situ experiments, born out of curiosity given the reported role of atonal in the pou4 cascade in other organisms. However, these exploratory experiments lacked a strong rationale for inclusion, particularly given that pou4-2 and the atonal homologs do not share expression patterns, co-expression, or differential expression in our RNA-seq dataset. Therefore, we decided to omit the atonal in situs following pou4-2 RNAi. We retained the experiments showing that knockdown of the atonal genes does not show robust effects on the mechanosensory neuron pattern, as expected. We thank the reviewing editor and reviewers for pinpointing the concern. We agree that additional experiments, such as qPCR experiments, would be needed. We reasoned that while these additional experiments could be informative, they are unlikely to alter the key conclusions of this study substantially.

(3) There seem to be typos at bottom of Figure 10 and top of page 11 when referencing to Figure 4B (should be to 5B instead): "While mechanosensory neuronal patterned expression of Eph1 was downregulated after pou4-2 and soxB1-2 inhibition, low expression in the brain branches of the ventral cephalic ganglia persisted (Figure 4B)." 

Thank you! We have fixed those.

(4) Typo (page 13; kernel?): "...to test to what extent the Pou4 gene regulatory kernel is conserved among these widely divergent animals." 

Regulatory kernels are defined as the minimal sets of interacting genes that drive developmental processes and are the core circuits within a gene regulatory network, but we recognize that this might not be as well known, so we have changed the term to “network” for clarity.

Reviewer #1 (Recommendations for the authors): 

(1) The authors indicate that they are interested in finding out whether POU4-2 is important in the creation of mechanosensory neurons in adulthood as well as in embryogenesis (in other words, whether the mechanism is "reused during adult tissue maintenance and regeneration"). The manuscript clearly shows that planarian POU4 -2 is important in adult neurogenesis in planarians, but there is no evidence presented to show that this is a recapitulation of embryogenesis. Is pou4-2 expressed in the planarian embryo? This might be possible to examine by ISH or through the evaluation of sequencing data that already exists in the literature. 

We agree that these statements should be precise. We have clarified when we make comparisons to the role of Pou4 in sensory system development in other organisms versus its role in the adult planarian. We examined its expression using the existing database of embryonic gene expression. Thanks for hinting at this idea. We performed BLAST in Planosphere (Davies et al., 2017) to cross-reference our clone matching dd_Smed_v6_30562_0_1, which is identical to SMED30002016. The embryonic gene expression for SMED30002016 indicates this gene is expressed at the expected stages given prior knowledge of the timing of organ development in Schmidtea mediterranea (a positive trend begins at Stage 5, with a marked increase by Stage 6 that remains comparable to the asexual expression levels shown). We thank the reviewer for pointing out this oversight. We have incorporated this result in the paper as a Supplementary Figure and discuss how we can only speculate that it has a similar role as we detect in the adult asexual worms.

(2) Can it be determined whether the punctate pou4-2+ cells outside of the stripes are progenitors or other neural cell types? Are there pou4-2+ neurons that are not mechanosensory cell types? Could there be other roles for POU4-2 in the neurogenesis of other cell types? It might help to show percentages of overlap in Figure 4A and discuss whether the two populations add up to 100% of cells. 

These are good questions that arise in part from other statements that need clarification in the text (pointed out by Reviewer 2). We think some of the dorsal pou4-2⁺ might represent progenitor cells undergoing terminal differentiation (see Supplementary Figure 4). We attempted BrdU pulse chase experiments but were not successful in consistently detecting pou4-2 at sufficient levels with our protocol. In response to this helpful comment, we have included this question as a future direction in the revised Discussion. Finally, we have edited our description of the expression pattern. We already pointed out that there are other cells on the ventral side that are not affected when soxB1-2 is knocked down. We attempted to resolve the potential identity of those cells working with existing scRNA-seq data in collaboration with colleagues, but their low abundance made it difficult to distinguish other populations. While we acknowledge this interesting possibility, we have chosen to focus this report on the role of pou4-2 downstream of soxB1-2, as this represents the most well-supported aspect of the dataset and was positively highlighted by both the reviewer and editor.

(3) The authors discuss many genes from their analysis that play conserved roles in mechanosensation and hearing. Were there any conserved genes that came up in the analysis of pou4-2(RNAi) planarians that have not yet been studied in human hearing and neurodevelopment? I am wondering the extent to which planarians could be used as a discovery system for mechanosensory neuron function and development, and discussion of this point might increase the impact of this paper or provide critical rationale for expanding work on planarian mechanosensation. 

Indeed, we agree that planarians could be used to identify conserved genes with roles in mechanosensation and have included this point in the Discussion. In this study, we have focused on demonstrating the conservation of gene regulation. While this study was initially based on a graduate thesis project, we have since generated a more comprehensive dataset from isolated heads, which we are currently analyzing. This has been emphasized in the revised Discussion.

Minor: 

(1) For Figure 6E, the authors could consider showing data along a negative axis to indicate a decrease in length in response to vibration and to more clearly show that this decrease doesn't occur as strongly after pou4-2(RNAi). 

We displayed this behavior as the percent change, as this is a standard way to represent this data. As the percent change is a positive value, we represent the data as these positive values.

(2) The authors should consider quantifying the decrease of pou4-2 mRNA after atonal(RNAi) conditions, either by RT-qPCR or cell quantification. Visually, the signal in the stripes after atoh8-2(RNAi) seems lower, particularly in the tail. The punctate pattern outside the stripes may also be decreased after atoh8-1(RNAi). But quantification might strengthen the argument. 

We agree with the reviewer and acknowledge that we should have been more cautious in interpreting these results. Those two genes are difficult to detect and did not show specific patterns in Cowles et al. (2013). The reviewer is correct that additional experiments are necessary before reaching conclusions, but we do not think as discussed earlier we do not think new experiments would provide insights for the major conclusions. These experiments were exploratory in nature and tangential to our main conclusions, especially in the absence of reciprocal evidence (e.g., shared expression patterns, co-expression, or differential expression in our RNA-seq data. Therefore, we decided to eliminate the atonal in situs following pou4-2 RNAi.

Reviewer #2 (Recommendations for the authors): 

A. Expression of pou4-2 in ciliated mechanosensory neurons: 

(1) The conclusion that pou4-2 is expressed in ciliated mechanosensory neurons is primarily based on co-expression analysis using a published single-cell dataset. Although the authors later show that a subset of pou4-2 cells also express pkd1L-2 (Figure 4A), a known marker of ciliated mechanosensory neurons, this finding is not properly quantified. I recommend moving Figure 4A to earlier in the manuscript (e.g., to Figure 2) and expanding the analysis to include additional known markers of this cell type. Proper quantification of the extent of co-localization is necessary to support the claim robustly. 

As pointed out by the reviewer, there is substantive evidence from our lab and other reports. King et al. also showed pou4-2 and pkd1L-2 ‘regulation’ by their scRNA-seq data, and this function is conserved in the acoel Hofstenia miamia (Hulett et al., PNAS 2024 ). Our analysis shows convincing co-localization by scRNA-seq and expression of soxB1-2 and neural markers in the respective populations. Furthermore, we included colocalization of pou4-2 with mechanosensory genes using fluorescence in situ hybridization (Figure 3B, Supplementary Figure 4, and Supplementary File S7). We are confident the data conclusively show pou4-2 regulates pkd1L-2 expression in a subset of mechanosensory neurons. Given the strength of existing observations and previously published data, we believe that additional staining experiments are not essential to support this conclusion. 

(2) There appears to be a conceptual inconsistency in the interpretation of pou4-2 expression dynamics. On one hand, the authors suggest that delayed pou4-2 expression indicates a role in late-stage differentiation (p.6). On the other hand, they propose that pou4-2 may be expressed in undifferentiated progenitors to initiate downstream transcriptional programs (p.8). These interpretations should be reconciled. Additionally, claims regarding pou4-2 expression in progenitor populations should be supported by co-localization with established stem cell or progenitor markers, rather than inferred from signal intensity alone. 

This is an excellent point, and we agree with the reviewer that this section requires editing. As described in response to Reviewer 1, we attempted BrdU pulse chase experiments but were not successful in consistently detecting pou4-2 at sufficient levels with our protocol. Furthermore, we could not obtain strong signals in double labeling experiments in pou4-2 in situs combined with piwi-1 or PIWI-1 antibodies. We will include those experiments as a future direction and amend our conclusions accordingly.

(3) The expression pattern shown in Figure 1B raises questions about the precise anatomical localization of pou4-2 cells. It is unclear whether these cells reside in the subepidermal plexus or the deeper submuscular plexus, which represent distinct neuronal layers (Ross et al., 2017). The observed signals near the ventral nerve cords could suggest submuscular localization. To clarify this, higher-resolution imaging and co-staining with region-specific neural markers are recommended. 

In Ross et al. (2018), we showed that the pkd1L-2⁺ cells are located submuscularly. The pkd1L-2 cells express pou4-2, thus the pou4-2⁺ cells are located in the same location. Based on co-expression data and co-expression with PKD genes, we are confident it is submuscular.

B. The functional requirements of pou4-2 in the maintenance of mechanosensory neurons: 

(1) To evaluate the functional role of pou4-2 in maintaining mechanosensory neurons, the authors performed whole-animal RNA-seq on pou4-2(RNAi) and control animals, identifying a significant downregulation of genes associated with mechanosensory neuron expression. However, the presentation of these findings is fragmented across Figures 3, 4, and 5. I recommend consolidating the RNA-seq results (Figure 3) and the subsequent validation of downregulated genes (Figures 4 and 5) into a single, cohesive figure. This would improve the logical flow and clarity of the manuscript. 

As suggested by the reviewer, we have combined Figures 3 and 4 (new Figure 3), which we believe improves the flow. We decided to keep Figure 5 (new Figure 4) as a standalone because it focuses on the characterization of new genes revealed by RNAseq and scRNA-seq data mining that were not previously reported in Ross et al. 2018 and

2024.

(2) In pou4-2(RNAi) animals, pkd1L-2 expression appears to be entirely lost, while hmcn-1-L shows faint expression in scattered peripheral regions. The authors suggest that an extended RNAi treatment might be necessary to fully eliminate hmcn-1-L expression. However, an alternative explanation is that pou4-2 is not essential for maintaining all hmcn-1-L cells, particularly if pou4-2 expression does not fully overlap with that of hmcn-1-L. This possibility should be acknowledged and discussed. 

We agree and have acknowledged this point in the revised text.

(3) On page 9, the section title claims that "Smed-pou4-2 regulates genes involved in ciliated cell structure organization, cell adhesion, and nervous system development." While some differentially expressed genes are indeed annotated with these functions based on homology, the manuscript does not provide experimental evidence supporting their roles in these biological processes in planarians. The title should be revised to avoid overstatement, and the limitations of extrapolating a function solely from gene annotation should be acknowledged. 

Excellent point. We have edited the text to indicate that the genes were annotated or implicated.

(4) The cilia staining presented in Figure 6B to support the claim that pou4-2 is required for ciliated cell structure organization is unconvincing. Improved imaging and more targeted analysis (e.g., co-labeling with mechanosensory markers) are needed to support this conclusion. 

We have addressed this concern by adjusting the language to be more precise and indicate that the stereotypical banded pattern is disrupted with decreased cilia labeling along the dorsal ciliated stripe. Indeed, our conclusion overstated the observations made with the staining and imaging resolution. Thank you.

C. The functional requirements of pou4-2 in the regeneration of mechanosensory neurons: 

To evaluate the role of pou4-2 in the regeneration of mechanosensory neurons, the authors performed amputations on pou4-2(RNAi) and control(RNAi) animals and assessed the expression of mechanosensory markers (pkd1L-2, hmcn-1-L) alongside a functional assay. However, the results shown in Figure 4B indicate the presence of numerous pkd1L-2 and hmcn-1-L cells in the blastema of pou4-2(RNAi) animals. This observation raises the possibility that pou4-2 may not be essential for the regeneration of these mechanosensory neurons. The authors should address this alternative interpretation. 

Our interpretation is that there were very few cells expressing the markers compared to controls. The pattern was predominantly lost, which is consistent with other experiments shown in the paper. However, we have added the additional caveat suggested by the reviewer.

Minor points: 

(1) On p.8, the authors wrote "every 12 hours post-irradiation". However, this is not consistent with the figure, which only shows 0, 3, 4, 4.5, 5, and 5.5 dpi. 

We corrected this. Thank you for catching the mistake!

(2) On p.12, the authors wrote "Analysis of pou4-2 RNAi data revealed differentially expressed genes with known roles in mechanosensory functions, such as loxhd-1, cdh23, and myo7a. Mutations in these genes can cause a loss of mechanosensation/transduction". This is misleading because, to my knowledge, the role of these genes in planarians is unknown. If the authors meant other model systems, they should clearly state this in the text and include proper references. 

The reviewer is correct that we are referencing findings from other organisms. We have clarified this point in the revised text. The appropriate references were included and cited in the first version.

(3) On p.7, the authors wrote, "conversely, the expression of atonal genes was unaffected in pou4-2 RNAi-treated regenerates (Supplementary Figure S2B)". However, it is unclear whether the Atoh8-1 and Atoh8-2 signals are real, as the quality of the in situ results is too low to distinguish between real signals and background noise/non-specific staining. 

This valid concern was addressed in our response to Reviewer 1. We have adjusted the figure and the text accordingly.

(4) On p.6 the authors wrote "pinpointed time points wherein the pou4-2 transcripts were robustly downregulated". However, the current version of the manuscript does not provide data explaining why Pou4-2 transcripts are robustly downregulated on day 12. 

Yes, we determined the appropriate time points using qPCR for all sample extractions. As an example, see the figure for qPCR validation at day 12 showing that pou4-2 and pkd1L2 are down.

Author response image 1.

In this graph, samples labeled “G” represent four biological controls of gfp(RNAi) control animals, and samples labeled “P” represent four biological controls of pou4-2(RNAi)animals at day 12 in the RNAi protocol.

(5) On p.13, the authors wrote "collecting RNA from how animals." Is this a typo? 

Thanks for catching the typo. It should read “whole” animals. We have corrected this.

(6) On p.14, the authors wrote "but the expression patterns of planarian atonal genes indicated that they represent completely different cell populations from pou4-2-regulated mechanosensory neurons". However, this is unclear from the images, as the in situ staining of Atoh8-1 and Atoh82 are potentially failed stainings. 

We agree. We have edited accordingly.

eLife Assessment

This valuable manuscript presents an open-source and low-cost acoustic system for quantifying biting and chewing in mice. The approach is carefully validated against human observers, demonstrating strong methodological reliability and enabling high-resolution analysis of feeding microstructure. The tool has broad relevance for studies of appetite circuits and pharmacological interventions. A significant contribution is the identification of previously unrecognized "meal-related" neurons in the lateral hypothalamus, providing novel biological insight into food consumption. While the support for the methodological advances is compelling and robust, some circuit-level conclusions are preliminary or incomplete, relying on small pilot samples and manual classification, and should be interpreted with caution. This paper will be of interest to those interested in ingestive behavior and/or hypothalamus.

Reviewer #1 (Public review):

This is an interesting and valuable paper by Gil-Lievana, Arroyo et al. that presents an open-source method (the "Crunchometer") for quantifying biting and chewing behavior in mice using audio detection. The work addresses an important and unmet need in the field: quantitative measures of feeding behavior with solid foods, since most prior approaches have been limited to liquids. The authors make a clear and compelling case for why this problem is important, and I fully agree with their motivation.

The system is carefully validated against human-scored video data and is shown to be at least as accurate, and in some cases more accurate, than human observers. This is a major strength of the study. I also particularly appreciate the demonstration of the technology in the context of LHA circuitry, which nicely illustrates its utility and importance for mechanistic studies of feeding. I also appreciate the ability to readily time-lock neural data to individual crunches. Overall, the manuscript is well-executed and represents a useful contribution to the field.

The comments I have are largely minor and should be straightforward to address:

(1) The authors should report sample sizes for all mouse cohorts, either alongside the statistics or in the figure legends for mean data.

(2) Clarification is needed as to whether crunch detection fidelity is influenced by the hardness or softness of the food. The focus here is on standard pellets, with some additional high-fat pellet data, but it would be useful to know how generalizable the method is across different textures.

(3) The authors should comment on how susceptible the Crunchometer is to background noise. For example, how well does it perform in the presence of white noise, experimenter movement, or other task-related sounds?

(4) Chemogenetic activation of LHA GABAergic neurons is used. DREADD-based activation may strongly drive these neurons in a way that is not directly comparable to optogenetic or more physiological manipulations. While I do not think additional experiments are required, it would strengthen the discussion to briefly acknowledge this limitation.

Reviewer #2 (Public review):

Summary:

This manuscript introduces the Crunchometer, a low-cost, open-source acoustic platform for monitoring the microstructure of solid food intake in mice. The Crunchometer is designed to overcome the limitations of existing methods for studying feeding behavior in rodents. The goal was to provide a tool that could precisely capture the microstructure of solid food intake, something often overlooked in favor of liquid-based assays, while being affordable, scalable, and compatible with neural recording techniques. By doing so, the authors aimed to enable detailed analysis of how physiological states, drugs, and specific neural circuits shape naturalistic feeding behaviors.

Strengths:

The study's strengths lie in its clear innovation, methodological rigor in validation against human annotation, and demonstration of broad utility across behavioral and neuroscience paradigms. The approach addresses a significant methodological gap in the field by moving beyond liquid-based feeding assays and provides an accessible tool for precisely dissecting ingestive behavior. The system is validated across multiple contexts, including physiological state (fed vs. fasted), pharmacological manipulation (semaglutide), and circuit-level interventions (chemogenetic activation of LH neurons), and is further shown to integrate seamlessly with both electrophysiology and calcium imaging.

(1) Introduces a low-cost, open-source acoustic tool for measuring solid food intake, filling a critical gap left by expensive and proprietary systems.

(2) Makes the method easily adoptable across labs with detailed setup instructions and shared benchmark datasets.

(3) Provides high temporal precision for detecting bite events compared to human observers.

(4) Successfully distinguishes feeding microstructure (bites, bouts, IBIs, gnawing vs. consumption) with greater objectivity than manual annotation.

(5) Demonstrates compatibility with electrophysiology and calcium imaging, enabling fine-scale alignment of neural activity with feeding behavior.

(6) Effectively discriminates between fed vs. fasted states, validating physiological sensitivity.

(7) Captures the pharmacological effects of semaglutide, although this is really just reduced feeding and associated readouts (bouts, latency, etc).

(8) Has potential to distinguish consummatory vs. non-consummatory behaviors (e.g., food spillage, gnawing); however, the current SVM model struggles to separate biting from gnawing due to similar acoustic profiles, and manual validation is still required.

(9) Provides potential for closed-loop experiments.

Weaknesses:

Several limitations temper the strength of the conclusions: the supervised classifier still requires manual correction for gnawing, generalizability across different setups is limited, and the neuroscience findings, particularly calcium imaging of GABAergic and glutamatergic neurons, are based on small pilot samples. These issues do not undermine the value of the tool, but mean that the neural circuit findings should be interpreted as preliminary.

(1) Some neuroscience findings (calcium imaging of GABAergic vs. glutamatergic neurons) are based on small pilot samples (n=2 mice per condition), limiting generalizability.

(2) Chemogenetic and pharmacological experiments used small cohorts, raising statistical power concerns.

(3) Correlation with actual food intake is modest and sometimes less accurate than human observers.

(4) Sensitive to hoarding behavior, which can reduce detection accuracy and requires manual correction for misclassifications (e.g., tail movements, non-food noises). However, these limitations are discussed and not ignored.

Conclusion:

Overall, this is an exciting and impactful methodological advance that will likely be widely adopted in the field. I recommend minor revisions to clarify the limits of classifier generalizability, better contextualize the small-sample neuroscience findings as pilot data, and discuss future directions (e.g., real-time closed-loop applications).

Reviewer #3 (Public review):

Summary:

The manuscript provides detailed information on the construction of open-source systems to monitor ingestive behavior with low-cost equipment. Overall, this is a welcome addition to the arsenal of equipment that could be used to make measurements. The authors show interesting applications with data that reveal important neurophysiological properties of neurons in the lateral hypothalamus. The identification of previously unknown "meal-related" neurons in the LH highlights the utility of the device and is a novel insight that should spark further investigation on the LH. This manuscript and videos provide a wealth of useful information that should be a must-read for anyone in the ingestive behavior or hypothalamus fields.

A scholarly introduction to the history and utility of various ways feeding is measured in rodents is provided. One point - the microstructure of eating solid food - has been studied extensively (for one of many studies, see https://doi.org/10.1371/journal.pone.0246569 ). However, I agree that the crunchometer will allow for more people to access recordings during food intake and temporally lock consummatory behavior to neural activity.

Questions on results:

(1) It is unclear why 10% sucrose solution was used as a liquid instead of water, given that the study is focusing on the solid food source.

(2) It is unclear how essential the human verification is in the pipeline - results for Figure 1 keep referring to the verification as essential. Is that dispensable once the ML algorithms have been trained?

(3) The ability to extrapolate food quantity consumed is limited, with high variability. This limitation does not undercut the utility of the crunchometer, but should be highlighted as one of the parameters that are not suitable for this system. This limitation should be added to the limitations section.

(4) The ability to discriminate between gnawing and consummatory behavior is a strength (Figure 5), and these findings are important. However, it is unclear what can be made of mice that have 'gnawing' behavior in the fasted state (like in Figure 3). It seems they would need to be eliminated from the analysis with this tool?

(5) Why is there a post-semaglutide fed group and not a fasted group in Figure 4? It seems both would have been interesting, as one could expect an effect on feeding even 24h after semaglutide treatment. This would help parse the preference better because the animals eat such a small amount on semaglutide, that it is hard to compare to the fasted condition with saline treatment.

(6) The identification of 'meal-related' neurons in the LH is another strength of the manuscript. Although there is currently insufficient data, could similar recordings be used to give a neurophysiological definition of a 'meal' duration/size? Typically, these were somewhat arbitrarily defined behaviorally. Having a neural correlate to a 'meal' would be a powerful tool for understanding how meals are involved in overall caloric intake.

(7) The conclusion in the title of Figure 8 is premature, given the pilot nature and small number of neurons and mice sampled.

Conclusion:

Overall, this report on the Crunchometer is well done and provides a valuable tool for all who study food intake and the behaviors around food intake. Clarification or answers to the points above will only further the utility and understanding of the tool for the research community. I am excited to see the future utility of this tool in emerging research.

eLife Assessment

This paper is an important overview of the currently published literature on low-intensity focused ultrasound stimulation (TUS) in humans, providing a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects. The overall synthesis is convincing. The database proposed by the paper has the potential to become a key community resource if carefully curated and developed.

Reviewer #1 (Public review):

This paper is a relevant overview of the currently published literature on low-intensity focused ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems, nevertheless, relevant to summarise the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data free permutation of the parameter space.

A database summarising the literature and allowing for quantitative assessment of these studies is a key contribution of the paper. If curated well, it can become a valuable community resource.

Comments on revisions:

The paper is much improved. There remain a few caveats the authors may want to address.

I'm not going to dwell on this if the authors don't agree, but remain critical about the inclusion of TPS in the discussion. It's comparing apples and oranges, and unless there's a personal interest the authors have in TPS, it remains puzzling why it is included in the first place. As per my previous review, the literature on TPS, and especially the main example cited, has been highly criticised, including national patient and medical associations. A mere disclaimer that more work is needed isn't enough, in this reviewer's opinion - I simply don't understand why the authors go out on a limb here when the rest of the paper is done so well and thoroughly.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

Summary:

This paper is a relevant overview of the currently published literature on lowintensity focused ultrasound stimulation (TUS) in humans, with a meta-analysis of this literature that explores which stimulation parameters might predict the directionality of the physiological stimulation effects.

The pool of papers to draw from is small, which is not surprising given the nascent technology. It seems nevertheless relevant to summarize the current field in the way done here, not least to mitigate and prevent some of the mistakes that other non-invasive brain stimulation techniques have suffered from, most notably the theory- and data-free permutation of the parameter space.

The meta-analysis concludes that there are, at best, weak trends toward specific parameters predicting the direction of the stimulation effects. The data have been incorporated into an open database that will ideally continue to be populated by the community and thereby become a helpful resource as the field moves forward.

Strengths:

The current state of human TUS is concisely and well summarized. The methods of the meta-analysis are appropriate. The database is a valuable resource.

We thank the reviewer for their positive assessment of the revised manuscript and the potential importance of the resource to the TUS community. 

Suggestions:

The paper remains lengthy and somewhat unfocused, to the detriment of readability. One can understand that the authors wish to include as much information as possible, but this reviewer is sceptical that this will aid the use of the databank, or help broaden the readership. For one, there is a good chunk of repetition throughout. The intro is also somewhat oscillating between TMS, tDCS and TUS. While the former two help contextualizing the issue, it doesn't seem necessary. In the section on clinical applications of TUs and possible outcomes of TUS, there's an imbalance of the content across examples. That's in part because of the difference in knowledge base but some sections could probably be shortened, eg stroke. In any case, the authors may want to consider whether it is worth making some additional effort in pruning the paper

We thank the reviewer for these suggestions. We have checked for redundancy and that the clinical review section is more balanced, although some of the sections have more TUS studies than others, therefore some imbalance is unavoidable. As some examples, we have condensed the “Stroke and neuroprotection in brain injury” section (lines 624-647). This helps to improve the clarity and readability of the manuscript.

The terms or concept of enhancement and suppression warrant a clearer definition and usage. In most cases, the authors refer to E/S of neural activity. Perhaps using terms such as "neural enhancement" etc helps distinguish these from eg behavioural or clinical effects. Crucially, how one maps onto the other is not clear. But in any case, a clear statement that the changes outlined on lines 277ff do not

We thank the reviewer for this point and agree that it is important to distinguish neural E/S, as we had intended, from behavioral effects. In the first instance and in several places we add ‘neural’ before enhancement/suppression.  Also see Lines 276-279: “Probable net neural enhancement versus suppression was characterised as follows. Note that our use of the terms enhancement and suppression refers exclusively to the increase or decrease of neural activity, respectively, as measured by, neurophysiological methods (EEG-ERPs, BOLD fMRI, etc.) and does not imply equivalent changes in behavioural responses” 

Please see also lines 108-116.

Re tb-TUS (lines 382ff), it is worth acknowledging here that independent replication is very limited (eg Bao et al 2024; Fong et al bioRxiv 2024) and seems to indicate rather different effects

We have updated this section by referencing Bao et al. and Fong et al., as examples of the limited independent replication of tbTUS results. Please see lines 392-396. “However, independent replication of these findings remains limited. For example, Bao, found reduced motor cortex excitability – measured as decreased TMS-MEP amplitude in M1 -- that lasted up to 30 minutes post-sonication (Bao et al., 2024). Whereas Fong reported no significant effects between tbTUS and sham conditions in M1 excitability (Fong et al., 2024).”

The comparison with TPS is troublesome. For one, that original study was incredibly poorly controlled and designed. Cherry-picking individual (badly conducted) proof-of-principle studies doesn't seem a great way to go about as one can find a match for any desired use or outcome. Moreover, other than the concept of "pulsed" stimulation, it is not clear why that original study would motivate the use of TUS in the way the authors propose; both types of stimulation act in very different ways (if TPS "acts" at all). But surely the cited TPS study does not "demonstrate the capability for TUS for pre-operative cognitive mapping". As an aside, why the authors feel the need to state the "potential for TPS... to enhance cognitive function" is unclear, but it is certainly a non-sequitur. This review feels quite strongly that simplistic analogies such as the one here are unnecessary and misleading, and don't reflect the thoughtful discussion of the rest of the paper. In the other clinical examples, the authors build their suggestions on other TUS studies, which seems more sensible.

This is an excellent point, and we have removed that statement replacing it with: “However, TPS effects studies remain highly limited and would require further study and comparison to effects with other TUS protocols.”. Please see lines 561-562. We thank the reviewer for the supportive comments on the rest of the review.

eLife Assessment

This important study addresses a topic that is frequently discussed in the literature but is under-assessed, namely correlations among genome size, repeat content, and pathogenicity in fungi. Contrary to previous assertions, the authors found that repeat content is not associated with pathogenicity. Rather, pathogenic lifestyle was found to be better explained by the number of protein-coding genes, with other genomic features associated with insect association status. The results are considered solid, although there remain concerns about potential biases stemming from the underlying data quality of the analyzed genomes.

Reviewer #1 (Public review):

Summary:

The manuscript "Lifestyles shape genome size and gene content in fungal pathogens" by Fijarczyk et al. presents a comprehensive analyses of a large dataset of fungal genomes to investigate what genomic features correlate with pathogenicity and insect associations. The authors focus on a single class of fungi, due to the diversity of life styles and availability of genomes. They analyze a set of 12 genomic features for correlations with either pathogenicity or insect association and find that, contrary to previous assertions, repeat content does not associate with pathogenicity. They discover that the number of protein coding genes, including total size of non-repetitive DNA does correlate with pathogenicity. However, unique features are associated to insect associations. This work represents an important contribution to the attempts to understand what features of genomic architecture impact the evolution of pathogenicity in fungi.

Strengths:

The statistical methods appear to be properly employed and analyses thoroughly conducted. The size of the dataset is impressive and likely makes the conclusions robust. The manuscript is well written and the information, while dense, is generally presented in a clear manner.

Weaknesses:

My main concerns all involve the genomic data, how they were annotated, and the biases this could impart to the downstream analyses. The three main features I'm concerned with are sequencing technology, gene annotation, and repeat annotation. The authors have done an excellent investigation into these issues, but these show concerning trends, and my concerns are not as assuaged as the authors.

The collection of genomes is diverse and includes assemblies generated from multiple sequencing technologies including both short- and long-read technologies. From the number of scaffolds its clear that the quality of the assemblies varies dramatically, even within categories of long- and short-read. This is going to impact many of the values important for this study, as the authors show.

I have considerable worries that the gene annotation methods could impart biases that significantly effect the main conclusions. Only 5 reference training sets were used for the Sordariomycetes and these are unequally distributed across the phylogeny. Augusts obviously performed less than ideally, as the authors observe in their extended analysis. While the authors are not concerned about phylogenetic distance from the training species, due to prevailing trends, I am not as convinced. In figure S12, the Augustus features appear to have considerably more variation in values for the H2 set and possible the microascales. It is unclear how this would effect the conclusions in this study.

Unfortunately, the genomes available from NCBI will vary greatly in the quality of their repeat masking. While some will have been masked using custom libraries generated with software like Repeatmodeler, others will probably have been masked with public databases like repbase. As public databases are again biased towards certain species (Fusarium is well represented in repbase for example), this could have significant impacts on estimating repeat content. Additionally, even custom libraries can be problematic as some software (like RepeatModeler) will included multicopy host genes leading to bona fide genes being masked if proper filtering is not employed. A more consistent repeat masking pipeline would add to the robustness of the conclusions. The authors show that there is a significant bias in their set.

To a lesser degree I wonder what impact the use of representative genomes for a species has on the analyses. Some species vary greatly in genome size, repeat content and architecture among strains. I understand that it is difficult to address in this type of analysis, but it could be discussed.

Reviewer #2 (Public review):

Summary:

In this paper, the authors report on the genomic correlates of the transition to the pathogenic lifestyle in Sordariomycetes. The pathogenic lifestyle was found to be better explained by the number of genes, and in particular effectors and tRNAs, but this was modulated by the type of interacting host (insect or not insect) and the ability to be vectored by insects.

Strengths:

The main strengths of this study lie in (i) the size of the dataset, and the potentially high number of lifestyle transitions in Sordariomycetes, (ii) the quality of the analyses and the quality of the presentation of the results, (iii) the importance of the authors' findings.

Weaknesses:

The weakness is a common issue in most comparative genomics studies in fungi, but it remains important and valid to highlight it. Defining lifestyles is complex because many fungi go through different lifestyles during their life cycles (for instance, symbiotic phases interspersed with saprotrophic phases). In many fungi, the lifestyle referenced in the literature is merely the sampling substrate (such as wood or dung), which does not necessarily mean that this substrate is a key part of the life cycle. The authors discuss this issue, but they do not eliminate the underlying uncertainties.

Author response:

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

Reviewer #1 (Public review):

Summary:

The manuscript "Lifestyles shape genome size and gene content in fungal pathogens" by Fijarczyk et al. presents a comprehensive analysis of a large dataset of fungal genomes to investigate what genomic features correlate with pathogenicity and insect associations. The authors focus on a single class of fungi, due to the diversity of lifestyles and availability of genomes. They analyze a set of 12 genomic features for correlations with either pathogenicity or insect association and find that, contrary to previous assertions, repeat content does not associate with pathogenicity. They discover that the number of proteincoding genes, including the total size of non-repetitive DNA does correlate with pathogenicity. However, unique features are associated with insect associations. This work represents an important contribution to the attempts to understand what features of genomic architecture impact the evolution of pathogenicity in fungi.

Strengths:

The statistical methods appear to be properly employed and analyses thoroughly conducted. The manuscript is well written and the information, while dense, is generally presented in a clear manner.

Weaknesses:

My main concerns all involve the genomic data, how they were annotated, and the biases this could impart to the downstream analyses. The three main features I'm concerned with are sequencing technology, gene annotation, and repeat annotation.

We thank the reviewer for all the comments. We are aware that the genome assemblies are of heterogeneous quality since they come from many sources. The goal of this study was to make the best use of the existing assemblies, with the assumption that noise introduced by the heterogeneity of sequencing methods should be overcome by the robustness of evolutionary trends and the breadth and number of analyzed assemblies. Therefore, at worst, we would expect a decrease in the power to detect existing trends. It is important to note that the only way to confidently remove all potential biases would be to sequence and analyze all species in the same way; this would require a complete study and is beyond the scope of the work presented here. Nevertheless some biases could affect the results in a negative way, eg. is if they affect fungal lifestyles differently. We therefore made an attempt to explore the impact of sequencing technology, gene and repeat annotation approach among genomes of different fungal lifestyles. Details are described in Supplementary Results and below. Overall, even though the assembly size and annotations conducted with Augustus can sometimes vary compared to annotations from other resources, such as JGI Mycocosm, we do not observe a bias associated with fungal lifestyles. Comparison of annotations conducted with Augustus and JGI Mycocosm dataset revealed variation in gene-related features that reflect biological differences rather than issues with annotation.  

The collection of genomes is diverse and includes assemblies generated from multiple sequencing technologies including both short- and long-read technologies. Not only has the impact of the sequencing method not been evaluated, but the technology is not even listed in Table S1. From the number of scaffolds it is clear that the quality of the assemblies varies dramatically. This is going to impact many of the values important for this study, including genome size, repeat content, and gene number.

We have now added sequencing technology in Table S1 as it was reported in NCBI. We evaluated the impact of long-read (Nanopore, PacBio, Sanger) vs short-read assemblies in Supplementary Results. In short, the proportion of different lifestyles (pathogenic vs. nonpathogenic, IA vs non-IA) were the same for short- and long-read assemblies. Indeed, longread assemblies were longer, had a higher fraction of repeats and less genes on average, but the differences between pathogenic vs. non-pathogenic (or IA vs non-IA) species were in the same direction for two sequencing technologies and in line with our results. There were some discrepancies, eg. mean intron length was longer for pathogens with long-read assemblies, but slightly shorter on average for short-read assemblies (and to lesser extent GC and pseudo tRNA count), which could explain weaker or mixed results in our study for these features.

Additionally, since some filtering was employed for small contigs, this could also bias the results.

The reason behind setting the lower contig length threshold was the fact that assemblies submitted to NCBI have varying lower-length thresholds. This is because assemblers do not output contigs above a certain length, and this threshold can be manipulated by the user. Setting a common min contig length was meant to remove this variation, knowing that any length cut-off will have a larger effect on short-read based assemblies than long-read-based assemblies. Notably, genome assemblies of corresponding species in JGI Mycocosm have a minimum contig length of 865 bp, not much lower than in our dataset. Importantly, in a response to a comment of previous reviewer, repeat content was recalculated on raw assembly lengths instead of on filtered assembly length. 

I have considerable worries that the gene annotation methods could impart biases that significantly affect the main conclusions. Only 5 reference training sets were used for the Sordariomycetes and these are unequally distributed across the phylogeny. Augusts obviously performed less than ideally, as the authors reported that it under-annotated the genomes by 10%. I suspect it will have performed worse with increasing phylogenetic distance from the reference genomes. None of the species used for training were insectassociated, except for those generated by the authors for this study. As this feature was used to split the data it could impact the results. Some major results rely explicitly on having good gene annotations, like exon length, adding to these concerns. Looking manually at Table S1 at Ophiostoma, it does seem to be a general trend that the genomes annotated with Magnaporthe grisea have shorter exons than those annotated with H294. I also wonder if many of the trends evident in Figure 5 are also the result of these biases. Clades H1 and G each contain a species used in the training and have an increase in genes for example.

We have applied 6 different reference training sets (instead of one) precisely to address the problem of increasing phylogenetic distance of annotated species. To further investigate the impact of chosen species for training, we plotted five gene features (number of genes, number of introns, intron length, exon length, fraction of genes with introns) as a function of   branch length distance from the species (or genus) used as a training set for annotation. We don’t see systematic biases across different training sets. However,  trends are very clear for clades annotated with fusarium. This set of species includes Hypocreales and Microascales, which is indeed unfortunate since Microascales is an IA group and at the same time the most distant from the fusarium genus in this set. To clarify if this trend is related to annotation bias or a biological trend, we compared gene annotations with those of Mycocosm, between Hypocreales Fusarium species, Hypocreales non-Fusarium species, and Microascales, and we observe exactly the same trends in all gene features. 

Similarly, among species that were annotated with magnaporthe_grisea, Ophiostomatales (another IA group) are among the most distant from the training set species. Here, however, another order, Diaporthales, is similarly distant, yet the two orders display different feature ranges. In terms of exon length, top 2 species in this training set include Ophiostoma, and they reach similar exon length as the Ophiostoma species annotated using H294 as a training set. In summary, it is possible that the choice of annotation species has some effect on feature values; however, in this dataset, these biases are likely mitigated by biological differences among lifestyles and clades. 

Unfortunately, the genomes available from NCBI will vary greatly in the quality of their repeat masking. While some will have been masked using custom libraries generated with software like Repeatmodeler, others will probably have been masked with public databases like repbase. As public databases are again biased towards certain species (Fusarium is well represented in repbase for example), this could have significant impacts on estimating repeat content. Additionally, even custom libraries can be problematic as some software (like RepeatModeler) will include multicopy host genes leading to bona fide genes being masked if proper filtering is not employed. A more consistent repeat masking pipeline would add to the robustness of the conclusions.

We have searched for the same species in JGI Mycocosm and were able to retrieve 58 genome assemblies with matching species, with 19 of them belonging to the same strain as in our dataset. Overall we found no differences in genome assembly length. Interestingly, repeat content was slightly higher for NCBI genome assemblies compared to JGI Mycocosm assemblies, perhaps due to masking of host multicopy genes, as the reviewer mentioned. By comparing pathogenic and non-pathogenic species for the same 19 strains, we observe that JGI Mycocosm annotates fewer repeats in pathogenic species than Augustus annotations (but trends are similar when taking into account 58 matching species). Given a small number of samples, it is hard to draw any strong conclusions; however, the differences that we see are in favor of our general results showing no (or negative) correlation of repeat content with pathogenicity. 

To a lesser degree, I wonder what impact the use of representative genomes for a species has on the analyses. Some species vary greatly in genome size, repeat content, and architecture among strains. I understand that it is difficult to address in this type of analysis, but it could be discussed.

In our case the use of protein sequences could underestimate divergence between closely related strains from the same species. We also excluded strains of the same species to avoid overrepresentation of closely related strains with similar lifestyle traits. We agree that some changes in the genome architecture can occur very rapidly, even at the species level, though analyzing emergence of eg. pathogenicity at the population level would require a slightly different approach which accounts for population-level processes. 

Reviewer #2 (Public review):

Summary:

In this paper, the authors report on the genomic correlates of the transition to the pathogenic lifestyle in Sordariomycetes. The pathogenic lifestyle was found to be better explained by the number of genes, and in particular effectors and tRNAs, but this was modulated by the type of interacting host (insect or not insect) and the ability to be vectored by insects.

Strengths:

The main strength of this study lies in the size of the dataset, and the potentially high number of lifestyle transitions in Sordariomycetes.

Weaknesses:

The main strength of the study is not the clarity of the conclusions.

(1) This is due firstly to the presentation of the hypotheses. The introduction is poorly structured and contradictory in some places. It is also incomplete since, for example, fungusinsect associations are not mentioned in the introduction even though they are explicitly considered in the analyses.

We thank the reviewer for pointing this out. We strived to address all comments and suggestions of the reviewer to clarify the message and remove the contradictions. We also added information about why we included insect-association trait in our analysis. 

(2) The lack of clarity also stems from certain biases that are challenging to control in microbial comparative genomics. Indeed, defining lifestyles is complicated because many fungi exhibit different lifestyles throughout their life cycles (for instance, symbiotic phases interspersed with saprotrophic phases). In numerous fungi, the lifestyle referenced in the literature is merely the sampling substrate (such as wood or dung), which doesn't mean that this substrate is a crucial aspect of the life cycle. This issue is discussed by the authors, but they do not eliminate the underlying uncertainties.

We agree with the reviewer that lack of certainty in the lifestyle or range of possible lifestyles of studied species is a weakness in this analysis. We are limited by the information available in the literature. We hope that our study will increase interest in collecting such data in the future.

Reviewer #3 (Public review):

Summary:

This important study combines comparative genomics with other validation methods to identify the factors that mediate genome size evolution in Sordariomycetes fungi and their relationship with lifestyle. The study provides insights into genome architecture traits in this Ascomycete group, finding that, rather than transposons, the size of their genomes is often influenced by gene gain and loss. With an excellent dataset and robust statistical support, this work contributes valuable insights into genome size evolution in Sordariomycetes, a topic of interest to both the biological and bioinformatics communities.

Strengths:

This study is complete and well-structured.

Bioinformatics analysis is always backed by good sampling and statistical methods. Also, the graphic part is intuitive and complementary to the text.

Weaknesses:

The work is great in general, I just had issues with the Figure 1B interpretation.

I struggled a bit to find the correspondence between this sentence: "Most genomic features were correlated with genome size and with each other, with the strongest positive correlation observed between the size of the assembly excluding repeats and the number of genes (Figure 1B)." and the Figure 1B. Perhaps highlighting the key p values in the figure could help.

We thank the reviewer for pointing out this sentence. Perhaps the misunderstanding comes from the fact that in this sentence one variable is missing. The correct version should be “Most genomic features were correlated with genome size and with each other, with the strongest positive correlation observed between the genome size, the genome size excluding repeats and the number of genes (Figure 1B)”. Also, the variable names now correspond better to those shown on the figure.

Reviewer #1 (Recommendations for the authors):

The authors have clearly done a lot of good work, and I think this study is worthwhile. I understand that my concerns about the underlying data could necessitate rerunning the entire analysis with better gene models, but there may be another option. JGI has a fairly standard pipeline for gene and repeat annotation. Their gene predictions are based on RNA data from the sequenced strain and should be quite good in general. One could either compare the annotations from this manuscript to those in mycocosm for genomes that are identical and see if there are systematic biases, or rerun some analyses on a subset of genomes from mycocosm. Indeed, it's possible that the large dataset used here compensates for the above concerns, but without some attempt to evaluate these issues, it's difficult to have confidence in the results.

We very appreciate the positive reception of our manuscript. Following the reviewer’s comments we have investigated gene annotations in comparison with those of JGI Mycocosm, even though only 58 species were matching and only 19 of them were from the same strain. This dataset is not representative of the Sordariomycetes diversity (most species come from one clade), therefore will not reflect the results we obtained in this study. To note, the reason for not choosing JGI Mycocosm in the first place, was the poor representation of the insect-associated species, which we found key in this study. In general, we found that assembly lengths were nearly identical, number of genes was higher, and the repeat content was lower for the JGI Mycocosm dataset. When comparing different lifestyles (in particular pathogens vs. non-pathogens), we found the same differences for our and JGI Mycocosm annotations, with one exception being the repeat content. In the small subset (19 same-strain assemblies), our dataset showed the same level of repeats between the two lifestyles, whereas JGI Mycocosm showed lower repeat content for pathogens (but notably for all 58 species, the trend was same for our and JGI Mycocosm annotations). None of these observations are in conflict with our results where we find no or negative association of repeat content with pathogens. 

The figures are very information-dense. While I accept that this is somewhat of a necessity for presenting this type of study, if the authors could summarize the important information in easier-to-interpret plots, that could help improve readability.

We put a lot of effort into showing these complicated results in as approachable manner as possible. Given that other reviewers find them intuitive we decided to keep most of them as they are. To add more clarification, we added one supplementary figure showing distributions of genomic traits across lifestyles. Moreover, in Figure 5, a phylogenetic tree was added with position of selected clades, as well as a scatterplot showing distributions of mean values for genome size and number of genes for those clades. If the reviewer has any specific suggestions on what to improve and in which figure, we’re happy to consider it. 

Reviewer #2 (Recommendations for the authors):

I have no major comments on the analyses, which have already been extensively revised. My major criticism is the presentation of the background, which is very insufficient to understand the importance or relevance of the results presented fully.

Lines are not numbered, unfortunately, which will not help the reading of my review.

(1) The introduction could better present the background and hypotheses:

(a) After reading the introduction, I still didn't have a clear understanding of the specific 'genome features' the study focuses on. The introduction fails to clearly outline the current knowledge about the genetic basis of the pathogenic lifestyle: What is known, what remains unknown, what constitutes a correlation, and what has been demonstrated? This lack of clarity makes reading difficult.

We thank the reviewer for pointing this out. We have now included in the introduction a list of genomic traits we focus on. We also tried to be more precise about demonstrated pathogenic traits and other correlated traits in the introduction. 

(b) Page 3. « Various features of the genome have been implicated in the evolution of the pathogenic lifestyle. » The cited studies did not genuinely link genome features to lifestyle, so the authors can't use « implicated in » - correlation does not imply causation.

This sentence also somehow contradicts the one at the end of the paragraph: « we still have limited knowledge of which genomic features are specific to pathogenic lifestyle

We thank the reviewer for this comment. We added a phrase “correlated with or implicated in” and changed the last sentence of the paragraph into “Yet we still have limited knowledge of how important and frequent different genomic processes are in the evolution of pathogenicity across phylogenetically distinct groups of fungi and whether we can use genomic signatures left by some of these processes as predictors of pathogenic state.”.

(c) Page 3: « Fungal pathogen genomes, and in particular fungal plant pathogen genomes have been often linked to large sizes with expansions of TEs, and a unique presence of a compartmentalized genome with fast and slow evolving regions or chromosomes » Do the authors really need to say « often »? Do they really know how often?

We removed “often”.

(d) Such accessory genomic compartments were shown to facilitate the fast evolution of effectors (Dong, Raffaele, and Kamoun 2015) ». The cited paper doesn't « show » that genomic compartments facilitate the fast evolution of effectors. It's just an observation that there might be a correlation. It's an opinion piece, not a research manuscript.

We changed the sentence to “Such accessory genomic compartments could facilitate the fast evolution of effectors”.

(e) even though such architecture can facilitate pathogen evolution, it is currently recognized more as a side effect of a species evolutionary history rather than a pathogenicity related trait ». This sentence somehow contradicts the following one: « Such accessory genomic compartments were shown to facilitate the fast evolution of effectors".

Here we wanted to point out that even though accessory genome compartments and TE expansions can facilitate pathogen evolution the origin of such architecture is not linked to pathogenicity. We reformulated the sentence to “Even though such architecture can facilitate pathogen evolution, it is currently recognized that its origin is more likely a side effect of a species evolutionary history rather than being caused by pathogenicity”.

(f) As the number of genes is strongly correlated with fungal genome size (Stajich 2017), such expansions could be a major contributor to fungal genome size. » This sentence suggests that pathogens might have bigger genomes because they have more effectors. This is contradictory to the sentence right after « At the end of the spectrum are the endoparasites Microsporidia, which have among the smallest known fungal genomes ».

The authors state that pathogens have bigger genomes and then they take an example of a pathogen that has a minimal genome. I know it's probably because they lost genes following the transition to endoparasitism and not related to their capacity to cause disease. I just want to point out that their writing could be more precise. I invite authors to think of young scholars who are new to the field of fungal evolutionary genomics.

We thank the reviewer for prompting us to clarify the text. We rewrote this short extract as follows “Notably, not all pathogenic species experience genome or gene expansions, or show compartmentalized genome architecture. While gene family expansions are important for some pathogens, the contrary can be observed in others, such as Microsporidia. Due to transition to obligatory intracellular lifestyle these fungi show signatures of strong genome contractions and reduced gene repertoire (Katinka et al. 2001) without compromising their ability to induce disease in the host. This raises questions about universal genomic mechanisms of transition to pathogenic state.”

(g) I find it strange that the authors do not cite - and do not present the major results of two other studies that use the same type of approach and ask the same type of question in Sordariomycetes, although not focusing on pathogenicity:

Hensen et al.: https://pubmed.ncbi.nlm.nih.gov/37820761/

Shen et al.: https://pubmed.ncbi.nlm.nih.gov/33148650/

We thank the reviewer for pointing out this omission. We now added more information in the introduction to highlight the importance of the phylogenetic context in studying genome evolution as demonstrated by these studies. The following part was added to introduction:  “Other phylogenomic studies investigating a wide range of Ascomycete species, while not explicitly focusing on the neutral evolution hypothesis, have found strong phylogenetic signals in genome evolution, reflected in distinct genome characteristics (e.g., genome size, gene number, intron number, repeat content) across lineages or families (Shen et al. 2020; Hensen et al. 2023). Variation in genome size has been shown to correlate with the activity of the repeat-induced point mutation (RIP) mechanism (Hensen et al. 2023; Badet and Croll 2025), by which repeated DNA is targeted and mutated. RIP can potentially lead to a slower rate of emergence of new genes via duplication (Galagan et al. 2003), and hinder TE proliferation limiting genome size expansion (Badet and Croll 2025). Variation in genome dynamics across lineages has also been suggested to result from environmental context and lifestyle strategies (Shen et al. 2020), with Saccharomycotina yeast fungi showing reductive genome evolution and Pezizomycotina filamentous fungi exhibiting frequent gene family expansions. Given the strong impact of phylogenetic membership,  demographic history (Ne) and host-specific adaptations of pathogens on their genomes, we reasoned that further examination of genomic sequences in groups of species with various lifestyles can generate predictions regarding the architecture of pathogenic genomes.”

(h) Genome defense mechanisms against repeated elements, such as RIP, are not mentioned while they could have a major impact on genome size (Hensen et al cited above; Badet and Croll https://www.biorxiv.org/content/10.1101/2025.01.10.632494v1.full).

This citation is added in the text above.

(i) Should the reader assume that the genome features to be examined are those mentioned in the first paragraph or those in the penultimate one?

In the last paragraph of the introduction we included the complete list of investigated genomic traits.

(j) The insect-associated lifestyle is mentioned only in the research questions on page 4, but not earlier in the introduction. Why should we care about insect-associated fungi?

We apologize for this omission. We added a sentence explaining how neutral evolution hypotheses can explain patterns of genome evolution in endoparasites and species with specialized vectors (traits present in insect-associated species) and added a sentence in the last paragraph that this is the reason why we have selected this trait for analysis.  

(2) Why use concatenation to infer phylogeny?

(a) Kapli et al. https://pubmed.ncbi.nlm.nih.gov/32424311/ « Analyses of both simulated and empirical data suggest that full likelihood methods are superior to the approximate coalescent methods and to concatenation »

(b) It also seems that a homogeneous model was used, and not a partitioned model, while the latter are more powerful. Why?

We thank the reviewer for the comment. When we were reconstructing the phylogenetic tree  we were not aware of the publication and we followed common practices from literature for phylogenetic tree reconstruction even though currently they are not regarded as most optimal. In fact, in the first round of submission, we have included both concatenation as well as a multispecies coalescent method based on 1000 busco sequences and a concatenation method with different partitions for 250 busco sequences. All three methods produced similar topologies. Since the results were concordant, we chose to omit these analyses from the manuscript to streamline the presentation and focus on the most important results.

(3) Other comments:

Is there a table listing lifestyles?

Yes, lifestyles (pathogenicity and insect-association) are listed in Supplementary Table S1. 

(4) Summary:

(a) seemingly similar pathogens »: meaning unclear; on what basis are they similar? why « seemingly »?

We removed “seemingly” from the sentence.

(b) Page 4: what's the difference between genome feature and genome trait?

There is no difference. We apologize for the confusion. We changed “feature” to “trait” whenever it refers to the specific 13 genomic traits analyzed in this study.

(c) Page 22: Braker, not Breaker

corrected

What do the authors mean when they write that genes were predicted with Augustus and Braker? Do they mean that the two sets of gene models were combined? Gene counts are based on Augustus (P24): why not Braker?

We only meant here that gene annotation was performed using Braker pipeline, which uses a particular version of Augustus. We corrected the sentence.

(d) Figure 2B and 2C:

'Undetermined sign' or 'Positive/Negative' would be better than « YES » or it's just impossible to understand the figure without reading the legend.

We changed “YES” to “UNDETERMINED SIGN” as suggested by the reviewer.

eLife Assessment

This valuable study uses a sophisticated array of techniques to investigate the mechanisms through which the chordotonal receptors in the locust ear (Müller's organ) sense auditory signals. Ultrastructural reconstruction of the sensory organ provides convincing evidence of the organization of the scolopidial structure that wraps the sensory neuron cilium. However, the recordings of sound-evoked motion and electrophysiological activity from the chordotonal sensory neurons provide incomplete evidence for the proposed axial stretch model of mechanotransduction.

Reviewer #1 (Public review):

Chaiyasitdhi et al. set out to investigate the detailed ultrastructure of the scolopidia in the locust Müller's organ, the geometry of the forces delivered to these scolopidia during natural stimulation, and the direction of forces that are most effective at eliciting transduction currents. To study the ultrastructure, they used the FIB-SEM technique, to study the geometry of natural stimulation, they used OCT vibrometry and high-speed light microscopy, and to study transduction currents, they used patch clamp physiology.

Strengths:

I believe that the ultrastructural description of the locust scolopidium is excellent and the first of its kind in any insect system. In particular, the finding of the bend in the dendritic cilium and the position of the ciliary dilation are interesting, and it would be interesting to see whether these are common features within the huge diversity of insect chordotonal organs.

I believe the use of OCT to measure organ movements is a significant strength of this paper; however, using ex vivo preparations undermines any conclusions drawn about the system's in vivo mechanics.

The choice of Group III scolopidia is also good. Research on the mechanics of locust tympana has shown that travelling waves are formed on the tympanum and waves of different frequencies show highest amplitudes at different positions on the tympanum, and therefore also on different groups of scolopidia within the Müller's organ (Windmill et al, 2005; 2008, and Malkin et al, 2013). The lowest frequency modal waves (F0) observed by Windmill et al 2008 were at about 4.4 kHz, which are slightly higher than the ~3 kHz frequencies studied in this paper but do show large deflections where these group III scolopidia attach at the styliform body (Windmill et al, 2005).

This should be mentioned in the paper since the electrophysiology justification to use group III neurons is less convincing, given that Jacobs et al 1999 clearly point out that group III neurons are very variable and some of them are tuned much higher to 10 kHz, and others even higher to 20-30 kHz.

Weaknesses:

Specifically, it is understandable that the authors decided to use excised ears for the light microscopy, where Müller's organ would not be accessible in situ. However, it is very likely that excision will change the system's mechanics, especially since any tension or support to Müller's organ will be ablated. OCT enables in vivo measurements in fully undissected systems (Mhatre et al, Biorxiv, 2021) or in systems with minimal dissection where the mechanics have not been compromised (Vavakou et al, 2021). The choice to entirely dissect out the membrane is difficult to understand here.

My main concern with this paper, however, is the use of light microscopy very close to the Nyquist limit to study scolopidial motion, and the fact that the OCT data contradict and do not match the light microscopy data.

The light microscopy data is collected at ~8 kHz, and hence the Nyquist limit is ~4 kHz. It is possible to measure frequencies reliably this close to the limit, but the amplitude of motion is quite likely to be underestimated, given that the technique only provides 2 sample points per cycle at 4 kHz and approximately 2.66 sample points at 3 kHz. At that temporal resolution, the samples are much more likely to miss the peak of the wave than not, and therefore, amplitudes will be misestimated. A much more reasonable sample rate for amplitude estimation is generally about 10 samples per cycle. I do not believe the data from the microscopy is reliable for what the authors wish to use them for.

Using the light microscopy data, the authors claim that the strains experienced by the group III scolopidia at 3 kHz are greater along the AP axis than the ML axis (Figure 4). However, this is contradicted by the OCT data, which show very low strain along the AP axis (black traces) at and around 3 kHz (Figure 3c and extended data Figure 2f) and show some movement along the ML axis (red traces, same figures). The phase at low amplitudes of motion cannot be considered very reliable either, and hence phase variations at these frequencies in the OCT cannot be considered reliable indicators of AP motion; hence, I'm unclear whether the vector difference in the OCT is a reliable indicator of movement.

The OCT data are significantly more reliable as they are acquired at an appropriate sampling rate of 90 kHz. The authors do not mention what microphone they use to monitor or calibrate their sound field and phase measurements in OCT, but I presume this was done since it is the norm. Thus, the OCT data show that the movement within the Müller's organ is complex, probably traces an ellipse at some frequencies as observed in bushcrickets (Vavkou et al, 2021) and also thought to be the case in tree crickets based on the known attachment points of the TO (Mhatre et al, 2021). The OCT data shows relatively low AP motion at frequencies near 3 kHz, and higher ML motion, which contradicts the less reliable light microscopy data. Given that the locust membrane shows peaks in motion at ~4.5 kHz, ~11 kHz, and also at ~20 kHz (Windmill et al, 2008), I am surprised that the authors limited their OCT experiments and analyses to 5 kHz.

In summary for this section, I am not convinced of the conclusion drawn by the authors that group III scolopidia receive significantly higher stimulation along the AP axis in their native configuration, if indeed they were studied in the appropriate force regime (altered due to excision).

In the scolopidial patch clamp data, the authors study transduction currents in response to steady state stimulation along the AP axis and the ML axis. The responses to steady state and periodic forces may well be different, and the authors do not offer us a way to clearly relate the two and therefore, to interpret the data.

In addition, both stimulation types, along the AP axis and the ML, elicit clear transduction responses. Stimulation along the AP axis might be slightly higher, but there is over 40% variation around the mean in one case (pull: 26.22 {plus minus} 10.99 pA) and close to 80% variation in the other (push: 10.96 {plus minus} 8.59 pA). These data are indeed from a very high displacement range (2000 nm), which is very high compared to the native displacement levels, which are in the 1-10 nm range.

The factor change from sample to sample is not reported, and is small even overall. The statistical analyses of these data are not clearly reported, and I don't see the results of the overall ANOVA in the results section. I also find the dip in the reported transduction currents between 10 and 100 nm quite odd (Figure 5 j-m) and would like to know what the authors' interpretation of this behaviour is. It seems to me that those currents increase continuously linearly after ~50-100 nm and that the data below that range are in the noise. Thus, the transduction currents observed at the relevant displacement range (1-10 nm) may not actually be reliable. How were these small displacements achieved, and how closely were the actual levels monitored? Is it possible to reliably deliver 1-10 nm displacements using a micromanipulator?

What is clear, despite the difficulty in interpreting this data, is that both AP and ML stimulation evoke transduction currents, and their relative differences are small. Additionally, in Müller's organ itself, in the excised organ, the scolopidia are stimulated along both axes. Thus, in my opinion, it is not possible to say that axial stretch along the cilium is 'the key mechanical input that activates mechano-electrical transduction'.

Reviewer #2 (Public review):

Summary of strengths and weaknesses:

Using several techniques-FIB-SEM, OCT, high-speed light microscopy, and electrophysiology-Chaiyasitdhi et al. provide evidence that chordotonal receptors in the locust ear (Müller's organ) sense the stretch of the scolapale cell, primarily of its cilium. Careful measurements certainly show cell stretch, albeit with some inconsistencies regarding best frequencies and amplitudes. The weakest argument concerns the electrophysiological recordings, because the authors do not show directly that the stimulus stretches the cells. If this latter point can be clarified, then our confidence that ciliary stretch is the proximal stimulus for mechanotransduction will be increased. This conclusion will not come as a surprise for workers in the field, as the chordotonal organ is known as a stretch-receptor organ (e.g., Wikipedia). But it is a useful contribution to the field and allows the authors to suggest transduction mechanisms whereby ciliary stretch is transduced into channel opening.

Reviewer #3 (Public review):

Summary:

The paper 'A stretching mechanism evokes mechano-electrical transduction in auditory chordotonal neurons' by Chaiyasitdhi et al. presents a study that aims to address the mechanical model for scolopidia in Schistocerca gregaria Müller's organ, the basic mechanosensory units in insect chordotonal organs. The authors combine high-resolution ultrastructural analysis (FIB-SEM), sound-evoked motion tracking (OCT and high-speed light microscopy), and electrophysiological recordings of transduction currents during direct mechanical stimulation of individual scolopidia. They conclude that axial stretching along the ciliary axis is an adequate mechanical stimulus for activating mechanotransduction channels.

Strengths/Highlights:

(1) The 3D FIB-SEM reconstruction provides high resolution of scolopidial architecture, including the newly described "scolopale lid" and the full extent of the cilium.

(2) High-speed microscopy clearly demonstrates axial stretch as the dominant motion component in the auditory receptors, which confirms a long-standing question of what the actual motion of a stretch receptor is upon auditory stimulation.

(3) Patch-clamp recordings directly link mechanical stretch to transduction currents, a major advance over previous indirect models.

Weaknesses/Limitations:

(1) The text is conceptually unclear or written in an unclear manner in some places, for example, when using the proposed model to explain the sensitivity of Nanchung-Inactive in the discussion.

(2) The proposed mechanistic models (direct-stretch, stretch-compression, stretch-deformation, stretch-tilt) are compelling but remain speculative without direct molecular or biophysical validation. For example, examining whether the organ is pre-stretched and identifying the mechanical components of cells (tissues), such as the extracellular matrix and cytoskeleton, would help establish the mechanical model and strengthen the conclusion.

(3) To some extent, the weaknesses of the paper are part of its strengths and vice versa. For example, the direct push/pull and up/down stimulations are a great experimental advance to approach an answer to the question of how the underlying cellular components are deformed and how the underlying ion channels are forced. However, as the authors clearly state, neither of their stimulations can limit all forces to only one direction, and both orthogonal forces evoke responses in the neurons. The question of which of the two orthogonal forces 'causes' the response cannot be answered with these experiments and has not been answered by this manuscript. But the study has brought the field a considerable step closer to answering the question. The answer, however, might be that both longitudinal ('stretch') and perpendicular ('compression') forces act together to open the ion channels and that both dendritic extension via stretch and bending can provide forces for ion channel gating. The current paper has identified major components (longitudinal stretch components) for the neurons they analysed, but these will surely have been chosen according to their accessibility, and as such, the variety of mechanical responses in Müller's organ might be greater. In light of these considerations, the authors might acknowledge such uncertainties more clearly in their paper. The paper is an impressive methodological progress and breakthrough, but it simply does not "demonstrate that axial stretch along the cilium is the adequate stimulus or the key mechanical input that activates mechano-electrical transduction" as the authors write at the start of their discussion. They do show that axial stretch dominates for the neurons they looked at, which is important information. The same applies to the end of the discussion: The authors write, "This relative motion within the organ then drives an axial stretch of the scolopidium, which in turn evokes the mechano-electrical transduction current." Reading the manuscript, the certainty and display of confidence are not substantiated by the data provided. But they are also not necessary. The study has paved the road to answer these questions. Instead, the authors are encouraged to make suggestions on how the remaining uncertainties could be removed (and what experiments or model might be used).

Author response:

Reviewer #1 (Public review):

Chaiyasitdhi et al. set out to investigate the detailed ultrastructure of the scolopidia in the locust Müller's organ, the geometry of the forces delivered to these scolopidia during natural stimulation, and the direction of forces that are most effective at eliciting transduction currents. To study the ultrastructure, they used the FIB-SEM technique, to study the geometry of natural stimulation, they used OCT vibrometry and high-speed light microscopy, and to study transduction currents, they used patch clamp physiology.

Strengths:

I believe that the ultrastructural description of the locust scolopidium is excellent and the first of its kind in any insect system. In particular, the finding of the bend in the dendritic cilium and the position of the ciliary dilation are interesting, and it would be interesting to see whether these are common features within the huge diversity of insect chordotonal organs.

Thank you very much for your comments. We indeed plan to extend and continue our approach to exploit and understand diverse chordotonal organs in insects and crustaceans.

I believe the use of OCT to measure organ movements is a significant strength of this paper; however, using ex vivo preparations undermines any conclusions drawn about the system's in vivo mechanics.

Having re-read the manuscript, we failed to explicitly describe our ex vivo preparation of Müller’s organ including key references that detail the largely retained physiological function of Müller’s organ. We have now revised this detail in the method section:

“We used an excised locust ear preparation for all experiments, following a previously described dissection protocol [9]. In short, the tympanum, with Muller’s organ attached was left intact suspended between the cuticular rim. The cuticular rim of the tympanum was fixed into a hole in a preparation dish that allowed Muller’s organ to be submerged with extracellular saline, whilst the outside of the tympanum was dry and could be stimulated with airborne sound. This ex vivo preparation of Muller’s organ retained frequency tuning (Warren & Matheson, 2018), similar electrophysiological function as freshly dissected Muller’s organs (Hill, 1983a, 1983b; Michelsen, 1968: frequency discrimination in the locust ear by means of four groups of receptor cells), and amplitude coding (Warren & Matheson, 2018). Since Müller’s organ is backed by an air-filled trachea in vivo, the addition of saline solution in the ex vivo preparation decreased its displacements ~100 fold due to a dampening effect (Warren et al., 2020).”

And in the last section of the introduction:

“Here, we combined FIB-SEM to resolve the 3D ultrastructure of a scolopidium, OCT and high-speed microscopy to examine sound-evoked motion at both the organ and individual scolopidium levels, and direct mechanical stimulation of the scolopale cap, where the ciliary tip is anchored, whilst simultaneously recording transduction currents. Here, Muller’s organ and the tympanum was excised from the locust for physiological experiments. This ex vivo preparation of Muller’s organ retained frequency tuning, amplitude coding and electrophysiological function. This preparation also permitted the enzymatic isolation of individual scolopidia whilst recording transduction currents (Warren & Matheson, 2018).”  

To further clarify physiological differences between the in vivo and ex vivo operation of the tympanum and Müller’s organ, we will perform an additional experiment for the revised manuscript by quantifying the changes in the sound-evoked tonotopic travelling wave of the tympanum using Laser Doppler Vibrometry (LDV). This result will be added to the Supplementary Text.

The choice of Group III scolopidia is also good. Research on the mechanics of locust tympana has shown that travelling waves are formed on the tympanum and waves of different frequencies show highest amplitudes at different positions on the tympanum, and therefore also on different groups of scolopidia within the Müller's organ (Windmill et al, 2005; 2008, and Malkin et al, 2013). The lowest frequency modal waves (F0) observed by Windmill et al 2008 were at about 4.4 kHz, which are slightly higher than the ~3 kHz frequencies studied in this paper but do show large deflections where these group III scolopidia attach at the styliform body (Windmill et al, 2005).

Thank you very much. We accept that the frequencies studied in this manuscript were lower than the lowest modal wave observed by Windmill et al., 2008. Other authors, according to Jacobs et al. 1999, found broad tuning form 3.4-3.74 kHz (Michelson et al., 1971) and 2-3.5 kHz (Halex et al., 1988). We settled on tuning previously measured for Group-III neurons in the same kind of preparation as in this manuscript, which was broadly around 3 kHz (Warren & Matheson, 2018).

This should be mentioned in the paper since the electrophysiology justification to use group III neurons is less convincing, given that Jacobs et al 1999 clearly point out that group III neurons are very variable and some of them are tuned much higher to 10 kHz, and others even higher to 20-30 kHz.

Looking at Fig. 7 from Jacobs et al., 1999, we indeed see that the four Group-III neurons recorded in this study are broadly tuned to 3-4 kHz. Often these tuning curves have threshold dips at higher frequencies at least 20 dB higher. We settled on the most sensitive frequency that we previously measured, and which also overlaps the most sensitive frequencies from several other studies.

Weaknesses:

Specifically, it is understandable that the authors decided to use excised ears for the light microscopy, where Müller's organ would not be accessible in situ. However, it is very likely that excision will change the system's mechanics, especially since any tension or support to Müller's organ will be ablated.

We completely understand this criticism. We have now added descriptions in the methodology and introduction (as detailed previously). In short, the tympanum was left intact suspended on the cuticle. Müller’s organ retains all (measured) physiological properties: frequency tuning, amplitude coding and electrophysiological function. To further investigate whether this excised preparation is a representative of the in vivo conditions, we plan to measure tympanal mechanics, such as the travelling wave, as part of the revisions.

OCT enables in vivo measurements in fully undissected systems (Mhatre et al, Biorxiv, 2021) or in systems with minimal dissection where the mechanics have not been compromised (Vavakou et al, 2021). The choice to entirely dissect out the membrane is difficult to understand here.

The pioneering OCT works by Mhatre et al, Biorxiv, 2021 and Vavakou et al, 2021 set the new standard of in vivo measurements in the field. We also totally agree with Reviewer#1’s view that OCT is best performed on in vivo Müller’s organ and we tried OCT imaging of Müller’s organ for several months in vivo. Although the OCT penetrates the tympanum the OCT beam does not penetrate the tracheal air sac that surrounds Müller’s organ and therefore OCT cannot be used in vivo. Please also see previous comment with regards to the intact physiological operation of Muller’s organ in the ex vivo preparation.

My main concern with this paper, however, is the use of light microscopy very close to the Nyquist limit to study scolopidial motion, and the fact that the OCT data contradict and do not match the light microscopy data. The light microscopy data is collected at ~8 kHz, and hence the Nyquist limit is ~4 kHz. It is possible to measure frequencies reliably this close to the limit, but the amplitude of motion is quite likely to be underestimated, given that the technique only provides 2 sample points per cycle at 4 kHz and approximately 2.66 sample points at 3 kHz. At that temporal resolution, the samples are much more likely to miss the peak of the wave than not, and therefore, amplitudes will be mis-estimated. A much more reasonable sample rate for amplitude estimation is generally about 10 samples per cycle. I do not believe the data from the microscopy is reliable for what the authors wish to use them for.

We understand your concern that the study of sound-evoked motion of the scolopidium using light microscopy was done near the Nyquist limit (with our average sampling rate at 8.6 ± 0.3 kHz and the Nyquist limit at 4.3 kHz). We also agree with your comment that amplitude of the motion could be underestimated at frequencies closer to the limit. However, we find that this systematic error does not change the key observation from our direct light microscopy observation that axial stretch of the scolopidium occurs around 3 kHz.

To address this concern, we plan to study the scolopidial motion within Group 1 auditory neurons, which are tuned to lower frequencies (0.5-1.5 kHz). This new set of data will allow us to obtain more data points per cycle (up to ~8.6 data points at 1 kHz). We will consider adding this result into the revised Fig. 4 or its extended data.

Regarding increasing the sampling rate, we did try to achieve higher sampling rate (> 10 kHz), however, there is a technical limitation of our camera and a trade-off between other key parameters, such as the size of the region of interest (ROI) and magnification. To increase the sampling rate, we will have to reduce the magnification or the ROI and in turn lose the spatial resolution required for quantification of the scolopidial motion or the ROI does not cover the whole scolopidial motion. The sampling rate at 8.6 ± 0.3 kHz was the best we could achieve.

Using the light microscopy data, the authors claim that the strains experienced by the group III scolopidia at 3 kHz are greater along the AP axis than the ML axis (Figure 4). However, this is contradicted by the OCT data, which show very low strain along the AP axis (black traces) at and around 3 kHz (Figure 3c and extended data Figure 2f) and show some movement along the ML axis (red traces, same figures). The phase at low amplitudes of motion cannot be considered very reliable either, and hence phase variations at these frequencies in the OCT cannot be considered reliable indicators of AP motion; hence, I'm unclear whether the vector difference in the OCT is a reliable indicator of movement.

This is our fault for not clearly explaining the orientation of the light microscopy measurement, which then leads to the reviewer’s concern about contradiction between OCT and light microscopy. Our OCT measurements was done along the Antero-Posterior (AP) and Mesio-Lateral axes (ML), while the axial stretch of the scolopidium occurs along the Dorso-Ventral (DV) axis. We recognise that the anatomical references in this manuscript can be confusing, and we tried to show the orientation of the scolopidium relative to Müller’s organ in Fig. 3b. To further clarify the orientation of our observations, we will add anatomical references in Fig. 4a and Fig. 5a. in the revised manuscript.

As stated in our result section (Line 165-167)

“Notably, we could not resolve the Group-III scolopidia along the ventro-dorsal axis—which runs parallel to the dendrite—as the OCT beam was obstructed by either the cuticle or the elevated process”

We did try to perform OCT measurement along the VD axis, but we could not resolve the scolopidial region along the scolopidial or ciliary axes because the OCT beam could not go through the thick cuticle at the edge of the tympanic membrane and the elevated process. For this reason, it is impossible for us to find an agreement or rule out any contradiction between the OCT and light microscopy since they are measuring motion along different axes. We plan to address this accessibility issue in a separate work using OCT measurements in combination with mirrors.

The OCT data are significantly more reliable as they are acquired at an appropriate sampling rate of 90 kHz. The authors do not mention what microphone they use to monitor or calibrate their sound field and phase measurements in OCT, but I presume this was done since it is the norm.

We use a condenser microphone (MK301, Microtech) and measuring amplifier (type 2610, Brüle & Kjær) for calibration. The calibration microphone was also calibrated beforehand using  a sound calibrator type 4231 from B&K.

Thus, the OCT data show that the movement within the Müller's organ is complex, probably traces an ellipse at some frequencies as observed in bushcrickets (Vavkou et al, 2021) and also thought to be the case in tree crickets based on the known attachment points of the tympanal organ (Mhatre et al, 2021). The OCT data shows relatively low AP motion at frequencies near 3 kHz, and higher ML motion, which contradicts the less reliable light microscopy data. Given that the locust membrane shows peaks in motion at ~4.5 kHz, ~11 kHz, and also at ~20 kHz (Windmill et al, 2008), I am surprised that the authors limited their OCT experiments and analyses to 5 kHz.

We found that immediately above 5 kHz the displacements reduced to undetectable magnitudes. We accept that there may be other modes of vibration at higher frequencies >10 kHz (based on Jacobs et al., 1999) that we could have detected with OCT. However, we focused our analysis on Group-III neurons at the best frequency and frequencies that we could cross-compere between our high-speed imaging system and OCT.

In summary for this section, I am not convinced of the conclusion drawn by the authors that group III scolopidia receive significantly higher stimulation along the AP axis in their native configuration, if indeed they were studied in the appropriate force regime (altered due to excision).

Again, we accept our faults for not clearly displaying the anatomical references of the scolopidial and ciliary axes in Fig. 4 and Fig. 5. We also did not clearly describe in detail that our ex vivo preparation largely retains its physiological properties. We will address the errors of our measurement near Nyquist and provide additional information from Group 1 scolopidia where we could achieve higher data points per cycle.

In the scolopidial patch clamp data, the authors study transduction currents in response to steady state stimulation along the AP axis and the ML axis. The responses to steady state and periodic forces may well be different, and the authors do not offer us a way to clearly relate the two and therefore, to interpret the data.

We will revise the Fig. 5a to clarify that the push-pull were done along the Dorso-Ventral (DV) axis and the push-pull were done along the Antero-Posterior (AP) axis. We do agree that steady-state and periodic forces may well be very different. However, valuable insight can be gained from mechanical systems when displaced outside of their normal physiological frequency (e.g. the transformative work on vertebrate hair bundle mechanics, Howard & Hudspeth, 1988). For the same reason, we believe artificial stimulation of the scolopidium gives us new and crucial information to understand scolopidial mechanics. Our main finding that stretch is the dominant stimulus should still, or at least provide strong support, that stretch is the dominant stimulus in periodical motion.

In addition, both stimulation types, along the AP axis and the ML, elicit clear transduction responses. Stimulation along the AP axis might be slightly higher, but there is over 40% variation around the mean in one case (pull: 26.22 {plus minus} 10.99 pA) and close to 80% variation in the other (push: 10.96 {plus minus} 8.59 pA). These data are indeed from a very high displacement range (2000 nm), which is very high compared to the native displacement levels, which are in the 1-10 nm range.

In this experiment, we wished to establish the upper limits (and plateau region) of displacement-transduction current response. However, even at 2000 nm we still did not see a plateau. Therefore, we believe that the strain on the scolopidium is still in the operating range even though our displacement is not. This discrepancy can be explained because the base of the scolopidium is not fixed. Therefore, the displacement imposed in our experiment is not equivalent to the strain on the cilium but a combination of pulling and stretching along the length of the dendrite. The force, however, remains along that particular axis, supporting our main finding.

Another important consideration is that the cilium is surrounded by the scolopale wall. It is assumed that the scolopale wall is far stiffer than the ciliary and will therefore limit the amount of ciliary strain.

The factor change from sample to sample is not reported and is small even overall. The statistical analyses of these data are not clearly reported, and I don't see the results of the overall ANOVA in the results section.

We reported the statistical analyses in the Fig. 5 Source Data. We will now add tables displaying these statistics in the supplementary text of the revised manuscript.

I also find the dip in the reported transduction currents between 10 and 100 nm quite odd (Figure 5 j-m) and would like to know what the authors' interpretation of this behaviour is. It seems to me that those currents increase continuously linearly after ~50-100 nm and that the data below that range are in the noise. Thus, the transduction currents observed at the relevant displacement range (1-10 nm) may not actually be reliable. How were these small displacements achieved, and how closely were the actual levels monitored? Is it possible to reliably deliver 1-10 nm displacements using a micromanipulator?

One interpretation is that the cilium has both sensitive and insensitive mechanically gated ion channels. A finding that is also supported by Effertz et al., 2012. We will add a sentence in the discussion highlighting this interpretation. We will also provide our calibration of displacement vs voltage delivered to the piezo in the Supplementary Text.

What is clear, despite the difficulty in interpreting this data, is that both AP and ML stimulation evoke transduction currents, and their relative differences are small. Additionally, in Müller's organ itself, in the excised organ, the scolopidia are stimulated along both axes. Thus, in my opinion, it is not possible to say that axial stretch along the cilium is 'the key mechanical input that activates mechano-electrical transduction'.

We confirm that the scolopidia are displaced along both. We also note that displacements of the scolopidium limited to the up-down axis will also produce a strain on the scolopidium along the push-pull axis. However, we tried to disentangle this complex motion by limiting the displacements to one axis during recordings of the transduction current. We found that displacement along the scolopidial axis generated the largest transduction currents. Even though there is large variation our statistical analysis confirmed a significant difference as stated in the result section (Line 283 – 286)

“Additionally, the transduction current evoked by pull from the resting position was larger than displacement upward, 12.17 ± 5.37 pA (N = 11, n = 11) (Tukey's procedure, p = 1.75e-03, t = -3.83) or downward 7.28 ± 9.76 pA (N = 11, n = 11) (Tukey's procedure, p = 5.10e-06, t = -4.53).”

The reason for large variation is that the discrete depolarisations (random depolarisations of unknown function and a common feature of chordotonal neurons so far recorded) have a similar magnitude to the transduction current produced by the step displacements. We will highlight these discrete depolarisations in Figure 4d and mention them in the results.

Reviewer #2 (Public review):

Summary of strengths and weaknesses:

Using several techniques-FIB-SEM, OCT, high-speed light microscopy, and electrophysiology-Chaiyasitdhi et al. provide evidence that chordotonal receptors in the locust ear (Müller's organ) sense the stretch of the scolapale cell, primarily of its cilium. Careful measurements certainly show cell stretch, albeit with some inconsistencies regarding best frequencies and amplitudes.

Thank you very much for acknowledging the strength of our study. Regarding the inconsistencies between best frequencies and amplitude, we believe that this concern largely arises from our faults for not clearly displaying the anatomical references of the scolopidial and ciliary axes in Fig. 4 and Fig. 5. As previously addressed in our response to Reviewer#1, we will add the anatomical references and revised the text to clarify the orientation of our measurements.

The weakest argument concerns the electrophysiological recordings, because the authors do not show directly that the stimulus stretches the cells. If this latter point can be clarified, then our confidence that ciliary stretch is the proximal stimulus for mechanotransduction will be increased.

We agree that the displacement is not solely stretching the scolopidium. However, the force is still constrained and acting along the push-pull axis. Due to this reason, we overestimate the displacement required to open the MET channels but stand by our conclusion that stretch is the dominant stimulus. For future work, we wish to devise a technique to mechanically clamp the base of the scolopidium and measure the more physiological relevant current-strain relationship.

This conclusion will not come as a surprise for workers in the field, as the chordotonal organ is known as a stretch-receptor organ (e.g., Wikipedia). But it is a useful contribution to the field and allows the authors to suggest transduction mechanisms whereby ciliary stretch is transduced into channel opening.

One of the goals of this manuscript is to highlight the lack of direct evidence for stretch-sensitivity of chordotonal organs, as this is assumed from their structure. More importantly the acceptance of chordotonal organs, as being stretch sensitive does not address the mechanism of how organs work. For instance, one candidate for the MET channel, NompC, is shown to be sensitive to compression (Wang et al., 2021). We find that a preconceived concept of “stretch-sensitive” mechanism, without an appreciation of scolopidium mechanics, cannot explain how NompC can be opened in chordotonal organs.

P. .E. Howse wrote in his work on ‘The Fine Structure and Functional Organisation of Chordotonal Organs’ in 1968 (Symp. Zool. Soc. Lon.) No. 23

“There is, however, a common tendency to refer to chordotonal organs in which scolopidia are contained in a connective tissue strand as “stretch receptor”. This is unfortunate in two senses, for firstly the implied function may not have been proved and secondly even if the organ responds to stretch the scolopidia may not.” then he proceeded to cite a pioneering work in the chordotonal organs of the hermit crab by R.C. Taylor (Comp. Biochem. Physiol. 1966) showing that the scolopidia may experience flexing when the connective strand are stretched.

This work represents the first efforts to investigate the problematic assumption of stretch-sensitivity of scolopidia since it was first highlighted 57 years ago.

Reviewer #3 (Public review):

Summary:

The paper 'A stretching mechanism evokes mechano-electrical transduction in auditory chordotonal neurons' by Chaiyasitdhi et al. presents a study that aims to address the mechanical model for scolopidia in Schistocerca gregaria Müller's organ, the basic mechanosensory units in insect chordotonal organs. The authors combine high-resolution ultrastructural analysis (FIB-SEM), sound-evoked motion tracking (OCT and high-speed light microscopy), and electrophysiological recordings of transduction currents during direct mechanical stimulation of individual scolopidia. They conclude that axial stretching along the ciliary axis is an adequate mechanical stimulus for activating mechanotransduction channels.

Strengths/Highlights:

(1) The 3D FIB-SEM reconstruction provides high resolution of scolopidial architecture, including the newly described "scolopale lid" and the full extent of the cilium.

(2) High-speed microscopy clearly demonstrates axial stretch as the dominant motion component in the auditory receptors, which confirms a long-standing question of what the actual motion of a stretch receptor is upon auditory stimulation.

(3) Patch-clamp recordings directly link mechanical stretch to transduction currents, a major advance over previous indirect models.

Weaknesses/Limitations:

(1) The text is conceptually unclear or written in an unclear manner in some places, for example, when using the proposed model to explain the sensitivity of Nanchung-Inactive in the discussion.

We will rephrase and make clearer the context of our findings for Nanchung-Inactive mechanism of MET in the introduction and the discussion. We will also refine and simplify unclear text overall.

(2) The proposed mechanistic models (direct-stretch, stretch-compression, stretch-deformation, stretch-tilt) are compelling but remain speculative without direct molecular or biophysical validation. For example, examining whether the organ is pre-stretched and identifying the mechanical components of cells (tissues), such as the extracellular matrix and cytoskeleton, would help establish the mechanical model and strengthen the conclusion.

We agree with the speculative nature of our four proposed hypotheses. We have, however, narrowed down from at least ten previous hypotheses (Field and Matheson, 1998). These hypotheses will enable us, and hopefully the field, to test them and more rapidly advance our understanding of how scolopidia work. We will add a section in the discussion as to the best way to experimentally test these four hypotheses (e.g pushing directly onto the cap should elicit sensitive responses for the cap-compression hypothesis).

(3) To some extent, the weaknesses of the paper are part of its strengths and vice versa. For example, the direct push/pull and up/down stimulations are a great experimental advance to approach an answer to the question of how the underlying cellular components are deformed and how the underlying ion channels are forced. However, as the authors clearly state, neither of their stimulations can limit all forces to only one direction, and both orthogonal forces evoke responses in the neurons. The question of which of the two orthogonal forces 'causes' the response cannot be answered with these experiments and has not been answered by this manuscript. But the study has brought the field a considerable step closer to answering the question. The answer, however, might be that both longitudinal ('stretch') and perpendicular ('compression') forces act together to open the ion channels and that both dendritic extension via stretch and bending can provide forces for ion channel gating.

Thank you very much for your acknowledgement of our experimental advances. We agree that this study cannot identify and localise the forces on the cilium as it is enclosed in the scolopidial unit. As previously explained, we plan to address this question in our next work by improving and expanding our experimental techniques, including modelling, to study the scolopidial mechanics based on our experiments using patch-clamp recording in combination with individual and direct manipulation the scolopidium.

The current paper has identified major components (longitudinal stretch components) for the neurons they analysed, but these will surely have been chosen according to their accessibility, and as such, the variety of mechanical responses in Müller's organ might be greater. In light of these considerations, the authors might acknowledge such uncertainties more clearly in their paper.

Our high-speed and OCT imaging confirms complex multi-dimensional displacements (and presumably forces) acting on the scolopidium. We agree that our mechanical stimulation cannot recapitulate such complex motions. But for future work we wish to extend our mechanical stimulation to three axis and also to pivot on the axis of the scolopidial cap.

The paper is an impressive methodological progress and breakthrough, but it simply does not "demonstrate that axial stretch along the cilium is the adequate stimulus or the key mechanical input that activates mechano-electrical transduction" as the authors write at the start of their discussion.

We rephrase to clarity that stretching along the “scolopidial axis”, not “along the ciliary axis” is the adequate stimulus. We cannot yet verify how this translates to forces acting on the cilium, hence the four speculative hypotheses. We will re-write the discussion to make clear that we are only interpretating the forces and displacements at the level of the cilium.

They do show that axial stretch dominates for the neurons they looked at, which is important information. The same applies to the end of the discussion: The authors write, "This relative motion within the organ then drives an axial stretch of the scolopidium, which in turn evokes the mechano-electrical transduction current." Reading the manuscript, the certainty and display of confidence are not substantiated by the data provided. But they are also not necessary. The study has paved the road to answer these questions. Instead, the authors are encouraged to make suggestions on how the remaining uncertainties could be removed (and what experiments or model might be used).

We will moderate our conclusion in the discussion, but we are confident that we have experimental repeats, and the statistical test, to support our conclusion that stretching of the scolopidium provides that largest transduction current responses (although not at the level of the cilium). As mentioned previously, we will include a section in the discussion for the best way to test the hypotheses arising from this work.

eLife Assessment

This study provides new and interesting findings that SCoR2 acts as a denitrosylase to control cardioprotective metabolic reprogramming and prevent injury following ischemia/reperfusion. The compelling evidence is supported by a novel multi-omics approach, but questions remain regarding the stability and human relevance of BDH1 as well as the sufficiency of SCoR2. Overall, the work will be of interest to cardiovascular researchers and provides valuable information to the field, though some mechanistic aspects require further clarification.

Reviewer #1 (Public review):

Summary:

This study shows a novel role for SCoR2 in regulating metabolic pathways in the heart to prevent injury following ischemia/reperfusion. It combines a new multi-omics method to determine SCoR2 mediated metabolic pathways in the heart. This paper would be of interest to cardiovascular researchers working on cardioprotective strategies following ischemic injury in the heart.

Strengths:

(1) Use of SCoR2KO mice subjected to I/R injury.

(2) Identification of multiple metabolic pathways in the heart by a novel multi-omics approach.

Comments on revisions:

Authors have addressed all concerns raised in the previous round of review. Substantial modifications have been made in response to those concerns. There are no further comments.

Reviewer #2 (Public review):

Summary:

This manuscript addresses the gap in knowledge related to the cardiac function of the S-denitrosylase SNO-CoA Reductase 2 (SCoR2; product of the Akr1a1 gene). Genetic variants in SCoR2 have been linked to cardiovascular disease, yet its exact role in heart remains unclear. This paper demonstrates that mice deficient in SCoR2 show significant protection in a myocardial infarction (MI) model. SCoR2 influenced ketolytic energy production, antioxidant levels, and polyol balance through the S-nitrosylation of crucial metabolic regulators.

Strengths:

Addresses a well-defined gap in knowledge related to the cardiac function of SNO-CoA Reductase 2. Besides the in-depth case for this specific player, the manuscripts sheds more light on the links between S-nytrosylation and metabolic reprogramming in heart.

Rigorous proof of requirement through the combination of gene knockout and in vivo myocardial ischemia/reperfusion

Identification of precise Cys residue for SNO-modification of BDH1 as SCoR2 target in cardiac ketolysis

Weaknesses:

The experiments with BDH1 stability were performed in mutant 293 cells. Was there a difference in BDH1 stability in myocardial tissue or primary cardiomyocytes from SCoR2-null vs -WT mice? Same question extends to PKM2.

In the absence of tracing experiments, the cross-sectional changes in ketolysis, glycolysis or polyol intermediates presented in Figures 4 and 5 are suggestive at best. This needs to be stressed while describing and interpreting these results.

The findings from human samples with ischemic and non-ischemic cardiomyopathy do not seem immediately or linearly in line with each other and with the model proposed from the KO mice. While the correlation holds up in the non-ischemic cardiomyopathy (increased SNO-BDH1, SNO-PKM2 with decreased SCoR2 expression), how do the Authors explain the decreased SNO-BDH1 with preserved SCoR2 expression in ischemic cardiomyopathy? This seems counterintuitive as activation of ketolysis is a quite established myocardial response to the ischemic stress. It may help the overall message clarity to focus the human data part on only NICM patients.

(partially linked to the point above) an important proof that is lacking at present is the proof of sufficiency for SCoR2 in S-Nytrosylation of targets and cardiac remodeling. Does SCoR2 overexpression in heart or isolated cardiomyocytes reduce S-nitrosylation of BDH1 and other targets, undermining heart function at baseline or under stress?

Comments on revisions:

Some of my points have been addressed. However, the points related to 1) BDH1 stability effect in cardiomyocytes; 2) human relevance of SNO-BDH1; 3) SCoR2 sufficiency remain unclear. That said, this manuscript will provide useful information to the field as such.

Reviewer #3 (Public review):

Summary:

This manuscript demonstrates that mice lacking the denitrosylase enzyme SCoR2/AKR1A1 demonstrate a robust cardioprotection resulting from reprogramming of multiple metabolic pathways, revealing<br /> widespread, coordinated metabolic regulation by SCoR2.

Strengths:

The extensive experimental evidence provided the use of the knockout model

Weaknesses:

No direct evidence for the underlying mechanism.

The mouse model used is not a tissue-specific knock-out.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

This study shows a novel role for SCoR2 in regulating metabolic pathways in the heart to prevent injury following ischemia/reperfusion. It combines a new multi-omics method to determine SCoR2 mediated metabolic pathways in the heart. This paper would be of interest to cardiovascular researchers working on cardioprotective strategies following ischemic injury in the heart. 

Strengths:

(1) Use of SCoR2KO mice subjected to I/R injury. 

(2) Identification of multiple metabolic pathways in the heart by a novel multi-omics approach.

We thank the Reviewer for the positive review of our manuscript.

Weaknesses:

(1) Use of a global SCoR2KO mice is a limitation since the effects in the heart can be a combination of global loss of SCoR2. 

(2) Lack of a cell type specific effect. 

We agree that global KOs limit the cell type-specific mechanistic conclusions that can be drawn. Global knockouts are nonetheless informative in their own right and serve to identify phenotypes worthy of further study.

Reviewer #2 (Public review):

Summary: 

This manuscript addresses the gap in knowledge related to the cardiac function of the S-denitrosylase SNOCoA Reductase 2 (SCoR2; product of the Akr1a1 gene). Genetic variants in SCoR2 have been linked to cardiovascular disease, yet their exact role in the heart remains unclear. This paper demonstrates that mice deficient in SCoR2 show significant protection in a myocardial infarction (MI) model. SCoR2 influenced ketolytic energy production, antioxidant levels, and polyol balance through the S-nitrosylation of crucial metabolic regulators. 

Strengths: 

(1) Addresses a well-defined gap in knowledge related to the cardiac function of SNO-CoA Reductase 2. Besides the in-depth case for this specific player, the manuscript sheds more light on the links between Snitrosylation and metabolic reprogramming in the heart.

(2) Rigorous proof of requirement through the combination of gene knockout and in vivo myocardial ischemia/reperfusion. 

(3) Identification of precise Cys residue for SNO-modification of BDH1 as SCoR2 target in cardiac ketolysis 

We thank the Reviewer for their kind words.

Weaknesses: 

(1) The experiments with BDH1 stability were performed in mutant 293 cells. Was there a difference in BDH1 stability in myocardial tissue or primary cardiomyocytes from SCoR2-null vs -WT mice? The same question extends to PKM2. 

We have not assessed BDH1 stability directly in cardiomyocytes. However, S-nitrosylation increased BDH1 stability in HEK293 cells, and BDH1 expression was increased in (injured) hearts of SCoR2KO mice, together with increased SNO-BDH1. 

For PKM2, there is a wealth of published evidence from us and others that S-nitrosylation does not regulate protein stability but rather inhibits tetramerization required for full activity.  

(2) In the absence of tracing experiments, the cross-sectional changes in ketolysis, glycolysis, or polyol intermediates presented in Figures 4 and 5 are suggestive at best. This needs to be stressed while describing and interpreting these results. 

We now acknowledge this limitation in the ‘Limitations’ section of the manuscript and in edits made to the text. 

(3) The findings from human samples with ischemic and non-ischemic cardiomyopathy do not seem immediately or linearly in line with each other and with the model proposed from the KO mice. While the correlation holds up in the non-ischemic cardiomyopathy (increased SNO-BDH1, SNO-PKM2 with decreased SCoR2 expression), how do the authors explain the decreased SNO-BDH1 with preserved SCoR2 expression in ischemic cardiomyopathy? This seems counterintuitive as activation of ketolysis is a quite established myocardial response to ischemic stress. It may help the overall message clarity to focus the human data part on only NICM patients. 

We find it interesting and important that SNO-BDH1 is readily detected in human heart tissue and its level is correlated to disease state. Our findings suggest conservation of this mechanism in human heart failure. However, we caution against drawing further conclusions related to NICM or ICM. Our animal model (based on a single time point) cannot faithfully recapitulate patients with chronic heart disease or differences between NICM and ICM. 

(4) This is partially linked to the point above. An important proof that is lacking at present is the proof of sufficiency for SCoR2 in S-nitrosylation of targets and cardiac remodeling. Does SCoR2 overexpression in the heart or isolated cardiomyocytes reduce S-nitrosylation of BDH1 and other targets, undermining heart function at baseline or under stress? 

The Reviewer proposes to test the effect of SCoR2 overexpression on cardioprotection. This is an interesting experiment for future study with the following caveats. First, it presupposes that native expression of SCoR2 is insufficient to control basal steady state S-nitrosylation of SNO-BDH1 and SNO-PKM2 (this does not seem to be the case). Second, overexpressed SCoR2 may be mislocalized within cells or associated with unnatural targets. Thank you.

Reviewer #3 (Public review): 

Summary: 

This manuscript demonstrates that mice lacking the denitrosylase enzyme SCoR2/AKR1A1 demonstrate a robust cardioprotection resulting from reprogramming of multiple metabolic pathways, revealing widespread, coordinated metabolic regulation by SCoR2. 

Strengths: 

(1) The extensive experimental evidence. 

(2) The use of the knockout model. 

We thank the Reviewer for identifying strengths in our work.

Weaknesses: 

(1) The connection of direct evidence for the mechanism. 

We believe we have identified a novel mechanism for cardioprotection entailing coordinate reprogramming of multiple metabolic pathways and suggesting a widescale role for SCoR2 in metabolic regulation. This is the key message we convey. While genetic dissection of individual pathways may be worthwhile, these investigations will have their own limitations. 

(2) The mouse model used is not tissue-specific. 

Please see our response to Reviewer 1, above. 

Reviewer #1 (Recommendations for the authors):

In the study, titled "The denitrosylase SCoR2 controls cardioprotective metabolic reprogramming", Grimmett ZW et al., describe a role for SNO-CoA Reductase 2 (SCoR2) in promoting cardioprotection via metabolic reprogramming in the heart after I/R injury. Authors show that loss SCoR2 coordinates multiple metabolic pathways to limit infarct size. Overall, the hypothesis is interesting, however there are some limitations as described below: 

(1) It is unclear whether SCoR2 mice are global or cardiomyocyte specific. 

We apologize for any confusion. These are global SCoR2^-/- mice. This is now stated in the Results when first identifying the strain, as well as in the Methods.  

(2) Can the authors clarify how divergent metabolic pathways such as Ketone oxidation, glycolysis, PPP and polyol metabolism work downstream of SCoR2 to impact cardioprotection in mice with I/R. 

The metabolic pathways of ketone oxidation, glycolysis, PPP and polyols appear to converge to support ischemic cardioprotection in SCoR2^-/- mice, as depicted in the model shown in Fig. 5L. Subsequent to SNO-PKM2 blockade of flux through glycolysis (detailed in this manuscript and in Zhou et al, 2019, PMID: 30487609, as well as by others), substrates of ketolysis and glycolysis are funneled into the PPP, producing the antioxidant NADPH and energy precursor phosphocreatine, which are well-known to be cardioprotective. This occurs more readily in SCoR2^-/- mice due to elevated SNO-BDH1 (detailed in this manuscript). 

Polyols, thought to be products of the PPP carbohydrate intermediates arabinose, ribulose, xylulose (among others), have recently been shown to be harmful to cardiovascular health in humans. These polyols are uniformly downregulated in SCoR2^-/- mice. We suggest this is likely the result of S-nitrosylation of SCoR2-substrate enzymes that form polyols (SCoR2/Akr1a1 is unable to directly reduce carbohydrates to their corresponding polyols). Regulation of endogenous polyol production in humans is a new concept and the mechanisms whereby these compounds increase risk of cardiac events are a subject of active investigation. This is detailed in the final paragraph of both the Results and Discussion sections, and in Fig. 5L. 

(3) The only functional outcome of SCoR2 loss in echocardiography and measurements for apoptosis. However, it would be important to determine whether the cardioprotective effect persists. It seems cardiac function was recorded 24hours post injury and whether the benefit remains till later time point such as 2 or 4 weeks is not shown. Without this time point, loss of SCoR2 only leads to an acute increment in function. 

Loss of SCoR2 reduced post-MI mortality at 4 hr; cardiac functional changes (plus troponin, LDH, and apoptosis) were studied in surviving animals at 24 hr post-MI. Cardiac response to acute injury and to chronic injury (weeks post-MI) are not the same metabolically. This is well elucidated in the literature and exemplified by the role of PKM2, which is protective in the chronic response to MI (28 days post-MI; PMID: 32078387), but implicated in injury at shorter timepoints post-MI (PMID: 33288902, 28964797). All that said, functional changes at 2-4 weeks will be important to determine in the future, as the Reviewer indicates. 

Reviewer #2 (Recommendations for the authors): 

(1) The last paragraph of the Results section should be divided into the statement related to Table S2 in the Results section, and the rest of the paragraph should be put somewhere in the Discussion. 

Thank you for this suggestion, which we have taken. 

(2) The number of mice alive/dead should be reported in the histogram in Figure 1G. 

Done.

(3) A concise Graphical Abstract will be useful to grasp the overall logic and message of the manuscript from the beginning. 

We thank you for this suggestion and have added a graphical abstract to the manuscript.

Reviewer #3 (Recommendations for the authors): 

I would suggest having more evidence on the effect of metabolic reprogramming on which cell type. The use of a global knockout is a major limitation, and probably some in vitro experiments with shRNA knockdown in endothelial cells and fibroblasts would provide more insights. 

The reviewer suggests one direction for future study. We identify a novel mechanism for cardioprotection entailing coordinate reprogramming of multiple metabolic pathways and suggesting a widescale role for SCoR2 in metabolic regulation. This is the message we wish to convey. The role of cardiomyocytes vs contributing cell types is a thoughtful direction for future study. Thank you. 

Editor's additional comment:

The editors wish to highlight a critical issue concerning the characterization of the SCoR2−/− mice employed in this study. 

In the Methods section (page 20), the manuscript states that "SCoR2+/− mice were made by Deltagen, Inc. as described previously (33)." However, reference 33 does not describe SCoR2−/− mice; instead, it refers to other genetically modified strains, including Akr1a1+/−, eNOS−/−, and PKM2−/− mice, with no mention of a SCoR2-targeted model. 

The editors fully acknowledge that the authors may be using the term "SCoR2" as a functional synonym for Akr1a1, based on its described role as a mammalian homologue of yeast SCoR. If this is the case, such equivalence should be explicitly stated in the manuscript to prevent potential confusion. Moreover, considering that the genetic deletion of Akr1a1 (i.e., SCoR2) underlies the key mechanistic findings presented, it is essential that the manuscript include a clear and comprehensive description of the generation and validation of the mouse model used. 

We therefore ask the authors to (1) clarify the nomenclature and relationship between "SCoR2" and Akr1a1, and (2) provide full details on the generation of the knockout mice, including the targeting strategy and the genotyping procedures. This information is necessary not only to ensure transparency and reproducibility but also to allow readers to fully appreciate the biological relevance of the findings.

Thank you for identifying this inconsistency. We have adjusted the manuscript text accordingly to clearly state that SCoR2 is a functional name for the product of the Akr1a1 gene and that these SCoR2^-/- mice are the same as Akr1a1^-/- mice described in Ref 33. We have augmented the Methods text to describe the generation and genotyping of these SCoR2/Akr1a1 knockout mice.

eLife Assessment

Using high-throughput small-molecule screening, this study discloses novel modulators of the mitochondrial transcription factor A (TFAM), a key regulator of mitochondrial function. Reviewers viewed the targeting of TFAM as innovative and the study's conclusions as potentially important (especially the effects on inflammation). However, the lack of evidence for a direct effect of the compounds on TFAM activity weakens the paper's key conclusion and renders the study incomplete.

Reviewer #1 (Public review):

Summary:

The authors identify small-molecule compounds modulating the stability of the mitochondrial transcription factor A (TFAM) using a high-throughput CETSA screen and subsequent secondary assays. The identified compounds increased the protein levels of TFAM without affecting its RNA levels and led to an increase in mtDNA levels. As a read-out for dose-dependent action of the identified compounds, the authors investigated cGAS-STING and ISG activation in cellular inflammation models in the presence or absence of their compounds. The addition of TFAM modulators led to a decrease in cGAS-STING/ISG activation and decreased mtDNA release. Furthermore, beneficial effects could be determined in models of mtDNA disease (rescue of ATP rates), sclerotic fibroblasts (decreased fibrosis), and regulatory T cells (decreased activation of effector T cells). The study thus proposes novel first-in-class regulators of TFAM as a therapeutic option in conditions of mitochondrial dysfunction.

Strengths:

The authors identified TFAM as a promising target in conditions of mitochondrial dysfunction, as it is a key regulator of mitochondrial function, serving both as a transcription and packaging factor of mtDNA. Importantly, TFAM is a key regulator of mtDNA copy number, and a moderate increase in TFAM/mtDNA levels has been shown to be beneficial in a number of pathological conditions. Furthermore, mtDNA release leading to activation of inflammatory responses has been linked to a variety of pathological conditions in the last decade. Thus, the identification of small molecule modulators of TFAM that have the potential to increase mtDNA copy number and decrease inflammatory signaling is of great importance. Furthermore, the authors highlight potential applications in the field of mitochondrial disease, fibrosis, and autoimmune disease.

Weaknesses:

The central weakness of the study is the fact that the authors propose compounds as modulators or even activators of TFAM without sufficiently proving a direct effect on TFAM itself. There are no data indicating a direct effect on TFAM activity (e.g., mtDNA transcription, replication, packaging), and it is not sufficiently ruled out that other proteins (e.g., LONP1) mediate the effect. Additionally, important information on the performed screen is not provided. Thus, the data presented is currently incomplete to support the described findings. Furthermore, the introduction and discussion are lacking key references.

Reviewer #2 (Public review):

Summary:

The present paper aims to identify small molecules that could possibly affect mitochondrial DNA (mtDNA) stability, limiting cytosolic mtDNA abundance and activation of interferon signaling. The authors developed a high-throughput screen incorporating HiBiT technology to identify possible target compounds affecting mitochondrial transcription factor A (TFAM) content, a compound known to impact mtDNA stability. Cells were subsequently exposed to target compounds to investigate the impact on TNFα-stimulated interferon signaling, a process activated by cytosolic mtDNA abundance. Compound 2, an analog of arylsulfonamide, was highlighted as a possible mitochondrial transcription factor A (TFAM)-activator, and emphasized as a small molecule that could stabilize mtDNA and prevent stress-induced interferon signaling.

Strengths:

Identifying compounds that positively affect mitochondrial biology has diverse implications. The combination of high-throughput screening and assay development to connect identified compounds with cellular interferon signalling events is a strength of the current approach, and the authors should be commended for identifying compounds that broadly impact interferon signalling. The authors have incorporated diverse measurements, including TFAM content, mtDNA content, interferon signaling, and ATP content, as well as verified the necessity of TFAM in mediating the beneficial effects of the emphasized small molecule (Compound 2).

Weaknesses:

(1) While the identified compound clearly works through TFAM, Compound 2 was identified as an arylsulfonamide, which would be expected to affect voltage-gated sodium channels (e.g. PMID: 31316182). Alterations in cellular sodium content and membrane polarization could affect metabolism to indirectly influence mtDNA and TFAM content. It remains unclear if this compound directly or indirectly affects TFAM content, especially as the authors have utilized various cancer cell lines, which could have aberrant sodium channels.

(2) TFAM is nuclear encoded - if this compound directly functions to 'activate TFAM', why/how would TFAM content increase independent of nuclear transcription?

(3) While a listed strength is the incorporation of diverse readouts, this is also a weakness, as there is a lack of consistency between approaches. For instance, data is not provided to show compound 2 increases TFAM or mtDNA content following TNFα stimulation, and extrapolating between cell lines may not be appropriate. The authors are encouraged to directly report TFAM and mtDNA for target compounds 2 and 15 to support their data reported in Figure 2. Ideally, the authors would also report for compound 1 as a control.

(4) While the authors indicate compound 11 displayed the strongest effect on ISRE activity, this appears not to be identified in Figure 1B as a compound affecting TFAM content? Can the authors identify various Compounds in Figure 1B to better highlight the relationship between compounds and TFAM content?

(5) The authors suggest Compound 2 increases cellular ATP - but they are encouraged to normalize luminescence to cellular protein and OXPHOS content to better interpret this data. Additionally, the authors are encouraged to report cellular ATP content following TNFα stimulation/stress (the key emphasis of the present data) and test compound 11, which the authors have implicated as a more sensitive compound.

The discussion is really a perspective, theorizing the diverse implications of small molecule activation of TFAM. The authors are encouraged to provide a balanced discussion, including a critical evaluation of their own work, including an acknowledgement that evidence is not provided that Compound 2 directly activates TFAM or decreases mtDNA cytosolic leakage.

eLife Assessment

This study presents a useful inventory of genes that are up- and down-regulated in the mouse small intestine (duodenum and ileum) during the first postnatal month; the data were collected and analyzed using solid and validated methodology and can be used as a starting point for additional validation of specific markers and for follow-up functional studies. Some aspects of the study were incomplete, with claims being only partially supported by the data, and it is suggested that additional validation be performed. The authors attempted to correlate gene expression changes with periods of high and low NEC susceptibility, but these correlations are speculative and not supported by functional follow-up studies. Discussion of gene expression changes with NEC susceptibility would be more appropriate to include in the Discussion section and to be tempered in the results section.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors aimed to clarify the transcriptional changes across murine postnatal small intestinal development (0 days to 1 month) in both the duodenum and ileum, a period that shows morphological similarity to 20-30 week old fetal humans. This is an especially critical stage in human intestinal development, as necrotizing enterocolitis (NEC) usually manifests during these stages.

Strengths:

The authors assessed numerous timepoints between 0 days and 1 month in the postnatal mouse duodenum and ileum using bulk RNA transcriptomics of bulk-isolated tissues. Cellular deconvolution, based on relative marker expression, was used to clarify immune cell proportions in the bulk RNA sequencing data. They confirmed some transcriptional targets found in vivo primarily in mouse via qrtPCR and immunohistochemistry, but also in human fetal tissues and isolated organoids, and are of decent quality.

Weaknesses:

The overall weakness of this study, as mentioned by the authors themselves, is that the bulk transcriptomic data generated for the study were isolated from non-fractionated bulk intestinal tissue. This makes it difficult to interpret much of this data regarding cellular fractions found across developmental time. It is difficult to rationalize the approach here, as even isolation protocols of epithelial-only or mesenchyme-only tissues for bulk RNA sequencing are well established. The authors address some of these concerns using cellular deconvolution for immune cell populations, which I think might be helpful if they expanded this analysis to other cell types (mesenchyme, endothelium, glia). However, I would assume that bulk isolations across developmental time are going to be influenced primarily by the bulk of tissue-type found at each time point - primarily epithelium. But this is also confirmed by the immune transcripts becoming more apparent later in their time series, as this system becomes more established during weaning. This study might also be strengthened by comparison with data that is publicly available for early fetal stage development in humans. Comparisons between the duodenum and ileum could be strengthened by what we already know from adult data, from both epithelial- and mesenchyme-isolated fractions. The rationale of using the postnatal mouse as a comparison to NEC is also a little unclear- perhaps some of the developmental processes are similar, however, the environments are completely different. For example, even in early postnatal mouse development, you would find microbial activity and milk.

Reviewer #2 (Public review):

Summary:

This work presents a valuable resource by generating a comprehensive bulk RNA sequencing catalogue of gene expression in the mouse duodenum and ileum during the first postnatal month. The central findings of this work are based on an analysis of this dataset. Specifically, the authors characterized molecular shifts that occur as the intestine matures from an immature to an adult-like state, investigating both temporal changes and regional differences between the proximal and distal small intestine. A key objective was to identify gene expression patterns relevant to understanding the region-specific susceptibility and resistance to necrotizing enterocolitis (NEC) observed in humans during the postnatal period. They also sought to validate key findings through complementary methods and to provide comparative context with human intestinal samples. This study will provide a solid reference dataset for the community of researchers studying postnatal gastrointestinal development and diseases that arise during these stages. However, the study lacks functional validation of the interpretations.

Strengths:

(1) The inclusion of numerous time points (day 0 through 4 weeks) and comparative analyses throughout the first postnatal month.

(2) Validation of key interpretations of RNA-seq data by other methods.

(3) Linking mouse postnatal development to human premature infant development, enhancing its clinical relevance, particularly for NEC research. The inclusion of human intestinal biopsy and organoid data for comparison further strengthens this link.

(4) The investigation covers a wide array of developmental gene categories with known significance, including epithelial differentiation markers (e.g., Vil1, Muc2, Lyz1), intestinal stem cell markers (e.g., Lgr5, Olfm4, Ascl2), mesenchymal markers (e.g., Pdgfra, Vim), Wnt signaling components (e.g., Wnt3, Wnt5a, Ctnnb1), and various immune genes (e.g., defensins, T cell, B cell, ILC, macrophage markers).

Weaknesses:

(1) The primary limitation is that there is no functional validation. The study primarily focuses on the interpretation of RNA expression. This is a common limitation of transcriptomic "atlas" studies, but the functional and mechanistic relevance of these interpretations remains to be determined.

(2) The data are derived from bulk RNA-Seq of full-thickness intestinal tissue. While this approach helps capture rare cell types and both epithelial and mesenchymal components simultaneously, it does not provide cell-type-specific gene expression profiles, which might obscure important nuances. Future investigations using single-cell sequencing would be a logical follow-up.

(3) The day 4 samples were omitted due to quality issues, which might have led to missing some dynamic changes, especially given that some ISC genes show dynamic changes around day 6.

Reviewer #3 (Public review):

Summary:

This study uses bulk mRNA sequencing to profile transcriptional changes in intestinal cells during the early postnatal period in mice - a developmental window that has received relatively little attention despite its importance. This developmental stage is particularly significant because it parallels late gestation in humans, a time when premature infants are highly vulnerable to necrotizing enterocolitis (NEC). By sampling closely spaced timepoints from birth through postnatal week four, the authors generate a resource that helps define transcriptional trajectories during this phase. Although the primary focus is on murine tissue, the authors also present limited data from human fetal intestinal biopsy samples and organoids. In addition, they discuss potential links between observed gene expression changes and factors that may contribute to NEC.

Strengths:

The close temporal sampling in mice offers a detailed view of dynamic transcriptional changes across the first four weeks after birth. The authors leverage these close timepoints to perform hierarchical clustering to define relationships between developmental stages. This is a useful approach, as it highlights when transcriptional states shift most dramatically and allows for functional predictions about classes of genes that vary over time. This high-level analysis provides an effective entry point into the dataset and will be useful for future investigations. The inclusion of human fetal intestinal samples, although limited, is especially notable given the scarcity of data from late fetal timepoints. The authors are generally careful in their presentation of results, acknowledging the limitations of their approach and avoiding over-interpretation. As they note, this dataset is intended as a foundation for their lab and others, with secondary approaches required to more fully explore the biological questions raised.

Weaknesses:

One limitation of the study is the use of bulk mRNA sequencing to draw conclusions about individual cell types. It has been documented that a few genes are exclusively expressed in single cell types. For instance, markers such as Lgr5 and Olfm4 are enriched in intestinal stem cells (ISCs), but they are also expressed at lower levels in other lineages and in differentiating cells. Using these markers as proxies for specific cell populations lowers confidence in the conclusions, particularly without complementary validation to confirm cell type-specific dynamics.

Validation of the sequencing data was itself limited, relying primarily on qPCR, which measures expression at the same modality rather than providing orthogonal support. It is unclear how the authors selected the subset of genes for validation; many key genes highlighted in the sequencing data were not assessed. Moreover, the regional differences reported in Lgr5, Olfm4, and Ascl2, appearing much higher in proximal samples than in distal ones, were not recapitulated by qPCR validation of Olfm4, and this discrepancy was not addressed. Resolving such inconsistencies will be important for interpreting the dataset.

The basis for linking particular gene sets to NEC susceptibility rests largely on their spatial restriction to the distal intestine and their temporal regulation between early (day 0-14) and later (weeks 3-4) developmental stages. While this is a reasonable approach for generating hypotheses, the correlations have limited interpretive power without experimental validation, which is not provided here. Many factors beyond NEC may drive regional and temporal differences in intestinal development.

Finally, the contribution of human fetal biopsy samples is minimal. The central figure presenting these data (Figure 4A) shows immunofluorescence for LGR5, a single stem cell marker. The staining at day 35 is not convincing, and the conclusions that can be drawn are limited to confirming the localization of LGR5-positive cells to crypts as early as 26 weeks.

eLife Assessment

This valuable study examined the roles of the posterior parietal cortex in rats performing an auditory change-detection decision task. It provided solid evidence for two subpopulations with opposing modulation patterns during decision formation and for a correspondence between neural and behavioral measures of the short timescale used for evidence evaluation.

Joint Public Review:

In this study, the authors sought to characterize the relationship between the timescales of evidence integration in an auditory change detection task and neural activity dynamics in the rat posterior parietal cortex (PPC), an area that has been implicated in the accumulation of sensory evidence. Using the state-of-the-art Neuropixel recording techniques, they identified two subpopulations of neurons whose firing rates were positively and negatively modulated by auditory clicks. The timescale of click-related response was similar to the behaviorally measured timescale for evidence evaluation. The click-related response of positively modulated neurons also depended on when the clicks were presented, which the authors hypothesized to reflect a time-dependent gain change to implement an urgency signal. Using muscimol injections to inactivate the PPC, they showed that PPC inactivation affected the rats' choices and reaction times.

There are several strengths of this study, including:

(1) Compelling evidence for short temporal integration in behavioral and neural data for this task.

(2) Well-executed and interpretable comparisons of psychophysical reverse correlation with single-trial, click-triggered neuronal analyses to relate behavior and neural activity.

(3) Inactivation experiments to test for causality.

(4) Characterization of neural subpopulations that allows for complex relationships between a brain region and behavior.

(5) Experimental evidence for an interesting way to use sensory gain change to implement urgency signals.

There are also some concerns, including:

(1) The work could be better contextualized. From a normative Bayesian perspective, the observed adaptation of timescales and gain aligns closely with optimal strategies for change detection in noisy streams: placing greater weight on recent sensory samples and lowering evidence requirements as decision urgency grows. However, the manuscript could go further in explicitly connecting the experimental findings to normative models, such as leaky accumulator or dynamic belief-updating frameworks. This would strengthen the broader impact of the work by making clear how the observed PPC dynamics instantiate computationally optimal strategies.

(2) It is unclear how the rats are performing the task, both in terms of the quality of performance (they only show hit rates, but the rats also seem to have high false alarm rates), and in terms of the underlying strategy that they seem to be using.

(3) A major conceptual weakness lies in the claim that PPC "dynamically modulates evidence evaluation in a time-adaptive manner to suit the behavioral demands of a free-response change detection task." To support this claim, it would require direct comparison of neural activity between two task demands, either in two tasks or in one task with manipulations that promote the adoption of different timescales.

(4) Some analyses of neural data are lacking or seem incomplete, without considering alternative interpretations.

(5) The muscimol inactivation results did not provide a clear interpretation about the link between PPC activity and decision performance.

eLife Assessment

This study presents valuable findings regardingg a rare mode of reproduction called hybridogenesis in a species pair of frogs. While parts of the study provide solid support for the claim of hybridogenesis, other parts are incomplete with certain claims being only partially supported, as alternative modes of reproduction cannot be fully ruled out.

Reviewer #1 (Public review):

Summary:

(1) Introduction Hybridogenesis involves one genome being clonally transmitted while the other is replaced by backcrossing. It results in high heterozygosity and balanced ancestry proportions in hybrids. Distinguishing it from other hybrid systems requires a combination of nuclear, mitochondrial, and population-genetic evidence. Hybridogenesis has been identified in only a few taxa (e.g., some fish, frogs, and stick insects), but no new cases have been reported in over a decade. Advancements in high-throughput sequencing now allow for the detection of high individual heterozygosity, which can indicate hybridization, but it is difficult to distinguish hybridogenesis from other similar asexual systems based solely on genome-wide data. To differentiate these systems, researchers look at several key indicators: Presence of pure-species offspring from hybrids (possible only in hybridogenesis); sex ratio (male presence in hybridogenetic systems); nuclear and mitochondrial haplotype sharing with co-distributed parental species; geographic distribution patterns, especially the lack of both parental species in hybrid populations.

(2) What the authors were trying to achieve The paper studies Quasipaa Frogs. Q. robertingeri (narrowly endemic) and Q. boulengeri (widespread), which are morphologically similar and found sympatrically in parts of China. Preliminary RAD-seq data revealed bimodal heterozygosity in Q. boulengeri samples. Some individuals had extremely high heterozygosity, consistent across loci and suggestive of F1 hybrids. These high-heterozygosity individuals had one haplotype from each species. The study investigates the high heterozygosity observed in Quasipaa frogs, particularly in individuals morphologically resembling Q. boulengeri but genetically appearing to be F1 hybrids with Q. robertingeri. The goal is to determine whether these patterns are consistent with hybridogenesis, rather than other atypical reproductive modes. The authors also suggest the hypothesis that hybridogenesis could enable range expansion of an endemic species through hybridization with a widespread relative.

(3) Methods A total of 107 individuals from 53 localities were collected for the study. This sample included 58 sexed adults-27 males and 31 females-as well as a majority of tadpoles. Of these individuals, 31 had previously determined karyotypes. DNA was extracted and sequenced. Individual heterozygosity and ancestry were estimated using bioinformatics tools. F1 hybrids were compared to one of the parental species to examine patterns of fixed heterozygous loci. Mitochondrial DNA was also extracted from sequencing data, and phylogenetic trees were constructed

(4) Results Two groups of individuals were detected based on heterozygosity: one group exhibited high heterozygosity and consisted of F1 hybrids, while the other group showed low heterozygosity, representing pure-species types. The F1 hybrids demonstrated approximately equal ancestry from Q. robertingeri and Q. boulengeri, consistently maintaining a high proportion of heterozygous loci at around 16.7%. In contrast, pure individuals had much lower heterozygosity, approximately 2.9%. F1 hybrids were found across 21 different sites, including both male and female individuals. The presence of numerous fixed heterozygous loci in F1 hybrids confirmed their hybrid origin, and these loci were absent in pure Q. boulengeri samples. F1 individuals typically carried one haplotype from each parental species. There was minimal haplotype sharing between the two pure species, but extensive sharing was observed between F1 hybrids and co-occurring pure-species individuals. In fact, F1 types shared haplotypes with local Q. boulengeri in over 90% of cases, which supports the occurrence of local backcrossing and parental contribution. In terms of mitochondrial DNA, F1 hybrids possessed mitochondrial haplotypes that clustered with Q. boulengeri and often shared these haplotypes directly. Genetic structure and phylogenetic analyses, revealed three distinct genetic clusters corresponding to F1 hybrids, Q. boulengeri, and Q. robertingeri. The F1 hybrids positioned themselves intermediate between the two pure species. Neighbor-joining trees and TreeMix analyses confirmed a strong separation between pure-species types, with F1 hybrids clustering alongside local Q. boulengeri subpopulations, indicating local formation of hybrids.

(5) Discussion In summary, the study reveals hybridogenesis (a reproductive system where hybrids clonally transmit one parental genome) in Quasipaa boulengeri and Q. robertingeri. Hybrids show high genetic heterozygosity and coexist with parental species, ruling out other reproductive modes like parthenogenesis or kleptogenesis. Evidence suggests hybridogenesis enables Q. robertingeri genomes to appear far outside their normal range, possibly aiding range expansion. Chromosomal abnormalities are linked to hybrid hybrids, supporting clonal genome transmission. The genetic divergence between parental species fits patterns seen in other hybridogenetic systems, highlighting a unique, understudied case in East Asia.

Strengths:

Overall, the authors carefully interpret their genetic data to support hybridogenesis as the reproductive mode in this system and propose that this mechanism may aid range expansion. They also appropriately acknowledge the need for further cytogenetic and ecological studies, demonstrating scientific caution. In summary, the discussion reasonably follows from the results, offering cautious interpretation where necessary.

Weaknesses:

Direct reproductive or cytological evidence is still lacking. While alternative reproductive modes are discussed and mostly ruled out logically, some require further empirical testing. The authors maintain a cautious interpretation, appropriately suggesting further research. Some outstanding questions remain.

(1) The elevated heterozygosity and presence of fixed heterozygous loci in hybrids compared to parental species strongly indicate hybridogenesis. However, alternative explanations such as repeated F1 hybridization or some form of balanced polymorphism, while less likely, are not fully excluded.

(2) The coexistence of hybrids and parental species, along with high nuclear and mitochondrial haplotype sharing between hybrids and Q. boulengeri, argues against reproductive modes like parthenogenesis, gynogenesis, or kleptogenesis. However, the assumption that hybrid sterility or multiple local hybrid origins are unlikely could be challenged if undetected local variation or cryptic reproductive strategies exist.

(3) The presence of Q. robertingeri nuclear genomes far outside their known geographic range, genetically linked to nearby populations, fits a hybridogenetic-mediated dispersal model. Although the authors dismiss human-mediated or accidental transport as explanations, these scenarios are not necessarily unlikley.

Reviewer #2 (Public review):

This study describes F1 hybrid frog lineages that use an "unusual" form of reproduction, perhaps hybridogenesis. Identifying such species is important for understanding the biodiversity of reproduction in animals, and animals that do not reproduce via "canonical" sex can be useful model systems in ecology and evolution. The conclusion of the study are based on reduced representation sequencing (RAD-seq with a de-novo assembly of loci) of 107 wild-caught individuals from 53 localities (plus 4 outgroup individuals), including 27 males, 31 females, and 49 juveniles of unknown sex. Conclusive inferences of unusual forms of reproduction typically require breeding studies and parent-offspring genotype comparisons but such information is not available (and perhaps impossible to generate) for the focal frog lineages.

(1) Conclusion 1: there are two pure species and F1 hybrids

The authors infer that there are two lineages RR and BB (corresponding to two named species), and F1 interspecific hybrids RB. This inference is based on the results presented in Figure 1 (PCA, admixture, and heterozygosity analyses) as well as analyses of fixed SNP differences between R and B. I think that this conclusion is well supported; my only comment on this part is that it would be useful to have the admixture plots & cross-validation for the 107 samples with other k values (not only k=2) as a supplemental figure. The plots in the supplemental file S1 are for the subset of 55 inds inferred to be BB only.

(2) Conclusion 2: F1 hybrids most likely reproduce via hybridogenesis

This conclusion is based on the sex ratio of hybrids and haplotype sharing between species and lineages at different, ~150 bp long loci. Parthenogenesis (including sperm-dependent parthenogenesis) is unlikely to generate males, yet sexed F1 hybrid individuals include 18 females and 10 males which prompts the exclusion of parthenogenesis in the present paper. Specific haplotype-sharing patterns are also discussed in the study and used as further support, but these arguments (and the related main and supplementary figures) are difficult to read/interpret. To clarify the arguments related to haplotype sharing and haplotype diversities, I suggest that the authors phase the R and B haplotypes from all their hybrids by using their pure (RR and BB individuals) as references. The concatenated lineage-specific haplotypes can then be used to reconstruct a single phylogenetic tree for all loci (easier to visualize and interpret that the separate haplotype networks for the loci). The authors can then draw cartoon phylogenies for what would be the expected pattern for haplotype clustering and diversity for different reproductive modes, and discuss their observed phylogenies in this regard. Similarly, the migration weights (represented in Figure 4) can then also be computed for separate haplotypes in the hybrids.

However, independently of the outcome of the phasing, it is important to note that there is no a priori reason why all F1 hybrid individuals would reproduce via the same reproductive mode. Notably, work by Barbara Mantovani and Valerio Scali on stick insects has shown that different F1 hybrid lineages involving the same parental species reproduce via hybridogenesis or parthenogenesis. I don't see how the presented data can allow excluding that some F1 hybrid frogs are parthenogenetic while others are hybridogenetic for example.

(3) Conclusion 3: Crosses between hybridogenetic RB males and hybridogenetic RB females gave rise to a new population of RR individuals outside of the RR species range (this new population would correspond to location 30 from Figure 1).

It is not entirely clear to me which data this conclusion is based on, I believe it is the combination of known species ranges for the species R (location 30 being outside of this) and the relatively low heterozygosity of RR individuals at location 30.

However, as the authors point out, the study focuses on an understudied geographic range. Isolated or rare populations of the R species may easily have been overlooked in the past, especially since the R and B species are morphologically difficult to distinguish. Furthermore, an isolated, perhaps vestigial population may also likely be inbred/feature low diversity. It seems most appropriate to discuss different (equally likely) scenarios for the RR population at location 30 rather than implying a hybridogenetic origin of RR individuals. I would also choose a title that does not directly imply this scenario but reflects the solid (not speculative) findings of the study.

Reviewer #3 (Public review):

Summary:

This work reports a new case of hybridogenetic reproduction in the frog genus Quasipaa. Only one other example of this peculiar reproductive mode is known in amphibians, and fewer than a dozen across the tree of life. Interestingly, a population of one of the parental species (Q. robertingeri) was found away from the core of its distribution, within the distribution of the hybridogens. This range expansion might have been mediated by hybridogenesis, whereby two copies of the same parental genome came together again after many generations of hybridogenesis.

Strengths:

Evidence for hybridogenesis is solid. The state of the art would be to genotype parents and offspring, but other known alternative scenarios have been considered carefully and can be ruled out convincingly. In addition, the authors are very careful in their phrasing and made sure to never overinterpret their data.

The explicit predictions under different reproductive modes (and Table 1) are a useful resource for future studies and could inspire new findings of unusual reproductive modes in other taxa.

The sampling is very impressive, with over 50 populations sampled across a very large area.

The comparison of p-distances between pairs of species involved in hybridogenesis is interesting.

Weaknesses:

The current phylogenetic reconstruction with the F1s does not enable to infer the number of origins of hybridogenesis, nor whether the population of Q. robertingeri that was found far from the core of the species' distribution indeed derives from hybridogenesis. This is because some of the signal is driven by the Q. boulengeri haplome, which is replaced every generation and therefore does not reflect the evolutionary history of the lineage.

All known reproductive modes except hybridogenesis can be excluded, but without genotyping parents and offspring, it is impossible to rule out another, yet undescribed reproductive mode.

eLife Assessment

This study provides valuable insights into the influence of sex on bile acid metabolism and the risk of hepatocellular carcinoma (HCC). The data to support that there are inter-relationships between sex, bile acids, and HCC in mice are convincing, although this is a largely descriptive study. Future studies are needed to understand the interaction of sex hormones, bile acids, and chronic liver diseases and cancer at a mechanistic level. Also, there is not enough evidence to determine the clinical significance of the findings given the differences in bile acid composition between mice and men.

Reviewer #1 (Public review):

Liver cancer shows a high incidence in males than females with incompletely understood causes. This study utilized a mouse model that lacks the bile acid feedback mechanisms (FXR/SHP DKO mice) to study how dysregulation of bile acid homeostasis and a high circulating bile acid may underlie the gender-dependent prevalence and prognosis of HCC. By transcriptomics analysis comparing male and female mice, unique sets of gene signatures were identified and correlated with HCC outcomes in human patients. The study showed that ovariectomy procedure increased HCC incidence in female FXR/SHP DKO mice that were otherwise resistant to age-dependent HCC development, and that removing bile acids by blocking intestine bile acid absorption reduced HCC progression in FXR/SHP DKO mice. Based on these findings, the authors suggest that gender-dependent bile acid metabolism may play a role in the male-dominant HCC incidence, and that reducing bile acid level and signaling may be beneficial in HCC treatment. This study include many strengths: 1. Chronic liver diseases often proceed the development of liver and bile duct cancer. Advanced chronic liver diseases are often associated with dysregulation of bile acid homeostasis and cholestasis. This study takes advantage of a unique FXR/SHP DKO model that develop high organ bile acid exposure and spontaneous age-dependent HCC development in males but not females to identify unique HCC-associated gene signatures. The study showed that the unique gene signature in female DKO mice that had lower HCC incidence also correlated with lower grade HCC and better survival in human HCC patients. 2. The study also suggests that differentially regulated bile acid signaling or gender-dependent response to altered bile acids may contribute to gender-dependent susceptibility to HCC development and/or progression. 3. The sex-dependent differences in bile acid-mediated pathology clearly exist but are still not fully understood at the mechanistic level. Female mice have been shown to be more sensitive to bile acid toxicity in a few cholestasis models, while this study showed a male dominance of bile acid promotion of HCC. This study used ovariectomy to demonstrate that female hormones are possible underlying factors. Future studies are needed to understand the interaction of sex hormones, bile acids, and chronic liver diseases and cancer.

eLife Assessment

This is an important study of critical period plasticity, focused on temperature manipulations, and how different parts of the Drosophila larval motor circuit adapt or maladapt. The work convincingly demonstrates that components of the motor network respond in distinct ways to the heat shock, and the combination of functional, structural, and electrophysiological approaches makes the study of significant interest. The work points to central interneurons as primary drivers of maladaptive changes, while motoneurons and neuromuscular junctions show compensatory or homeostatic adjustments. The study is methodologically rigorous, contributing significant insights into critical period biology using a tractable invertebrate model.

Reviewer #1 (Public review):

Summary:

The authors examine the impact of heat stress during an embryonic CP in Drosophila, focusing on the larval locomotor network. They show that elevated temperature increases neuronal activity and, when applied during the CP, results in long-term instability of the network, which manifests in prolonged seizure recovery times. At the neuromuscular junction, substantial structural changes occur, including terminal overgrowth and altered receptor composition, yet synaptic transmission remains preserved due to homeostatic regulation. Motoneurons display reduced excitability but receive increased synaptic input from premotor interneurons. These findings suggest that maladaptive instability originates within the central circuitry rather than at the neuromuscular junction, where changes seem to be homeostatically compensated. The study concludes that different network components exhibit distinct and hierarchical responses to CP perturbations, with premotor interneurons setting the tone for downstream adjustments in motoneurons.

Strengths:

The work takes advantage of the unique accessibility of the Drosophila system. A major strength of the study is the integration of structural, physiological, and behavioral analyses, which allows the authors to draw a comprehensive picture of how CP perturbations shape the locomotor network. The choice of an ecologically relevant stimulus (heat stress) is particularly convincing, as it links experimental manipulations more closely to natural environmental conditions. The experiments are carefully designed, and the results are robust and consistent with previous findings in the field, while also extending them in new directions.

Weaknesses:

The study leaves some uncertainty regarding the experimental design and interpretation. The change from short to prolonged heat shock manipulations raises the possibility that the effects observed may not be confined to the critical period alone - this could be experimentally addressed or simply rephrased in the text. In addition, the maladaptive (seizure recovery) and adaptive/homeostatic phenotypes are not always clearly distinguished or highlighted, which makes it harder to appreciate how the different levels of the network plasticity fit together into a single mechanistic framework.

Reviewer #2 (Public review):

Summary:

This manuscript presents a thoughtful and well-executed study of critical period plasticity in the Drosophila larval motor circuit. The authors examined how transient heat, 32 {degree sign}C, during the embryonic stage, altered network properties, showing that premotor interneurons A27h increase excitatory drive onto motoneurons, which respond with a reduction in excitability. At the NMJ, synaptic terminals expand and GluRIIA distribution shifts, yet synaptic transmission remains largely unaffected. Despite these local compensations, the treated larvae display slower crawling and prolonged recovery from seizures, indicating that the network is functionally compromised.

Strengths:

(1) One of the major strengths of this study is the elegant dissection of a defined circuit, tracking changes from premotor interneurons through motoneurons to the NMJ. The multimodal approach provides a comprehensive view of how connected elements respond to CP perturbations.

(2) An interesting finding is that NMJ morphology changes dramatically without corresponding deficits in synaptic transmission, challenging the common assumption that larger boutons necessarily indicate stronger synapses.

(3) Another intriguing result is that even with two layers of homeostatic compensation, locomotor behavior is still impaired, highlighting the limits of compensation and underscoring the critical role of CP timing.

(4) Beyond these scientific insights, the study benefits from a well-defined, tractable system and simple experimental manipulations, which together make the results highly interpretable and reproducible.

Weaknesses:

There are a few areas where the manuscript could be strengthened.

(1) Although A27h premotor neurons are well characterized, the claim that they are the causal driver of downstream changes would be strengthened by additional experiments or a clearer discussion of the temporal hierarchy.

(2) While 32 {degree sign}C heat stress is presented as ecologically relevant, it produces maladaptive behavioral outcomes, raising questions about the ecological and mechanistic interpretation of the model. In particular, most experiments, with the exception of Figure 1, used prolonged (24h) heat treatments, which could introduce developmental effects beyond the CP itself. Comparing shorter and longer heat exposures would help clarify the specificity of the CP response.

(3) While there are schematics for experimental procedures, a circuit diagram tracing information flow and indicating where structural and functional changes occur would help readers better understand the findings.

(4) Finally, the main paradox of the study, that robust homeostatic compensations occur yet behavior remains impaired, could be explored in more depth in the Discussion.

Reviewer #3 (Public review):

Summary:

During development, neural circuits undergo brief windows of heightened neuronal plasticity (e.g., critical periods) that are thought to set the lifelong functional properties of underlying circuits. These authors, in addition to others within the Drosophila community, previously characterized a critical period in late fly embryonic development, during which alterations to neuronal activity impact late-stage larval crawling behavior. In the current study, the authors use an ethologically-relevant activation paradigm (increased temperature) to boost motor activity during embryogenesis, followed by a series of electrophysiology and imaging-based experiments to explore how 3 distinct levels of the circuit remodel in response to increases in embryonic motor activity. Specifically, they find that each level of the circuit responds differently, with increased excitatory drive from excitatory pre-motor neurons, reduced excitability in motor neurons, and no physiological changes at the NMJ despite dramatic morphological differences. Together, these data suggest that early life experience in the motor neuron drives compensatory changes at each level of the circuit to stabilize overall network output.

Strengths:

The study was well-written, and the data presented were clear and an important contribution to the field.

Weaknesses:

The sample sizes and what they referred to throughout the distinct studies were unclear. In the legends, the authors should clearly state for each experiment N=X, and if N refers to an NMJ, for example, instead of an individual animal, they should state N=X NMJs per N=X animals. This will help readers better understand the statistical impact of the study.

eLife Assessment

This study provides important evidence that negative affect is associated with slower cognitive processing in daily life, with findings replicated across three independent samples and supported by rigorous statistical analyses. The strength of evidence is convincing, though reliance on a proxy measure of processing speed limits the completeness of the conclusions.

Reviewer #1 (Public review):

Summary:

A study researching the relationship between affective shifts and cognitive performance in a daily life setting.

Strengths:

The evidence provided is compelling: the findings are conceptually replicated in three samples of adequate size and statistical rigor in analyzing the data, with methods beyond the current state of the art in applied research. For example, using two-step multilevel vector autoregressive models that were adopted to allow the inclusion of covariates, and contemporaneous effects corrected for temporal relations and background covariates. In addition, the authors use beautiful visualizations to convey the different samples used (Figure 1) and intuitive and rich figures to convey their obtained results.

In summary, the authors were able to convincingly show that higher negative affect is linked to slower cognitive processing speed, with results supporting their conclusions.

Weaknesses:

I have one major concern. Although a check for careless responding has been conducted on the basis of long reaction times, I wonder whether, beyond long response times, any other sanity checks with respect to, e.g., careless responding were done? For example, a lack of variability of EMA items over subsequent occasions, e.g., say 15, is often seen as an indicator of careless responding, especially when using VAS items. In line 693, it is stated, "We added a small amount of random noise, ranging from -0.1 to +0.1, to each EMA time series to allow models to converge when EMA time series showed minimal variance over time", which I understand, but this lack of variability could also be caused by participants stopping to take the study seriously. For datasets 1 and 2, this might be more difficult to assess (due to the limited response values), but maybe the authors can get an indication of this in dataset 3?

Reviewer #2 (Public review):

Summary:

In this paper, Fittipaldi et al. assessed whether cognitive processing speed - as operationalized by the Digital Questionnaire Response Time (DQRT) - and affect (both positive and negative) are related in contemporaneous and temporaneous ways, both between and within-subject. At the between-person level, they found positive relationships with DQRT and negative affect, and the opposite for positive affect. This was similar at the within-subject contemporaneous level.

The authors further test Granger-causality in the dynamics, for both Affect -> DQRT and DQRT -> Affect. They find that affect and t-1 is associated with DQRT in the same manner as in the other models (positively for negative affect, and negatively for positive affect). Interestingly, DQRT -> Affect was largely non-significant for most affect items.

This study adds important information on the associations between affect and cognitive measures outside the lab, showcasing a methodological approach to translate laboratory research to new contexts.

Strengths

Overall, this study has a strong methodological approach, which is commendable. The use of three independent samples with different affective measures is a good way to showcase the validity of the findings. The multi-level modelling approach is also done thoroughly and appropriately within the context of MLVAR modelling. The findings are also well visualized, making it easy to follow along with the interconnected and potentially confusing analyses.

Weaknesses

The authors use the DQRT as a measure of cognitive processing, which isn't fully validated or substantiated as such. The authors do address this as a limitation, but I believe it warrants a much broader discussion, as the construct being assessed may not be the construct intended by the authors. This makes it difficult to ascertain whether the conclusion drawn (that affect impacts cognitive function) is valid. I would rather frame it that there are associations between affect and response times, which can indicate many different things, be it potentially careless responding or other mechanisms at play.

eLife Assessment

This important work develops the C. elegans as a model organism for studying effort-based discounting by asking the worms to choose between patches of easy and hard to digest bacteria. The authors provide convincing evidence that the nematodes are effort discounting. They also provide solid evidence of involvement of dopamine in the food preference and that the finding is not restricted to lab-acclimated strains.

Reviewer #1 (Public review):

Summary:

Millet et al. show that C. elegans systematically prefers easy-to-eat bacteria but will switch its choice when harder-to-eat bacteria are offered at higher densities, producing indifference points that fit standard economic discounting models. Detailed kinetic analysis reveals that this bias arises from unchanged patch-entry rates but significantly elevated exit rates on effortful food, and dop-3 mutants lose the preference altogether, implicating dopamine in effort sensitivity. These findings extend effort-discounting behavior to a simple nematode, pushing the phylogenetic boundary of economic cost-benefit decision-making.

Strengths:

Extends the well-characterized concept of effort discounting into C. elegans, setting a new phylogenetic boundary and opening invertebrate genetics to economic-behavior studies.

Elegant use of cephalexin-elongated bacteria to manipulate "effort" without altering nutritional or olfactory cues, yielding clear preference reversals and reproducible indifference points.

Application of standard discounting models to predict novel indifference points is both rigorous and quantitatively satisfying, reinforcing the interpretation of worm behavior in economic terms.

The three-state patch-model cleanly separates entry and exit dynamics, showing that increased leaving rates-rather than altered re-entry-drive choice biases.

Demonstrates that _dop-3_ mutants lose normal effort discounting, firmly tying monoaminergic signaling to this behavior and paralleling vertebrate findings.

Demonstration of discounting in wild strain (solid evidence).

Weaknesses:

Only _dop-3_ shows an effect, whereas _cat-2_/_dat-1_ do not, leaving the broader role of dopamine synthesis and reuptake ambiguous.

With only five wild isolates tested, and only one clearly showing clear evidence of preference for the easy to eat bacteria, it's hard to conclude that effort discounting isn't a lab-strain artifact or how broadly it varies in natural populations.

Reviewer #2 (Public review):

Summary:

Here Millet et al. adapted a t-maze paradigm for use in C. elegans to understand whether nematodes exhibit effort discounting behaviors comparable to other species. C. elegans worms were reliably sensitive to how effortful the food was to consume, allowing for the application of standard economic models of decision-making to be applied to their behavior. The authors then demonstrated the necessity of dopamine signaling for this behavior, identifying dop-3 mutants in particular as insensitive to effort. Together, this work establishes a new model system for the study of discounting behavior in cost-benefit decision-making.

Strengths:

The question is well-motivated and the approach taken here is novel; it is uncommon for worms to undergo such behavioural procedures (although this lab has previously been integral to pushing the extent of the complexity of behaviours studied in C. elegans). The authors are careful in their approach to altering and testing the properties of the elongated bacteria. Similarly, they go to some effort to understand what exactly is driving behavioural choices in this context, both through application of simple standard models of effort discounting and a kinetic analysis of patch leaving. The comparisons to various dopamine mutants further extends the translational potential of their findings. I also appreciate the comparison to natural isolate strains as the question of whether this behaviour may be driven by some sort of strain-specific adaptation to the environment is not regularly addressed in mammalian counterparts to this work.

Weaknesses:

The authors have now addressed concerns about whether the mechanisms underlying the choice behavior here are generalizable to other organisms. Specifically, their work speaks to foraging-inspired effort discounting paradigms in rodents and humans in which the decision is whether to stay or leave a given resource, rather than to simultaneous decision-making across two options in a T-maze.

The dopamine results are interesting but still difficult to interpret. As the authors discuss, the lack of an effect in the cat-2 and dat-1 mutants is surprising given the effect in the dop-3 mutants. Understanding what exactly the role of dop-3 is here therefore requires further study.

Reviewer #3 (Public review):

Summary:

The authors establish a behavioral task to explore effort discounting in C. elegans. By using bacterial food that takes longer to consume, the authors show that for equivalent effort, as measured by pumping rate, animals obtain less food, as measured by fat deposition.

The authors formalize the task by applying a neuroeconomic decision making model that includes, value, effort, and discounting. They use this to estimate the discounting C. elegans apply based on ingestion effort by using a population level 2-choice T-maze.

They then analyze the behavioral dynamics of individual animals transitioning between on-food and off-food states. Harder to ingest bacteria led to increased food patch leaving.

Finally, they examined a set of mutants defective in different aspects of dopamine signaling, as dopamine plays a key role in discounting in vertebrates and regulates certain aspects of C. elegans foraging.

In their response to the first set of reviews, the authors take care to ensure their task is analogous to at least some of those used in mammals and make changes to the text to better clarify some of their conclusions. My view is the same--that this is an interesting paper for methodological and scientific reasons that brings an important theoretical framework to bear on C. elegans foraging behavior. While I think the mutant results are somewhat unsatisfying, this is not the principal contribution of the work.

Strengths:

The behavioral experiments and neuroeconomic analysis framework are compelling and interesting and make a significant contribution to the field. While these foraging behaviors have been extensively studied, few include clearly articulated theoretical models to be tested.

Demonstrating that C. elegans effort discounting fits model predictions and has stable indifference points is important for establishing these tasks as a model for decision making.

Weaknesses:

The dopamine experiments are harder to interpret. The authors point out the perplexing lack of an effect of dat-1 and cat-2. dop-3 leads to general indifference. I am not sure this is the expected result if the argument is a parallel functional role to discounting in vertebrates. dop-3 causes a range of locomotor phenotypes and may affect feeding (reduced fat storage), and thus there may be a general defect in the ability to perform the task rather than anything specific to discounting.

That said, some of the other DA mutants also have locomotor defects and do not differ from N2. But there is no clear result here-my concern is that global mutants in such a critical pathway exhibit such pleiotropy that it's difficult to conclude there is a clear and specific role for DA in effort discounting. This would require more targeted or cell-specific approaches. The authors state these experiments are outside the scope of the current study, and that at minimum their results implicate dopamine signaling in some form. I tend to agree but still think locomotion defects of DA mutants complicate this question.

Meanwhile, there are other pathways known to affect responses to food and patch leaving decisions-5HT, PDF, tyramine, etc. in their response the authors state they focus on dopamine because of its role in discounting behavior in mammals.

Author response:

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

Reviewer #1(Public Reviews):

Summary: 

Here, Millet et al. consider whether the nematode C. elegans 'discounts' the value of reward due to effort in a manner similar to that shown in other species, including rodents and humans. They designed a T-maze effort choice paradigm inspired by previous literature, but manipulated how effortful the food is to consume.C. elegans worms were sensitive to this novel manipulation, exhibiting effort-discountinglike behaviour that could be shaped by varying the density of food at each alternative in order to calculate an indifference point. This discounting-like behaviour was related to worms' rates of patch leaving, which differed between the low and high effort patches in isolation. The authors also found a potential relationship to dopamine signalling, and also that this discounting behaviour was not specific to lab-based strains of C. elegans . 

Strengths: 

The question is well-motivated, and the approach taken here is novel. The authors are careful in their approach to altering and testing the properties of the effortful, elongated bacteria. Similarly, they go to some effort to understand what exactly is driving behavioural choices in this context, both through the application of simple standard models of effort discounting and a kinetic analysis of patch leaving. The comparisons to various dopamine mutants further extend the translational potential of their findings. I also appreciate the comparison to natural isolate strains, as the question of whether this behaviour may be driven by some sort of strain-specific adaptation to the environment is not regularly addressed in mammalian counterparts. The manuscript is well-written, and the figures are clear and comprehensible. 

Weaknesses: 

Discounting is typically defined as the alteration of a subjective value by effort (or time, risk, etc.), which is then used to guide future decision-making. By adapting the standard t-maze task for C. elegans as a patch-leaving paradigm, the authors observe behaviour strongly consistent with discounting models, but that is likely driven by a different process, in particular by an online estimate of the type of food in the current patch, which then influences patch-leaving dynamics (Figure 3). This is fundamentally different from decision-making strategies relating to effort that have been described in the rodent and human literatures. 

We agree that in our study worms are likely making an on-line estimate of food quality in the current patch, but we wish to point out that rodents and humans also use on-line estimates in some significant effort-discounting paradigms. With respect to rodents, we call attention to effort discounting studies involving the widely used progressive ratio task (references in Discussion). In this task, animals can either lever-press for a preferred food or consume a less preferred food that is freely available nearby. However, the number of lever presses required to obtain preferred food increases as a function of the cumulative number of lever presses until the effort-cost of obtaining preferred food becomes too high and the animal switches to a freely available food. In essence, the lever and the freely available food are patches and the animal decides whether or not to leave the “lever” patch. It seems inescapable that the progressive ratio task involves an on-line assessment of the cost/benefit relationship associated with lever pressing. With respect to humans, one highly cited study (reference in Discussion) presented participants with a series of virtual apple trees. They could see how many apples are in the current tree and how much effort (squeezing a handgrip) is required to gather them. Their task was to decide whether or not to gather apples from that tree based on the perceived cost and benefit. Thus, on-line estimation is a common strategy used by animals and humans as shown in the effort discounting literature. We now make this point in the Discussion section titled A model of effort-discounting like behavior.

Similarly, the calculation of indifference points at the group instead of at the individual level also suggests a different underlying process and limits the translational potential of their findings. The authors do not discuss the implications of these differences or why they chose not to attempt a more analogous trial-based experiment.  

It is not clear to us why changing the read-out –– from the individual level to the population level –– necessarily suggests that a different biological mechanism is at work. In our view, there is one mechanism and it can be seen from different perspectives (e.g., individual vs population). Furthermore, the analogous trial-based experiment, as we understand it, would be to record behavior one worm at a time in the T-maze. This design is not practical because it entails recording a large number of single worms in the T-maze for 60 min each. 

In the case of both the dopamine and natural isolate experiments, the data are very noisy despite large (relative to other C. elegans experiments) sample sizes. In the dopamine experiment, disruption of dop1, dop-2, and cat-2 had no statistically significant effect. There do not appear to be any corrections for multiple comparisons, and the single significant comparison, for dop-3, had a small effect size. 

An ANOVA followed by a Dunnett test was used to test differences between groups in Fig. 4 and 5. The Dunnett test is a multiple comparison test comparing experimental groups to a single control group. It is used to minimize type I error while maintaining statistical power and does not require further correction for multiple comparisons. We have clarified the use of the Dunnett test in the statistical table.  The effect size for dop-3 is 0.5 (Cohen’s d), which is typically interpreted as a medium, not small, effect size.(e.g. Cohen, Psychological Bulletin, 1992, Vol. 112. No. 1,155-159). 

More detailed behavioural analyses on both these and the wild isolate strains, for example by applying their kinetic analysis, would likely give greater insight as to what is driving these inconsistent effects. 

More detailed behavioral analysis could reveal why we observe a difference in effort discounting in some strains and not others. However, it is not obvious what type of behavioral analysis would be needed to differentiate between pleiotropic effects of the mutations/natural isolates and more specific effects on effort discounting. A simple kinetic analysis in particular may not be enough to reveal relevant differences between mutants/natural isolates. For this reason, we think that such experiments may be better suited for future follow up studies.

Reviewer #2 (Public Reviews)

Summary: 

Millet et al. show that C. elegans systematically prefers easy-to-eat bacteria but will switch its choice when harder-to-eat bacteria are offered at higher densities, producing indifference points that fit standard economic discounting models. Detailed kinetic analysis reveals that this bias arises from unchanged patch-entry rates but significantly elevated exit rates on effortful food, and dop-3 mutants lose the preference altogether, implicating dopamine in effort sensitivity. These findings extend effortdiscounting behavior to a simple nematode, pushing the phylogenetic boundary of economic costbenefit decision-making. 

Strengths: 

(1) Extends the well-characterized concept of effort discounting into C. elegans , setting a new phylogenetic boundary and opening invertebrate genetics to economic-behavior studies. 

(2) Elegant use of cephalexin-elongated bacteria to manipulate "effort" without altering nutritional or olfactory cues, yielding clear preference reversals and reproducible indifference points. 

(3) Application of standard discounting models to predict novel indifference points is both rigorous and quantitatively satisfying, reinforcing the interpretation of worm behavior in economic terms. 

(4) The three-state patch-model cleanly separates entry and exit dynamics, showing that increased leaving rates-rather than altered re-entry-drive choice biases. 

(5) Investigates the role of dopamine in this behavior to try to establish shared mechanisms with vertebrates. 

(6) Demonstration of discounting in wild strain (solid evidence). 

Weaknesses: 

(1) The kinetic model omits rich trajectory details-such as turning angles or hazard functions-that could distinguish a bona fide roaming transition from other exit behaviors. 

The overarching goal of present paper was to develop a simple model for effort discounting in a small, genetically tractable organism.  Accordingly,  we focused on quantitative assays that are easy to implement and analyze. The patch-leaving assay and its associated kinetic analysis are one such assay. To keep things simple in this assay, we counted the number of  transitions between the three states shown in Fig. 3A. We chose not to analyze the data in terms of turning angles or hazard functions because the metrics we developed seemed sufficient. Finally, we note that there are new modeling data showing that the presumptive transitions into the roaming state can be explained in terms of a one-state stochastic model in which there is no discrete roaming state (Elife. 2025 Jul 30;14:RP104972. doi:

10.7554/eLife.104972.PMID: 40736321).

(2) Only dop-3 shows an effect, and the statistical validity of this result is questionable. It is not clear if the authors corrected for multiple comparisons, and the effect size is quite small and noisy, given the large number of worms tested. Other mutants do not show effects. Given these two concerns, the role of dopamine in C. elegans effort discounting was unconvincing. 

An ANOVA followed by a Dunnett test was used to test statistical significance in figures 4 and 5 (see above for a discussion of these tests). We believe this approach is rigorous, and the use of these tests is statistically valid. We note that the effect size for this comparison was medium.

(3) With only five wild isolates tested (and variable data quality), it's hard to conclude that effort discounting isn't a lab-strain artifact or how broadly it varies in natural populations. 

The fact that four of the five natural isolates tested display levels of effort discounting similar to N2 (only one natural isolate does not display effort discounting) argues against effort discounting being a laboratory adaption.  We have nevertheless weakened the claim regarding natural isolates. We now say effort discounting-like behavior may not be an adaptation to the laboratory environment.  

(4) Detailed analysis of behavior beyond preference indices would strengthen the dopamine link and the claim of effort discounting in wild strains. 

Going beyond preference in the behavioral analysis might or might not reveal new phenotypes that strengthen the link with dopamine. At present, however, we think such experiments are beyond the scope of the paper.

(5) A few mechanistic statements (e.g., tying satiety exclusively to nutrient signals) would benefit from explicit citations or brief clarifications for non-worm specialists. 

We are unable to identify a mechanistic statement tying satiety to nutrient signals in our manuscript.

Reviewer #3 (Public Reviews)

Summary: 

The authors establish a behavioral task to explore effort discounting in C. eleganss . By using bacterial food that takes longer to consume, the authors show that, for equivalent effort, as measured by pumping rate, they obtain less food, as measured by fat deposition. The authors formalize the task by applying a formal neuroeconomic decision-making model that includes value, effort, and discounting. They use this to estimate the discounting that C. elegans applies based on ingestion effort by using a population-level 2-choice T-maze. They then analyze the behavioral dynamics of individual animals transitioning between on-food and off-food states. Harder to ingest bacteria led to increased food patch leaving. Finally, they examined a set of mutants defective in different aspects of dopamine signaling, as dopamine plays a key role in discounting in vertebrates and regulates certain aspects of C. elegans foraging. 

Strengths: 

The behavioral experiments and neuroeconomic analysis framework are compelling, interesting, and make a significant contribution to the field. While these foraging behaviors have been extensively studied, few include clearly articulated theoretical models to be tested. 

Demonstrating that C. elegans effort discounting fits model predictions and has stable indifference points is important for establishing these tasks as a model for decision making. 

Weaknesses: 

The dopamine experiments are harder to interpret. The authors point out the perplexing lack of an effect of dat-1 and cat-2. dop-3 leads to general indifference. I am not sure this is the expected result if the argument is a parallel functional role to discounting in vertebrates. dop-3 causes a range of locomotor phenotypes and may affect feeding (reduced fat storage), and thus, there may be a general defect in the ability to perform the task rather than anything specific to discounting.

That said, some of the other DA mutants also have locomotor defects and do not differ from N2. But there is no clear result here - my concern is that global mutants in such a critical pathway exhibit such pleiotropy that it's difficult to conclude there is a clear and specific role for DA in effort discounting. This would require more targeted or cell-specific approaches. 

We agree with the reviewer that the results of the dopamine experiments are puzzling and getting a better understanding of the role of dopamine in effort-discounting will require more sensitive assays and different experimental approaches (e.g. cell-specific rescues). However, as mentioned by the reviewer, all the mutations tested have some pleiotropic effects, yet only dop-3 displays a defect in effort discounting. This, in our opinion, points to a specific role of dop-3 in effort-discounting in C. elegans. This point is now made in the Discussion in the section titled Role of dopamine signaling in effort discountinglike behavior.

Meanwhile, there are other pathways known to affect responses to food and patch leaving decisions: serotonin, pigment-dispersing factor, tyramine, etc. The paper would have benefited from a clarification about why these were not considered as promising candidates to test (in addition to or instead of dopamine). 

We focused on DA because of its well-established effect on effort discounting in rodents.

Testing other pathways is a goal for future research.

Reviewer #1 (Recommendations for the authors):

The current results are more a reframing of data gathered from a patch-leaving paradigm, but described in the form of economic choice modelling in which discounting is one possible explanation. One more parsimonious explanation that worms estimate in real-time some rate of reward and leave the patch at some threshold, consistent with canonical foraging models, previous experiments in C. elegans, and the authors' own data (Figure 3). Therefore, I am wary about some of the claims made in this manuscript, such as 'decision-making strategies based on effort-cost trade-offs are evolutionarily conserved'. 

These points are now addressed in the Discussion in a revised section titled A model of effortdiscounting like behavior. (i) We now call attention to the fact that our T-maze assay is a patch-leaving foraging paradigm. (ii) We now propose a revised model in which “worms make an on-line assessment of food value in the current patch which in turn alters patch-leaving dynamics, increasing the exit rates from cephalexin-treated patches as shown in Figure 3.” (iii) We now provide evidence from the rodent and human literature that the strategy of on-line assessment of reward value may be evolutionarily conserved in the case of a class of effort discounting tasks whose solution requires on-line assessments. 

If the reason the authors chose to do a patch-leaving style task rather than a traditional t-maze is because C. elegans is unable to retain the sort of information necessary to make such simultaneous decisions - e.g., if pre-training on the two options isn't possible - then this in itself suggests that mechanisms underlying these decisions in worms and mammals are unlikely to be the same. I mention this because I would like to suggest to the authors an alternative interpretation: that patch foraging is actually 'the' canonical computation that translates across species. This would, in fact, be nicely consistent with some other recent modelling work in humans, e.g., https://www.biorxiv.org/content/10.1101/2025.05.06.652482v1. 

Please see the previous response.

Reviewer #2 (Recommendations for the authors):

Can you provide a picture of the regular and CEPH bacteria? 

Done (see Figure 1––figure supplement 1).

Reviewer #3 (Recommendations for the authors):

I would recommend testing representative mutants in other pathways in the choice task. If possible, more targeted experiments with dop-3, including either cell-specific KOs or rescues, would very much strengthen this aspect of the paper. 

While valuable, these experiments are out of scope for the present study.

eLife Assessment

This important study combines behavioural psychophysics with image-computable models to contrast a view-selective model of face recognition with a view-tolerant process. Although diagnostic orientations vary with viewpoint (horizontal for frontal, vertical for profile), human recognition remains consistently tuned to horizontal information, aligning with the view-tolerant model's predictions. The evidence for view-invariant recognition is solid, though testing more plausible model variants and considering generalisability to more naturalistic face stimuli would strengthen the conclusions.

Reviewer #1 (Public review):

Summary:

The authors describe the results of a single study designed to investigate the extent to which horizontal orientation energy plays a key role in supporting view-invariant face recognition. The authors collected behavioral data from adult observers who were asked to complete an old/new face matching task by learning broad-spectrum faces (not orientation filtered) during a familiarization phase and subsequently trying to label filtered faces as previously seen or novel at test. This data revealed a clear bias favoring the use of horizontal orientation energy across viewpoint changes in the target images. The authors then compared different ideal observer models (cross-correlations between target and probe stimuli) to examine how this profile might be reflected in the image-level appearance of their filtered images. This revealed that a model looking for the best matching face within a viewpoint differed substantially from human data, exhibiting a vertical orientation bias for extreme profiles. However, a model forced to match targets to probes at different viewing angles exhibited a consistent horizontal bias in much the same manner as human observers.

Strengths:

I think the question is an important one: The horizontal orientation bias is a great example of a low-level image property being linked to high-level recognition outcomes, and understanding the nature of that connection is important. I found the old/new task to be a straightforward task that was implemented ably and that has the benefit of being simple for participants to carry out and simple to analyze. I particularly appreciated that the authors chose to describe human data via a lower-dimensional model (their Gaussian fits to individual data) for further analysis. This was a nice way to express the nature of the tuning function, favoring horizontal orientation bias in a way that makes key parameters explicit. Broadly speaking, I also thought that the model comparison they include between the view-selective and view-tolerant models was a great next step. This analysis has the potential to reveal some good insights into how this bias emerges and ask fine-grained questions about the parameters in their model fits to the behavioral data.

Weaknesses:

I will start with what I think is the biggest difficulty I had with the paper. Much as I liked the model comparison analysis, I also don't quite know what to make of the view-tolerant model. As I understand the authors' description, the key feature of this model is that it does not get to compare the target and probe at the same yaw angle, but must instead pick a best match from candidates that are at different yaws. While it is interesting to see that this leads to a very different orientation profile, it also isn't obvious to me why such a comparison would be reflective of what the visual system is probably doing. I can see that the view-specific model is more or less assuming something like an exemplar representation of each face: You have the opportunity to compare a new image to a whole library of viewpoints, and presumably it isn't hard to start with some kind of first pass that identifies the best matching view first before trying to identify/match the individual in question. What I don't get about the view-tolerant model is that it seems almost like an anti-exemplar model: You specifically lack the best viewpoint in the library but have to make do with the other options. Again, this is sort of interesting and the very different behavior of the model is neat to discuss, but it doesn't seem easy to align with any theoretical perspective on face recognition. My thinking here is that it might be useful to consider an additional alternate model that doesn't specifically exclude the best-matching viewpoint, but perhaps condenses appearance across views into something like a prototype. I could even see an argument for something like the yaw-averages presented earlier in the manuscript as the basis for such a model, but this might be too much of a stretch. Overall, what I'd like to see is some kind of alternate model that incorporates the existence of the best-match viewpoint somehow, but without the explicit exemplar structure of the view-specific model.

Besides this larger issue, I would also like to see some more details about the nature of the cross-correlation that is the basis for this model comparison. I mostly think I get what is happening, but I think the authors could expand more on the nature of their noise model to make more explicit what is happening before these cross-correlations are taken. I infer that there is a noise-addition step to get them off the ceiling, but I felt that I had to read between the lines a bit to determine this.

Another thing that I think is worth considering and commenting on is the stimuli themselves and the extent to which this may limit the outcomes of their behavioral task. The use of the 3D laser-scanned faces has some obvious advantages, but also (I think) removes the possibility for pigmentation to contribute to recognition, removes the contribution of varying illumination and expression to appearance variability, and perhaps presents observers with more homogeneous faces than one typically has to worry about. I don't think these negate the current results, but I'd like the authors to expand on their discussion of these factors, particularly pigmentation. Naively, surface color and texture seem like they could offer diagnostic cues to identity that don't rely so critically on horizontal orientations, so removing these may mean that horizontal bias is particularly evident when face shape is the critical cue for recognition.

Reviewer #2 (Public review):

This study investigates the visual information that is used for the recognition of faces. This is an important question in vision research and is critical for social interactions more generally. The authors ask whether our ability to recognise faces, across different viewpoints, varies as a function of the orientation information available in the image. Consistent with previous findings from this group and others, they find that horizontally filtered faces were recognised better than vertically filtered faces. Next, they probe the mechanism underlying this pattern of data by designing two model observers. The first was optimised for faces at a specific viewpoint (view-selective). The second was generalised across viewpoints (view-tolerant). In contrast to the human data, the view-specific model shows that the information that is useful for identity judgements varies according to viewpoint. For example, frontal face identities are again optimally discriminated with horizontal orientation information, but profiles are optimally discriminated with more vertical orientation information. These findings show human face recognition is biased toward horizontal orientation information, even though this may be suboptimal for the recognition of profile views of the face.

One issue in the design of this study was the lowering of the signal-to-noise ratio in the view-selective observer. This decision was taken to avoid ceiling effects. However, it is not clear how this affects the similarity with the human observers.

Another issue is the decision to normalise image energy across orientations and viewpoints. I can see the logic in wanting to control for these effects, but this does reflect natural variation in image properties. So, again, I wonder what the results would look like without this step.

Despite the bias toward horizontal orientations in human observers, there were some differences in the orientation preference at each viewpoint. For example, frontal faces were biased to horizontal (90 degrees), but other viewpoints had biases that were slightly off horizontal (e.g., right profile: 80 degrees, left profile: 100 degrees). This does seem to show that differences in statistical information at different viewpoints (more horizontal information for frontal and more vertical information for profile) do influence human perception. It would be good to reflect on this nuance in the data.

eLife Assessment

This important study uses a combination of behavioral and molecular techniques to identify neuromodulators that influence blood-feeding behavior in the disease vector, Anopheles stephensi. Through a combination of gene expression analysis and RNA knockdown, the authors identify neuropeptides RYamide and sNPF as candidate regulators for blood-feeding, demonstrate behavioral changes upon co-knockdown, and anatomically characterize their expression patterns. While the evidence for behavioral characterization and expression mapping is solid, the evidence supporting a direct causal role for these neuropeptides in promoting host-seeking remains unproven.

Reviewer #1 (Public review):

Summary:

Bansal et al. present a study on the fundamental blood and nectar feeding behaviors of the critical disease vector, Anopheles stephensi. The study encompasses not just the fundamental changes in blood feeding behaviors of the crucially understudied vector, but then uses a transcriptomic approach to identify candidate neuromodulation pathways which influence blood feeding behavior in this mosquito species. The authors then provide evidence through RNAi knockdown of candidate pathways that the neuromodulators sNPF and Rya modulate feeding either via their physiological activity in the brain alone or through joint physiological activity along the brain-gut axis (but critically not the gut alone). Overall, I found this study to be built on tractable, well-designed behavioral experiments.

Their study begins with a well-structured experiment to assess how the feeding behaviors of A. stephensi change over the course of its life history and in response to its age, mating, and oviposition status. The authors are careful and validate their experimental paradigm in the more well-studied Ae. aegypti, and are able to recapitulate the results of prior studies, which show that mating is a prerequisite for blood feeding behaviors in Ae. aegypt. Here they find A. Stephensi, like other Anopheline mosquitoes, has a more nuanced regulation of its blood and nectar feeding behaviors.

The authors then go on to show in a Y-maze olfactometer that ,to some degree, changes in blood feeding status depend on behavioral modulation to host cues, and this is not likely to be a simple change to the biting behaviors alone. I was especially struck by the swap in valence of the host cues for the blood-fed and mated individuals, which had not yet oviposited. This indicates that there is a change in behavior that is not simply desensitization to host cues while navigating in flight, but something much more exciting is happening.

The authors then use a transcriptomic approach to identify candidate genes in the blood-feeding stages of the mosquito's life cycle to identify a list of 9 candidates that have a role in regulating the host-seeking status of A. stephensi. Then, through investigations of gene knockdown of candidates, they identify the dual action of RYa and sNPF and candidate neuromodulators of host-seeking in this species. Overall, I found the experiments to be well-designed. I found the molecular approach to be sound. While I do not think the molecular approach is necessarily an all-encompassing mechanism identification (owing mostly to the fact that genetic resources are not yet available in A. stephensi as they are in other dipteran models), I think it sets up a rich line of research questions for the neurobiology of mosquito behavioral plasticity and comparative evolution of neuromodulator action.

Strengths:

I am especially impressed by the authors' attention to small details in the course of this article. As I read and evaluated this article, I continued to think about how many crucial details could potentially have been missed if this had not been the approach. The attention to detail paid off in spades and allowed the authors to carefully tease apart molecular candidates of blood-seeking stages. The authors' top-down approach to identifying RYamide and sNPF starting from first principles behavioral experiments is especially comprehensive. The results from both the behavioral and molecular target studies will have broad implications for the vectorial capacity of this species and comparative evolution of neural circuit modulation.

Weaknesses:

There are a few elements of data visualizations and methodological reporting that I found confusing on a first few read-throughs. Figure 1F, for example, was initially confusing as it made it seem as though there were multiple 2-choice assays for each of the conditions. I would recommend removing the "X" marker from the x-axis to indicate the mosquitoes did not feed from either nectar, blood, or neither in order to make it clear that there was one assay in which mosquitoes had access to both food sources, and the data quantify if they took both meals, one meal, or no meals.

I would also like to know more about how the authors achieved tissue-specific knockdown for RNAi experiments. I think this is an intriguing methodology, but I could not figure out from the methods why injections either had whole-body or abdomen-specific knockdown.

I also found some interpretations of the transcriptomic to be overly broad for what transcriptomes can actually tell us about the organism's state. For example, the authors mention, "Interestingly, we found that after a blood meal, glucose is neither spent nor stored, and that the female brain goes into a state of metabolic 'sugar rest', while actively processing proteins (Figure S2B, S3)".

This would require a physiological measurement to actually know. It certainly suggests that there are changes in carbohydrate metabolism, but there are too many alternative interpretations to make this broad claim from transcriptomic data alone.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated females, but not unmated (virgin) females, exhibit suppression in their blood-feeding behaviour. Using brain transcriptomic analysis comparing sugar-fed, blood-fed, and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools, including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding), although the impact was observed only after both neuropeptide genes underwent knockdown.

Strengths and/or weaknesses:

Overall, the manuscript was well-written; however, the authors should review carefully, as some sections would benefit from restructuring to improve clarity. Some statements need to be rectified as they are factually inaccurate.

Below are specific concerns and clarifications needed in the opinion of this reviewer:

(1) What does "central brains" refer to in abstract and in other sections of the manuscript (including methods and results)? This term is ambiguous, and the authors should more clearly define what specific components of the central nervous system was/were used in their study.

(2) The abstract states that two neuropeptides, sNPF and RYamide are working together, but no evidence is summarized for the latter in this section.

(3) Figure 1<br /> Panel A: This should include mating events in the reproductive cycle to demonstrate differences in the feeding behavior of Ae. aegypti.<br /> Panel F: In treatments where insects were not provided either blood or sugar, how is it that some females and males had fed? Also, it is unclear why the y-axis label is % fed when the caption indicates this is a choice assay. Also, it is interesting that sugar-starved females did not increase sugar intake. Is there any explanation for this (was it expected)?

(4) Figure 3<br /> In the neurotranscriptome analysis of the (central) brain involving the two types of comparisons, can the authors clarify what "excluded in males" refers to? Does this imply that only genes not expressed in males were considered in the analysis? If so, what about co-expressed genes that have a specific function in female feeding behaviour?

(5) Figure 4<br /> The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well. Relatedly, blood-feeding is decreased when both neuropeptide transcripts are targeted compared to uninjected (panel C) but not compared to dsGFP injected (panel E). Why is this the case if authors showed earlier in this figure (panel B) that dsGFP does not impact blood feeding? In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data, and how do transcript levels of RYa and sNPF compare in the brain versus the abdomen (the presentation of data doesn't make this relationship clear).

(6) As an overall comment, the figure captions are far too long and include redundant text presented in the methods and results sections.

(7) Criteria used for identifying neuropeptides promoting blood-feeding: statement that reads "all neuropeptides, since these are known to regulate feeding behaviours". This is not accurate since not all neuropeptides govern feeding behaviors, while certainly a subset do play a role.

(8) In the section beginning with "Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels...", the authors state that there was no change in blood-feeding and later state the opposite. The wording should be clarified as it is unclear.

(9) Just before the conclusions section, the statement that "neuropeptide receptors are often ligand-promiscuous" is unjustified. Indeed, many studies have shown in heterologous systems that high concentrations of structurally related peptides, which are not physiologically relevant, might cross-react and activate a receptor belonging to a different peptide family; however, the natural ligand is often many times more potent (in most cases, orders of magnitude) than structurally related peptides. This is certainly the case for various RYamide and sNPF receptors characterized in various insect species.

(10) Methods<br /> In the dsRNA-mediated gene knockdown section, the authors could more clearly describe how much dsRNA was injected per target. At the moment, the reader must carry out calculations based on the concentrations provided and the injected volume range provided later in this section.

It is also unclear how tissue-specific knockdown was achieved by performing injection on different days/times. The authors need to explain/support, and justify how temporal differences in injection lead to changes in tissue-specific expression. Does the blood-brain barrier limit knockdown in the brain instead, while leaving expression in the peripheral organs susceptible? For example, in Figure 4, the data support that knockdown in the head/brain is only effective in unfed animals compared to uninjected animals, while there is no evidence of knockdown in the brain relative to dsGFP-injected animals. Comparatively, evidence appears to show stronger evidence of abdominal knockdown mostly for the RYa transcript (>90%) while still significantly for the sNPF transcript (>60%).

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(3) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(4) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated (for example, with peptide injection or overexpression experiments).

(2) The proposed model regarding central versus peripheral (gut) peptide action is inconsistently presented and lacks strong experimental support.

(3) Some conclusions appear premature based on the current data and would benefit from additional functional validation.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

Bansal et al. present a study on the fundamental blood and nectar feeding behaviors of the critical disease vector, Anopheles stephensi. The study encompasses not just the fundamental changes in blood feeding behaviors of the crucially understudied vector, but then uses a transcriptomic approach to identify candidate neuromodulation pathways which influence blood feeding behavior in this mosquito species. The authors then provide evidence through RNAi knockdown of candidate pathways that the neuromodulators sNPF and Rya modulate feeding either via their physiological activity in the brain alone or through joint physiological activity along the brain-gut axis (but critically not the gut alone). Overall, I found this study to be built on tractable, well-designed behavioral experiments.

Their study begins with a well-structured experiment to assess how the feeding behaviors of A. stephensi change over the course of its life history and in response to its age, mating, and oviposition status. The authors are careful and validate their experimental paradigm in the more well-studied Ae. aegypti, and are able to recapitulate the results of prior studies, which show that mating is a prerequisite for blood feeding behaviors in Ae. aegypt. Here they find A. Stephensi, like other Anopheline mosquitoes, has a more nuanced regulation of its blood and nectar feeding behaviors.

The authors then go on to show in a Y-maze olfactometer that ,to some degree, changes in blood feeding status depend on behavioral modulation to host cues, and this is not likely to be a simple change to the biting behaviors alone. I was especially struck by the swap in valence of the host cues for the blood-fed and mated individuals, which had not yet oviposited. This indicates that there is a change in behavior that is not simply desensitization to host cues while navigating in flight, but something much more exciting is happening.

The authors then use a transcriptomic approach to identify candidate genes in the blood-feeding stages of the mosquito's life cycle to identify a list of 9 candidates that have a role in regulating the host-seeking status of A. stephensi. Then, through investigations of gene knockdown of candidates, they identify the dual action of RYa and sNPF and candidate neuromodulators of host-seeking in this species. Overall, I found the experiments to be well-designed. I found the molecular approach to be sound. While I do not think the molecular approach is necessarily an all-encompassing mechanism identification (owing mostly to the fact that genetic resources are not yet available in A. stephensi as they are in other dipteran models), I think it sets up a rich line of research questions for the neurobiology of mosquito behavioral plasticity and comparative evolution of neuromodulator action.

We appreciate the reviewer’s detailed summary of our work. We thank them for their positive comments and agree with them on the shortcomings of our approach.

Strengths:

I am especially impressed by the authors' attention to small details in the course of this article. As I read and evaluated this article, I continued to think about how many crucial details could potentially have been missed if this had not been the approach. The attention to detail paid off in spades and allowed the authors to carefully tease apart molecular candidates of blood-seeking stages. The authors' top-down approach to identifying RYamide and sNPF starting from first principles behavioral experiments is especially comprehensive. The results from both the behavioral and molecular target studies will have broad implications for the vectorial capacity of this species and comparative evolution of neural circuit modulation.

We really appreciate that the reviewer has recognised the attention to detail we have tried to put, thank you!

Weaknesses:

There are a few elements of data visualizations and methodological reporting that I found confusing on a first few read-throughs. Figure 1F, for example, was initially confusing as it made it seem as though there were multiple 2-choice assays for each of the conditions. I would recommend removing the "X" marker from the x-axis to indicate the mosquitoes did not feed from either nectar, blood, or neither in order to make it clear that there was one assay in which mosquitoes had access to both food sources, and the data quantify if they took both meals, one meal, or no meals.

We thank the reviewer for flagging the schematic in figure 1F. As suggested, we have removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose in the assay. For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data, as it does not capture the variability in the data.

I would also like to know more about how the authors achieved tissue-specific knockdown for RNAi experiments. I think this is an intriguing methodology, but I could not figure out from the methods why injections either had whole-body or abdomen-specific knockdown.

The tissue-specific knockdown (abdomen only or abdomen+head) emerged from initial standardisations where we were unable to achieve knockdown in the head unless we used higher concentrations of dsRNA and did the injections in older females. We realised that this gave us the opportunity to isolate the neuronal contribution of these neuropeptides in the phenotype produced. Further optimisations revealed that injecting dsRNA into 0-10h old females produced abdomen-specific knockdowns without affecting head expression, whereas injections into 4 days old females resulted in knockdowns in both tissues. Moreover, head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts.

We have mentioned the knockdown conditions- time of injection and the amount dsRNA injected- for tissue-specific knockdowns in methods but realise now that it does not explain this well enough. We have now edited it to state our methodology more clearly (see lines 932-948).

I also found some interpretations of the transcriptomic to be overly broad for what transcriptomes can actually tell us about the organism's state. For example, the authors mention, "Interestingly, we found that  after a blood meal, glucose is neither spent nor stored, and that the female brain goes into a state of metabolic 'sugar rest', while actively processing proteins (Figure S2B, S3)".

This would require a physiological measurement to actually know. It certainly suggests that there are changes in carbohydrate metabolism, but there are too many alternative interpretations to make this broad claim from transcriptomic data alone.

We thank the reviewer for pointing this out and agree with them. We have now edited our statement to read:

“Instead, our data suggests altered carbohydrate metabolism  after a blood meal, with the female brain potentially entering a state of metabolic 'sugar rest' while actively processing proteins (Figure S2B, S3). However, physiological measurements of carbohydrate and protein metabolism will be required to confirm whether glucose is indeed neither spent nor stored during this period.” See lines 271-277.

Reviewer #2 (Public review):

Summary:

In this manuscript, Bansal et al examine and characterize feeding behaviour in Anopheles stephensi mosquitoes. While sharing some similarities to the well-studied Aedes aegypti mosquito, the authors demonstrate that mated females, but not unmated (virgin) females, exhibit suppression in their bloodfeeding behaviour. Using brain transcriptomic analysis comparing sugar-fed, blood-fed, and starved mosquitoes, several candidate genes potentially responsible for influencing blood-feeding behaviour were identified, including two neuropeptides (short NPF and RYamide) that are known to modulate feeding behaviour in other mosquito species. Using molecular tools, including in situ hybridization, the authors map the distribution of cells producing these neuropeptides in the nervous system and in the gut. Further, by implementing systemic RNA interference (RNAi), the study suggests that both neuropeptides appear to promote blood-feeding (but do not impact sugar feeding), although the impact was observed only  after both neuropeptide genes underwent knockdown.

Strengths and/or weaknesses:

Overall, the manuscript was well-written; however, the authors should review carefully, as some sections would benefit from restructuring to improve clarity. Some statements need to be rectified as they are factually inaccurate.

Below are specific concerns and clarifications needed in the opinion of this reviewer:

(1) What does "central brains" refer to in abstract and in other sections of the manuscript (including methods and results)? This term is ambiguous, and the authors should more clearly define what specific components of the central nervous system was/were used in their study.

Central brain, or mid brain, is a commonly used term to refer to brain structures/neuropils without the optic lobes (For example: https://www.nature.com/articles/s41586-024-07686-5). In this study we have focused our analysis on the central brain circuits involved in modulating blood-feeding behaviour and have therefore excluded the optic lobes. As optic lobes account for nearly half of all the neurons in the mosquito brain (https://pmc.ncbi.nlm.nih.gov/articles/PMC8121336/), including them would have disproportionately skewed our transcriptomic data toward visual processing pathways.

We have indicated this in figure 3A and in the methods (see lines 800-801, 812). We have now also clarified it in the results section for neuro-transcriptomics to avoid confusion (see lines 236-237).

(2) The abstract states that two neuropeptides, sNPF and RYamide are working together, but no evidence is summarized for the latter in this section.

We thank the reviewer for pointing this out. We have now added a statement “This occurs in the context of the action of RYa in the brain” to end of the abstract, for a complete summary of our proposed model.

(3) Figure 1

Panel A: This should include mating events in the reproductive cycle to demonstrate differences in the feeding behavior of Ae. aegypti.

Our data suggest that mating can occur at any time between eclosion and oviposition in An. stephensi and between eclosion and blood feeding in Ae. aegypti. Adding these into (already busy) 1A, would cloud the purpose of the schematic, which is to indicate the time points used in the behavioural assays and transcriptomics.

Panel F: In treatments where insects were not provided either blood or sugar, how is it that some females and males had fed? Also, it is unclear why the y-axis label is % fed when the caption indicates this is a choice assay. Also, it is interesting that sugar-starved females did not increase sugar intake. Is there any explanation for this (was it expected)?

We apologise for the confusion. The experiment is indeed a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. The x-axis indicates the choice made by the mosquitoes, not the choice provided in the assay, and the y-axis indicates the percentage of males or females that made each particular choice. We have now removed the “X” markers from the x-axis and revised the axis label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

In this assay, we scored females only for the presence or absence of each meal type (blood or sugar) and are therefore unable to comment on whether sugar-starved females consumed more sugar than sugarsated females. However, when sugar-starved, a higher proportion of females consumed both blood and sugar, while fewer fed on blood alone.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data as it does not capture the variability in the data.

(4) Figure 3

In the neurotranscriptome analysis of the (central) brain involving the two types of comparisons, can the authors clarify what "excluded in males" refers to? Does this imply that only genes not expressed in males were considered in the analysis? If so, what about co-expressed genes that have a specific function in female feeding behaviour?

This is indeed correct. We reasoned that since blood feeding is exclusive to females, we should focus our analysis on genes that were specifically upregulated in them. As the reviewer points out, it is very likely that genes commonly upregulated in males and females may also promote blood feeding and we will miss out on any such candidates based on our selection criteria.

(5) Figure 4

The authors state that there is more efficient knockdown in the head of unfed females; however, this is not accurate since they only get knockdown in unfed animals, and no evidence of any knockdown in fed animals (panel D). This point should be revised in the results test as well.

Perhaps we do not understand the reviewer’s point or there has been a misunderstanding. In figure 4D, we show that while there is more robust gene knockdown in unfed females, blood-fed females also showed modest but measurable knockdowns ranging from 5-40% for RYamide and 2-21% for sNPF.

Relatedly, blood-feeding is decreased when both neuropeptide transcripts are targeted compared to uninjected (panel C) but not compared to dsGFP injected (panel E). Why is this the case if authors showed earlier in this figure (panel B) that dsGFP does not impact blood feeding?

We realise this concern stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens.

4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomens. We have now added a schematic in the plots to make this clearer.

In addition, do the uninjected and dsGFP-injected relative mRNA expression data reflect combined RYa and sNPF levels? Why is there no variation in these data,…

In these qPCRs, we calculated relative mRNA expression using the delta-delta Ct method (see line 975). For each neuropeptide its respective control was used. For simplicity, we combined the RYa and sNPF control data into a single representation. The value of this control is invariant because this method sets the control baseline to a value of 1.

…and how do transcript levels of RYa and sNPF compare in the brain versus the abdomen (the presentation of data doesn't make this relationship clear).

The reviewer is correct in pointing out that we have not clarified this relationship in our current presentation. While we have not performed absolute mRNA quantifications, we extracted relative mRNA levels from qPCR data of 96h old unmanipulated control females. We observed that both sNPF and RYa transcripts are expressed at much lower levels in the abdomens, as compared to those in the heads, as shown in the graphs inserted below.

Author response image 1.

(6) As an overall comment, the figure captions are far too long and include redundant text presented in the methods and results sections.

We thank the reviewer for flagging this and have now edited the legends to remove redundancy.

(7) Criteria used for identifying neuropeptides promoting blood-feeding: statement that reads "all neuropeptides, since these are known to regulate feeding behaviours". This is not accurate since not all neuropeptides govern feeding behaviors, while certainly a subset do play a role.

We agree with the reviewer that not all neuropeptides regulate feeding behaviours. Our statement refers to the screening approach we used: in our shortlist of candidates, we chose to validate all neuropeptides.

(8) In the section beginning with "Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels...", the authors state that there was no change in blood-feeding and later state the opposite. The wording should be clarified as it is unclear.

Thank you for pointing this out. We were referring to an unchanged proportion of the blood fed females. We have now edited the text to the following:

“Two neuropeptides - sNPF and RYa - showed about 25% and 40% reduced mRNA levels in the heads but the proportion of females that took blood meals remained unchanged”. See lines 338-340.

(9) Just before the conclusions section, the statement that "neuropeptide receptors are often ligand promiscuous" is unjustified. Indeed, many studies have shown in heterologous systems that high concentrations of structurally related peptides, which are not physiologically relevant, might cross-react and activate a receptor belonging to a different peptide family; however, the natural ligand is often many times more potent (in most cases, orders of magnitude) than structurally related peptides. This is certainly the case for various RYamide and sNPF receptors characterized in various insect species.

We agree with the reviewer and apologise for the mistake. We have now removed the statement.

(10) Methods

In the dsRNA-mediated gene knockdown section, the authors could more clearly describe how much dsRNA was injected per target. At the moment, the reader must carry out calculations based on the concentrations provided and the injected volume range provided later in this section.

We have now edited the section to reflect the amount of dsRNA injected per target. Please see lines 921-931.

It is also unclear how tissue-specific knockdown was achieved by performing injection on different days/times. The authors need to explain/support, and justify how temporal differences in injection lead to changes in tissue-specific expression. Does the blood-brain barrier limit knockdown in the brain instead, while leaving expression in the peripheral organs susceptible?

To achieve tissue-specific knockdowns of sNPF and RYa, we optimised both the time of injection as well as the dsRNA concentration to be injected. Injecting dsRNA into 0-10h females produced abdomen specific knockdowns without affecting head expression, whereas injections into 96h old females resulted in knockdowns in both tissues. Head knockdowns in older females required higher dsRNA concentrations, with knockdown efficiency correlating with the amount injected. In contrast, abdominal knockdowns in younger females could be achieved even with lower dsRNA amounts, reflecting the lower baseline expression of sNPF in abdomens compared to heads and the age-dependent increase in head expression (as confirmed by qPCR). It is possible that the blood-brain barrier also limits the dsRNA entering the brain, thereby requiring higher amounts to be injected for head knockdowns.

We have now edited this section to state our methodology more clearly (see lines 932-948).

For example, in Figure 4, the data support that knockdown in the head/brain is only effective in unfed animals compared to uninjected animals, while there is no evidence of knockdown in the brain relative to dsGFP-injected animals. Comparatively, evidence appears to show stronger evidence of abdominal knockdown mostly for the RYa transcript (>90%) while still significantly for the sNPF transcript (>60%).

As we explained earlier, this concern likely stems from our representation of the data. Since we had earlier determined that dsGFP-injected females fed similarly to uninjected females (fig 4B), we used these controls interchangeably in subsequent experiments. To avoid confusion, we have now only used the label ‘control’ in figure 4 (and supplementary figure S9) and specified which control was used for each experiment in the legend.

In addition to this, we wanted to clarify that fig 4C and 4E are independent experiments. 4C is the behaviour corresponding to when the neuropeptides were knocked down in both heads and abdomens. 4E is the behaviour corresponding to when the neuropeptides were knocked down in only the abdomen. We have now added a schematic in the plots to make this clearer.

Reviewer #3 (Public review):

Summary:

This manuscript investigates the regulation of host-seeking behavior in Anopheles stephensi females across different life stages and mating states. Through transcriptomic profiling, the authors identify differential gene expression between "blood-hungry" and "blood-sated" states. Two neuropeptides, sNPF and RYamide, are highlighted as potential mediators of host-seeking behavior. RNAi knockdown of these peptides alters host-seeking activity, and their expression is anatomically mapped in the mosquito brain (sNPF and RYamide) and midgut (sNPF only).

Strengths:

(1) The study addresses an important question in mosquito biology, with relevance to vector control and disease transmission.

(2) Transcriptomic profiling is used to uncover gene expression changes linked to behavioral states.

(3) The identification of sNPF and RYamide as candidate regulators provides a clear focus for downstream mechanistic work.

(3) RNAi experiments demonstrate that these neuropeptides are necessary for normal host-seeking behavior.

(4) Anatomical localization of neuropeptide expression adds depth to the functional findings.

Weaknesses:

(1) The title implies that the neuropeptides promote host-seeking, but sufficiency is not demonstrated (for example, with peptide injection or overexpression experiments).

Demonstrating sufficiency would require injecting sNPF peptide or its agonist. To date, no small-molecule agonists (or antagonists) that selectively mimic sNPF or RYa neuropeptides have been identified in insects. An NPY analogue, TM30335, has been reported to activate the Aedes aegypti NPY-like receptor 7 (NPYLR7; Duvall et al., 2019), which is also activated by sNPF peptides at higher doses (Liesch et al., 2013). Unfortunately, the compound is no longer available because its manufacturer, 7TM Pharma, has ceased operations. Synthesising the peptides is a possibility that we will explore in the future.

(2) The proposed model regarding central versus peripheral (gut) peptide action is inconsistently presented and lacks strong experimental support.

The best way to address this would be to conduct tissue-specific manipulations, the tools for which are not available in this species. Our approach to achieve head+abdomen and abdomen only knockdown was the closest we could get to achieving tissue specificity and allowed us to confirm that knockdown in the head was necessary for the phenotype. However, as the reviewer points out, this did not allow us to rule out any involvement of the abdomen. This point has been addressed in lines 364-371.

(3) Some conclusions appear premature based on the current data and would benefit from additional functional validation.

The most definitive way of demonstrating necessity of sNPF and RYa in blood feeding would be to generate mutant lines. While we are pursuing this line of experiments, they lie beyond the scope of a revision. In its absence, we relied on the knockdown of the genes using dsRNA. We would like to posit that despite only partial knockdown, mosquitoes do display defects in blood-feeding behaviour, without affecting sugar-feeding. We think this reflects the importance of sNPF in promoting blood feeding.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Overall, I found this manuscript to be well-prepared, visually the figures are great and clearly were carefully thought out and curated, and the research is impacwul. It was a wonderful read from start to finish. I have the following recommendations:

Thank you very much, we are very pleased to hear that you enjoyed reading our manuscript!

(1) For future manuscripts, it would make things significantly easier on the reviewer side to submit a format that uses line numbers.

We sincerely apologise for the oversight. We have now incorporated line numbers in the revised manuscript.

(2) There are a few statements in the text that I think may need clarification or might be outside the bounds of what was actually studied here. For example, in the introduction "However, mating is dispensable in Anophelines even under conditions of nutritional satiety". I am uncertain what is meant by this statement - please clarify.

We apologise for the lack of clarity in the statement and have now deleted it since we felt it was not necessary.

(3) Typo/Grammatical minutiae:

a) A small idiosyncrasy of using hyphens in compound words should also be fixed throughout. Typically, you don't hyphenate if the words are being used as a noun, as in the case: e.g. "Age affects blood feeding.". However, you would hyphenate if the two words are used as a compound adjective "Age affects blood-feeding behavior". This may not be an all-inclusive list, but here are some examples where hyphens need to either be removed or added. Some examples:

"Nutritional state also influences other internal state outputs on blood-feeding": blood-feeding -> blood feeding

"... the modulation of blood-feeding": blood-feeding -> blood feeding

"For example, whether virgin females take blood-meals...": blood-meals -> blood meals

".... how internal and external cues shape meal-choice"-> meal choice

"blood-meal" is often used throughout the text, but is correctly "blood meal" in the figures.

There are many more examples throughout.

We apologise for these errors and appreciate the reviewer’s keen eye. We have now fixed them throughout the manuscript.

b) Figure 1 Caption has a typo: "co-housed males were accessed for sugar-feeding" should be "co-housed males were assessed for sugar feeding"

We apologise for the typo and thank the reviewer for spotting it. We have now corrected this.

c) It would be helpful in some other figure captions to more clearly label which statement is relevant to which part of the text. For example, in Figure 4's caption.

"C,D. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head (C). Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected blood-fed and unfed females, as compared to that in uninjected females, analysed via qPCR (D)."

I found re-referencing C and D at the end of their statements makes it look as thought C precedes the "Relative mRNA expression" and on a first read through, I thought the figure captions were backwards. I'd recommend reformating here and throughout consistently to only have the figure letter precede its relevant caption information, e.g.:

"C. Blood-feeding and sugar-feeding behaviour of females when both RYa and sNPF are knocked down in the head. D. Relative mRNA expressions of RYa and sNPF in the heads of dsRYa+dssNPF - injected bloodfed and unfed females, as compared to that in uninjected females, analysed via qPCR."

We have now edited the legends as suggested.

Reviewer #2 (Recommendations for the authors):

Separately from the clarifications and limitations listed above, the authors could strengthen their study and the conclusions drawn if they could rescue the behavioural phenotype observed following knockdown of sNPF and RYamide. This could be achieved by injection of either sNPF or RYa peptide independently or combined following knockdown to validate the role of these peptides in promoting blood-feeding in An. stephensi. Additionally, the apparent (but unclear) regionalized (or tissue-specific) knockdown of sNPF and RYamide transcripts could be visualized and verified by implementing HCR in situ hyb in knockdown animals (or immunohistochemistry using antibodies specific for these two neuropeptides).

In a follow up of this work, we are generating mutants and peptides for these candidates and are planning to conduct exactly the experiments the reviewer suggests.

Reviewer #3 (Recommendations for the authors):

The loss-of-function data suggest necessity but not sufficiency. Synthetic peptide injection in non-host seeking (blood-fed mated or juvenile) mosquitoes would provide direct evidence for peptide-induced behavioral activation. The lack of these experiments weakens the central claim of the paper that these neuropeptides directly promote blood feeding.

As noted above, we plan to synthesise the peptide to test rescue in a mutant background and sufficiency.

Some of the claims about knockdown efficiency and interpretation are conflicting; the authors dismiss Hairy and Prp as candidates due to 30-35% knockdown, yet base major conclusions on sNPF and RYamide knockdowns with comparable efficiencies (25-40%). This inconsistency should be addressed, or the justification for different thresholds should be clearly stated.

We have not defined any specific knockdown efficacy thresholds in the manuscript, as these can vary considerably between genes, and in some cases, even modest reductions can be sufficient to produce detectable phenotypes. For example, knockdown efficiencies of even as low as about 25% - 40% gave us observable phenotypes for sNPF and RYa RNAi (Figure S9B-G).

No such phenotypes were observed for Hairy (30%) or Prp (35%) knockdowns. Either these genes are not involved in blood feeding, or the knockdown was not sufficient for these specific genes to induce phenotypes. We cannot distinguish between these scenarios.

The observation that knockdown animals take smaller blood meals is interesting and could reflect a downstream effect of altered host-seeking or an independent physiological change. The relationship between meal size and host-seeking behavior should be clarified.

We agree with the reviewer that the reduced meal size observed in sNPF and RYa knockdown animals could result from their inability to seek a host or due to an independent effect on blood meal intake. Unfortunately, we did not measure host-seeking in these animals. We plan to distinguish between these possibilities using mutants in future work.

Several figures are difficult to interpret due to cluttered labeling and poorly distinguishable color schemes. Simplifying these and improving contrast (especially for co-housed vs. virgin conditions) would enhance readability.

We regret that the reviewer found the figures difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition). Wherever mated females were used, we have now appended “(m)” to the annotations and consistently depicted these females with striped abdomens in all the schematics. We believe these changes will improve clarity and readability.

The manuscript does not clearly justify the use of whole-brain RNA sequencing to identify peptides involved in metabolic or peripheral processes. Given that anticipatory feeding signals are often peripheral, the logic for brain transcriptomics should be explained.

The reviewer is correct in pointing out that feeding signals could also emerge from peripheral tissues. Signals from these tissues – in response to both changing nutritional and reproductive states – are then integrated by the central brain to modulate feeding choices. For example, in Drosophila, increased protein intake is mediated by central brain circuitry including those in the SEZ and central complex (Munch et al., 2022; Liu et al., 2017; Goldschmidt et al., 2023). In the context of mating, male-derived sex peptide further increases protein feeding by acting on a dedicated central brain circuitry (Walker et al., 2015). We, therefore focused on the central brain for our studies.

The proposed model suggests brain-derived peptides initiate feeding, while gut peptides provide feedback. However, gut-specific knockdowns had no effect, undermining this hypothesis. Conversely, the authors also suggest abdominal involvement based on RNAi results. These contradictions need to be resolved into a consistent model.

We thank the reviewer for raising this point and recognise their concern. Our reasons for invoking an involvement of the gut were two-fold:

(1) We find increased sNPF transcript expression in the entero-endocrine cells of the midgut in blood-hungry females, which returns to baseline  after a blood-meal (Fig. 4L, M).

(2) While the abdomen-only knockdowns did not affect blood feeding, every effective head knockdown that affected blood feeding also abolished abdominal transcript levels (Fig. S9C, F). (Achieving a head-only reduction proved impossible because (i) systemic dsRNA delivery inevitably reaches the abdomen and (ii) abdominal expression of both peptides is low, leaving little dynamic range for selective manipulation.) Consequently, we can only conclude the following: 1) that brain expression is required for the behaviour, 2) that we cannot exclude a contributory role for gut-derived sNPF. We have discussed this in lines 364-371.

The identification of candidate receptors is promising, but the manuscript would be significantly strengthened by testing whether receptor knockdowns phenocopy peptide knockdowns. Without this, it is difficult to conclude that the identified receptors mediate the behavioral effects.

We agree that functional validation of the receptors would strengthen the evidence for sNPF and RYa_mediated control of blood feeding in _An. stephensi. We selected these receptors based on sequence homology. A possibility remains that sNPF neuropeptides activate more than one receptor, each modulating a distinct circuit, as shown in the case of Drosophila Tachykinin (https://pmc.ncbi.nlm.nih.gov/articles/PMC10184743/). This will mean a systematic characterisation and knockdown of each of them to confirm their role. We are planning these experiments in the future.

The authors compared the percentage changes in sugar-fed and blood-fed animals under sugar-sated or sugar-starved conditions. Figure 1F should reflect what was discussed in the results.

Perhaps this concern stems from our representation of the data in figure 1F? We have now edited the xaxis and revised its label from “choice of food” to “choice made” to better reflect what food the mosquitoes chose to take.

For clarity, we have now also plotted the same data as stacked graphs at the bottom of Fig. 1F, which clearly shows the proportion of mosquitoes fed on each particular choice. We avoid the stacked graph as the sole representation of this data because it does not capture the variability in the data.

Minor issues:

(1) The authors used mosquitoes with belly stripes to indicate mated females. To be consistent, the post-oviposition females should also have belly stripes.

We thank the reviewer for pointing this out. We have now edited all the figures as suggested.

(2) In the first paragraph on the right column of the second page, the authors state, "Since females took blood-meals regardless of their prior sugar-feeding status and only sugar-feeding was selectively suppressed by prior sugar access." Just because the well-fed animals ate less than the starved animals does not mean their feeding behavior was suppressed.

Perhaps there has been a misunderstanding in the experimental setup of figure 1F, probably stemming from our data representation. The experiment is a choice assay in which sugar-starved or sugar-sated females, co-housed with males, were provided simultaneous access to both blood and sugar, and were assessed for the choice made (indicated on the x-axis): both blood and sugar, blood only, sugar only, or neither. We scored females only for the presence or absence of each meal type (blood or sugar) and did not quantify the amount consumed.

(3) The figure legend for Figure 1A and the naming convention for different experimental groups are difficult to follow. A simplified or consistently abbreviated scheme would help readers navigate the figures and text.

We regret that the reviewer found the figure difficult to follow. We have now revised our annotations throughout the manuscript for enhanced readability. For example, “D1^B” is now “D1^PBM” (post-bloodmeal) and “D1^O” is now “D1^PO” (post-oviposition).

(4) In the last paragraph of the Y-maze olfactory assay for host-seeking behaviour in An. stephensi in Methods, the authors state, "When testing blood-fed females, aged-matched sugar-fed females (bloodhungry) were included as positive controls where ever possible, with satisfactory results." The authors should explicitly describe what the criteria are for "satisfactory results".

We apologise for the lack of clarity. We have now edited the statement to read:

“When testing blood-fed females, age-matched sugar-fed females (blood-hungry) were included wherever possible as positive controls. These females consistently showed attraction to host cues, as expected.” See lines 786-790.

(5) In the first paragraph of the dsRNA-mediated gene knockdown section in Methods, dsRNA against GFP is used as a negative control for the injection itself, but not for the potential off-target effect.

We agree with the reviewer that dsGFP injections act as controls only for injection-related behavioural changes, and not for off-target effects of RNAi. We have now corrected the statement. See lines 919-920.

To control for off-target effects, we could have designed multiple dsRNAs targeting different parts of a given gene. We regret not including these controls for potential off-target effects of dsRNAs injected.

(6) References numbers 48, 89, and 90 are not complete citations.

We thank the reviewer for spotting these. We have now corrected these citations.

eLife Assessment

This manuscript investigates inter-hemispheric interactions in the olfactory system of Xenopus tadpoles. Using a combination of electrophysiology, pharmacology, imaging, and uncaging, the transection of the contralateral nerve is shown to lead to larger odor responses in the unmanipulated hemisphere, and implicates dopamine signaling in this process. The study uses a rich and sophisticated array of tools to investigate olfactory coding and uncovers valuable mechanisms of signaling. However, the data is incomplete, with a few of the conclusions not being well-supported by the data; the interpretation should be adjusted with some caveats, or additional experiments should be done to support these conclusions.

Reviewer #1 (Public review):

In this study, the authors investigate LFP responses to methionine in the olfactory system of the Xenopus tadpole. They show that this response is local to the glomerular layer, arises ipsilaterally, and is blocked by pharmacological blockade of AMPA and NMDA receptors, with little modulation during blockade of GABA-A receptors. They then show that this response is translently enlarged following transection of the contralateral olfactory nerve, but not the optic lobe nerve. Measurement of ROS- a marker of inflammation- was not affected by contralateral nerve transection, and LFP expansion was not affected by pharmacological blockade of ROS production. Imaging biased towards presynaptic terminals suggests that the enlargement of the LFP has a presynaptic component. A D2 antagonist increases the LFP size and variability in intact tadpoles, while a GABA-B antagonist does not. On this basis, the authors conclude that the increase driven by contralateral nerve transection is due to DA signaling.

Overall, I found the array of techniques and approaches applied in this study to be creatively and effectively employed. However, several of the conclusions made in the Discussion are too strong, given the evidence presented. For example, the authors state that "The observed potentiation was not related to inflammatory mediators associated to inury, because it was caused by a release of the inhibition made by D2 dopamine receptor present in OSN axon terminals." This statement is too strong - the authors have shown that D2 receptors are sufficient to cause an increase in LFP, but not that they are required for the potentiation evoked by nerve transection. The right experiment here would be to get rid of the D2 receptors prior to transection and show that the potentiation is now abolished. In addition, the authors have not shown any data localizing D2 receptors to OSN axon terminals.

Similarly, the authors state, "the onset of LFP changes detected in glomeruli is determined by glutamate release from OSNs." Again, the authors have shown that blockade of AMPA/NMDA receptors decreases the LFP, and that uncaging of glutamate can evoke small negative deflections, but not that the intact signal arises from glutamate release from OSNs. The conclusions about the in vivo contribution of this contralateral pathway are also rather speculative. Acute silencing of one hemisphere would likely provide more insight into the moment-to-moment contributions of bilateral signals to those recorded in one hemisphere.

Author response:

Thank you for your time and for considering our manuscript as a Reviewed Preprint. We also would like to thank Reviewer 1 for their evaluation of our manuscript.

Here, we present a provisional response to reviewer comments and following their suggestions we will make an effort to: i) increase evidence for the role of dopamine in olfactory glomeruli and ii) delineate the circuit involved mediating the observed potentiation. Next, we briefly describe the set of experiments that are in progress or will be performed to improve our paper.

We will carry out immunostainings for tyrosine hydroxylase to certify that dopamine can be released on the genetically labelled glomerulus. There is a lack of good commercial antibodies for Xenopus (we already tried one and did not work, PA1-4679, Thermofisher scientific), but we will look for alternatives. In a previous set of experiments, we attempted to measure dopamine release in the glomerular layer by electroporating olfactory sensory neurons or olfactory bulb neurons with the dopamine sensors dLight1.1 (Addgene #111053) or dLight1.3 (Addgene # 111056). In our hands, fluorescence signals were extremely weak, barely undetectable. Similar results were obtained after electroporating the tectum or the rhombencephalon. We propose to repeat experiments using a more sensitive sensor such as GRAB_DA2m. Other approaches, such as performing single cell transcriptomics of olfactory sensory neurons might be considered to confirm the expression of D2 receptors.

We agree with the reviewer that we should obtain more lines of evidence in support for a presynaptic inhibition mediated by D2 receptors.To gain insight on the bilateral circuit mediating the observed potentiation of glomerular responses we are currently investigating the role of dorsolateral pallium neurons. In Xenopus tadpoles the lateral pallium plays an analogous role to the olfactory cortex in amniotes. Preliminary observations show that neurons located in this pallial region respond to ipsilateral stimulation of the olfactory epithelium and if damaged, a contralateral potentiation of glomerular output occurs. We aim to conclude this set of experiments and include it in the paper as we believe it clarifies the circuitry involved.