Author Response

Reviewer #3 (Public Review):

Comment 1: I'm having some difficulty understanding the logic of Figure 5 in determining cis processing. It is an inverse of figure 4, and in my view, provides further evidence of trans processing. A better experiment would be to use WT-citrine tagged protein with catalytic dead mcherry and image them together. This would show WT cis processing occurs faster than trans processing as citrine specks should appear earlier than the mCherry ones. Can also do colocalization and FRET-based assays with the pair.

We thank the reviewer for pointing this out. While our data demonstrate that the same molecule must be catalytically active and competent for processing at the IDL (Figure 5), we agree that the data do not rule out trans-processing as a mechanism for speck formation. We have therefore modified the interpretation of these findings accordingly (pp. 7-8). We agree that some of the quantitative assays the reviewer has suggested would strengthen this logic, and we are making efforts to carry out a kinetic FRET-based assay for our upcoming biochemistry-focused manuscript to better characterize the enzymatic affinity of Casp11 for cis- vs. trans- based autoprocessing, and how either impacts Casp11 speck assembly.

Comment 2: Do those casp11 specks still contain CARDs?- i.e. is the second cleavage necessary for speck formation? Is CARD necessary at all? Would adding the TEV site at CDL and b/w p20 and p10 rescue? i.e. trans-activate?

We are grateful to the reviewer for these insightful questions, which we also had considered. We addressed this question in two ways – first by replacing the CARD with a DmrB dimerizable domain that undergoes inducible dimerization of Casp11 in the presence of the dimerizing drug AP20187. Critically, inducible dimerization of DmrB-ΔCARD-Casp11-mCherry significantly enhances Casp11-mCherry speck formation, and this speck formation requires catalytic activity, even in the presence of dimerizer (Figure 6A-C). Moreover, we generated CARD-less Casp11-mCherry constructs containing wild-type p20-p10 and catalytically inactive p20-p10. Intriguingly, the CARD was dispensable for spontaneous Casp11-mCherry speck formation, which again was dependent on catalytic activity (Figure 6-figure supplement 2A-B). While we do not currently have data with a TEV-cleavable CDL construct, our data here demonstrate that the CARD is dispensable for speck formation in an overexpression system, implying that the p20/p10 contains all the information that is necessary and sufficient to mediate spontaneous assembly of Casp11 specks in HEK293T cells. Nonetheless, as forced dimerization enhances speck formation (Figure, we hypothesize that CARD-LPS interactions act to facilitate catalytic activity and push cooperative assembly of the Casp11 speck.

To address whether both the N-terminal CARD and C-terminal p10 domains are present in Casp11 specks, we performed a dual-fluorophore co-localization assay in which we transiently expressed C-terminal mCherry-tagged Casp11 constructs (Casp11-mCherry) in HEK293T cells that stably express N-terminal Flag-tagged Casp11 (2xFLAG-Casp11). As expected, Casp11-mCherry formed specks spontaneously in this setting (Figure 3-figure supplement 1). Critically, both the N-terminal FLAG and C-terminal mCherry were found together in these specks, indicating the presence of both Casp11 N- and C- termini within the specks. Moreover, the wild-type Casp11-mCherry also recruited catalytically inactive 2xFLAG-Casp11C254A, again supporting the finding that wild-type Casp11 can recruit a catalytic mutant to noncanonical inflammasome complexes.

Comment 3: What are the equations that fit experimental data points and R2 for? E.g. Figure 1E. What are the parameters being fitted/compared and how are those interpreted? A table of fitted values and proper interpretation should be provided.

We thank the reviewer for this request to clarify how the curves were fit to the experimental data points. We have modified our ‘Statistical Analysis’ section and all figure legends that contain dose-response curves to reflect the equations used to fit each curve. Additionally, please find a table of raw values in the corresponding source data provided for each dose-response curve (Figure 2 Source Data 5; Figure 4 Source Data 3, 6; Figure 5 Source Data 3, 4; Figure 7 Source Data 2; and Figure 4-figure supplement 1 Source Data 1).

Author Response

Reviewer #1 (Public Review):

This paper examines different signaling networks and attempts to give general results for when the network will exhibit biphasic behavior, which is the situation when the output of the network is a non-monotonic function of its inputs. The strength of the paper is in the approach it takes. It starts with the simplest network motifs that produce biphasic behavior and then asks too what happens when these motifs are parts of larger networks. Their approach is in contrast to the usual way in which this question is tackled, which tends to be within the confines of a specific signaling network, where general results like the ones that the authors are after, might be hard to spot.

We thank the reviewer for the careful reading of the manuscript and for the comments and appreciate the fact the reviewer regards the approach as the strength of the paper.

The weakness of the paper, in my opinion, is the rather formal description of the results which I am afraid will be of rather limited utility to experimental groups seeking to make use of them. The paper attempts to provide general rules for when to expect biphasic behavior and it was hard to assess to what extent such rules exist as behaviors can change depending on the context of a larger network in which the smaller biphasic one is embedded. The other thing that made assessing the generality of the results difficult is that the input-output functions shown in all the figures are computed for a specific choice of parameters and I was left wondering how different choices of parameters might change the reported behaviors. The lack of specific proposals for how their results should guide future experiments on different signaling networks is another weakness.

We address these points in a number of ways. Initially our presentation was intended to highlight unambiguously which systems (especially the substrate modification building blocks) were capable of biphasic response and which were not, and highlighting parameter dependence on intrinsic kinetic parameters. Based on both referee comments, we make a number of changes

(a) We highlight the rationale for choosing the suite of biochemical substrate modification systems: enzyme/substrate sharing is a key driver for the origins of biphasic responses and the suite of systems we employ allows us to systematically explore this (see Response to Essential Revisions). These are building blocks of many pathways,

(b) Biphasic responses emerge from a built in competing effect. In every instance of substrate modification systems, we now highlight the mechanistic underpinning which gives rise to the competing effect responsible for the biphasic response. This will help experimentalists and modellers alike obtain insights into how such behaviour may arise, and the associated ingredients which facilitate that (which may be relevant in other systems). Similarly, we highlight how altered behaviour at the network level may arise from a biphasic interaction pattern, providing the intuition therein and guide further experimental investigation (also see Response to Essential Revisions).

(c) With regard to parameters (also see Response to Essential Comments) firstly we emphasize that we completely characterize at the substrate modification level, whether biphasic responses are possible as a function of intrinsic kinetic constants. This is done for every system studied. In Fig 2, we depict this, along with sample biphasic dose responses, for pictorial depiction. However, the essential point is that the parametric dependence on intrinsic kinetic parameters is completely done. We indicate in which cases biphasic responses are impossible irrespective of intrinsic kinetic parameters, where they can be obtained for every value of the intrinsic kinetic parameters, and where there are partial restrictions in the intrinsic kinetic parameter space for obtaining this. In the revision we have performed further parametric analysis to assess the impact of species total amount providing further insights. We have also shown that in all these systems biphasic responses can be obtained in ranges of kinetic parameters similar to those found experimentally (eg Wistel et al 2018) and for reasonable species total amounts in systems and synthetic biology. This is analyzed, and depicted in Figure 2-figure supplement 3 and Figure 2-figure supplement 4.

(d) Also, in response to another comment (about behaviour changing in networks): we first emphasize that we start at the substrate modification level to uncover drivers of biphasic responses at this level. Biphasic responses arise from an inbuilt competing effect and we demonstrate different ways in which such an inbuilt competing effect arises, through sharing of enzymes or substrates. While it is true that the behaviour can change as part of a network (a) It still remains that there are these in-built competing effects which can generate biphasic responses (both substrate and enzyme) and this can manifest at a pathway or network level under suitable conditions (b) the fact that behaviour at a network level may be altered is exactly why we consider studies at the network level showing both biphasic patterns in interaction (the overall behaviour is determined by the motif and the biphasic pattern of interaction and studies involving interaction of biphasic responses at both the network and substrate modification level!! (subsection: The network level)

(e) We have also expanded on a paragraph on testable predictions in the conclusions (p10).

Taken together, we believe that these results should interest both experimentalists and modellers and have intrinsic value as well.

While I appreciate that the authors adopted a style of presenting their results such that all the mathematics is buried in the figures, I found that it made reading the paper quite difficult, and contributed to my confusion about which results are general and insensitive to parameter choices and which are not. I believe a narrative that integrated the math with some simple intuition might have been more effective. For example, when the authors say in the text that model M0 is incapable of displaying biphasic response, how general is that result? Later on, when discussing model M2, they provide a criterion for biphasic response in terms of products of rate constants satisfying an inequality, but the meaning of this condition is not described. Such things make it hard to learn from the authors' work.

This has indeed been incorporated, and we agree that presenting the intuition and mechanistic underpinning for the behaviour aids readability. In addition to the points about parameters which are now explained at length in the paper , there are a number of paragraphs providing the mechanistic underpinning and intuition for why the behaviour is obtained. Both these are discussed at length in Response to Essential Revisions. Thus, both the mechanistic intuition and the role of parameters are addressed in detail in the revision.

When M0 is mentioned to be incapable of yielding biphasic responses we mean just that: irrespective of any parameter choice in the model. The meaning of the criterion in Model M2 is now discussed. We take the point about not being able to learn from the work seriously and have made various changes both on the intuition and clarifying the impact of parameters.

The text is sprinkled with statements like "this reveals the plurality of information processing behaviors..." where the meaning is quite opaque (for this example, there is no description of "information processing" and what it might mean in this context) and therefore it makes it hard to understand what are the lessons learned from these calculations. Another example is found in the description of Erk regulation where the authors speak of "significant robustness" but what is meant by "significant" is also unclear.

Yes, we agree that these phrases are distracting and not adding much and so we have removed them.

Overall, I think this is an interesting attempt to provide a general mathematical framework for analyzing biphasic response of signaling networks, but the authors fall short for the reasons described above. I think a lot can be fixed by improving the way the results are presented.

We have indeed taken these comments on board and aimed to improve the presentation

Reviewer #2 (Public Review):

Biphasic responses are widely observed in biological systems and the determination of general design principles underlying biphasic responses is an important problem. The authors attempt to study this problem using a range of biochemical signaling models ranging from simple enzymatic modification and de-modification of a single substrate to systems with multiple enzymes and substrates. The authors used analytical and computational calculations to determine conditions such as network topology, range of concentrations, and rate parameters that could give rise to biphasic responses. I think the approach and the result of their investigation are interesting and can be potentially useful. However, the conditions for biphasic responses are described in terms of parameter ranges or relationships in particular biochemical models, and these parameters have not been connected to the values of concentrations or rates in real biological systems. This makes it difficult to evaluate how these findings would be applicable in nature or in experiments. It might also help if some general mechanisms in terms of competition/cooperation of time scales/processes are gleaned which potentially can be used to analyze biphasic responses in real biological systems.

We thank the reviewer for a careful reading of the manuscript and for the various comments and are happy to see the reviewer find the approach interesting. We address these comments in more detail below.

Reading these comments, we recognized how various analysis and algebraic equations could appear opaque to a reader both in terms of what it conveys and its import. To address this, we made a number of changes.

1. First and foremost, we provide the mechanistic underpinning and intuition for why a competing effect emerges in the first place. We do this for every substrate modification system we analyze and make further comments in the subsection focussing on the network level as well as ERK This intuition should help a reader where the result is coming from and be then able to see if it might apply in a quite different system. This is discussed in detail in Response to Essential Revisions.

2. Secondly, we have discussed many aspects of the parameters in more detail. Our goal, especially in substrate modification systems was to be able to completely characterize the role of intrinsic kinetic parameters: whether biphasic responses was impossible irrespective of parameters, whether they were possible for every value of intrinsic kinetic parameters or whether they were possible in a subset of kinetic parameter space. This has been done for every substrate modification system, and has been discussed more explicitly in the revision. Furthermore, when biphasic responses were possible, we aimed to determine the impact of species total amounts which facilitated the response. Here we performed additional analytical and semi-analytical work. Additionally with the semi-analytical work and parameters chosen in ranges very similar to those found experimentally (eg Wistel et al 2018), we are able to show that biphasic responses can indeed be obtained in experimentally feasible ranges. Further aspects of the parameters are discussed in detail in the Response to Essential Revisions. In particular, a number of new paragraphs (p2-3, p6) and plots Figure 2-figure supplement 3 and Figure 2-figure supplement 4 specifically deal with this.

Taken together these address the reviewers points.

Author Response

Reviewer #1 (Public Review):

This interesting manuscript sets out to develop for the mouse a series of important concepts and models that this group has previously developed for models of monkey brains, where they showed that in a large-scale model, anterior → posterior spatial gradients such as spine density (and thus inferred strength of local coupling) lead to a transition from transient stimulus responses to persistent responses, capable of supporting working memory (WM). No such spine density gradient is found in the mouse. Here, the authors propose and use modeling to explore the idea, that the corresponding gradient may be that of density of inhibitory PV cells in different regions of the brain.

The goal of the study - a large-scale, anatomically-constrained model of WM - is an extremely valuable one, and the authors' efforts in this direction should be supported. That said, some of the main claims in the manuscript were not, at least as currently written, clearly supported by the data, a number of important clarifications need to be made, and some claims of novelty are made in a way that, for a typical reader, may obscure the actual contribution being made.

The biggest issue is that one of the main claims, that together with cell-type specific long-range targeting, "density of cell classes define working memory representations" (abstract), is not terribly clear. For example, Figs. 2D and 2E show that a brain region's hierarchical location tightly predicts its persistent firing rate (2D), but that PV cell fraction has a far weaker correlation (2E). Is hierarchical location sufficient? If PV cell fraction were constant across model brain regions, would we still get persistent activity modes? It seems likely that the answer may be "yes", but the answer, easily within reach of the authors, is surprisingly not in the current version of the manuscript. Figure 3D, for the thalamocortical model, shows no significant correlation of firing rate with PV density.

Given the claim about PV density (in the abstract and the first main point of the discussion), this is a big concern. Yet it seems easily addressable: e.g. if indeed the authors found that hierarchy was sufficient and PV density immaterial, the model would be no less interesting. And if the authors demonstrated clearly that a PV density gradient is required, that would make the claim a solid one. If, within the model, such a causal demonstration is present, this reader at least missed it.

MAJOR CONCERNS:

(1) The model appears to be a model of a single side of the brain. Perhaps each brain region in the model could be considered an amalgam of that region across both sides of the brain. Yet given results like Li et al. Nature 2016, who show that persistent activity is robust to inhibition of one side, but not both sides of ALM, at the very least discussion of the issue is warranted.

The model is indeed a one-hemisphere model, and an expansion to a bihemispheric model is considered for future work. We have added the following sentence in the Discussion section:

“Future versions of the large-scale model may consider different interneuron types to understand their contributions to activity patterns in the cortex (Kim et al,2017; Meng et al., 2023; Tremblay and Rudy, 2016; Nigro et al., 2022), the role of interhemispheric projections in providing robustness for short-term memory encoding (Ni et al., 2016), and the inclusions of populations with tuning to various stimulus features and/or task parameters that would allow for switching across tasks (Yang et al, 2018).”

(2) The authors make an interesting attempt to distinguish core WM regions from other regions such as "readout" regions, defined as showing persistent activity yet not having an effect on persistent activity elsewhere in the network.

However, this definition seemed problematic: for example, consider a network that consists of 20 brain regions, all interconnected to each other, and all equivalent to each other, capable of displaying persistent activity thanks to mutual connectivity. Imagine that inhibition of any one of these regions is not sufficient to significantly perturb persistent activity in the other 19. Then they would all be labeled as "readout". Yet, by construction in this thought experiment, they are all equivalent to each other and are all core areas. Such redundancy may well be present in the brain. How would the authors address this redundancy issue?

We acknowledge the importance of this thought experiment. Although we initially restricted the definition of core area to how a single area contributes to working memory, we proceeded with concurrent inhibition of multiple readout areas (see Essential Revisions response 6 above).

(3) Also important to discuss would be the fact that every brain region in this model is set up as composed of two populations, and when long-range interactions are strong and the attractors strongly coupled, the entire brain is set up as a 1-bit working memory. How would results and the approach be impacted by considering WM for more flexible situations?

We have used a model of two populations as the simplest way to integrate large-scale connectivity and inhibitory gradients. Indeed, future work should consider more realistic connectivity and populations with various degrees of tuning to different task parameters. (see Reviewer 1 response 1 above)

(4) Another concern that is important yet easily addressed is the authors' use of the term "novel cell-type specific graph theory measures". Describing in the abstract and elsewhere the fact that what they mean is to take into account the sign of connections, not just their magnitude, would transmit to readers the essence of the contribution in a manner very simple to understand. Most readers would fail to grasp the essential point of the current labeling, which sounds potentially very vague and complex.

We have reworded the abstract - see also Essential reviews response 2 above.

(5) Finally, the overall significance of the study, and advances over previous work, were not entirely clear. In the discussion, the authors identify three major findings: (1) WM function is shaped by the PV cell density gradient. But as above, further work is required to make it clear that this claim is supported by the model. (2) if local recurrent excitation is insufficient to generate persistent activity, then long-range recurrent excitation is needed to generate it. I had trouble understanding why a model was needed to reach this conclusion - it seems as if it is simply a question of straightforward logic. The discussion states that in this regard, the work here "offers specific predictions to be tested experimentally", but I had trouble identifying what these specific predictions are. (3) Taking into account sign, not only magnitude, of connections, is important. This last point once again seemed a matter of straightforward logic, making its novelty difficult to assess.

We thank the reviewer, we have addressed these issues in the Essential Revisions 3) above.

Reviewer #2 (Public Review):

This paper uses the mouse mesoscale connectome, combined with data on the number and fraction of PV-type interneurons, to build a large-scale model of working memory activity in response to inputs from various sensory modalities. The key claims of the paper are two-fold. First, previous work has shown that there does not appear to be an increase in the number of excitatory inputs (spines) per pyramidal neuron along the cortical hierarchy (and this increase was previously suggested to underlie working memory activity occurring preferentially in higher areas along the cortical hierarchy). Thus, the claim is that a key alternative mechanism in the mouse is the heterogeneity in the fraction of PV interneurons. Second, the authors claim to develop novel cell type-specific graph theory.

I liked seeing the authors put all of the mouse connectomic information into a model to see how it behaved and expect that this will be useful to the community at large as a starting point for other researchers wishing to use and build upon such large-scale models. However, I have significant concerns about both primary scientific claims. With regard to the PV fraction, this does not look like a particularly robust result. First, it's a fairly weak result to start, much smaller than the simple effect of the location of an area along the cortical hierarchy (compare Figs. 2D, 2E; 3C, 3D). Second, the result seems to be heavily dependent upon having subdivided the somatosensory cortex into many separate points and focusing the main figures of the paper (and the only ones showing rates as a function of PV cell fraction) solely on simulations in which the sensory input is provided to the visual cortex. With regards to the claim of novel cell type-specific graph theory, there doesn't appear to be anything particularly novel. The authors simply make sure to assign negative rather than positive weights to inhibitory connections in their graph-theoretic analyses.

Major issues:

1) Weakness of result on effect of PV cell fraction. Comparing Figures 2D and 2E, or 3C and 3D, there is a very clear effect of cortical hierarchy on firing rate during the delay period in Figures 2D and 3C. However, in Figure 2E relating delay period firing rate to PV cell fraction, the result looks far weaker. (And similarly for Figs. 3C, 3D, with the latter result not even significant). Moreover, the PV cell fraction results are dominated by the zero firing rate brain regions (as opposed to being a nice graded set of rates, both for zeros and non-zeros, as with the cortical hierarchy results of Figures 2D), and these zeros are particularly contributed to by subdividing somatosensory (SS) into many subregions, thus contributing many points at the lower right of the graph.

Further, it should be noted that Figure 2E is for visual inputs. In the supplementary Figure 2 - supplement 1, the authors do apply sensory inputs to auditory and somatosensory cortex...but then only show the result that the delay period firing rate increases along the cortical hierarchy (as in Figure 2D for the visual input), but strikingly omit the plots of firing rate versus PV cell fraction. This omission suggests that the result is even weaker for inputs to other sensory modalities, and thus difficult to justify as a defining principle.

We have now made an effort to exhaustively compare the contributions of PV versus hierarchy in defining the firing rate activity patterns in the model - see Essential Revisions response 1 above. Moreover, we included plots of firing rate versus PV cell fraction for other sensory modalities, and the results would still support a common architecture for short-term memory maintenance.

2) Graph theoretic analyses. The main comparison made is between graph-theoretic quantities when the quantities account for or do not account for, PV cells contributing negative connection strengths. This did not seem particularly novel.

See Essential Revisions response 2 above

3) It was not clear to me how much the cell-type specific loop strength results were a result of having inhibitory cell types, versus were a result of the assumption ('counter-stream inhibitory bias') that there is a different ratio of excitation to inhibition in top-down versus bottom-up connections. It seems like the main results were more a function of this assumed asymmetry in top-down vs. bottom-up than it was a function of just using cell-type per se. That is, if one ignored inhibitory neurons but put in the top-down vs. bottom-up asymmetry, would one get the same basic results? And, likewise, if one didn't assume asymmetry in the excitatory vs. inhibitory connectivity in top-down versus bottom-up connections, but kept the Pyramidal and PV cell fraction data, would the basic result go away?

We have addressed the issue of cell-type specific loop strength in Essential Revisions response 2 above.

4) In the Discussion, there is a third 'main finding' claimed: "when local recurrent excitation is not sufficient to sustain persistent activity...distributed working memory must emerge from long-range interactions between parcellated areas". Isn't this essentially true by definition?

We have addressed this important issue in Essential Revisions response 3 above.

5) I don't know if it's even "CIB" that's important or just "any asymmetry (excitatory or inhibitory) between top-down vs. bottom-up directions along the hierarchy". This is worth clarifying and thinking more about, as assigning this to inhibition may be over-attributing a more basic need for asymmetry to a particular mechanism.

We found that this asymmetry is indeed crucial, which may be provided by CIB or, in some regimes, it is sufficient that a PV gradient is present - see Essential Revisions response 1 above.

Other questions:

1) Is it really true that less than 2% of neurons are PV neurons for some areas? Are there higher fractions of other inhibitory interneuron types for these areas, and does this provide a confound for interpreting model results that don't include these other types?

Maybe related to the above, the authors write in the Results that local excitation in the model is proportional to PV interneuron density. However, in the methods, it looks like there are two terms: a constant inhibition term and a term proportional to density. Maybe this former term was used to account for other cell types. Also, is local excitation in the model likewise proportional to pyramidal interneuron density (and, if not, why not?)?

The reviewer is correct in pointing out that the ‘constant inhibition term’, which we interpret as a minimal inhibition, accounts for other cell types. We have added the respective explanation in the Methods section. Future versions of the model may include different interneuron types - see Reviewer 1 Response 1 above.

2) Non-essential areas. The categorization of areas as 'non-essential' as opposed to, e.g. "inputs" is confusing. It seems like the main point is that, since the delay period activity as a whole is bistable, certain areas' contributions may be small enough that, alone, they can't flip the network between its bistable down and up states. However, this does not mean that such areas (such as the purple 'non-essential' area in Figure 5a) are 'non-essential' in the more common sense of the word. Rather, it seems that the purple area is just a 'weaker input' area, and it's confusing to thus label it as 'non-essential' (especially since I'd guess that, whether or not an area flips on/off the bistability may also depend on the assumed strength of the external input signal, i.e. if one made the labeled 'input area' a bit too weak to alone trigger the bistability, then the purple area might become 'essential' to cross the threshold for triggering a bistable-up state).

This is an important point, and a similar point was also raised by Reviewer 1. For simplicity, we have restricted the definition of the function of an area (e.g., input, vs core vs non essential) to how a single area contributes to working memory. The existence of ‘subnetworks’ for any of these functions is indeed plausible - and potentially important, but we have left this for future modeling work. (see Essential Revisions response 6 above). The point that distinguishes ‘input’ and ‘non-essential’ areas is simply whether inhibiting said area during the stimulus period affects stimulus-specific persistent activity. Surely some of the areas that we have classified as ‘non-essential’ have important roles, even for the contents of working memory, however they are not essential to produce the activity pattern we observe here.

3) Relation between 'core areas' and loop strength. The measure underlying 'prediction accuracy = 0.93' in Figure 6D and the associated results seems incomplete by being unidirectional. It captures the direction: 'given high cell-type specific loop strength, then core area' but it does not capture the other direction: 'given a cell is part of a core area, is its predicted cell-type specific loop strength strong?'. It would be good to report statistics for both directions of association between loop strength and core area.

Indeed the prediction accuracy refers to the direction loop strength->core area, for which we estimate how well a continuous variable (loop strength) predicts a binary variable (whether core area or not). A prediction in the reverse direction is not well defined, namely to predict a continuous variable from a binary variable, so the reverse association may be only indirectly inferred from Figure 8D.

4) More justification would be useful on the assumption that the reticular nucleus provides tonic inhibition across the entire thalamus.

Relatively little is known about how specific this inhibition may be. We have included references in the Discussion section that speak to this fact. (Crabtree 2018, Hardinger et al., 2023).

5) Is NMDA/AMPA ratio constant across areas and is this another difference between mice and monkeys? I am aware of early work in the mouse (Myme et al., J. Neurophys., 2003) suggesting no changes at least in comparing two brain regions' layer 2/3, but has more work been performed related to this?

Recent anatomical in-vitro autoradiography work in the macaque shows that NMDA/AMPA ratio (in terms of receptor density) varies across the cortical hierarchy (Klatzmann et al., 2022). Functionally NMDA receptors seem important in PFC L2/3 for persistent activity, while in V1, they contribute relatively little to the stimulus response, which is dominated by AMPA-mediated excitation. This was shown by a recent physiological study in the macaque (Yang et al., 2018). This could indeed point to a species difference, although like-for-like comparisons of equivalent experiments across species are lacking in the literature.. We have included this and other related references in our Discussion - see Essential Revision 4 above.

6) Are bilateral connections between the left and right sides of a given area omitted and could those be important?

These potentially important connections were omitted for simplicity in the model, please see Reviewer 1 Responses 1, 3 above.

Reviewer #3 (Public Review):

Combining dynamical modelling and recent findings of mouse brain anatomy, Ding et al. developed a cell-type-specific connectome-based dynamical model of the mouse brain underlying working memory. The authors find that there is a gradient across the cortex in terms of whether mnemonic information can be sustained persistently or only transiently, and this gradient is negatively correlated to the local density of parvalbumin (PV) positive inhibitory cells but positively correlated with mesoscale-defined cortical hierarchy. In addition, weighing connectivity strength by PV density at target areas provides a more faithful relationship between input strength and delay firing rate. The authors also investigate a model where cortical persistent activity can only be sustained with thalamus input intact, although this result is rather separate from the rest of the study. The authors then use this model to test the causal contributions of different areas to working memory. Although some of the in silico perturbations are consistent with existing experimental data, others are rather surprising and need to be further discussed. Finally, the authors investigate patterns of attractor states as a result of different local and long-range connections and suggest that distinct attractor states could underlie different task demands.

The importance of PV density as a predictor for working memory activity patterns in the mouse brain is in contrast to recent computational findings in the primate brain where the number of spines (excitatory synapses per pyramidal cell) is the key predictor. This finding reveals important species differences and provides complementary mechanisms that can shape distributed patterns of working memory representation across cortical regions. The method of biologically-based near-whole-brain dynamical modeling of a cognitive function is compelling, and the main conclusions are mostly well supported by evidence. However, some aspects of the method, result, and discussion need to be clarified and extended.

1) Based on existing anatomical data, the authors reveal a negative correlation between cortical hierarchy (defined by mesoscale connectivity; this concept needs to be explicitly defined in the Results session, not just in the Method section) and local PV density (Fig. 1). In the dynamical model, the authors find that working memory activity is positively (and strongly) correlated with cortical hierarchy and negatively (and less strongly) correlated with PV cell density (Fig. 2), and conclude that working memory activity depends on both. But could the negative correlation between activity and PV density simply result from the inherent relationship between hierarchy and PV density across regions? To strengthen this result, the authors should quantify the predictive power of local PV density on working memory activity beyond the predictive power of cortical hierarchy.

We have systematically compared the relationship between PV and hierarchy in generating delay-patterns of activity - see Essential Revisions response 1 above.

2) In Fig. 4, the authors find that cell-type-specific graph measures more accurately predict delay-period firing rates. Specifically, the authors weigh connections with a cell-type-projection coefficient, which is smaller when the PV cell fraction is higher in the target area. Considering that local PV cell fraction is already correlated with delay activity patterns, weighing the input with the same feature will naturally result in a better input-output relationship. This result will be strengthened if there is a more independent measure of cell-type-projection coefficient, such as the spine density of PV vs excitatory cells across regions, or even the percentage of inhibitory versus excitatory cells targeted by upstream region (even just for an example set of brain regions).

We have compared different measures of cell-type projection coefficients and how they predict delay-patterns of activity and whether an area is a core area - see Essential Revisions response 2 above.

3) The authors aim to identify a core subnetwork that generates persistent activity across the cortex by characterising delay activity as well as the effects of perturbations during the stimulus and delay period. Consistent with existing data, the model identifies frontal areas and medial orbital areas as core areas. Surprisingly, areas such as the gustatory area are also part of the core areas. These more nuanced predictions from the model should be further discussed. Also surprisingly, the secondary motor cortex (MOs), which has been indicated as a core area for short-term memory and motor planning by many existing studies is classified as a readout area. The authors explain this potential discrepancy as a difference in task demand. The task used in this study is a visual delayed response task, and the task(s) used to support the role of MOs in short-term memory is usually a whisker-based delayed response task or an auditory delay response task. In all these tasks, activity in the delay period is likely a mixture of sensory memory, decision, and motor preparation signals. Therefore, task demand is unlikely the reason for this discrepancy. On the other hand, motor effectors (saccade, lick, reach, orient) could be a potential reason why some areas are recruited as part of the core working-memory network in one task and not in another task. The authors should further discuss both of these points.

We have addressed this important point in Essential Revisions response 5 above.

4) As a non-expert in the field, it is rather difficult to grasp the relationship between the results in Fig. 7 and the rest of the paper. Are all the attractor states related to working memory? If so, why are the core regions for different attractor states so different? And are the core regions identified in Fig. 5 based on arbitrary parameters that happen to identify certain areas as core (PL)? The authors should at least further clarify the method used and discuss these results in the context of previous results in this study.

Attractor states that have a stable baseline are, by definition, related to working memory in that there is a baseline and a memory state associated with the model. Some areas, such as PL are more likely to be associated with different core subnetworks given its position in the hierarchy. In the current version of the manuscript, we provide a motivation for the different attractor states and how they may relate to cognitive function.

Author Response

Reviewer #1

While the article clearly outlines the strengths of the chosen approach, it lacks an equally clear exposition of its limitations and a more thorough comparison to established approaches. Two examples of limitations that should be stated more clearly, in my opinion: models need to be small enough to fit on a single machine (in contrast to e.g. NEURON and NEST which support distributed computation via MPI), and only single-compartment models are supported; both limitations are mentioned in passing in the discussion, but would merit a more upfront mention.

We agree that our paper could be improved by more clearly stating the limitations of our approach and comparing it to established approaches. We have revised the paper and added two new subsections in the Discussion section to address these specific concerns:

1. The Limitations subsection (L448 - L491) acknowledges restrictions of BrainPy paradigm which uses a Python-based object-oriented programming. It highlights three main categories of limitations: (a) approach limitations, (b) functionality limitations, (c) parallelization limitations. These limitations highlight areas where BrainPy may require further development to improve its functionality, performance, and compatibility with different modeling approaches.

2. The Future Work subsection (L493 - L526) outlines development enhancements we aim to pursue in the near term. It emphasizes the need for further development in order to meet the demands of the field. Three key areas requiring attention are highlighted: (a) multi-compartment neuron models, (b) ultra-large-scale brain simulations, (c) bridging with acceleration computing systems.

In addition to these changes, we have also made a number of other minor changes to the paper to improve its clarity and readability.

The study does not verify the accuracy of the presented framework. While its basic approach (time-step-based simulation, standard numerical integration algorithms) is sufficiently similar to other software to not expect major discrepancies, an explicit comparison would remove any doubt. Quantitative measures of accuracies are particularly important in the context of benchmarks (see below), since simulations can be made arbitrarily fast by sacrificing performance.

We agree that an explicit comparison would help alleviate any doubts and provide a more comprehensive understanding of our framework's accuracy. We have revised our manuscript to include a dedicated section, particularly Appendix 11. In this section, we verified that all simulators generated consistent average firing rates for the given benchmark network models (figure 1 and figure 2 in Appendix 11). These verifications were performed under different network sizes (ranging from 4e^3 to 4e^5) and different computing platforms (CPU, GPU and TPU). We also qualitatively compared the overall network activity patterns produced by each simulator to ensure they exhibited the same dynamics (figure 3 and figure 4 in Appendix 11). While exact spike-to-spike reproducibility was not guaranteed between different simulator implementations, we confirmed that our simulator produced activity consistent with the reference simulators for both firing rates and network-level dynamics. Additionally, BrainPy did not sacrifice simulation accuracy for speed performance. Despite using single precision floating point, BrainPy was able to produce consistent firing rates and raster diagrams across all simulations (see figure 3 and figure 4 in Appendix 11).

We hope these revisions can ensure that our manuscript provides a clear and robust validation of the accuracy of our simulator.

Benchmarking against other software is obviously important, but also full of potential pitfalls. The current article does not state clearly whether the results are strictly comparable. In particular: are the benchmarks on the different simulators calculating results to the same accuracy (use of single or double precision, same integration algorithm, etc.)? Does each simulator use the fastest possible execution mode (e.g. number of threads/processes for NEST, C++ standalone mode in Brian2, etc.)? What is exactly measured (compilation time, network generation time, simulation execution time, ...) - these components will scale differently with network size and simulation duration, so summing them up makes the results difficult to interpret. Details are also missing for the comparison between the XLA operator customization in C++ vs. Python: was the C++ variant written by the authors or by someone else? Does the NUMBA→XLA mechanism also support GPUs/TPUs? This comparison also seems to be missing from the GitHub repository provided for reproducing the paper results.

We have carefully considered these comments and addressed each of these concerns regarding the benchmarks and examples presented in our paper.

1. Lack of Details in Examples: In the revised version of the paper, we provide additional information and any other pertinent details to enhance the clarity and replicability of our results. Particularly, in Appendix 9, we provide the mathematical description, the number of neurons, the connection density, and delay times used in our multi-scale spiking network; in Appendix 10, we provide the detail description of reservoir models, evaluation metrics, training algorithms, and their implementations in BrainPy; in Appendix 11, we elaborate the hardware and software specifications and experimental details for benchmark comparisons.

2. Inadequate Description of Benchmarking Procedures: In the revised paper, particularly, in L328-L329 of the main text at section of "Efficient performance of BrainPy" and in Appendix 11, we elaborate on the integration methods, simulation time steps, and floating-point precision used in our experiments. We also ensure that these parameters are clearly stated and identical across all simulators involved in the benchmarking process, see "Accuracy evaluations" in Appendix 11 (L1550 - L1580).

3. Clarification on Measured Time: In the revised paper, we state that all simulations only measured the model execution time, excluding model construction time, synapse creation time, and compilation time, see "Performance measurements" in Appendix 11 (L1539 - L1548).

4. Consideration of Acceleration Modes: In the revised version, we provide the simulation speed of other brain simulators on different acceleration modes, see Figure 8. For instance, we utilize the fastest possible option --- the C++ standalone mode in Brian2 --- for speed evaluations. Furthermore, we have requested the developers of the comparison simulators for optimizing the benchmark models, ensuring a fair and accurate comparison.

5. Scaling Networks to Maintain Activity: In the revised manuscript, we adopt the suggestion of Reviewer #3 and apply the appropriate scaling techniques to maintain consistent network activity throughout our experiments. These details can be found in Appendix 11 (also see Appendix 11—figure 1 and Appendix 11—figure 2).

Regarding the comparison between XLA operator customization in C++ and Python, we utilized our self-implemented C++ version, which is accessible in the Appendix 8 Listing 2. Presently, the NUMBA→XLA mechanism does not support GPUs/TPUs; however, we are working on expanding this capability to other platforms. We have made this clarification in the revised manuscript as well (see L1278 - L1285).

While the authors convincingly argue for the merits of their Python-based/object-oriented approach, in my opinion, they do not fully acknowledge the advantages of domain-specific languages (NMODL, NestML, equation syntax of ANNarchy and Brian2, ...). In particular, such languages aim at a strong decoupling of the mathematical model description from its implementation and other parts of the model. In contrast, models described with BrainPy's approach often need to refer to such details, e.g. be aware of differences between dense and sparse connectivity schemes, online, or batch mode, etc. It might also be worth mentioning descriptive approaches to synaptic connectivity as supported by other simulators (connection syntax in Brian2, Connection Set Algebra for NEST).

We have made revisions to better acknowledge the merits of DSLs while providing a more comprehensive comparison. These revisions are incorporated in Discussion (L452 - L466) and Appendix 1 (L778 - L788).

Reviewer #2

While the results presented are impressive, publishing further details of the benchmarks in an appendix would be helpful for evaluating the claims and the overall conclusion would be more convincing if the performance benefits were demonstrated on a wider selection of test cases. Unsatisfyingly, the authors gave up on making a direct comparison to Brian running on GPUs with GeNN which would have been a fairer comparison than CPU-based simulations. The code for the chosen benchmarks is also likely to be highly optimised by the authors for running on BrainPy but less so for the other platforms - a fairer test would be to invite the authors of the other simulators to optimise the same models and re-evaluate the benchmarks.

We have carefully considered these comments and addressed each of these concerns regarding the benchmarks and examples presented in our paper.

1. Lack of Details in Examples: In the revised version of the paper, we provide additional information and any other pertinent details to enhance the clarity and replicability of our results. Particularly, in Appendix 9, we provide the mathematical description, the number of neurons, the connection density, and delay times used in our multi-scale spiking network; in Appendix 10, we provide the detail description of reservoir models, evaluation metrics, training algorithms, and their implementations in BrainPy; in Appendix 11, we elaborate the hardware and software specifications and experimental details for benchmark comparisons.

2. Inadequate Description of Benchmarking Procedures: In the revised paper, particularly, in L328-L329 of the main text at section of "Efficient performance of BrainPy" and in Appendix 11, we elaborate on the integration methods, simulation time steps, and floating-point precision used in our experiments. We also ensure that these parameters are clearly stated and identical across all simulators involved in the benchmarking process, see "Accuracy evaluations" in Appendix 11 (L1550 - L1580).

3. Clarification on Measured Time: In the revised paper, we state that all simulations only measured the model execution time, excluding model construction time, synapse creation time, and compilation time, see "Performance measurements" in Appendix 11 (L1539 - L1548).

4. Consideration of Acceleration Modes: In the revised version, we provide the simulation speed of other brain simulators on different acceleration modes, see Figure 8. For instance, we utilize the fastest possible option --- the C++ standalone mode in Brian2 --- for speed evaluations. Furthermore, we have requested the developers of the comparison simulators for optimizing the benchmark models, ensuring a fair and accurate comparison.

5. Scaling Networks to Maintain Activity: In the revised manuscript, we adopt the suggestion of Reviewer #3 and apply the appropriate scaling techniques to maintain consistent network activity throughout our experiments. These details can be found in Appendix 11 (also see Appendix 11—figure 1 and Appendix 11—figure 2).

Regarding the wider selection of test cases, we understand the importance of demonstrating the performance benefits on a broader range of scenarios. Particularly, we have designed two kinds of benchmark models:

• Sparse connection models. This category models include COBA-LIF network and COBA-HH network. The former is a standard E/I balanced network for comparing simualtion speed of a brain simulator, while the latter uses the complex computational expensive HH neuron model as the elements. Both models can be effectively to demonstrate the capability of a brain simulator for the sparse and event-driven computation.

• Dense connection models. The local circuits of a cortical column are usually connected densely (Science 366, 1093). Particularly, we use the decision making network proposed by (Wang, 2002) for evaluations.

In the revised version, we include extensive experiments on these three test cases under different kinds of computing platforms (including CPU, GPU, and TPU) to strengthen the overall conclusion and provide a more comprehensive evaluation of our approach.

Regarding the comparison to Brian running on GPUs with GeNN, we apologize for not including that in our initial submission. We have conducted the necessary experiments on all three benchmark models we have used in our evaluations and include these results in the revised version of the paper (see Figure 8). This addition will enhance the credibility of our findings and allow for a more meaningful comparison between different simulation platforms. Furthermore, we have also reached out to the authors of other simulators and invite them to optimize the same models used in our benchmarks. We believe this collaborative approach will ensure a more equitable evaluation of the simulators and provide a more robust and convincing analysis of our work.

Furthermore, the manuscript reads like an advertisement for the platform with very little discussion of its limitations, weaknesses, or directions for further improvement. A more frank and balanced perspective would strengthen the manuscript and give the reader greater confidence in the platform.

We agree that our paper could be improved by more clearly stating the limitations of our approach and comparing it to established approaches. We have revised the paper and added two new subsections in the Discussion section to address these specific concerns:

1. The Limitations subsection (L448 - L491) acknowledges restrictions of BrainPy paradigm which uses a Python-based object-oriented programming. It highlights three main categories of limitations: (a) approach limitations, (b) functionality limitations, (c) parallelization limitations. These limitations highlight areas where BrainPy may require further development to improve its functionality, performance, and compatibility with different modeling approaches.

2. The Future Work subsection (L493 - L526) outlines development enhancements we aim to pursue in the near term. It emphasizes the need for further development in order to meet the demands of the field. Three key areas requiring attention are highlighted: (a) multi-compartment neuron models, (b) ultra-large-scale brain simulations, (c) bridging with acceleration computing systems. In addition to these changes, we have also made a number of other minor changes to the paper to improve its clarity and readability.

Since simulators wax and wane in popularity, it would be reassuring to see a roadmap for development with a proposed release cadence and a sustainable governance policy for the project. This would serve to both clearly indicate the areas of active development where community contributions would be most valuable and also to reassure potential users that the project is unlikely to be abandoned in the near future, ensuring that their time investment in learning to use the framework will not be wasted.

We appreciate the reviewer raising the point for demonstrating the project's sustainability. In response to this feedback, we have made the following efforts.

Firstly, we add and maintain a "Development roadmap" section in the BrainPy GitHub homepage (https://github.com/brainpy/BrainPy). This will enable the community to have a clear understanding of the project's direction and the areas of active development. Additionally, the "Future work" section in our revised paper has also outlined a comprehensive roadmap for next stages of the BrainPy development.

Secondly, to address the concern about the sustainability of our project and the potential risk of abandonment, we have incorporated a ACKNOWLEDGMENTS.md file in the GitHub (https://github.com/brainpy/BrainPy/blob/master/ACKNOWLEDGMENTS.md) to outline our sustainable funding support. These supports demonstrates our commitment to the long-term maintenance and development of the project, thus may help to dispel doubts of users for the project abandonment.

Similarly, a complex set of dependencies, which need to be modified for BrainPy, will likely make the project hard to maintain and so a similar plan to those given for the CI pipeline and documentation generation for automation of these modifications would be a good addition. It is also important to periodically reflect on whether it still makes sense to combine all the disparate tools into one framework as the codebase grows and starts to strain under modifications required to maintain its unification.

We appreciate the reviewer's valuable suggestions on the BrainPy framework.

First, BrainPy is a self-contained package designed specifically for brain dynamics programming. It boasts minimal dependencies, relying only on fundamental packages within the Python scientific computing ecosystem. In essence, BrainPy relies on numpy for array-based computations and utilizes jax and jaxlib for JIT compilation. While we currently utilize numba to customize dedicated operators, we can also remove this dependency by rewriting these operators with C++ code. We incorporate the use of brainpylib, a package developed by ourselves, which provides dedicated operators for CPUs and GPUs in the context of brain dynamics modeling. Additionally, BrainPy leverages mature solutions within the field for certain auxiliary functions. For instance, we integrate the use of tqdm to facilitate the display of a progress bar during model execution, and employ matplotlib for visualization purposes, capitalizing on its well-established capabilities in the scientific community.

Second, we agree that there is a risk of overly complex dependencies and architectural strains. To mitigate this risk, we have taken the following changes:

• We prioritize good software engineering practices like loose coupling, high cohesion and modularity in the framework design. This will isolate dependencies and changes to specific components. For example, brainpy.visualize nodule defines abstract visualization functions in which the visualization backend can be changed anytime.

• We invest in automating aspects of the build, test, and release process to relieve manual maintenance burdens. We heavily use the GitHub actions for testing BrainPy codes and building documentations.

• We document dependencies clearly and maintain backwards compatibility when possible. New APIs will be clearly stated supported after which BrainPy version, and deprecated APIs will be deprecated over multiple release cycles.

• We continuously monitor code complexity metrics and refactor/simplify the architecture when needed.

• When new tools have significantly different requirements, we will consider spinning them off into separate projects rather than forcing them into the core framework.

Finally, a live demonstration would be a very useful addition to the project. For example, a Jupyter notebook hosted on mybinder.org or similar, and a fully configured Docker image, would each enable potential users to quickly experiment with BrainPy without having to install a stack of dependencies and troubleshoot version conflicts with their pre-existing setup. This would greatly lower the barrier to adoption and help to convince a larger base of modellers of the potential merits of BrainPy, which could be major, both in terms of the computational speed-up and ease of development for a wide range of modelling paradigms.

We appreciate the reviewer's valuable feedback and suggestion. We have hosted a Jupyter notebook and a fully configured Docker image on mybinder.org (https://mybinder.org/v2/gh/brainpy/BrainPy-binder/main). Users can easily experiment with BrainPy without the need to install multiple dependencies or troubleshoot version conflicts.

Reviewer #3

One potential issue is that the scope of the neuro-simulator is not very clearly explained and the target audience is not well defined: is BrainPy primarily intended for computational neuroscientists or for neuro-AI practitioners? The simulator offers very detailed neural models (HH, fractional order models), classical point-models (LIF, AdEx), rate-coded models (reservoirs), but also deep learning layers (Conv, MaxPool, BatchNorm, LSTM). Is there an advantage to using BrainPy rather than PyTorch for purely deep networks? Is it possible to build hybrid models combining rate-coded reservoirs or convnets with a network of HH neurons? Without such a hybrid approach, it is unclear why the deep learning layers are needed.

We appreciate the reviewer's concern regarding the scope of BrainPy and the need for clarification regarding the target audience.

BrainPy is designed to cater to both computational neuroscientists and neuro-AI practitioners by integrating detailed neural models, classical point models, rate-coded models, and deep learning models. The platform aims to provide a general-purpose programming framework for modeling brain dynamics, allowing users to explore the dynamics of brain or brain-inspired models that combines insights from biology and machine learning.

Particularly, brain dynamics models (provided in brainpy.dyn module) and deep learning models (provided in brainpy.dnn module) are closely integrated with each other in BrainPy. First, to build brain dynamics models, users should use the building blocks in brainpy.dnn module to create synaptic projections.

Second, to build brain-inspired computing models for machine learning, users could also take advantages of neuronal and synaptic dynamics have been provided in brainpy.dyn module.

To that end, BrainPy provides building blocks of detailed conductance-based models like Hodgkin-Huxley, as well as common deep learning layers like convolutions.

Regarding the advantage of using BrainPy over PyTorch for purely deep networks, we acknowledge that existing deep learning libraries like Flax in the JAX ecosystem provide extensive tools and examples for constructing traditional deep neural networks. While BrainPy does implement standard deep learning layers, our primary focus is not to compete directly with those libraries. Instead, we provide these models for the seamless integration of deep learning layers within BrainPy's core modeling abstractions, including variables and dynamical systems. This integration allows researchers to incorporate common deep learning layers into their brain models. Additionally, the inclusion of deep learning layers in BrainPy serves as examples for customization and facilitates the development of tailored layers for neuroscience research. Researchers can modify or extend the implementations to suit their specific needs.

In summary, BrainPy's scope focuses on the general-purpose brain dynamics programming. The target audience includes computational neuroscientists who want to incorporate insights from machine learning, as well as some ML researchers interested in integrating brain-like components.

In terms of plasticity, only external training procedures are implemented (backpropagation, FORCE, surrogate gradients). No local plasticity mechanism (Hebbian learning for rate-coded networks, STDP and its variants for spiking networks) seems to be implemented, apart from STP. Is it a planned feature? Appendix 8 refers to bp.synplast.STDP(), but it is not present in the current code (https://github.com/brainpy/BrainPy/tree/master/brainpy/_src/dyn/synplast). Spiking networks without STDP are not going to be very useful to computational neuroscientists, so this suggests that the simulator targets primarily neuro-AI, i.e. AI researchers interested in using spiking models in a machine learning approach.

We appreciate that the reviewer raising the limitations of BrainPy in terms of local plasticity mechanisms. We are sorry for the delay of implementing STDP models in BrainPy. Currently, we provide very general implementations of STDP. It can be compatible with any synaptic model (such as Exponential, Dual Exponential, AMPA, GABA, and NMDA dynamics), and common connection patterns (such as Dense, and Sparse connection patterns).

bp.dyn.STDP_Song2001(pre, post, delay, syn, comm, out)

It can also be easily used with the combination of short-term plasticity models. The modular design of BrainPy's framework also make the plasticity component straightforward to be implemented and integrated into existing models.

A second weakness of the paper concerns the demos and benchmarks used to demonstrate the versatility and performance of BrainPy, which are not sufficiently described. In Fig. 4, it is for example not explained how the reservoirs are trained (only the readout weights, or also the recurrent ones? Using BPTT only makes sense when the recurrent weights are also trained.), nor how many neurons they have, what the final performance is, etc. The comparison with NEURON, NEST, and Brian2 is hard to trust without detailed explanations. Why are different numbers of neurons used for COBA and COBAHH? How long is the simulation in each setting? Which time is measured: the total time including compilation and network creation, or just the simulation time? Are the same numerical methods used for all simulators? It would also be interesting to discuss why the only result involving TPUs (Fig 8c) shows that it is worse than the V100 GPU. What could be the reason? Are there biologically-realistic networks that would benefit from a TPU? As the support for TPUs is a major selling point of BrainPy, it would be important to investigate its usage further.

We appreciate the reviewer for raising the important question about the demos and benchmarks used to demonstrate the versatility and performance of BrainPy. To address these concerns, we have added more details in the revised paper, including:

• In Fig. 4, we explain how the reservoirs are trained in Appendix 10, in which only the readout weights are trained, and they are trained using backpropagation, FORCE learning, and ridge regression algorithms, respectively. We also specify the number of neurons in each reservoir (see L1397), and the final performance of the reservoirs on the task (see Figure 4).

• To enable readers to better interpret the simulator comparisons in Fig. 8, we have also added more detailed explanations of the comparison with NEURON, NEST, and Brian2 in Appendix 11.

• In the current revised paper, we provide a comprehensive analysis of BrainPy's compatibility with different hardware platforms, including TPUs, and to identify the specific conditions under which TPUs may offer advantages (see Figure 8 and Appendix 11—figure 7 ). We have also discussed the potential benefits of TPUs for biologically-realistic networks (see L514 - L521). Particularly, for the biological network with arbitrary sparsity, TPUs does not show advantage over GPUs (see Appendix 11—figure 7). TPUs are best at exploiting certain kinds of structured sparsity, for example block sparsity.

Author Response

Reviewer #1 (Public Review):

Due complicated and often unpredictable idiosyncratic differences, comparing fMRI topography between subjects typically would require extra expensive scan time and extra laborious analyzing steps to examine with specific functional localizer scan runs that contrast fMRI responses of every subject to different stimulus categories. To overcome this challenge, hyperaligning tools have recently been developed (e.g., Guntupalli et al., 2016; Haxby et al., 2011) based on aligning in a high-dimensional space of voxels of subjects' fMRI responses to watching a given movie. In the present study, Jiahui and colleagues propose a significantly improved version of hyperaligning functional brain topography between individuals. This new version, based on fMRI connectivity, works robustly on datasets when subjects watched different movies and were scanned with different parameters/scanners at different MRI centers.

Robustness is the major strength of this study. Despite the fact that datasets from different subjects watching different movies at different MRI centers with different scan parameters were used, the results of functional brain topography from between-subject hyperalignment based on fMRI connectivity were comparable to the golden standard of within-subject functional localizations, and significantly better than regular surface anatomical alignments. These results also support the claim that the present approach is a useful improvement from previous hyperalignments based on time-locked fMRI voxel responses, which would require normative samples of subjects watching a same movie.

We thank the reviewer for the appreciation of our work.

Given the robustness, this new version of hyperalignment would provide much stronger statistical power for group-level comparisons with less costs of time and efforts to collect and analyze data from large sample size according to the current stringent standard, likely being useful to the whole research community of functional neuroimaging. That said, more discussions of the limit of the present hyperalignment approach would be helpful to potential eLife readers. For example, to what extend the present hyperalignment approach would be applicable to individuals with atypical functional brain topography such as brain lesion patients with e.g., acquired prosopagnosia? Even in typical populations, while bilateral fusiform face areas can be identified in the majority through functional localizer scans, the left fusiform face area sometimes cannot be found. Moreover, many top-down factors are known to modulate functional brain topography. Due to these factors, brain responses and functional connectivity may be different even when a same subject watched a same movie twice (e.g., Cui et al., 2021).

We thank the reviewer for the suggestion and agree that it would be fascinating if the predictions can be made with high fidelity in neuropsychological populations. Although we are optimistic that our algorithm is able to generalize across diverse populations, to date, no previous literature has provided empirical evidence to illustrate the effectiveness, including optimizations and special applications beyond typical brains. Besides the neuropsychological population, it would also be valuable to study the generalization across a broad age range, for example, from infants to the elderly. The brain changes across age both anatomically and functionally, so it is a challenge to predict functional topographies based on a normative group that only includes young participants. With all these potential applications in mind, future research is needed to illustrate the efficacy, build the pipeline, and construct the representative normative groups to meet the requirements of accurate individualized predictions in diverse populations.

In typical populations, although participants have great individual variabilities in their functional topographies, for instance, some participants have distinguishable patches of activations in their left ventral temporal cortex while some participants don’t, our algorithms successfully captured these individualized differences in the prediction. The figure below shows, as an example, the face-selective topographies of two individuals that have markedly different face-selective topographies on the left ventral temporal cortex. The left participant has prominent face-selective areas on the left ventral temporal cortex that are in similar sizes as the right side, while the right participant only has a few scattered small face-selective spots on the left side. No matter what their face-selective areas look like, our algorithm accurately recovered the individualized locations, shapes, and sizes, retaining the individual variability in the functional topographies.

Functional connectivity profiles based on naturalistic stimuli are very stable across the cortex, even when participants watch different movies. In Figure 4-figure supplement 9, the mean correlations of fine-scaled connectome for most searchlights (r = 15mm) when participants watched The Grand Budapest Hotel and the Raiders of the Lost Ark were generally around 0.8. The mean correlations were about 0.9 between the first and second half of the same movie although the stimuli contents were different between the two halves. Thus, the fine-grained functional connectivity profiles remain highly stable and reliable across movie contents, which contributes to the robustness of cross-movie, time, and other parameters (e.g., scanner models, scanning parameter) predictions using our algorithms.

We added a paragraph in the discuss section to address the concerns (page 18-19):

“This study successfully illustrated that accurate individualized predictions are both robust and applicable across a variety of conditions, including movie types, languages, scanning parameters, and scanner models. Importantly, the intricate connectivity profiles remain consistent even when participants view entirely different movies, as evidenced by Figure 4-figure supplement 9, reinforcing the prediction's stability in various scenarios. However, all four datasets in this study only included typical participants with anatomically intact brains. An unanswered question is whether individualized topographies of neuropsychological populations with atypical cortical function (e.g., developmental prosopagnosics) or with lesioned brains (e.g., acquired prosopagnosics) could also be accurately predicted using the hyperalignment-based methods. Up to now, as far as we know, no previous literature has investigated this question. Beyond neuropsychological groups, it is also valuable to investigate how well the predictions will be across a wide range of age, from infants to the elderly. Future research is essential to adapt our algorithms to diverse populations.”

Reviewer #2 (Public Review):

Guo and her colleagues develop a new approach to map the category-selective functional topographies in individual participants based on their movie-viewing fMRI data and functional localizer data from a normative sample. The connectivity hyperalignment are used to derived the transformation matrices between the participants according to their functional connectomes during movies watching. The transformation matrices are then used to project the localizer data from the normative sample into the new participant and create the idiosyncratic cortical topography for the participant. The authors demonstrate that a target participant's individualized category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. The new approach allows researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate datasets from laboratories worldwide to map functional areas for individuals. The topic is of broad interest for neuroimaging community; the rationale of the study is straightforward and the experiments were well designed; the results are comprehensive. I have some concerns that the authors may want to address, particularly on the details of the pipeline used to map individual category-selective functional topographies.

We thank the reviewer for the encouragement.

1) How does the length of the scan-length of movie-viewing fMRI affect the accuracy in predicting the idiosyncratic cortical topography? Similarly, how does the number of participants in the normative database affect the prediction of the category-selective topography? This information is important for the researchers who are interested in using the approach in their studies.

To investigate the influence of movie-viewing data length and the number of participants in the normative database on prediction performance, we systematically varied these parameters. Specifically, we altered the number of runs utilized in the analysis for both the normative and target data and experimented with varying the number of participants in the normative dataset using the Budapest and the Sraiders datasets. We have included a new Figure 4-figure supplement 5 to present a summary of these findings.

The results reveal that both within-dataset and between-dataset prediction performances are positively correlated with the length of movie-viewing fMRI data used for both the normative and target groups. A similar trend was observed with respect to the number of participants included in the normative dataset. It is important to highlight, though, that, even when analyzing as little as one run of movie-viewing data—roughly 10-15 minutes, our hyperalignment-based prediction performance was significantly higher than that achieved using traditional surface alignment. This held true even when the normative dataset included as few as five participants.

In summary, our results show that prediction performance generally improves with longer movie-viewing sessions and larger normative datasets. However, it is noteworthy that even with minimal data—10 minutes of movie-viewing and a small number of participants in the normative dataset—our algorithm still outperforms traditional surface alignment methods significantly.

We also added sentences in the discussion section (page 15):

“We investigated the influence of naturalistic movie length and the size of the training group on the prediction accuracy of individualized functional topographies. By incrementally increasing both the number of movie runs in the training and target dataset and the participants in the training group in the Budapest and Sraiders dataset, we observed enhanced prediction accuracy (Figure 4-figure supplement 5). Notably, even with just one movie run in the training or target dataset, or with a mere five participants in the training group, our prediction performance (Pearson r) ranged from about 0.6 to 0.7. This accuracy significantly outperformed results obtained using surface-based alignment.”

2) The data show that category-selective topography can be accurately estimated using connectivity hyperalignment, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. I'm wondering whether the functional connectome from resting state fMRI can do the same job as the movie-watching fMRI. If it is yes, it will expand the approach to broader data.

We agree with the reviewer that demonstrating the applicability of the resting state data will expand the application scenarios of this approach. Most previous findings on resting state connectivity, including the comparison between the naturalistic and the resting state paradigms, focused on the macro-scale similarities and differences (e.g., Samara et al., 2023). Very few studies have investigated the fine-scaled connectome based on resting state data. The study on connectivity hyperalignment (Guntupalli et al., 2018) demonstrated a shared fine-scale connectivity structure among individuals that co-exists with the common coarse-scale structure and built the algorithm to successfully hyperalign individuals to the shared fine-scaled space. Another study from our lab (Feilong et al., 2021) revealed that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence, indicating reliable and biologically relevant fine-scaled resting state connectome structures. Thus, it is highly plausible that our approach is able to be generalized to the resting state data, generating significantly better predictions of individualized functional topographies than traditional surface alignment. However, due to the limitations of the current datasets, we do not have resting state data available in the current datasets to perform this analysis. We are in the process of collecting new data to explore this hypothesis in future work.

We added sentences to the discussion section to discuss this idea (page 18):

“Studies comparing movie-viewing and resting state functional connectivity have shown that both paradigms yield overlapping macroscale cortical organizations (29), though naturalistic viewing introduces unique modality-specific hierarchical gradients. However, there remains a gap in research comparing the fine-scaled connectomes of naturalistic and resting state paradigms. Guntupalli and colleagues (14) revealed a shared fine-scale structure that coexists with the coarse-scale structure, and connectivity hyperalignment successfully improved intersubject correlations across a wide variety of tasks. Feilong et al. (13) noted that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence. This suggests a reliable and biologically relevant fine-scale resting state connectivity structure among individuals. Therefore, it is plausible that individualized functional topography could be effectively estimated using resting state functional connectivity, expanding the applicability of our approach. Future studies are needed to explore this direction.”

3) The authors averaged the hyper-aligned functional localizer data from all of subjects to predict individual category-selective topographies. As there are large spatial variability in the functional areas across subjects, averaging the data from many subjects may blur boundaries of the functional areas. A better solution might be to average those subjects who show highly similar connectome to the target subjects.

We appreciate the reviewer’s insightful question about optimizing prediction performance by selecting participants most similar in functional connectivity to the target individuals. This is a promising direction and difficult problem as well. Our approach is based on fine-scale connectome to hyperalign participants, thus different groups of participants may be similar to the target participant in different searchlights. In addition, based on results discussed in the response to Q2, the more participants included in the normative dataset, the better the prediction performance. Thus, there is a trade-off between the number of participants included in the normative dataset for the prediction and the overall similarity of those participants to the target participant.

To quantitatively explore this idea, we used a searchlight in the right ventral temporal cortex, roughly at the location of posterior fusiform face area (pFFA).We sorted participants by their connectome similarity to each target participant and then examined prediction performance based on either the top nine most similar participants or the bottom nine least similar participants. Our results, presented in Figure 4-figure supplement 8, reveal that hyperalignment consistently outperforms surface alignment regardless of the subset of participants used. Notably, using the nine most similar participants did not significantly alter prediction performance (Tukey Test, z = -0.09, p = 0.996), while using the least similar participants did negatively impact it (Tukey Test, z = 2.492, p = 0.034). Interestingly, the stability of hyperalignment-based predictions remained high even when only a subset of participants was used, contrasting with the variability observed in surface-alignment-based predictions.

Overall, these findings suggest that while selecting functionally similar participants is a promising avenue for future optimization, the process will require nuanced, searchlight-specific criteria. Each searchlight may necessitate its own set of optimal participants to balance between the performance boost from having more participants and the fidelity gained from participant similarity.

We added the following to the discussion in the manuscript (page 16):

“In our study, we used fine-scale connectomes, noting that some participants are more similar to the target participant in specific searchlights. It is an interesting question whether predictions could be enhanced by exclusively selecting those more similar participants for the target participant. To explore this option, we examined a searchlight in the right ventral temporal cortex that was roughly at the location of the posterior fusiform area (pFFA) using the top and bottom nine participants similar to each target participant measured by their fine-scale connectome similarities in the budapest dataset. Generally, using all or part of the participants for the prediction generated similar results (Figure 4-figure supplement 8). Compared to using all the participants, using only the top nine participants who are the most similar to the target participants did not significantly improve the prediction (Tukey Test, z = -0.09, p = 0.996), but using only the bottom nine participants generated significantly lower prediction accuracies (Tukey Test, z = 2.492, p = 0.034). This suggests a trade-off between the number of participants included in the prediction and the similarity of the participants. Future studies are needed to explore the optimal threshold for the number of participants included for each searchlight to refine the algorithm.”

4) It is good to see that predictions made with hyperalignment were close to and sometimes even exceeded the reliability values measured by Cronbach's alpha. But, please clarify how the Cronbach's alpha is calculated.

Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. For example, Guntupalli et al. (2016) used correlations of category-selectivity maps between odd and even localizer runs as the measure of reliability. The odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.

Cronbach’s alpha has been used in many previously published works from our lab (e.g., Feilong et al., 2021; Jiahui et al., 2020, 2023). The code for implementing this metric is publicly accessible on the first author’s Github repository (https://github.com/GUOJiahui/face_DCNN/blob/main/code/cronbach_alpha.py).

We added the detailed explanation above to the Material and Methods section (page 24):

“Cronbach’s alpha calculates the correlation score between localizer-based maps across the runs, and it reflects the amount of noise in maps based on individual localizer runs. Traditionally, the reliability was estimated based on split-half correlations. The common odd/even split measure underestimated reliability and necessitated recalculation of correlations between maps for only half the data to provide valid comparisons. In contrast, Cronbach’s alpha involves all localizer runs and provides a more accurate statistical estimate of the reliability of the topographies estimated with localizer runs.”

5) Which algorithm was used to perform surface-based anatomical alignment? Can the state-ofthe-art Multimodal Surface Matching (MSM) algorithm from HCP achieve better performance?

We preprocessed our datasets using fMRIPrep, which employs algorithms from FreeSurfer’s recon-all for surface-based anatomical alignment. It is worth noting that different alignment methods can yield varying degrees of performance. For instance, a study by Coalson et al. (2018) compared the localization performance of multiple surface-based alignment methods, including Multimodal Surface Matching (MSM) and FreeSurfer. The study found that MSM outperformed FreeSurfer in terms of peak probabilities and spatial clustering, suggesting better overall localization.

Additionally, Guntupalli et al. (2018) evaluated intersubject correlations (ISC) of functional connectivity from movie-viewing data using both Connectivity Hyperalignment (CHA) and MSM-All with the Human Connectome Project (HCP) dataset. The study showed that although MSM-All yielded marginally better ISC than traditional surface alignment, CHA’s performance was significantly superior.

In summary, while using a more advanced alignment algorithm like MSM could marginally improve prediction performance, its advantages may not be substantial when compared to our CHA-based predictions. The combination of MSM and CHA represents an intriguing direction for future research, although it falls outside the scope of our current study.

6) Is it necessary to project to the time course of the functional localizer from the normative sample into the new participants? Does it work if we just project the contrast maps from the normative samples to the new subjects?

It is an interesting question and a practical alternative to researchers to know whether time series of the localizer runs are required to obtain reasonable predictions, as in some scenarios, contrast maps may be the only accessible data in the analysis. To quantitatively explore this possibility, we applied transformation matrices derived from the movie data to training participants’s individual pre-calculated contrast maps of all four categories, and evaluated the predictions. We found nearly similar prediction performance between the two flavors within and across datasets (Figure 4-figure supplement 7). However, it is worth noting that applying transformation matrices directly to contrast maps did not get as much improvement in the interactive steps as the other flavor in the advanced CHA, perhaps due to the scale changes when multiple iterations were implemented and the difficulty to properly normalize the t-maps compared to the regular time series.

Overall, although our algorithm is originally designed to be used on the time course of the functional localizer runs, relatively comparable results can be generated even when the contrast maps are directly projected from the normative group to the target participant. However, to derive the best results with our approach, time series are recommended when the situation permits.

We have also added the contents into the Discussion section (page 16):

“Our original algorithm is designed to apply transformation matrices to the time series of localizer data of training participants before generating contrast maps. To explore whether directly applying these matrices to pre-calculated contrast maps yields comparable results, we conducted an additional analysis across the four categories. Our findings indicate that the prediction outcomes were indeed quite similar between the two approaches for both the within- and across-datasets predictions (Figure 4-figure supplement 7). However, it is worth noting that the improvements observed with enhanced CHA were not as pronounced when applied directly to the contrast maps as opposed to the time series.”

7) Saygin and her colleagues have demonstrated that structural connectivity fingerprints can predict cortical selectivity for multiple visual categories across cortex (Osher DE et al, 2016, Cerebral Cortex; Saygin et al, 2011, Nat. Neurosci). I think there's a connection between those studies and the current study. If the author can discuss the connection between them, it may help us understand why CHA work so well.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

Reviewer #3 (Public Review):

In this paper, Jiahui and colleagues propose a new method for learning individual-specific functional resonance imaging (fMRI) patterns from naturalistic stimuli, extending existing hyperalignment methods. They evaluate this method - enhanced connectivity hyperalignment (CHA) - across four datasets, each comprising between nine (Raiders) and twenty (Budapest, Sraiders) participants.

The work promises to address a significant need in existing functional alignment methods: while hyperalignment and related methods have been increasingly used in the field to compare participants scanned with overlapping stimuli (or lack thereof, in the case of resting state data), their use remains largely tied to naturalistic stimuli. In this case, having non-overlapping stimuli is a significant constraint on application, as many researchers may have access to only partially overlapping stimuli or wish to compare stimuli acquired under different protocols and at different sites.

It is surprising, however, that the authors do not cite a paper that has already successfully demonstrated a functional alignment method that can address exactly this need: a connectivitybased Shared Response Model (cSRM; Nastase et al., 2020, NeuroImage). It would be relevant for the authors to consider the cSRM method in relation to their enhanced CHA method in detail. In particular, both the relative predictive performance as well as associated computational costs would be useful for researchers to understand in considering enhanced CHA for their applications.

We thank the reviewer for raising this point that provides us with the chance of clarifying how our approach differs with methods previously reported in the literature. The computational logic underlying our approach is that we derived the transformation matrices between the training and the target participants in the high-dimensional space based on functional connectivity calculated from the movie data. Then, we applied these transformation matrices to the training participant’s localizer data to accomplish the prediction. On the other hand, Saygin and colleagues directly used diffusion-weighted imaging (DWI) data and predicted participants’ functional responses based on the anatomical-functional correspondence. They evaluated the prediction by calculating the mean absolute errors (AE) of the difference between the actual and predicted contrast responses. Although AE linearly increases with the quality of the prediction, it is difficult to measure the prediction performance of the shape, size, and location of the functional areas precisely using this mean value. With our algorithm, we were able to predict the general location and size of the areas and recover the individualized shapes, generating more powerful predictions. We also used the searchlight analysis to evaluate the performance across the cortex systematically. In addition, Osher et al. (2016) and Saygin et al. (2012) always have a few participants failing to show better predictions based on the connectivity than the group averaged method. Our algorithm is more stable, as all participants across all four datasets had better predicted performance using our algorithm than using the group average. However, although we did not directly use the anatomical-functional correspondence with DWI, the relationships between individual structural connectivity and cortical visual category selectivity could be one of the biological underpinnings that contribute to this robust and accurate prediction.

The Connectivity-Based Shared Response Model (cSRM, Nastase et al., 2020) offers an alternative framework for aligning individuals through functional connectivity. While the overarching aim of cSRM and our methodology converges, substantial differences emerge in the respective implementation and application between the two methods that make our approach the more suitable for predicting individualized topographies. The most significant difference between the two is that, instead of focusing on within-individual connectivity profiles, cSRM used inter-subject functional connectivity (ISFC) in the initial step. This design requires that all participants must have time-locked time series, making the algorithm unusable for cross-content prediction and making it incompatible with resting-state data. Our approach, on the other hand, does not require time-locked stimuli, thereby offering a more flexible framework that permits generalization across different types of stimuli and experimental settings and enables bringing data across laboratories across the world together. Secondly, cSRM predominantly focuses on Region of Interest (ROI) analyses, whereas our model employs searchlight-based analyses designed to comprehensively cover the entire cortical sheet. Whole-brain coverage is needed to generate the topography that reflects the patterns across the cortex. Finally, with the optimized 1step method, our approach directly hyeraligns the training and target participants together, avoiding the accumulation of errors from the intermediate common space. cSRM, with an implementation similar to the classic connectivity hyperalignment, creates and hyperaligns all participants to a shared information space. In summary, while our approach and cSRM share a similar theoretical foundation, our approach has been specifically optimized to address the challenges and complexities in predicting individualized whole-brain functional topographies. Moreover, our approach demonstrates a remarkable ability to generalize across a variety of contexts and stimuli, offering a significant advantage in dealing with diverse experimental settings and datasets.

We have added the contents to the discussion section (page 16-17):

“By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging (DWI) has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (22, 23) and has established a notable congruence between structural and functional connectivities (24). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable finescale functional connectome that underpins our approach. The connectivity-based Shared Response Model (cSRM) proposed by Nastase and colleagues (25) used connectivity to functionally align individuals similar to the connectivity hyperalignment algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity (ISFC) rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly-spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subjectspecific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.”

With this in mind, I noted several current weaknesses in the paper:

First, while the enhanced CHA method is a promising update on existing CHA techniques, it is unclear why this particular six step, iterative approach was adopted. That is: why was six steps chosen over any other number? At present, it is not clear if there is an explicit loss function that the authors are minimizing over their iterations. The relative computational cost of six iterations is also likely significant, particularly compared to previous hyperalignment algorithms. A more detailed theoretical understanding of why six iterations are necessary-or if other researchers could adopt a variable number according to the characteristics of their data-would significantly improve the transferability of this method.

In the advanced connectivity hyperalignment implementation, we gradually increased the number of targets. The six steps were not intentionally chosen but were the result of the increase to the maximum number of fine-grained targets, namely single cortical vertices.

Our datasets were resampled to the cortical mesh with 18,742 vertices across both hemispheres (approximately 3 mm vertex spacing; icoorder 5; 20,484 vertices before removing non-cortical vertices). Step 1 was the classic standard connectivity hyperalignment implementation based on the anatomically-aligned data. Since using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data generates poor functional correspondence across participants (Busch et al., 2021), we used 1,284 vertices (icoorder 3, before removing the medial wall) as connectivity targets in step 1. However, it is beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales. To better align across participants, we iterated the alignment for another two times (step 2 and step 3) with the same number of 1,284 coarse connectivity targets to ensure improved alignment before increasing the number of targets in the later steps. In step 4, we increased the number of targets to 5,124 (icoorder 4, before removing the medial wall), and iterated with this number of vertices for two times in total (step 4 & step 5) before using all vertices as targets. In the final step (step 6), all vertices were used as connectivity targets.

It is true that the multiple iteration steps largely increased the computational complexity compared to the classic connectivity hyperalignment, but the prediction increase was steady across all datasets and became comparable to response hyperalignment performance which requires time-locked stimuli. We did not use an explicit loss function in the algorithm, but followed the natural progression of the number of potential connectivity targets in the implementation. On the other hand, the difference between the performance of the improved and the classic connectivity hyperalignment was relatively small (difference of r < 0.05), which indicates the effectiveness of our classic algorithm. It is up to the researchers’ own options to adopt the number of iterations and the pace of increasing the number of targets in each step. If computational resources are limited or if a shorter total computational time is the primary priority, using the classic connectivity hyperalignment may be the best option to balance the trade-offs.

The Materials and Methods section had the details of the implementation (page 22-23):

“Using dense connectivity targets (e.g., using all 18742 vertices on the surface) with anatomically-aligned data usually generates poor functional correspondence across participants (33). It is, however, beneficial to include more targets for calculating connectivity patterns after the first iteration of connectivity hyperalignment and repeated iterations to lead to a better solution by gradually aligning the information at finer scales.

We used six steps to further improve the connectivity hyperalignment method. Step 1 was the initial connectivity hyperalignment step as described above that was based on the raw anatomically aligned movie data. The resultant transformation matrices were applied to those movie runs, and the hyperaligned data were then used in step 2 to calculate new connectivity patterns and calculate new transformation matrices. We repeated this procedure iteratively six times and derived transformation matrices for each step. In steps 1, 2, and 3, 642 × 2 (icoorder3, before removing the medial wall) connectivity targets were defined with 13 mm searchlights. In step 4 and 5, 2562 × 2 (icoorder 4, before removing the medial wall) connectivity targets were used with 7 mm searchlights to calculate target mean time series. In the final step 6, all 18742 vertices were included as separate connectivity targets, using each vertex’s time series rather than calculating the mean in a searchlight. Each step of this advanced connectivity hyperalignment algorithm increased the prediction performance (Figure 4-figure supplement 2).”

But to help the readers understand the logic of the advanced connectivity hyperalignment algorithm used in this study, we expanded the discussion section (page 15):

“Because using dense connectivity targets (e.g., using all vertices as connectivity targets) with anatomically-alignment data often leads to suboptimal alignment across participants (33), we started with coarse connectivity targets and gradually increased the number of connectivity targets to form a denser representation of connectivity profiles. The iterations improved the prediction performance step by step, and at the final step (step 6, all vertices were used as connectivity targets) in this analysis, the enhanced CHA generated comparable performance with RHA (Figure 4-figure supplement 4).”

Second, the existing evaluations for enhanced CHA appear to be entirely based on imagederived correlations. That is, the authors compare the predicted image from CHA with the ground-truth image using correlation. While this provides promising initial evidence, correlation-based measures are often difficult to interpret given their sensitivity to image characteristics such as smoothness. Including Cronbach's alpha reliability as a baseline does not address this concern, as it is similarly an image-based statistic. It would be useful to see additional predictive experiments using frameworks such as time-segment classification, intersubject decoding, or encoding models.

We appreciate the reviewer’s concern regarding the stability of local correlations in relation to image characteristics. To address this, we conducted additional analysis using different searchlight sizes (with radii of 10 mm, 15 mm, and 20 mm) to evaluate the predicted categoryselective maps, focusing specifically on the Budapest dataset. The local correlations between the predicted category-selective maps (obtained using enhanced CHA) and participants’ own maps based on classic localizer runs were calculated for each searchlight. We averaged these correlations across participants and plotted the resulting maps, as shown in Figure 4-figure supplement 10. Although using a larger searchlight radius is similar to employing a larger smoothing kernel, the results remained relatively stable across different searchlight sizes, particularly in regions selectively responsive to the specific category. This stability suggests that while the evaluation may be influenced by image-related features, the conclusion would remain consistent under varying parameters.

As for the use of enhanced CHA, it serves as an optimized version of the classic CHA, specifically designed for predicting individualized functional topographies. Evaluating prediction performance in our study is based on t-value contrast maps for each participant. Given this, it's unclear how time-segment classification or other decoding/encoding models could be appropriately implemented for performance evaluation. However, prior research from our lab has already established the effectiveness of classic CHA. Specifically, Guntupalli et al. (2018) showed that classic CHA significantly improved intersubject correlations (ISC) of connectivity profiles across the cortex. They also revealed that CHA captured fine-scale variations in connectivity profiles for nearby cortical nodes across participants and led to improved betweensubject multivariate pattern classification accuracies (bsMVPC) of movie segments. These findings serve as robust evidence for the effectiveness of classic CHA, laying the groundwork for our enhanced CHA approach.

We added Figure 4-figure supplement 10 to the supplementary material:

Addressing these concerns and considering cSRM as a comparison model would significantly strengthen the paper. There are also notable strengths that I would encourage the authors to further pursue. In particular, the authors have access to a unique dataset in which the same Raiders of the Lost Ark stimulus was scanned for participants within the Budapest (SRaiders) dataset as well as non-overlapping participants in the Raiders dataset. Exploring the relative performance for cross-movie prediction within a dataset as compared to a shared movie prediction across datasets is particularly interesting for methods development. I would encourage the authors to explicitly report results in this framework to highlight both this unique testing structure as well as the performance of their enhanced CHA method.

We appreciate the reviewer's suggestion to examine a shared time-series but non-overlapping participants scenario using the Sraiders and Raiders datasets. However, there are significant differences between the two datasets that preclude such direct comparison. These differences include varying scanning parameters, MRI scanners, localizer types, and data collection procedures. Due to these methodological divergences, the datasets cannot be treated as identical time-series.

Firstly, the scanning parameters vary considerably. Sraiders were scanned with TR = 1 s (TR/TE = 1000/33 ms, flip angle = 59 °, resolution = 2.5 mm3 isotropic voxels, matrix size = 96 × 96, FoV = 240 × 240 mm, multiband acceleration factor = 4, and no in-plane acceleration), and Raiders were scanned with TR = 2.5 s (TR = 2.5 s, TE = 35 ms, Flip angle = 90°, 80 × 80 matrix, FOV = 240 mm × 240 mm, resolution = 0.938 mm × 0.938 mm × 1.0 mm).

Secondly, participants in the Sraiders were scanned with a 3 T S Magnetom Prisma MRI scanner with a 32 channel head coil and the Raiders dataset, collected more than 10 years ago, used a 3T Philips Intera Achieva scanner with an eight-channel head coil.

Thirdly, the stimuli presentations were different. In the Sraiders dataset, the movie Raiders of the Lost Ark was split into eight parts (~15 min each), and the first four parts were watched outside of the scanner prior to the scanning (~56 min). The later four parts were watched in the scanner (57 min) with audio. And in the Raiders dataset, the audio-visual movie was split into eight parts (~15 min each). Participants watched all eight parts in the scanner with audio (one part / per run).

Fourthly and critically, the two datasets included two types of localizers. The Sraiders dataset included dynamic localizer runs, and the Raiders dataset only contained a static localizer that was similarly designed as in the Forrest dataset.

With all four points, it is not suitable to treat the two datasets as identical time-series. The difference in the localizer type is a further issue. The topographies generated from the two types of localizers are dissimilar in many ways. For all categories, the dynamic localizer elicited stronger and broader category-selective activations than the static localizer, and the searchlight analysis showed that the dynamic localizer had higher reliabilities across the cortex, especially in regions that were selectively responsive to the target category. Due to these differences, crossdataset predictions yielded lower correlations than within-dataset predictions. This is not indicative of methodological failure but reflects diverging topographies activated by different localizers.

In the manuscript, we have extensively analyzed cross-dataset predictions (Figure 2-figure supplement 1-Figure 4-figure supplement 4 & 6).

● Figure 2-figure supplement 1 demonstrates that, despite the limitations of cross-localizertype evaluation, both R-to-S (Raiders to Sraiders) and S-to-R (Sraiders to Raiders) predictions significantly outperformed surface alignment methods across categories.

● Figure Figure 2-figure supplement 2 confirms that the prediction performance remained stable across individual participants, underscoring the robustness of our methodology.

● Figure 3-figure supplement 1 & Figure 3-figure supplement 2 display contrast maps generated from both native and alternate localizers, revealing that the maps share similar topographies irrespective of the dataset origin.

● Figure 4-figure supplement 1 presents a correlation analysis of local similarities in R-to-S and S-to-R predictions, highlighting particularly strong correlations in the ventral face regions.

● Figure 4-figure supplement 2 employs histograms to showcase performance across major cortices and furnishes additional evidence regarding the influence of localizer types on the results.

● Figure 4-figure supplement 3 offers a searchlight analysis for other categories, enriching the scope of our investigation.

● Figure 4-figure supplement 4 affirms that the advanced CHA is effective in both R-to-S and S-to-R predictions.

● Figure 4-figure supplement 6 compares the efficacy of 1-step vs. 2-step prediction methods for R-to-S and S-to-R, showing a clear advantage for the 1-step approach.

These analyses affirmed that our approach outperforms surface alignment methods. But the inherent limitations in data collection and localizer types preclude a direct exploration of the reviewer’s hypothesis. These complexities necessitate further research to fully validate the proposed scenario.

Overall, I share the authors' enthusiasm for the potential of cross-movie, cross-dataset prediction, and I believe that methods such as enhanced CHA are likely to significantly improve our ability to make these comparisons in the near future. At present, however, I find that the theoretical and experimental support for enhanced CHA is incomplete. It is therefore difficult to assess how enhanced CHA meets its goals or how successfully other researchers would be able to adopt this method in their own experiments.

We hope our new analysis and replies addressed the reviewer’s concerns.

Author Response

Reviewer #1 (Public Review):

In this study, the authors describe an elegant genetic screen for mutants that suppress defects of MCT1 deletions which are deficient in mitochondrial fatty acid synthesis. This screen identified many genes, including that for Sit4. In addition, genes for retrograde signaling factors (Rtg1, Rtg2 and Rtg3), proteins influencing proteasomal degradation (Rpn4, Ubc4) or ribosomal proteins (Rps17A, Rps29A) were found. From this mix of components, the authors selected Sit4 for further analysis. In the first part of the study, they analyzed the effect of Sit4 in context of MCT1 mutant suppression. This more specific part is very detailed and thorough, the experiments are well controlled and convincing. The second, more general part of the study focused on the effect of Sit4 on the level of the mitochondrial membrane potential. This part is of high general interest, but less well developed. Nevertheless, this study is very interesting as it shows for the first time that phosphate export from mitochondrial is of general relevance for the membrane potential even in wild type cells (as long as they live from fermentation), that the Sit4 phosphatase is critical for this process and that the modulation of Sit4 activity influences processes relying on the membrane potential, such as the import of proteins into mitochondria. However, some aspects should be further clarified.

1) It is not clear whether Sit4 is only relevant under fermentative conditions. Does Sit4 also influence the membrane potential in respiring cells? Fig. S2D shows the membrane potential in glucose and raffinose. Both carbon sources lead to fermentative growths. The authors should also test whether Sit4 levels influence the membrane potential when cells are grown under respirative conditions, such in ethanol, lactate or glycerol. Even if deletions of Sit4 affect respiration, mutants with altered activity can be easily analyzed.

sit4Δ cells fail to grow on nonfermentable media as shown by us (Figure 2—figure supplement 1C) and others (Arndt et al., 1989; Dimmer et al., 2002; Jablonka et al., 2006). In our opinion, the exact reason is unclear, but there is an interesting observation that addition of aspartate can partially restore growth on ethanol (Jablonka et al., 2006). Despite the lack of thorough investigation on this sit4Δ defect, an early study speculated that this defect could be related to the cAMP-PKA pathway (Sutton et al., 1991). This study pointed out genetic interactions of SIT4 with multiple genes in cAMP-PKA (Sutton et al., 1991). In addition, sit4Δ cells have similar phenotypes as those cAMP-PKA null mutants, such as glycogen accumulation, caffeine resistant, and failure to grow on nonfermentable media (Sutton et al., 1991). We have not found sit4Δ mutants that could grow on nonfermentable media based on literature search.

2) The authors should give a name to the pathway shown in Fig. 4D. This would make it easier to follow the text in the results and the discussion. This pathway was proposed and characterized in the 90s by George Clark-Walker and others, but never carefully studied on a mechanistic level. Even if the flux through this pathway cannot be measured in this study, the regulatory role of Sit4 for this process is the most important aspect of this manuscript.

We now refer this mechanism as the mitochondrial ATP hydrolysis pathway.

3) To further support their hypothesis, the authors should show that deletion of Pic1 or Atp1 wipes out the effect of a Sit4 deletion. In these petite-negative mutants, the phosphate export cycle cannot be carried out and thus, Sit4, should have no effect.

The mitochondrial phosphate transport activity is electroneutral as it also pumps a proton together with inorganic phosphate. The F1 subunit of the ATP synthase (Atp1 and Atp2) is suggested among many literatures to be responsible for the ATP hydrolysis. We performed tetrad dissection to generate atp1Δ or atp2Δ in pho85Δ background. After streaking the single colony to a fresh plate, we noticed that atp1Δ mct1Δ and atp2Δ mct1Δ cells are lethal, and knocking out PHO85 rescued this synthetic lethality. It is not surprising that atp1Δ mct1Δ or atp2Δ mct1 Δ cells are lethal since the F1 subunit is important to generate a minimum of MMP in mct1 Δ cells when the ETC is absent (i.e., rho0 cells). However, knocking out PHO85 can generate MMP independent of F1 subunit of ATP synthase, which is suggested by the viable atp1Δ mct1Δ pho85Δ and atp2Δ mct1Δ pho85Δ cells. There are many ATPases in the mitochondrial matrix that could hydrolyze ATP for ADP/ATP carrier to generate MMP theoretically. However, we do not currently know exactly which ATPase(s) is activated by phosphate starvation. This data is now included as Figure 5—figure supplement 1F-G.

4) What is the relevance of Sit4 for the Hap complex which regulates OXPHOS gene expression in yeast? The supplemental table suggests that Hap4 is strongly influenced by Sit4. Is this downstream of the proposed role in phosphate metabolism or a parallel Sit4 activity? This is a crucial point that should be addressed experimentally.

To investigate the role of the Hap complex in MMP generation in sit4Δ cells, we overexpressed and knocked out HAP4, the catalytic subunit of the Hap complex, separately in wild-type and sit4Δ cells. We confirmed the HAP4 overexpression by the enriched abundance of ETC complexes as shown in the BN-PAGE (Figure 2—figure supplement 1E). However, we did not observe any rescue of ETC or ATP synthase in mct1Δ cells when HAP4 was overexpressed. The enriched level of ETC complexes by HAP4 overexpress is not sufficient to rescue the MMP (Figure 2—figure supplement 1F).

Next, we knocked out HAP4 in sit4Δ cells. Knocking out SIT4 could still increase MMP in hap4Δ cells with a much-reduced magnitude, which phenocopied ETC subunit and RPO41 deletion in sit4Δ cells (Figure 2—figure supplement 1G).

In conclusion, the Hap complex is involved in the MMP increase when SIT4 is absent. However, it is not sufficient to increase MMP by overexpressing HAP4. The Hap complex discussion is now included in the manuscript, and the data is presented as Figure 2—figure supplement 1E-G.

5) The authors use the accumulation of Ilv2 precursors as proxy for mitochondrial protein import efficiency. Ilv2 was reported before as a protein which, if import into mitochondria is slow, is deviated into the nucleus in order to be degraded (Shakya,..., Hughes. 2021, Elife). Is it possible that the accumulation of the precursor is the result of a reduced degradation of pre-Ilv2 in the nucleus rather than an impaired mitochondrial import? Since a number of components of the ubiquitin-proteasome system were identified with Sit4 in the same screen, a role of Sit4 in proteasomal degradation seems possible. This should be tested.

We thank the reviewer for pointing out this potential caveat with our Ilv2-FLAG reporter. With limited search and tests, we could not find another reporter that behaves like Ilv2FLAG. The reason Ilv2-FLAG is a perfect reporter for this study is because in wild-type cells, Ilv2-FLAG is not 100% imported. Therefore, we could demonstrate that mitochondria with higher MMP import more efficiently. Unfortunately, all of the mitochondrial proteins that we tested could efficiently import in wild-type cells. To identify other suitable mitochondrial proteins that behave like Ilv2-FLAG, we would need to conduct a more comprehensive screen.

To address the concern of the involvement of protein degradation in obscuring the interpretation of Ilv2-FLAG import, we performed two experiments. First, we measured the proteasomal activity in wild-type and our mutants using a commercial kit (Cayman). We did not observe a statistically significant difference in 20S proteasomal activity between wild-type and sit4Δ cells.

In the second experiment, we reduced the MMP of sit4 cells using CCCP treatment and measured the Ilv2-FLAG import. We first treated sit4Δ cells with different dosage of CCCP for six hours and measured their MMP. sit4Δ cells treated with 75 µM CCCP had comparable MMP to wild-type cells. When we treated sit4Δ cells with higher concentrations of CCCP, most of the cells did not survive after six hours. Next, we performed the Ilv2-FLAG import assay. We observed similar level of unimported Ilv2FLAG (marked with *) in sit4Δ cells treated with 75 µM CCCP. This result confirms that sit4Δ cells have similar Ilv2-FLAG turnover mechanism and activity as the wild-type cells, because when we lower the MMP in sit4Δ background we observe a similar level of unimported Ilv2-FLAG. We thus feel confident in concluding that the Ilv2-FLAG import results are indeed an accurate proxy for MMP level. These data are now included as Figure 1—figure supplement 1H-J in the manuscript.

Author response image 1.

Reviewer #2 (Public Review):

This study reports interesting findings on the influence of a conserved phosphatase on mitochondrial biogenesis and function. In the absence of it, many nucleus-encoded mitochondrial proteins among which those involved in ATP generation are expressed much better than in normal cells. In addition to a better understanding of th mechanisms that regulate mitochondrial function, this work may help developing therapeutic strategies to diseases caused by mitochondrial dysfunction. However there are a number of issues that need clarification.

1) The rationale of the screening assay to identify genes required for the gene expression modifications observed in mct1 mutant is not clear. Indeed, after crossing with the gene deletion libray, the cells become heterozygote for the mct1 deletion and should no longer be deficient in mtFAS. Thank you for clarifying this and if needed adjust the figure S1D to indicate that the mated cells are heterozygous for the mct1 and xxx mutations.

We updated the methods section and the graphic for the genetic screen to clarify these points within the SGA workflow overview. After we created the heterozygote by mating mct1Δ cells with the individual KO cells in the collection, these diploids underwent sporulation and selection for the desired double KO haploid. As a result, the luciferase assay was performed in haploid cells with MCT1 and one additional non-essential gene deleted.

2) The tests shown in Fig. S1E should be repeated on individual subclones (at least 100) obtained after plating for single colonies a glucose culture of mct1 mutant, to determine the proportion of cells with functional (rho+) mtDNA in the mct1 glucose and raffinose cultures. With for instance a 50% proportion of rho- cells, this could substantially influence the results of the analyses made with these cells (including those aiming to evaluate the MMP).

We agree that this would provide a more confident estimate for population-level characterization of these colonies. It is important to note that we randomly chose 10 individual subclones, and 100% of these colonies were verified to be rho+. This suggests the population has functional mtDNA, and thus felt confident in the identity of our populations.

3) The mitochondria area in mct1 cells (Fig.S1G) does not seem to be consistent with the tests in Fig. 1C. that indicate a diminished mitochondrial content in mct1 cells vs wild-type yeast. A better estimate (by WB for instance) of the mitochondrial content in the analyzed strains would enable to better evaluate MMP changes monitored with Mitotracker since the amount of mitochondria in cells correlate with the intensity of the fluorescence signal.

As this reviewer pointed out, we quantified mitochondrial area based on Tom70-GFP signal. This measurement is quantified by mitochondrial area over cell size. Cell size is an important parameter when measuring organelle size as most of the organelles scale up and down with the cell size. mct1Δ cells generally have smaller cell size than WT cells. Therefore, the mitochondrial area of mct1Δ cells was not significantly different from WT cells when scaled to cell size. We believe this is the best method to compare mitochondrial area. As for quantifying MMP from these microscopy images, we measured the average MitoTracker Red fluorescence intensity of each mitochondria defined by Tom70-GFP. This method inherently normalizes to subtract the influence of mitochondria area when quantifying MMP.

4) Page 12: "These data demonstrate that loss of SIT4 results in a mitochondrial phenotype suggestive of an enhanced energetic state: higher membrane potential, hyper-tubulated morphology and more effective protein import." Furthermore, the sit4 mutant shows higher levels of OXPHOS complexes compared to WT yeast.

Despite these beneficial effects on mitochondria, the sit4 deletion strain fails to grow on respiratory substrates. It would be good to know whether the authors have some explanation for this apparent contradiction.

We agree that this was initially puzzling. We provide a more complete explanation above (see comments to reviewer #1 - major concern #1). Briefly, the growth deficiency in non-fermentable media with sit4Δ cells was reported and studied by multiple groups (Arndt et al., 1989; Dimmer et al., 2002; Jablonka et al., 2006). These seems to indicate that sit4Δ cells contain more ETC complexes and more OCR but cannot respire on nonfermentable carbon source. However, we do not think there is yet a clear explanation for this phenotype. One interesting observation reported is the addition of aspartate partly restoring cells’ growth on ethanol (Jablonka et al., 2006). One early study speculates that this defect could be related to the cAMP-PKA pathway. Sutton et al. pointed out genetic interactions with sit4 and multiple genes in cAMP-PKA (Sutton et al., 1991). In addition, sit4Δ cells have similar phenotypes as those cAMP-PKA null mutants, such as glycogen accumulation, caffeine resistance, and failure to grow on non-fermentable media. However, to keep this manuscript succinct, we opted to stay focused on MMP.

Reviewer #3 (Public Review):

In this study, the authors investigate the genetic and environmental causes of elevated Mitochondrial Membrane Potential (MMP) in yeast, and also some physiological effects correlated with increased MMP.

The study begins with a reanalysis of transcriptional data from a yeast mutant lacking the gene MCT1 whose deletion has been shown to cause defects in mitochondrial fatty acid synthesis. The authors note that in raffinose mct1del cells, unlike WT cells, fail to induce expression of many genes that code for subunits of the Electron Transport Chain (ETC) and ATP synthase. The deletion of MCT1 also causes induction of genes involved in acetyl-CoA production after exposure to raffinose. The authors therefore conduct a screen to identify mutants that suppress the induction of one of these acetylCoA genes, Cit2. They then validate the hits from this screen to see which of their suppressor mutants also reduce expression in four other genes induced in a mct1del strain. This yielded 17 genes that abolished induction of all 5 genes tested in an mct1del background during growth on raffinose.

The authors chose to focus on one of these hits, the gene coding for the phosphatase SIT4 (related to human PP6) which also caused an increase in expression of two respiratory chain genes. The authors then investigated MMP and mitochondrial morphology in strains containing SIT4 and MCT1 deletions and surprisingly saw that sit4del cells had highly elevated MMP, more reticular mitochondria, and were able to fully import the acetolactate synthase protein Ilv2p and form ETC and ATP synthase complexes, even in cells with an mct1del background, rescuing the low MMP, fragmented mitochondria, low import of Ilv2 and an inability to form ETC and ATP synthase complexes phenotypes of the mct1del strain. Surprisingly, the authors find that even though MMP is high and ETC subunits are present in the sit4del mct1del double deletion strain, that strain has low oxygen consumption and cannot grow under respiratory conditions, indicating that the elevated MMP cannot come from fully functional ETC subunits. The authors also observe that deleting key subunits of ETC complex III (QCR2) and IV (COX5) strongly reduced the MMP of the sit4del mutant, which would suggest that the majority of the increase in MMP of the sit4del mutant was dependant on a partially functional ETC. The authors note that there was still an increase in MMP in the qcr2del sit4del and cox4del sit4del strains relative to qcr2del and cox4del strains indicating that some part of the increase in MMP was not dependent on the ETC.

The authors dismiss the possibility that the increase in MMP could have been through the reversal of ATP synthase because they observe that inhibition of ATP synthase with oligomycin led to an increase of MMP in sit4del cells. Indicating that ATP synthase is operating in a forward direction in sit4del cells.

Noting that genes for phosphate starvation are induced in sit4del cells, the authors investigate the effects of phosphate starvation on MMP. They found that phosphate starvation caused an increase in MMP and increased Ilv2p import even in the absence of a mitochondrial genome. They find that inhibition of the ADP/ATP carrier (AAC) with bongkrekic acid (BKA) abolishes the increase of MMP in response to phosphate starvation. They speculate that phosphate starvation causes an increase in MMP through the import and conversion of ATP to ADP and subsequent pumping of ADP and inorganic phosphate out of the mitochondria.

They further show that MMP is also increased when the cyclin dependent kinase PHO85 which plays a role in phosphate signaling is deleted and argue that this indicates that it is not a decrease in phosphate which causes the increase in MMP under phosphate starvation, but rather the perception of a decrease in phosphate as signalled through PHO85. Unlike in the case of SIT4 deletion, the increase in MMP caused by the deletion of pho85 is abolished when MCT1 is deleted.

Finally they show an increase in MMP in immortalized human cell lines following phosphate starvation and treatment with the phosphate transporter inhibitor phosphonoformic acid (PFA). They also show an increase in MMP in primary hepatocytes and in midgut cells of flies treated with PFA.

The link between phosphate starvation and elevated MMP is an important and novel finding and the evidence is clear and compelling. Based on their experiments in various mammalian contexts, this link appears likely to be generalizable, and they propose and begin to test an interesting hypothesis for how MMP might occur in response to phosphate starvation in the absence of the Electron Transport Chain.

The link between phosphate starvation and deletion of the conserved phosphatase SIT4 is also interesting and important, and while the authors' experiments and analysis suggest some connection between the two observations, that connection is still unclear.

Major points

Mitotracker is great fluorescent dye, but it measures membrane potential only indirectly. There is a danger when cells change growth rates, ion concentrations, or when the pH changes, all MMP indicating dyes change in fluorescence: their signal is confounded Change in phosphate levels can possibly do both, alter pH and ion concentrations. Because all conclusions of the manuscript are based on a change in MMP, it would be a great precaution to use a dye-independent measure of membrane potential, and confirm at least some key results.

Mitochondrial MMP does strongly influence amino acid metabolism, and indeed the SIT4 knockout has a quite striking amino acid profile, with histidine, lysine, arginine, tyrosine being increased in concentration. http://ralser.charite.de/metabogenecards/Chr_04/YDL047W.html Could this amino acid profile support the conclusions of the authors? At least lysine and arginine are down in petites due to a lack of membrane potential and iron sulfur cluster export.- and here they are up. Along these lines, according to the same data resource, the knock-outs CSR2, ASF1, SSN8, YLR0358 and MRPL25 share the same metabolic profile. Due to limited time I did not re-analyse the data provided by the authors- but it would be worth checking if any of these genes did come up in the screens of the authors.

We tested the mutants within the same cluster as SIT4 shown in this paper from the deletion collection and measured their MMP. yrl358cΔ cells have similar high MMP as observed in sit4Δ cells. However, this gene has a yet undefined function. Beyond YRL358C, we did not observe similar MMP increases in other gene deletions from this panel, which does not support the notion that amino acids such as histidine, lysine, arginine, or tyrosine play a determining effect in driving MMP.

The media condition and strain used in the suggested paper is very different from what we used in our study. Instead of growing prototrophic cells in minimal media without any amino acids, we used auxotrophic yeast strains and grew them in media containing complete amino acids. So far, none of the other defects or signaling associated with SIT4 deletion could influence MMP as much as the phosphate signaling. We interpret these data to support the hypothesis that the MMP observation in sit4Δ cells is connected with the phosphate signaling as illustrated by the second half of the story in our manuscript.

Author reponse image 2.

One important claim in the manuscript attempts to explain a mechanism for the MMP increase in response to phosphate starvation which is independent of the ETC and ATP synthase.

It seems to me the only direct evidence to support this claim is that inhibition of the AAC with BKA stops the increase of mitotracker fluorescence in response to phosphate starvation in both WT and rho0 cells (Figs 4B and 4C). It would strengthen the paper if the authors could provide some orthogonal evidence.

This is a similar comment as raised by reviewer #1 - major concern #3. We refer the reviewer to our discussion and the new data above. Briefly, we do not think F1 subunit is responsible for the ATP hydrolysis activity to generate MMP in phosphate depleted situation. We believe there are additional ATPase(s) in the mitochondrial matrix that can be utilized to couple to ADP/ATP carrier for MMP generation during phosphate starvation. However, we have not identified the relevant ATPase(s) at this point, and it is likely that multiple ATPases could contribute to this activity.

Introduction/Discussion The author might want to make the reader of the article aware that the 'reversal' of the ATP synthase directionality -i.e. ATP hydrolysis by the ATP synthase as a mechanism to create a membrane potential (in petites), has always been a provocative idea - but one that thus far could never be fully substantiated. Indeed some people that are very familiar with the topic, are skeptical this indeed happens. For instance, Vowinckel et al 2021 (PMID: 34799698) measured precise carbon balances for peptide cells, and found no evidence for a futile cycle - peptides grow slower, but accumulate the same biomass from glucose as peptides that re-evolve at a fast growth rate . Perhaps the manuscript could be updated accordingly.

We thank the reviewer for pointing out this additional relevant study. We have rephased the referenced sentence in the introduction. The MMP generation in phosphate starvation is independent of the F1 portion of ATP synthase. Therefore, our data neither supports or refutes either of these arguments.

In the introduction and conclusion there is discussion of MMP set points. In particular the authors state:

"Critically, we find that cells often prioritize this MMP setpoint over other bioenergetic priorities, even in challenging environments, suggesting an important evolutionary benefit."

This does not seem to be consistent with the central finding of the manuscript that MMP changes under phosphate starvation. MMP doesn't seem so much to have a 'set point' but rather be an important physiological variable that reacts to stimuli such as phosphate starvation.

The reviewer raises a rational alternative hypothesis to the one that we have proposed. In reality, both of these are complete speculations to explain the data and we can’t think of any way to test the evolutionary basis for the mechanisms that we describe. We recognize that untested/untestable speculative arguments have limitations and there are viable alternative hypotheses. We have softened our language to ensure that it is clear that this is only a speculation.

The authors suggest that deletion of Pho85 causes an increase in MMP because of cellular signaling. However, they also state in the conclusion:

"Unlike phosphate starvation, the pho85D mutant has elevated intracellular phosphate concentrations. This suggests that the phosphate effect on MMP is likely to be elicited by cellular signaling downstream of phosphate sensing rather than some direct effect of environmental depletion of phosphate on mitochondrial energetics."

The authors should cite the study that shows deletion of PHO85 causes increased intracellular phosphate concentrations. It also seems possible that the 'cellular signaling' that causes the increase in MMP could be a result of this increase in intracellular phosphate concentrations, which could constitute a direct effect of an environmental overload of phosphate on mitochondrial energetics.

We now cited the literature that shows higher intracellular phosphate in pho85Δ cells (Gupta et al., 2019; Liu et al., 2017). Depleting phosphate in the media drastically reduced intracellular phosphate concentration, which is the opposing situation as pho85Δ cells. Nevertheless, we observed higher MMP in either situation. We concluded from these two observations that the increase in MMP is a response to the signaling activated by phosphate depletion rather than the intracellular phosphate abundance.

Related to this point, in the conclusion, the authors state:

"We now show that intracellular signaling can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome."

In sum, the data shows that signaling is important here- but signaling alone is only the message - not the biophysical process that creates a membrane potential. The authors then could revise this slightly.

We have rephrased this sentence as suggested, which now reads “We now show that intracellular signaling triggers a process that can lead to an increased MMP even beyond the wild-type level in the absence of mitochondrial genome”.

The authors state in the conclusion that

"We first made the observation that deletion of the SIT4 gene, which encodes the yeast homologue of the mammalian PP6 protein phosphatase, normalized many of the defects caused by loss of mtFAS, including gene expression programs, ETC complex assembly, mitochondrial morphology, and especially MMP (Fig. 1)"

The data shown though indicates that a defect in mtFAS in terms of MMP, deletion of SIT4 causes a huge increase (and departure away from normality) whether or not mct1 is present (Fig 1D)

We changed the word “normalized” to “reversed”. In the discussion section, we also emphasized that many of these increases are independent of mitochondrial dysfunction induced by loss of mtFAS.

The language "SIT4 is required for both the positive and negative transcriptional regulation elicited by mitochondrial dysfunction" feels strong. SIT4 seems to influence positive transcriptional regulation in response to mitochondrial dysfunction caused by MCT1 deletion (but may not be the only thing as there appears to be an increase in CIT2 expression in a sit4del background following a further deletion of MCT1). In terms of negative regulation, SIT4 deletion clearly affects the baseline, but MCT1 deletion still causes down regulation of both examples shown in Fig 1B, showing that negative transcriptional regulation can still occur in the absence of SIT4. The authors might consider showing fold change of expression as they do in later figures (Figs 4B and C) to help the reader evaluate the quantitative changes they demonstrate.

We now displayed the fold change as suggested. This sentence now reads “These data suggest that SIT4 positively and negatively influences transcriptional regulation elicited by mitochondrial dysfunction”.

The authors induce phosphate starvation by adding increasing amounts of potassium phosphate monobasic at a pH of 4.1 to phosphate dropout media supplemented with potassium. The authors did well to avoid confounding effects of removing potassium. The final pH of YNB is typically around 5.2. Is it possible that the authors are confounding a change in pH with phosphate starvation? One would expect the media in the phosphate starvation condition to have a higher pH than the phosphate replacement or control media. Is a change in pH possibly a confounding factor when interpreting phosphate starvation? Perhaps the authors could quantify the pH of the media they use for the experiment to understand how much of a factor that could be. One needs to be careful with Miotracker and any other fluorescent dye when pH changes. Albeit having constraints on its own, MitoLoc as a protein rather than small molecule marker of MMP might be a good complement.

We followed the protocol used by many other studies that depleted phosphate in the media. The reason we and others adjusted the media without inorganic phosphate to a pH of 4.1 is because that is the pH of phosphate monobasic. From there, we could add phosphate monobasic to create +Pi media without changing the media pH. Therefore, media containing different concentrations of phosphate all have the exact same pH. We now emphasize that all media containing different levels of inorganic phosphate have the same pH to the manuscript to eliminate such concern (see page 18).

Even though all media have the similar pH, we also provided complementary data using a parallel approach to measure the MMP by assessing mitochondrial protein import as demonstrated previously with Ilv2-FLAG, which shares the same principle as mitoLoc.

Reference

Arndt, K. T., Styles, C. A., & Fink, G. R. (1989). A suppressor of a HIS4 transcriptional defect encodes a protein with homology to the catalytic subunit of protein phosphatases. Cell, 56(4), 527–537. https://doi.org/10.1016/00928674(89)90576-X

Dimmer, K. S., Fritz, S., Fuchs, F., Messerschmitt, M., Weinbach, N., Neupert, W., & Westermann, B. (2002). Genetic basis of mitochondrial function and morphology in Saccharomyces cerevisiae. Molecular Biology of the Cell, 13(3), 847–853. https://doi.org/10.1091/mbc.01-12-0588

Gupta, R., Walvekar, A. S., Liang, S., Rashida, Z., Shah, P., & Laxman, S. (2019). A tRNA modification balances carbon and nitrogen metabolism by regulating phosphate homeostasis. ELife, 8, e44795. https://doi.org/10.7554/eLife.44795

Jablonka, W., Guzmán, S., Ramírez, J., & Montero-Lomelí, M. (2006). Deviation of carbohydrate metabolism by the SIT4 phosphatase in Saccharomyces cerevisiae. Biochimica et Biophysica Acta (BBA) - General Subjects, 1760(8), 1281–1291. https://doi.org/10.1016/j.bbagen.2006.02.014

Liu, N.-N., Flanagan, P. R., Zeng, J., Jani, N. M., Cardenas, M. E., Moran, G. P., & Köhler, J. R. (2017). Phosphate is the third nutrient monitored by TOR in Candida albicans and provides a target for fungal-specific indirect TOR inhibition. Proceedings of the National Academy of Sciences, 114(24), 6346–6351. https://doi.org/10.1073/pnas.1617799114

Sutton, A., Immanuel, D., & Arndt, K. T. (1991). The SIT4 protein phosphatase functions in late G1 for progression into S phase. Molecular and Cellular Biology, 11(4), 2133–2148.

Author Response

Reviewer #2 (Public Review):

Weaknesses:

1)The authors demonstrate that Isw1 has a role in responding to antifungals in Cryptococcus. However, it is not clear if changes in Isw1 stability represent a general response to stress. This study would have benefited from experiments to test: (1) if levels of Isw1 change in response to other stressors (e.g., heat, osmotic, or oxidative stress) and (2) if loss of Isw1 impacts resistance to other stressors.

A series of experiments were conducted to illustrate and measure phenotypic traits associated with virulence. These traits encompassed capsule formation, melanin synthesis, cell proliferation under stressful conditions, and Isw1 expression levels in response to diverse environmental stimuli. Please see Figure 3a, 3b, 3c, Figure 3-figure supplement 1 and line 237-241.

2) The authors demonstrate a critical role in the acetylation of K97 and ubiquitination of K441 in regulating Isw1 stability. Additionally, this study shows that K113 is also likely involved in this process. However, it appears that K113 can be either acetylated or ubiquitinated, and it is, thus, less clear if one of the two modifications or both modifications is critical at this residue. Additional experiments may be required to answer this question. This study would have benefited from an additional discussion on the results related to the modification of K113.

We express our genuine gratitude for this insightful critique pertaining to the K113 site. In our study, we observed the presence of acetylation and ubiquitination changes at the K113 site in our mass spectrometry data. This finding suggests that a proportion of Isw1 is acetylated, while another proportion of Isw1 is ubiquitinated. In order to analyze the K113 function, a series of experiments were conducted, involving the production of triple, double, and single mutations at positions K89, K97, and K113. In addition, the utilization of K-to-R (mimicking deacetylation) and K-to-Q (mimicking acetylation) methodologies was implemented. To elucidate the significance of the acetylation modification of K113, a series of mutants were created. The K-to-R mutation was employed to indicate the deacetylation and deubiquitylation status, while the K-to-Q mutation was utilized to represent the acetylation and deubiquitylation status. In our dataset, it was shown that neither the single mutation of K113 K-to-R nor K-to-Q exhibited any discernible drug resistance phenotype. This finding suggests that, within the physiological context of the Isw1 protein, both post-translational modifications (PTMs) of K113 had minimal or no impact on the regulation of drug resistance. The reason for this phenomenon is because the acetylation modification of K97 imitates the process of ubiquitination of Isw1, hence reducing the interaction between Isw1 and Cdc4, which is an E3 ligase. Hence, the ubiquitination of K113 does not play a crucial role in the regulation of Isw1 protein stability under conditions where K97 is completely acetylated. Nevertheless, upon deacetylation of K97, we observed a notable increase in the abundance of Isw1 protein when K113 is substituted with R. This finding strongly supports the notion that ubiquitination of K113 plays a crucial role in maintaining the stability of the Isw1 protein. Hence, in the case of K97 acetylation, the PTM modifications of K113 are not required for maintaining Isw1 protein levels. However, in the event of K97 deacetylation, the ubiquitination of K113 becomes crucial in regulating protein stability. Considering the intricate post-translational modification (PTM) regulation observed at the K113 site, it would be advantageous to generate antibodies specific to K113ac and K113ub in order to comprehensively investigate the functional role of K113 in the regulatory processes. Nevertheless, the presence of antibodies targeting site-specific ubiquitination is infrequent in scientific literature. We regret any confusion that may have arisen from the previous remark and have made revisions to the manuscript to address this issue. Please refer to line 485-500.

3)The authors demonstrate that overexpression of ISW1 in select clinical isolates of Cryptococcus increases sensitivity to antifungals. However, these experiments would have benefited from additional controls, such as including overexpression of ISW1 in the wild-type strain (H99) and antifungal-sensitive isolate (CDLC120).

In response to your concern, we successfully generated the strains as required. In the revised manuscript, we demonstrated that the overexpression of the stable variant of Isw1 in H99 and CDLC120 strains induces heightened susceptibility to antifungal drugs. Please see Figure 8e, 8i and line 404-413.

Reviewer #3 (Public Review):

1) ISWI chromatin remodellers are well-characterised in many organisms. How many ISWI proteins does Cryptococcus contain? Why did the authors focus on ISWI?

We express our gratitude for this criticism. The identification of Isw1 was conducted as a further investigation building upon the findings presented in our previously published data (Li Y, 2019). In prior research, the acetylome in C. neoformans was comprehensively analyzed, and a series of knockout strains were created to investigate the relationship between fungal pathogenicity and acetylation. The Isw1 mutant has been discovered as a modifier of drug resistance. The identification of fungal paralogs of ISW genes was initially observed in Saccharomyces cerevisiae, a species of yeast that has experienced genome duplication. This process involves two paralogs, Isw1 and Isw2, which emerged as a result of the whole genome duplication event (Kellis M, 2004; Tsukiyama T, 1999; Wolfe KH, 1997). Because C. neoformans has not gone through the complete genome duplication event, its genome only encodes one copy of ISW gene. Please see line 129-134..

2) What is the ISWI protein complex(es)? The Mass-Spec analysis should reveal this.

Prior research conducted on Saccharomyces cerevisiae has provided evidence that the ISWI complex is comprised of several subunits, namely Isw1, Ioc genes, Itc1, Chd1, and Sua7 (Mellor J, 2004; Smolle M, 2012; Sugiyama and Nikawa, 2001; Vary JC Jr, 2003; Yadon AN, 2013). Upon a thorough examination of the C. neoformans genome, we have not been able to identifying a similar the IOC gene family. This absence likely suggests an evolutionary loss of the IOC gene family in C. neoformans, as suggested on the FungiDB website. However, C. neoformans has Itc1, Chd1, and Sua7. While we concur with the aforementioned statement on the capability of Mass-Spec data to elucidate potential protein-protein interactions and aid in the identification of subunits within the ISWI complex, it is important to acknowledge that the PTM Mass-Spec methodology is solely employed for the purpose of identifying potential sites of protein modification. In order to comprehensively investigate the cryptoccocal ISWI complex, we conducted a standardized Isw1-Flag protein immunoprecipitation procedure, followed by Mass-Spec analysis. In the present study, a total of 22 proteins that interact with Isw1 were found in our experimental data. Among these proteins, 11 have been previously reported to be associated with the regulatory networks including Isw1. In the mass spectrometry results, the protein Itc1 was found to be co-immunoprecipitated with the protein Isw1. Although the Mass-Spec analysis did not reveal the presence of Chd1 and Sua7, our study demonstrated that Chd1 can be coimmunoprecipitated with Isw1 through the utilization of co-IP and immunoblotting techniques. However, no interaction between Isw1 and Sua7 was shown utilizing any of these methods. In brief, cryptococcal ISWI regulatory machinery is distantly related to that from S. cerevisiae. Please see Figure 2 and line 206-219.

3) Is Cryptococcus ISWI a transcriptional activator or repressor?

We regret the erroneous representation of Isw1 in the prior iteration of the manuscript. The misclassification of Isw1 as a transcriptional regulator has been identified, since it has been determined to function as a chromatin remodeler instead. The text has been suitably revised in accordance with academic standards. In the revised publication, we have presented a comprehensive transcriptome analysis of the isw1 Δ strain under both FLC treatment and no treatment conditions. This analysis offers valuable insights into the gene regulatory patterns associated with Isw1. In our dataset, we observed that Isw1 exerts a negative regulatory effect on the expression of genes that encode drug pumps, while simultaneously exerting a positive regulatory effect on the expression of genes that are essential for 5-FC resistance. Moreover, the ChIP-PCR study demonstrated the binding of Isw1 to the promoter regions of genes of interest. Hence, the chromatin remodeler Isw1 has a dual role, wherein it both facilitates the activation of certain genes and suppresses the expression of others, in response to varying forms of drug resistance. Please see line 142-153.

4) Is ISWI function in drug resistance linked to its chromatin remodelling activity?

In order to investigate the potential role of Isw1 on chromatin activity in the modulation of multidrug resistance, we have conducted protein truncation experiments. Specifically, we deleted the DNA binding domain, the helicase domain, and the SNF2 domain, which have been previously shown to regulate Isw1 chromatin activity in the model organism S. cerevisiae (Grune T, 2003; Mellor J, 2004; Pinskaya M, 2009; Rowbotham SP, 2011). The new data demonstrated that all truncation variants of Isw1 mutants had a growth phenotype consistent with that of the deletional strain isw1Δ. In addition, the levels of gene expression observed in these strains were also similar to those observed in the deletion strain isw1Δ. This finding provides evidence that the regulation of the drug resistance mechanism is influenced by these critical domains involved in modifying chromatin activities. Moreover, the Isw1-Flag strain was utilized to conduct chromatin immunoprecipitation and PCR experiments, which revealed that Isw1 exhibits the ability to directly bind to the promoter regions of target genes. The new findings added evidence substantially supporting the hypothesis that the Isw1 chromatin activity plays a crucial role in modulating its protein function, and acting as a central regulator of drug resistance in C. neoformans. Please see revised Figure 1g, 1h, 1i and line 186-199 in the revised manuscript text.

5) Does ISWI interact with chromatin? If so, which are ISWI-target genes? Does drug treatment modulate chromatin binding?

To effectively tackle this concern, we have pursued two distinct approaches to demonstrate the chromatin regulatory effects of Isw1. In this study, the DNA binding domain was deliberately removed through genetic manipulation. The data presented indicates that the Isw1 mutants with shorter variations exhibited a growth phenotype that was characterized by multidrug resistance. This growth phenotype correlates with the growth phenotype obtained in the isw1Δ deletion strain. Additionally, it was observed that the levels of gene expression in the strain were comparable to those detected in the deletion strain isw1Δ. This discovery offers empirical support for the notion that the control of the drug resistance mechanism is indeed impacted by the DNA binding capability of Isw1. Furthermore, the Isw1-Flag strain was employed to perform chromatin immunoprecipitation and PCR assays, demonstrating the direct binding capacity of Isw1 to the promoter regions of target genes. The results obtained from this comprehensive analysis of the revised data offer significant evidence for the proposition that Isw1 interacts with chromatin and that its chromatin activity plays a pivotal role in modulating its protein function. This interaction serves as a central regulatory mechanism for drug resistance in C. neoformans. Furthermore, a transcriptome analysis was performed on both wildtype and isw1 deletion strains in the absence of FLC therapy. Upon comparing the results obtained from two unique experimental settings, specifically those with and without FLC administration, a notable disparity in the control of gene expression between these two situations was identified. In the context of the isw1 deletion strain exposed to FLC treatment, a set of 21 genes, including those belonging to the ABC/MFS family and efflux pumps, displayed significant changes in their gene expression patterns. In particular, a total of 9 genes exhibited downregulation, whilst 12 genes displayed upregulation. In contrast, in the absence of FLC supplementation, a total of 9 genes exhibited alterations in gene expression, with 3 genes showing downregulation and 6 genes showing upregulation. Therefore, the Isw1 protein plays a crucial role in the activation of certain genes, while simultaneously having a suppressive effect on other genes. Hence, the Isw1 undergoes a reconfiguration of its regulatory apparatus in response to drugs. Despite that the performance of ChIP-seq analysis was necessary in this study, it was observed that the treatment of fungal cells resulted in a notable decrease in the abundance of the Isw1 protein. This decrease can be attributed to the activation of Isw1 protein degradation. Consequently, there was an insufficient amount of Isw1 protein available for successful enrichment and subsequent ChIP-seq analysis (please see Figure 4a and 4c). However, the data collected collectively have demonstrated the idea that Isw1 serves as a crucial master regulator of drug resistance in C. neoformans. The text has undergone revisions in order to present our findings in a precise and thorough manner. Please see Figure 1c, 1g, Supplementary File 2, and line 145-153, 186-188.

Author Response

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

eLife assessment:

Multimodal experiences that for example contain both visual and tactile components are encoded as associative memories. This manuscript is a valuable contribution supporting structural and functional brain plasticity following associative training protocols that pair together different types of sensory stimuli. The results provide solid support for this plasticity being a basis for cross-modal associative memories.

We appreciate eLife assessments to our discovery about the recruitment of associative memory neurons in cerebral cortices as a hub for the fulfillment of the first order and the second order of associative memory. Synapse interconnections among associative memory neurons mediate the reciprocal retrieval, the conversion and the translation of associated signals learnt in life span.

Reviewer #1 (Public Review):

This manuscript by Xu and colleagues addresses the important question of how multi-modal associations are encoded in the rodent brain. They use behavioral protocols to link stimuli to whisker movement and discover that the barrel cortex can be a hub for associations. Based on anatomical correlations, they suggest that structural plasticity between different areas can be linked to training. Moreover, they provide electrophysiological correlates that link to behavior and structure. Knock-down of nlg3 abolishes plasticity and learning. This study provides an important contribution as to how multi-modal associations can be formed across cortical regions.

We sincerely thank Reviewer one’s comments, which is a great driving force for us to move forward to reveal the specific roles of neural circuits in associative memory and its relevant cognitive activities and emotional reactions.

Reviewer #2 (Public Review):

This manuscript by Xu et al. explores the potential joint storage/retrieval of associated signals in learning/memory and how that is encoded by some associative memory neurons using a mouse model. The authors examined mouse associative learning by pairing multimodal mouse learning including olfactory, tactile, gustatory, and pain/tail heating signals. The key finding is that after associative learning, barrel neurons respond to other multi-model stimulations. They found these barrel cortical neurons interconnect with other structures including piriform cortex, S1-Tr and gustatory cortical neurons. Further studies showed that Neuroligin 3 mediated the recruitment of associative memory neurons during paired stimulation group. The authors found that knockdown Neuroligin 3 in the barrel cortex suppressed the associative memory cell recruitment in the paired stimulation learning. Overall, while the findings of this study are interesting, the concept of associative learning involving multiple functionally connective cortical regions is not that novel. While some data presented are convincing, the other seems to lack rigor. In addition, more details and clarification of the experimental methods are needed.

Thank you so much for your comments on our studies in terms of the recruitment of associative memory neurons as the hub for the joint storage and reciprocal retrieval of multi-modal associated signals. You are right about that the concept of associative memory neuron and the new established interconnection among cerebral cortices for the formation of associative memory are not novel. The original finding has been reported by senior author’s lab many years ago, which has also been presented in a book by Jin-Hui Wang “Associative Memory Cells: Basic Units of Memory Trace” published by Springer-Nature 2019. In addition, we have made certain clarifications in our revision, but the detailed information about experimental approaches and concepts are expected to be seen in our previous publications and this book as well.

Reviewer #1 (Recommendations For The Authors):

I have two points that I find would strengthen the manuscript further:

1. Associative memories are also based on specificity, which is not addressed in this manuscript. The authors could discuss this and also the magnitude of plasticity. In general, I would suggest also testing plasticity in response to a non-linked stimulus to prove specificity.

This a good point. In terms of the specificity of associative memory in our model, we have shown this point in our previous studies, such as Wang, et al. “Neurons in the barrel cortex turn into processing whisker and odor signals: a cellular mechanism for the storage and retrieval of associative signals”. Frontiers in Cellular Neuroscience 9-320:1-17 2015, and Jin-Hui Wang “Associative Memory Cells: Basic Units of Memory Trace” published by Springer-Nature 2019.

1. Nlg3 knock-down is a strong intervention. The authors could discuss the implications of interfering with synapse assembly and mechanistic implications at the synaptic level. It could help to compare the consequences of this intervention to a post-training lesion.

This is a good point. To prevent the possibility of post-training lesion by the intervention of Nlg3 knockdown, we have conducted the use of shRNA-scramble control. In addition, the discussion about the intervention of Nlg3 knockdown at synapse level has been added in our discussion.

1. In general, the clarity of the wording in some sections/sentences could be improved.

The rewording of certain sentences has been done in our revision.

Reviewer #2 (Recommendations For The Authors):

1. The writing of the manuscript needs major editing, there are grammatical errors even in the title. The extremely long introduction and discussion section with repeated details can be distracting from the main focus of the work.

This point has been taken during our revision.

1. Many bar graphs, such as Figure 5C and 5G, Figure 6C-6G, have low-resolution images, meaning that the axis titles and labels are unreadable.

The resolution of Figures have been improved in our revision.

1. The bar graph with data points and illustration in Figure 1E and 1G are misplaced.

This mistake has been corrected in our revision.

1. On page 23, Figure 2B, which layer(s) of the PC, S1Tr and GC were the images taken from? In the PSG group, why is there no red axon terminal signal observed in the three regions? does it indicate that there is no significant projection from the BC axon to PC, S1Tr, or GC neurons? Given that Thy1-YFP labeled glutamatergic neurons at PC, S1Tr, and GC and there is no discernable co-localization of yellow and green cells, can we assume that the glutamatergic neurons at PC, S1Tr, and GC are not involved in the associative learning after PSG paradigm? Lastly, the number of synapse contacts in Figure 2E is only 1-2 per 100um dendrite, but this is not quite consistent with the confocal images in Figure 2D. In Figure 2D, there are at least three tdTomato boutons on the cropped dendrite which is ~16um according to the scale bar.

If we magnify Figure 2B, we are able to see red boutons, which can be seen in Figure 2C with a higher magnification. In addition, the distribution of synapse contacts is variable, we have demonstrated the averaged values of synapse contacts over dendrites in Figure 2E, such that the single original image may not exactly same as the statistical data.

1. Figure 4C and Figure 8C, how were the percentages of associative neurons calculated after LFP recording? More details are needed on the method of this in vivo LFP/single unit recordings, including the spike sorting algorithm.

In the section of Results, the total number of neurons recorded in each of groups has been given. For instance, the neurons recorded from PSG mice (Figure 4) were 70, which was used as denominator. With the number of neurons that responded to two or more signals, the percentage of associative memory neurons recruited in associative learning was calculated. This information has been added in our revision (please see the section of Results).

1. The rationale for the authors choosing Neuroligin 3 as the target for investigating the formation of new synapse interconnections between BC, PC, S1Tr, and GC after PSG should be more clearly spelled out. Synaptic CAMs include SynCAM, NCAM, Neurexin, Cadherin et al all play a role in new synapse formation. Neuroligin 1 is expressed specifically in the CNS at excitatory synapses. Why did the authors choose to study Neuroligin 3 instead of Neuroligin 1?

This is a good point. Based on our previous data, miRNA-324 is upregulated during the associative learning by our mouse model, which degrades neuroligin-3 mRNA. The role of neuroligin-3 in the formation of new synapses and the recruitment of associative memory neurons is studied in this paper.

1. The behavioral results in Figure 5B-5G indicated that after pair-stimulation of WS-OS, WS-TS, or WS-GS, the memory learned in piriform, S1-Tr and gustatory cortical neurons can be retrieved from each other, by jumping over the barrel cortex. Is it possible that there is some direct interconnection formed between piriform, S1-Tr, and gustatory cortical neurons? Maybe they can try to do barrel cortical lesion or chemogenetic inhibition after PGS training and then repeat the behavioral tests as in Figure 5B-5G.

We have done experiments to examine the potential direct interconnection among piriform, S1-Tr and gustatory cortical neurons, after the associative learning about twelve days. We have no convincing data to support this possibility at this moment.

1. Some of the images showing the location of virus injections look VERY similar, such as Figure 3A left and right, Figures 7A and 7D. Larger variability of different animals/injection sites is definitely expected.

The injected viruses in Figure 3 and Figure 7 are different, since AAV-carried fluorescent proteins in different cortical areas are different. In addition, if we carefully enlarge the images in the right and left panels of Figure 3A, we will see that the areas of AAV transfection in morphology are different. The similarity of injection areas as Reviewer two claimed indicates the more precision of our virus-injection sites.

1. On page 49, are the green neurons in Figure 9B the BC cells? Just to be consistent, the authors should use the same color for BC cells as in Figure 9A. Also, label the primary and the secondary associative memory cells in Figure 9.

Figure 9 has been thoroughly changed in our revision.

Author Response

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

Reviewer #1 (Public Review):

Soudi, Jahani et al. provide a valuable comparative study of local adaptation in four species of sunflowers and investigate the repeatability of observed genomic signals of adaptation and their link to haploblocks, known to be numerous and important in this system. The study builds on previous work in sunflowers that have investigated haploblocks in those species and on methodologies developed to look at repeated signals of local adaptations. The authors provide solid evidence of both genotype-environment associations (GEA) and genome-wide association study (GWAS), as well as phenotypic correlations with the environment, to show that part of the local adaptation signal is repeatable and significantly co-occur in regions harboring haploblocks. Results also show that part of the signal is species specific and points to high genetic redundancy. The authors rightfully point out the complexities of the adaptation process and that the truth must lie somewhere between two extreme models of evolutionary genetics, i.e. a population genetics view of large effect loci and a quantitative genetics model. The authors take great care in acknowledging and investigating the multiple biases inherent to the used methods (GEA and GWAS) and use a conservative approach to draw their conclusions. The multiplicity of analyses and their interdependence make them slightly hard to understand and the manuscript would benefit from more careful explanations of concepts and logical links throughout. This work will be of interest to evolutionary biologists and population geneticists in particular, and constitutes an additional applied example to the comparative local adaptation literature.

Some thoughts on the last paragraph of the discussion (L481-497): I think it would be fine to have some more thoughts here on the processes that could contribute to the presence/absence of inversions, maybe in an "Ideas and Speculation" subsection. To me, your results point to the fact that though inversions are often presented as important for local adaptation, they seem to be highly contingent on the context of adaptation in each species. First, repeatability results are only at the window/gene level in your results, the specific mutations are not under scrutiny. Is it possible that inversions are only necessary when sets of small effect mutations are used, opposite to a large effect mutation in other species? Additionally, in a model with epistasis, fitness effects of mutations are dependent on the genomic background and it is possible that inversions were necessary in only certain contexts, even for the same mutations, i.e. some adaptive path contingency. Finally, do you have specific demographic history knowledge in this system that maps to the observations of the presence of inversions or not? For example, have the species "using" inversions been subject to more gene flow compared to others?

Thank you for the great suggestions and helpful comments. Regarding the question of demography, each of the species actually harbours quite a large number of haploblocks (13 in H. annuus spanning 326Mb, 6 in H. argophyllus spanning 114 Mb, and 18 in H. petiolaris spanning 467 Mb; see Todesco et al. 2020 for more details) so there does not seem to be any clear association with demography. We agree about the complexities that might underly the evolution of inversions that you outline above, and have refined some of the text where we discuss their evolution in the Discussion.

Reviewer #2 (Public Review):

In this study the authors sought to understand the extent of similarity among species in intraspecific adaptation to environmental heterogeneity at the phenotypic and genetic levels. A particular focus was to evaluate if regions that were associated with adaptation within putative inversions in one species were also candidates for adaptation in another species that lacked those inversions. This study is timely for the field of evolutionary genomics, due to recent interest surrounding how inversions arise and become established in adaptation.

Major strengths

Their study system was well suited to addressing the aims, given that the different species of sunflower all had GWAS data on the same phenotypes from common garden experiments as well as landscape genomic data, and orthologous SNPs could be identified. Organizing a dataset of this magnitude is no small feat. The authors integrate many state-of-the-art statistical methods that they have developed in previous research into a framework for correlating genomic Windows of Repeated Association (WRA, also amalgamated into Clusters of Repeated Association based on LD among windows) with Similarity In Phenotype-Environment Correlation (SIPEC). The WRA/CRA methods are very useful and the authors do an excellent job at outlining the rationale for these methods.

Thank you!

Major weaknesses

The study results rely heavily on the SIPEC measure, but I found the values reported difficult to interpret biologically. For example, in Figure 4 there is a range of SIPEC from 0 to 0.03 for most species pairs, with some pairs only as high as ~0.01. This does not appear to be a high degree of similarity in phenotype-environment correlation. For example, given the equation on line 517 for a single phenotype, if one species has a phenotype-environment correlation of 1.0 and the other has a correlation of 0.02, I would postulate that these two species do not have similar evolutionary responses, but the equation would give a value of (1+0.02)10.02/1 = 0.02 which is pretty typical "higher" value in Figure 4. I also question the logic behind using absolute values of the correlations for the SIPEC, because if a trait increases with an environment in one species but decreases with the environment in another species, I would not predict that the genetic basis of adaptation would be similar (as a side note, I would not question the logic behind using absolute correlations for associations with alleles, due to the arbitrary nature of signing alleles). I might be missing something here, so I look forward to reading the author's responses on these thoughts.

The reviewer makes a very good point about the range of SIPEC, and we have changed our analysis to reflect this, now reporting the maximum value of SIPEC for each environment (across the axes of the PCA on phenotypes that cumulatively explain 95% of the variance), in Figure 4 and Supplementary Figures S2 and S13. For consistency among manuscript versions and to illustrate the effect of this change, we retain the mean SIPEC value in one figure in the supplementary materials (S12), which shows the small effect of this change on the qualitative patterns. Figure 4 now shows that the maximum SIPEC value is regularly quite strong, which should address the reviewer’s concern that this is not being driven by anomalous and small values. We appreciate this point and think this change now more closely reflects how we are trying to estimate the biological feature of interest – that some axis of phenotypic space is strongly (or not) responding to selection from the environmental variable.

With respect to the logic behind using absolute value, we still feel this is justified for traits, because if a trait evolves to be bigger or smaller, it may still use the same genes. For example, flowering time may change to be later or earlier, which would result in opposite correlations with a given environment, but might use the same gene (e.g. FT) for this. As such, we think keeping absolute value is more representative as otherwise species with strong but opposite patterns of adaptation would look like they were very different. We have added a statement on line 584 in the methods section to further clarify the reason for this choice.

An additional potential problem with the analysis is that from the way the analysis is presented, it appears that the 33 environmental variables were essentially treated as independent data points (e.g. in Figure 4, Figure 5). It's not appropriate to treat the environmental variables independently because many of them are highly correlated. For example in Figure 4, many of the high similarity/CRA values tend to be categorized as temperature variables, which are likely to be highly correlated with each other. This seems like a type of pseudo replication and is a major weakness of the framework.

This is a good point and we fully agree. It is for this reason that we didn’t present any p-values or statistical tests of the overall patterns that are shown in these figures (i.e. the linear relationship between SIPEC and number of CRAs in figure 4 and the tendency for most points to fall above the 1:1 line in figure 5). But to make sure this is even more clear, we have added statements to the captions of these figures to remind readers that points are non-independent. We still feel that in the absence of a formal test, the overall patterns are strongly consistent with this interpretation. A smaller number of non-pseudo-replicated points in Figure 4 would still likely show linear patterns. Similarly, there are almost no significant points falling below the 1:1 line in Figure 5, and it seems unlikely that pseudoreplication would generate this pattern.

Below I highlight the main claims from the study and evaluate how well the results support the conclusions.

"We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments" (abstract)<br /> Given the questions above about SIPEC, I did not find this conclusion well supported with the way the data are presented in the manuscript.

We have changed the reporting of the SIPEC metric so that it more clearly reflects whichever axis of phenotypic space is most strongly correlated with environment in both species (using max instead of mean). This shows similar qualitative patterns but illustrates that this happens across much higher values of SIPEC, showing that it is in fact driven by high correlations in each species (or non-similar correlations resulting in low values of SIPEC). While we agree about the pseudo-replication problem preventing formal statistical test of this hypothesis, the visual pattern is striking and seems unlikely to be an artefact, so we think this does still support this conclusion.

"We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an inversion in one species. " (Abstract) And "increased repeatability found in regions of the genome that harbour inversions" (Discussion)<br /> These claims are supported by the data shown in Figure 4, which shows that haploblocks are enriched for WRAs. I want to clarify a point about the wording here, as my understanding of the analysis is that the authors test if haploblocks are enriched with WRAs, not whether WRAs are enriched for haploblocks. The wording of the abstract is claiming the latter, but I think what they tested was the former. Let me know if I'm missing something here.

We are actually not interested in whether WRAs are enriched for haploblocks; we want to know if WRAs tend to occur more commonly within haploblocks than outside of them. We have tried to clarify that this is our aim in various places in the manuscript. Our analysis for Figure 5 is the one supporting these claims, and it uses the Chi-square test statistic to assess the number of WRAs and non-WRAs that fall within vs. outside of inversions, and a permutation test to assess the significance of this observation, for each environmental variable and phenotype. We don’t think that this test has any direction to it – it’s simply testing if there is non-random association between the levels of the two factors. Thus, we think the wording we have used is consistent with the test result and our aims. Perhaps the confusion arose from the two methods that we present in the Methods (one is used for Figure 5, the other for Figure S6C & D), so we have added clarifications there.

Notwithstanding the concerns about highly correlated environments potentially inflating some of the patterns in the manuscript, to my knowledge this is the first attempt in the literature to try this kind of comparison, and the results does generally suggest that inversions are more likely capturing, rather than accumulating adaptive variation. However, I don't think the authors can claim that repeated signatures are enriched with haploblock regions, and the authors should take care to refrain from stating the relative importance of different regions of the genome to adaptation without an analysis.

Actually, we don’t have a strong feeling about whether inversions are capturing vs. accumulating adaptive variation, as these results could be consistent with either. As described above, we do not understand why we can’t claim that repeated signatures are enriched within haploblocks. We thought the reviewer is perhaps referring to the fact that the points are pseudo-replicated in the figures due to environment? We note that a very large number of points are significantly different from random in terms of the distribution of WRAs within vs. outside of haploblocks (light- vs. dark-shaded symbols), and that almost all of them fall above the 1:1 line. While there may be pseudo-replication preventing a test of the bigger multi-environment/multi-species hypothesis across all phenotypes and environments, there is almost a complete lack of significant results in the other direction. This seems like quite strong evidence about enrichment of WRAs within haploblocks, across many environments/species contrasts. We have added some text to the description of patterns in figure 5 to try to clarify this.

"While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species." (Abstract)<br /> Figure 3B certainly makes it look like there is very little similarity among species in the genetic basis of adaptation, which leaves the question as to how important the repeated signatures really are for adaptation if there are very few of them. (Is 3B for the whole genome or only that region?). This result seems to be at odds with the large number of CRAs and the claims about the importance of haploblock regions to adaptation, which extend from my previous point.

Figure 3B is for the whole genome, we have added text to the figure caption to clarify this. We think that both interpretations are possible: that most of the regions of the genome that are driving adaptation are non-repeated, but that a small but significant proportion of regions driving adaptation are repeated above what would be expected at random. Thus, it seems that there is high redundancy, coupled with adaptation via some genes that seem particularly functionally important and non-redundant, and therefore repeated. We added clarifying text on lines 541-548.

"we have shown evidence of significant repeatability in the basis of local adaptation (Figure 4, 5), but also an abundance of species-specific, non-repeated signatures (Figure 3)"<br /> While the claim is a solid one, I am left wondering how much of these genomes show repeated vs. non-repeated signatures, how much of these genomes have haploblocks, and how much overlap there really is. Finding a way to intuitively represent these unknowns would greatly strengthen the manuscript.

We agree, and really struggled to find the best way to communicate both the repeated patterns and the large amount of non-repeated signatures. Unfortunately, we have more confidence in the validity of repeated patterns because for the non-repeated patterns, a strong signature of association to environment in only one species could just be the product of structureenvironment correlation, as we didn’t control for population structure. Thus, trying to quantify the proportion of non-repeated signatures is difficult to do with any accuracy and we preferred to avoid putting too much emphasis on the simple calculation of the proportion of top candidate windows that were also WRAs.

Overall, I think the main claims from the study, the statistical framework, and the results could be revised to better support each other.

Although the current version of the manuscript has some potential shortcomings with regards to the statistical approaches, and the impact of this paper in its present form could be stifled because the biology tended to get lost in the statistics, these shortcomings may be addressed by the authors.

With some revisions, the framework and data could have a high impact and be of high utility to the community.

Thank you for your very helpful comments and suggestions on our paper, we really appreciate it.

Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

Editor's comments:

The reviewers make a series of reasonable suggestions that I echo. I found the paper quite hard to follow, and got fairly lost in the various layers of analyses done. Partially, this represents the complexity of empirical genomic data, which rarely deliver simple stories of convergence at a few genes. However, the properties of the various statistics used to detail local adaptation and convergence are not particularly clear and the figures presented were not intuitive representations of the data. This leaves the reader with an incomplete view of how much weight to put in the various lines of evidence marshaled. I would suggest simplifying the presentation of the results considerably. I add a few additional comments below.

Great suggestion, we’ve added a schematic overview of the methods and main research questions to Figure S1 in the supplementary materials.

A figure would help showing some of the signals of SNPs with putative signals of convergent environmental correlations across species, e.g. frequencies plotted against climate variables. This would help readers get a sense of how strong these signals were. These could be accompanied by the statistics calculated for these SNPs, that would allow the reader to start to get some intuitive sense of what the numbers mean.

Great suggestion, we have added a schematic overview of the methods to Figure S1 that shows some of the values and illustrates how the methods work using visual examples from our data.

In general, the introduction and some of the discussion of the inversion results feel oddly framed:<br /> Abstract line 36: "This shows that while inversions may facilitate local adaptation, at least some of the loci involved can still make substantial contributions without the benefit of recombination suppression."

We have changed “some of the loci involved can still make substantial contributions without the benefit of recombination suppression” here to “some of the loci involved can still harbour mutations that make substantial contributions without the benefit of recombination suppression in species lacking a segregating inversion” as it hopefully clarifies that we’re not talking about individual alleles that are present in both species.

Models of the role of local adaptation in the establishment of inversions (Kirkpatrick & Barton) assume that there are multiple locally adapted alleles already present. It is the load created by these alleles being constantly maintained in the face of migration and subsequent recombination that allow an inversion to be selected for because it keeps together locally adapted alleles. Thus these models predict that there could well be standing local adaptation at these loci in the absence of the inversion in other species, and that these locally adapted alleles while not fixed may be at high frequency. (After establishment, inversions housing locally adapted alleles, can shield more weakly, locally beneficial alleles from migration allow other alleles to build up.) Empirically it's interesting to find signals of local adaptation in other species that don't contain putative inversions. But the logic of the different predictions is not particularly clear from the introduction, and only becomes somewhat clearer in the discussion.

Thank you for pointing out this murkiness, we have re-written portions of both the Introduction and Discussion to clarify this aspect.

From the introduction: Inversions have been implicated in local adaptation in many species (Wellenreuther and Bernatchez 2018), likely due to their effect to suppress recombination among inverted and noninverted haplotypes, and thereby maintain LD among beneficial combinations of locally adapted alleles (Rieseberg 2001; Noor et al. 2001; Kirkpatrick and Barton 2006). This has been approached by models studying the establishment of inversions that capture combinations of locally adapted alleles present as standing variation (e.g., Kirkpatrick and Barton 2006), as well as models examining the accumulation of locally adapted mutations within inversions (e.g., Schaal et al. 2022). If there is variation in the density of loci that can potentially contribute to local adaptation, inversions would be expected to preferentially establish and be retained in regions harbouring a high density of such loci (and this expectation would hold for both the capture and accumulation models). We would also expect to see stronger signatures of repeated local adaptation in such high density regions. Despite mounting evidence of their importance in adaptation, it is unclear how inversions may covary with repeatability of adaptation among species. A fundamental parameter of importance in these models is the relationship between migration rate and strength of selection on individual alleles, which may not make persistent contributions to local adaptation without the suppressing effects of recombination if selection is too weak (Yeaman and Whitlock 2011; Bürger and Akerman 2011). If most alleles have small effects relative to migration rate and can only contribute to local adaptation via the benefit of the recombination-suppressing effect of an inversion, then we would expect little repeatability at the site of an inversion – other species lacking the inversion would not tend to use that same region for adaptation because selection would be too weak for alleles to persist. On the other hand, if some loci are particularly important for local adaptation and regularly yield mutations of large effect, with these patterns being conserved among species, repeatability within regions harbouring inversions may be substantial. Thus, studying whether adaptation at the same genomic region harbouring an inversion is observed in other species lacking the inversion can give insights about the underlying architecture of adaptation, and the evolution and maintenance of inversions.

From the Discussion: The observed repeatability associated with inversions further supports the local adaptation model as an explanation for the long-term persistence of segregating inversions (at least in sunflowers, rather than mechanisms based on dominance or meiotic drive (Rieseberg 2001). If there is variation across the genome in the density of loci with the potential to be involved in local adaptation, then the establishment and maintenance of inversions would be biased towards regions harbouring a high density such loci under this model. If the genomic basis for local adaptation is conserved amongst species, then these same regions are more likely to have high repeatability. Thus, our observation of genomic regions harbouring inversions also being enriched for WRAs is consistent with this general model for inversion evolution. Unfortunately, our observations do not provide much insight into whether inversions evolve through the capture (e.g. Kirkpatrick and Barton 2006) or accumulation (e.g. Schaal et al. 2022) type of model, as either model would be consistent with our results. Most of the sunflower inversions are >1 My old, and therefore predate any current local adaptation patterns, but likely do not predate the genes underlying local adaptation (which appear to be shared among the species we studied). As for the alleles underlying local adaptation, they may be younger than the inversions, but as our work suggests, these regions are prone to harbouring locally adaptive alleles so it is possible that they also harboured other ancestral locally adaptive alleles.

As a minor comment, there's a fair number of places where a more nuanced view of the field is needed, e.g.:<br /> "Models in evolutionary genetics tend to focus on extremes: population genetic approaches explore cases where strong selection deterministically drives a change in allele frequency" --This seems like a strange strawman. Population genetic models span a huge parameter range. The empirical approaches of looking for sweeps by detecting genome-wide statistical outliers is predicated on strong selection, but there are numerous papers that have looked for signals of weak selection genome-wide.

Good point, we have changed our wording here.

Reviewer #1 (Recommendations For The Authors):

Comments

My main comment on the manuscript is that the different levels and diversity of analyses are slightly hard to follow on the first, and even second, read. As there are several layers of correlations and comparisons, as well as some independent analyses, I wonder if it might be helpful to have a summary schematic figure of how all analyses fit together.

Great idea, we have added Figure S1 that summarizes the main flow of the methods and research questions.

• L169-171: Would it be more accurate to say that SIPEC is maximized when both species have strong correlations for an environmental variable across the same phenotypes? But maybe I misunderstood the index.

Good point, we have now simplified SIPEC, reporting the max instead of the mean, which we think better reflects when similar patterns are happening in both species for some phenotype.

• L191: Given the discussion in the introduction and elsewhere about the correction for population structure, which version is used here? Same for Figure 3.

We have added clarification there.

• L348: One [environmental] variable?

Added

• L353: Maybe add a percentage indication for 387 so that it is comparable to the following 23.3%.

Good point, added

-> L388 and paragraph: You mention "significant repeatability" but it is hard from the results at this point to have a broad idea of the amount of signal that is repeatable. Would it be possible to add here some quantitative measure of the proportion of signal repeatable or not, even if approximated?

I wish we could, but I think the precision implied by such an approximation would involve a huge amount of uncertainty and likely inaccuracy. Because it is so hard to conclusively identify how many loci are significant but non-repeated, we really don’t have a good handle on the denominator here. We are pretty confident that the repeated loci are strongly enriched for true positives, but the non-repeated loci are also almost certainly strongly enriched for false positives. While we really want to be able to quantify this explicitly, we don’t think it’s possible given our data.

-L415-418: "If there is variation [...] involved in local adaptation", I do not follow this argument, could you rephrase?

Changed

-L447-450: As you say in the supplementary methods, your analyses exclude 3/4 of the genome. Do you think this choice has a large impact on the number of outliers observed here as the genome-wide baseline would change?

This is a very good question, but one that is quite complex and without a clear answer – we chose not to delve into it in the paper to keep the discussion streamlined. My (SY) feeling is that it is unlikely that regions harbouring transposable elements would contribute much to adaptation, but I think we really don’t know if that is true. Even excluding ¾ of the genome harbouring TEs, ¼ of the genome still constitutes a huge amount of sequence and a very large number of genes and it seems plausible that most genes and genic regions would not contribute to adaptation for a given trait, so I don’t think this would change the results too much in a qualitative way – but would almost certainly change the number of windows that are significant, etc.

• L455-457: "As we are unable [...] potentially important drivers" Could you provide the logical link here between loci of small effect and them being important drivers. I presume you mean that the large effect loci found here only account for a small proportion of the heritability?

Yes that’s what we meant here, so we’ve added some clarification.

• L482: "enriched within inversions" should that be 'in genomic regions where there exist inversions in at least one species'? Thanks for catching that, yes. Changed.

• Methods/SIPEC L512: Compared to the Results section it is unclear here what is referred to as an "environment" Is it a variable or a set of environment variables?

This is done per environmental variable.

I find the presence of the PCA for environment variables in Figure 2 misleading as my first interpretation was that PCs for environment were also used.

Good point, we have clarified this on line 190-193.

Maybe one potential addition to the formula would be to add an environment variable $j$ notation such that it reads "$SIPEC_j = \sum_i (|r_{ij,1}| + ...) ...$ where ... between environment variable $j$". I had initial difficulties to understand how this SIPEC was computed relating to environmental variables and this might help.

Given the other changes we made to SIPEC, we felt it was simpler to just present it as a single calculation on a given combination of phenotype and environment for a pair of species, and then discuss taking the mean and maximum of this later.

Finally, PCA axes explaining 95% of the variance are used, I would find it interesting to see how many PCs are used in comparison to the number of traits being measured.

We have added the following sentence to the methods describing this:

"For comparisons including H. argophyllus, 95% of the variance was typically explained by 8-10 PC axes (out of 28 or 29 phenotypes), whereas for comparisons among other taxa this included 21 or 22 PC axes (out of 65 or 66 phenotypes."

Typos

L52: --

Changed

L254: portions [of] their

Changed

L399: additional closing parenthesis

Changed

L458: signatures [of] repeated association

Changed

L554: performed [on]

Changed

L578: 5 ~~kp~~/kb windows

Changed

L601: ~~casual~~/causal SNPs

Changed

L615: ~~widow~~/window

Changed

L732: ~~Banding~~/Banting Postdoctoral Fellowship

Changed

L1002 & L960: [Supplementary] Figure

Changed

Supplementary: Some figure titles are in bold and others are not.

Changed

Reviewer #2 (Recommendations For The Authors):

Overall I found the writing to be very clear and easy to follow. Despite my comments, it was clear that a lot of thought went into how to conduct the tests and visualize the results. I recommend ending the Discussion on a positive note, rather than an impossible test.

Thanks for the positive suggestion, we have done this.

In Figure 5, is the temperature variable missing in the legend and in the plot?

No, for this plot we just combined the temperature/precipitation variables into one variable called “climate”.

Author Response

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

Reviewer #1 (Public Review):

The first major issue is related to the imaging and tracking experiment to examine the formation and migration of F-actin foci as illustrated in figure 3. The formation and centripetally migration of F-actin foci is a significant finding of this MS for the promotion of B cells to switch from spreading to contraction response. Thus, I may suggest to recommend the authors to conduct one more rigorous fluorescent molecular tracking experiment to confirm this phenomenon. Molecular tracking usually requires low labeling density, and the lifeact-GFP labeling here do not meet this requirement which may cause misidentification of the moving molecules. Permeable dye-based fluorescent speckle microscopy is recommended here to track the actin foci if applicable (P. Risteski, Nat. Rev. Mol. Cell Biol., 2023, DOI: 10.1038/s41580-023-00588w & K. Hu, et al, Science, 2007, 315, 111-115).

We thank the reviewer for the suggestion. We conducted the suggested experiment using membrane-permeable SiR-actin to track B-cell actin dynamics. Unfortunately, two significant issues prevented us from confirming the LifeAct-GFP results using fluorescent speckle microscopy. First, the concentration of SiR-actin required to visualize F-actin in the contact zone of mouse primary B-cells was relatively high due to their smaller sizes (~6 µm diameter) and non-adherent nature. With such a relatively high concentration of SiR-actin, we could not perform fluorescent speckle microscopy. Second, we observed that SiR-actin appeared to stabilize actin structures and reduce actin dynamics, further limiting its use in studying actin dynamics in B-cells.

Additionally, kymograph is used for foci tracking in figure3 and figure4. Kymograph is indeed a powerful tool for tracking cell protrusion and retraction but is not fairly suitable here, since a Factin focus is a concentrated point which may not move strictly along the selected eight lines generating kymograph. Other imaging processing method should be used to track the foci, for example, time series max projection is recommended if applicable.

We thank the reviewer for the suggestion and have tried the time series max projection. Unfortunately, it did not provide the resolution to identify individual actin foci, again probably due to the small size of primary mouse B-cells. While kymographs may not track the entire paths of these moving foci, we believe that the conclusions drawn from the kymography analysis in Figure 3 and 4 are reasonable. We generated eight kymographs for each cell in Figure 3 and three kymographs for each cell in Figure 4 to follow as many actin foci as possible within the spreading to contraction transition time window. Our analysis in Figure 3 identifies the fraction of actin foci originating from lamellipodia. In Figure 4, we used the kymographs to trace the path of putative clusters and used these to calculate their relative lifetimes and speed. While this is not what was suggested by the reviewer, our analysis provides qualitatively similar information to the time series max projection and reasonable comparisons between contracted and noncontracted cells, inhibitor-treated and untreated cells, and wild-type and WASP KO cells.

The second major issue is about the relationship between actin foci formation and NMII recruitment in figure 5. The author concludes that 'N-WASP and Arp2/3 mediated branched actin polymerization promotes the recruitment and the reorganization of NMII ring-like structures by generating inner F-actin foci in the contact zone'. However, there is a lack of strong evidence to directly show the mechanism by which myosin is recruited and the up and down stream relationship between actin foci migration and myosin recruitment. Since myosin-induced actin retrograde flow is a classical model in adherent cells, is it possible that, here also in activated B cells, the recruited myosin driven the formation and migration of actin foci? This reviewer may recommend the author to investigate whether Myosin blocking (e.g., using Y27632) can eliminate the F-actin foci formation and migration.

This is an excellent suggestion! In the revised manuscript, we have included new data showing that treatment with the non-muscle myosin II motor inhibitor blebbistatin, which is known to inhibit B-cell contraction but not spreading on Fab’-PLB (Seeley-Fallen et al. 2022. Frontiers in Immunology), interferes with the formation of inner actin foci ring-like structures, which are associated with B-cell contraction. These results together suggest that the generation of inner actin foci ring-like structure depends on the coordination between N-WASP-mediated actin polymerization and myosin contractile activity. We chose to use blebbistatin rather than Y27632 to inhibit non-muscle myosin II because in addition to the ROCK pathway, myosin light chain kinase can also activate myosin II, and Y27632 may have additional effects besides inhibiting myosin activity. The new data are shown in Figure 5G and H and discussed in the revised manuscript.

Reviewer #2 (Public Review):

Weaknesses: Minor as listed below. The working hypothesis of molecular crowding as a way to push out signalling molecules from the BCR dense foci is interesting. The authors provide evidence for that this is an active process mediated by N-WASP - Arp2/3 induced actin foci. Another possibility is that BCR dense foci formation is an indirect consequence of lamellipodia retraction. Future works should define the specific role of N-WASP, Arp2/3 and actin in the process to form BCR dense foci, especially as the BCR continue to signal in the cytoplasm.

We thank the reviewer for the comments. We have included the possibility that lamellipodial retraction may be involved in increasing the molecular density of BCR clusters and suggested future studies on the potential roles of N-WASP-dependent inner actin foci and actomyosin structures in BCR internalization and intracellular signaling in the Discussion section.

Reviewer #3 (Public Review):

The author prove their claims by mean of thorough image analysis, mainly observing and quantifying the fluorescence and the dynamics of single clusters of antigen and actin foci and analyzing two-colors dynamical images. They perform their observation in control cells, on pharmacologically perturbed cells where the action of Arp2/3 or N-WASP is inhibited, and on modified primary cells (primary derived from genetically engineered mice) to silence N-WASP or WASP. The work is sound and complete, the experiments technically excellent and well explained. Some experiments and discussions are objectively harder to describe, and given the length of the work, the reader might find itself lost some times. A graphical abstract/summary of the main way N-WASP ultimately control signal attenuation would solve this minor point.

We greatly appreciate the reviewer’s confirmation of our data quality and are delighted to accept the reviewer’s suggestion. In the revised manuscript, we have included a new figure (Figure 10) in the Discussion section, summarizing the results presented in the manuscript as a working model.

Reviewer #1 (Recommendations For The Authors):

Some minor points: Figure 1C, E, G and I shows three individual symbols, indicating three independent experiments described in legend. Please double check for accuracy.

It is better to show statistical data with representative repeat, not the merged means of independent experiments. For example, figure 1C even indicates three "0" data in CK-666 treated cells, meaning no contracting cell was found in ~75 cells, while there are other repeats showing 45% - 50% contracting cells. This applies to all figures involving individual cell imaging data, such as figure 2D, in which 30 cells from three independent experiments were pooled. The authors shall clearly state that those independent experiments are statistically indistinguishable before pooling the data.

We agree with the reviewer’s comments that these data have variability from individual mice, the quality of isolated primary B-cells, and the lateral mobility of planar lipid bilayers. To show the variability, we displayed the data from each experiment as individual data points. In the revised manuscript, we have utilized three colors of dots to represent three independent experiments in Figure 1C, E, G, and I, Figure 2B-G, and new Figure 5H, which show that the data from the three experiments have the same trend despite the variability.

In figure 7B-C, figure 8 and figure 9. The significant test results were hard to understand in which groups they compared. Please describe it in more detail in the figure legend or the method section.

In the legend, the authors claimed blue points in Figure 7B represented individual pCD79a clusters within an equal number of BCR clusters from each time points. The authors used means to qualify the change of blue points distribution. These shall be clearly stated in the Methods. Total BCR cluster numbers shall be shown also. This applies to Figure 7B, 7C, 7D and all figures in figure 8 and figure 9.

We thank the reviewer for pointing it out. We have revised Figures 7-9, where we utilized square braces to indicate groups of clusters (blue points) being compared. We have also provided additional information in the figure legend and Method sections.

Reviewer #2 (Recommendations For The Authors):

199-200: What is the consequence of increased WASP activation in N-WASP knockout B cells? Is this evaluated as increased pWASP activity and/or increased actin polymerization of WASP knockout B cells. Does WASP and N-WASP have an additive or counteractive effect on each other during spreading and contraction?

Indeed, the relationship between WASP and N-WASP, which are co-expressed in B-cells and other immune cells, is fascinating. Our previous studies, using WASP germline knockout, B-cellspecific N-WASP knockout, WASP and N-WASP double knockout mice, showed that WASP and N-WASP have both additive and counteractive effects during B-cell spreading, but B-cell contraction only depends on N-WASP (Liu et al. 2013. PLoS Biol). Double knockout B-cells fail to spread, and WASP knockout B-cells show reduced spreading but still contract, showing their additive effects. However, WASP and N-WASP suppress each other for activation, as detected by their phosphorylation. Phosphorylated WASP increases in the B-cell contact zone first, and phosphorylated N-WASP increases later when the phosphorylated WASP level decreases. Knocking out one of them enhances the phosphorylation of the other. Consequently, N-WASP knockout B-cells show increased spreading, probably due to enhanced activation of WASP, but exhibit delayed contraction. The revised manuscript has expanded the discussion on this area to relate it to the results presented in this manuscript.

560-563: Was Syk and SHIP-1 measured in the same cell? If not, the conclusion should be tempered.

Unfortunately, antibodies specific for Syk and SHIP-1 were from the same host, which did not allow us to stain them in the same cells. The revised manuscript has discussed this as a shortcoming of our work.

1204-1205: Explain better "three randomly positioned kymographs were generated" - how were they selected?

We apologize for this unclear sentence. The three kymographs were positioned to track as many inner F-actin foci as possible.

328: Change "abolished" to "reduced" to describe the data. 354-356: Unclear sentence, please edit. 1171: (H) should be (G). 1325: "PI" should be "FI".

We thank the reviewer for finding these typos and unclear sentences. We have made the corrections accordingly.

Methods: The description of the TIRF microscopy method is good. Regarding the image analysis, it is somehow difficult to have a good understanding of what was analyzed just by reading the text. Please show an example of the pipeline for the analysis from a raw image and the processing steps.

Figure 6-figure supplement 2 shows the image analysis process for tracking Fab’ clusters. We utilized the same approach for the image analysis of Figures 7-9.

Discussion: Add a paragraph to state the limitations of the study. How do the findings here translate into in vivo activation of B cells and how can this be addressed based on the data presented in this study.

We thank the reviewer for the suggestion. In several paragraphs of the revised Discussion section, we have brought up the limitations of the study and how these limitations affect the data interpretation. In addition, we have added Figure 10 and the associated text to present our working model, which explains how our findings reveal the cellular mechanism by which BCR surface signaling amplification transitions into attenuation, likely occurring in vivo.

Figure 2: Add an example of the image analysis for foci determination. From the images, it is not always clear what is a foci and what is not which makes the "number of foci" data difficult to evaluate.

We have added arrows to Figure 2A to indicate all identified inner F-actin foci in images.

Figure 3: add a kymograph for the WKO analysis.

In the revised Figure 4, we have provided a kymograph of a WKO B cell.

Figure 4M: the analysis of the "relative speed" of the "WT" samples is lower compared to the other control samples "DMSO" and "CK-689". The conclusion is that WKO have similar "relative speed" as "WT" cells, but in fact the "WT" cells may have responded poorly in this experiment. What is the author's experience and explanation?

We agree that the relative speeds of inner actin foci in the contact zone of WT and WKO B-cells are relatively low compared to DMSO and CK-689. Based on our experience, this parameter is very sensitive to the lateral mobility of planar lipid bilayers. We could only perform one pair of conditions using live cell images each time. The WT and WKO experiments were done at the end and might use relatively aged liposomes. However, it did not affect the number of inner actin foci formed and their relative lifetime, consistent with their similar relative speeds. Unfortunately, we lost the LifeAct-GFP-expressing WKO mouse colony and cannot redo this experiment using freshly made liposomes within a reasonable time.

Figure 7B-D: Add a more detailed legend for the black and brown lines in the dot plots.

We have expanded the legend for Figure 7B-D to provide additional details.

Figure 8-9: Show representative images for SYK, pSYK, SHIP-1 and pSHIP-1. Add a more detailed legend for the black and brown lines in the dot plots.

We have provided representative images for Syk, pSyk, SHIP-1, and pSHIP-1 in revised Figure 8 and 9.

Reviewer #3 (Recommendations For The Authors):

From the paper one understands that NMII is recruited by the actin foci and this recruitment pushes the foci towards the center of the synapse, in what resembles a positive feedback. Could the authors better elucidate this point? What happen at the peak of NMII recruitment? Could this be a mechanism used by the cell to end the contact and detach (which probably cannot be observed in this experimental setup)?

This is an excellent comment! We have recently shown that NMIIA recruitment peaks right before B-cell contraction occurs, and inhibition of NMII by inhibitors or B-cell conditional knockout blocks B-cell contraction and enhances signaling (Seeley-Fallen et al. 2022. Frontiers in Immunology). In the revised manuscript, we have included new data showing that treatment with the NMII motor inhibitor blebbistatin, which is known to inhibit B-cell contraction but not spreading on Fab’-PLB (Seeley-Fallen et al. 2022. Frontiers in Immunology), interferes with the formation of inner actin foci associated with B-cell contraction. These results together suggest that the generation of inner actin foci depends on the coordination between N-WASP-activated actin polymerization and myosin contractile activity, supporting the reviewer’s comment. The new data are shown in Figure 5G and H and discussed in the revised manuscript.

Whether the recruited NMII pulls B-cells away from antigen-presenting surfaces remains an interesting question. We have previously shown that high-affinity interaction of surface BCRs with membrane-anchored antigen can cause NMII-dependent B-cell membrane permeabilization, which triggers lysosome exocytosis and lysosomal enzyme-mediated antigen cleavage, allowing antigen internalization and presentation to T-cells (Maeda et al. 2021. eLife). Furthermore, NMII is required for B cells to internalize surface antigens (Natkanski et al. 2013. Science). These results support the possibility that actomyosin structures formed during B-cell contraction may further drive B-cells to internalize antigen. We have discussed this interesting point in the revised manuscript.

Some experiments/quantification are a bit more complex than others and a reader might find hard to follow them (in particular figs 7,8 and 9). The comprehension could be improved by providing a guide to read them. E.g. it is not clear what the population distribution represents (and it is not particularly affected by any manipulation. How were the group for test chosen? It seems they are based on intensity categories taken every 100 units: is it the case? even if arbitrary, this should be stated it in the legend.

We thank the reviewer for understanding the complexity of image analysis and pointing out the unclear points. Based on the reviewer’s comments, we have revised Figures 7-9 and the figure legend. We utilized square brackets to indicate groups of clusters (blue points) being compared. The comparison groups were chosen arbitrarily based on Fab’ peak fluorescence intensity every 90 units for Figure 7 and 8 and every 100 units for Figure 9.

Can the author speculate on how the actin organization passes from actin foci to recruitment of NMII and arc formation? Is it a rearrangement of the actin network (percolation) or simply recruitment of monomers?

Our previous and new results show that both N-WASP-activated Arp2/3 and NMII are required to form inner F-actin foci. Based on these results, we speculate that N-WASP and Arp2/3mediated actin polymerization may initiate the process and recruit NMII, and recruited NMII coordinates with actin polymerization to reorganize actin structures, promoting inner actin foci maturation and arc formation. We have included these possibilities in the revised discussion.

The role of SHIP recruitment as way to inhibit the signal downstream of the BCR is an interesting finding. Is this related to the termination of the synapse? Could we relate the time scales (accurately measured in this work) to contact times observed in vivo?

The reviewer raises an interesting question. In the discussion section, we have speculated that the actomyosin structures responsible for B-cell contraction are potentially the precursor cytoskeleton structures for antigen internalization. However, the relationship of B-cell contraction and signaling attenuation with the termination of the synapse remains unclear.

The BCR has been shown to be internalised mechanically: do these new data suggest a mechanisms for force generation in antigen internalization at the actin foci? Related to that, how do the dynamics of N-WASP recruitment relate to the force measurement highlighted in Traction Force Microscopy experiments (see for example Wang Sci.Signal. 2018, Kumari Nat.Comm.2019)? What happens in situation when the actin foci are unable to get transported, e.g. as on the more classical antigen on coverslip configuration?

Indeed, our results allow us to speculate that the actomyosin structures responsible for B-cell contraction potentially contribute to antigen internalization by mechanical forces. We previously showed that the B-cell-specific N-WASP knockout drastically reduced BCR internalization of soluble antigen (Liu et al. 2013. PLoS Biol), and that NMII is required for BCR internalization of membrane-associated antigen (Maeda et al. 2021. eLife and Natkanski et al. 2013. Science). The effect of N-WASP knockout on the internalization of membrane-associated antigen and traction forces generated at the contact membrane and whether traction forces are generated from the inner F-actin foci have not been determined but will be pursued in the future.

Our previous publication compared the BCR and actin dynamics of B-cells interacting with Fab’ tethered to planer lipid bilayers (Fab’-PLB) and cover glass (Fab’-G) (Ketchum et al. 2014. Biophys J). B-cells interacting with Fab’-G do not contract and generate inner F-actin foci and exhibit less dynamic BCR clusters and actin cytoskeleton than B-cells interacting with Fab’-PLB. Actin foci remain coincident with Fab’ clusters on glass rather than being positioned behind Fab’ clusters on PLB, thus driving their centripetal movement.

Minor remarks: When several experiments (mice) are presented in dot plots (e.g. fig 2D-G 4J-M), color dot plot (so called "smart plot") where each experiment is identified by a color, could be used to highlight the sample-to-sample variability.

This is an excellent suggestion. In the revised manuscript, we have utilized three shades of dots to represent the data points from three independent experiments.

Fig 6A: the fluorophore should be indicated in the picture (Fab'-AF546)

The suggested correction has been made.

Fig 6D: how is the contraction phase (purple rectangle) determined? Curve by curve or on the average curve? Please specify this in the legend.

The contraction phase (purple rectangle) was determined using the average curve of the contact area by IRM over time. We have added this sentence to the revised figure legend.

Minor typos in the material and methods: in some case C56BL/6 is written instead of C57BL/6 Corrected.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The investigators sought to determine whether Marco regulates the levels of aldosterone by limiting uptake of its parent molecule cholesterol in the adrenal gland. Instead, they identify an unexpected role for Marco on alveolar macrophages in lowering the levels of angiotensin-converting enzyme in the lung. This suggests an unexpected role of alveolar macrophages and lung ACE in the production of aldosterone.

Strengths:

The investigators suggest an unexpected role for ACE in the lung in the regulation of systemic aldosterone levels. The investigators suggest important sex-related differences in the regulation of aldosterone by alveolar macrophages and ACE in the lung. Studies to exclude a role for Marco in the adrenal gland are strong, suggesting an extra-adrenal source for the excess Marco observed in male Marco knockout mice.

Weaknesses:

While the investigators have identified important sex differences in the regulation of extrapulmonary ACE in the regulation of aldosterone levels, the mechanisms underlying these differences are not explored. The physiologic impact of the increased aldosterone levels observed in Marco -/- male mice on blood pressure or response to injury is not clear. The intracellular signaling mechanism linking lung macrophage levels with the expression of ACE in the lung is not supported by direct evidence.

Reviewer #2 (Public Review):

Summary:

Tissue-resident macrophages are more and more thought to exert key homeostatic functions and contribute to physiological responses. In the report of O'Brien and Colleagues, the idea that the macrophage-expressed scavenger receptor MARCO could regulate adrenal corticosteroid output at steady-state was explored. The authors found that male MARCO-deficient mice exhibited higher plasma aldosterone levels and higher lung ACE expression as compared to wild-type mice, while the availability of cholesterol and the machinery required to produce aldosterone in the adrenal gland were not affected by MARCO deficiency. The authors take these data to conclude that MARCO in alveolar macrophages can negatively regulate ACE expression and aldosterone production at steady-state and that MARCO-deficient mice suffer from secondary hyperaldosteronism.

Strengths:

If properly demonstrated and validated, the fact that tissue-resident macrophages can exert physiological functions and influence endocrine systems would be highly significant and could be amenable to novel therapies.

Weaknesses:

The data provided by the authors currently do not support the major claim of the authors that alveolar macrophages, via MARCO, are involved in the regulation of a hormonal output in vivo at steady-state. At this point, there are two interesting but descriptive observations in male, but not female, MARCO-deficient animals, and overall, the study lacks key controls and validation experiments, as detailed below.

Major weaknesses:

1) According to the reviewer's own experience, the comparison between C57BL/6J wild-type mice and knock-out mice for which precise information about the genetic background and the history of breedings and crossings is lacking, can lead to misinterpretations of the results obtained. Hence, MARCO-deficient mice should be compared with true littermate controls.

2) The use of mice globally deficient for MARCO combined with the fact that alveolar macrophages produce high levels of MARCO is not sufficient to prove that the phenotype observed is linked to alveolar macrophage-expressed MARCO (see below for suggestions of experiments).

3) If the hypothesis of the authors is correct, then additional read-outs could be performed to reinforce their claims: levels of Angiotensin I would be lower in MARCO-deficient mice, levels of Antiotensin II would be higher in MARCO-deficient mice, Arterial blood pressure would be higher in MARCO-deficient mice, natremia would be higher in MARCO-deficient mice, while kaliemia would be lower in MARCO-deficient mice. In addition, co-culture experiments between MARCO-sufficient or deficient alveolar macrophages and lung endothelial cells, combined with the assessment of ACE expression, would allow the authors to evaluate whether the AM-expressed MARCO can directly regulate ACE expression.

Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors

Reviewer #1 (Recommendations For The Authors):

1. Corticosterone levels in male Marco -/- mice are not significantly different, but there is (by eye) substantially more variability in the knockout compared to the wild type. A power analysis should be performed to determine the number of mice needed to detect a similar % difference in corticosterone to the difference observed in aldosterone between male Marco knockout and wild-type mice. If necessary the experiments should be repeated with an adequately powered cohort.

We thank the reviewer for their comments. We are prepared to carry out these power calculations and repeat the experiment if necessary.

1. All of the data throughout the MS (particularly data in the lung) should be presented in male and female mice. For example, the induction of ACE in the lungs of Marco-/- female mice should be absent. Similar concerns relate to the dexamethasone suppression studies. Also would be useful if the single cell data could be examined by sex--should be possible even post hoc using Xist etc.

We are prepared to measure the levels of Ace, biosynthetic enzyme expression in female mice by qPCR, and ACE protein expression by IF. Additionally, we will test females using the dexamethasone suppression study. The single cell RNA seq analysis was used primarily to inform our model, not for experimental readout. We will explore the dataset as the reviewer suggests and will add additional plots if the analysis substantively changes our previous findings.

1. IF is notoriously unreliable in the lung, which has high levels of autofluorescence. This is the only method used to show ACE levels are increased in the absence of Marco. Orthogonal methods (e.g. immunoblots of flow-sorted cells, or ideally CITE-seq that includes both male and female mice) should be used.

We have negative controls for antibody staining. Additionally, we also used qPCR to show an increase in Ace mRNA expression in the lung.

1. Given the central importance of ACE staining to the conclusions, validation of the antibody should be included in the supplement.

The vendor of this antibody has verified by cell treatment to ensure that the antibody binds to the antigen stated .We are prepared to additionally validate the antibody using other tissues as control, though we point out that ACE is expressed, albeit at lower levels, in endothelial cells throughout the body and so some signal is to be expected in most if not all tissues.

1. The link between alveolar macrophage Marco and ACE is poorly explored.

We are prepared do co-culture experiments of alveolar macrophages and endothelial cells and measure ACE/Ace expression as a consequence.

1. Mechanisms explaining the substantial sex difference in the primary outcome are not explored.

We argue that this would be outside the scope if this project, though we would consider exploring such experiments in future studies.

1. Are there physiologic consequences either in homeostasis or under stress to the increased aldosterone (or lung ACE levels) observed in Marco-/- male mice?

We are prepared to measure blood electrolytes and blood pressure in Marco-deficient and Marco-sufficient mice.

Reviewer #2 (Recommendations For The Authors):

Below is a suggestion of important control or validation experiments to be performed in order to support the authors' claims.

1) It is imperative to validate that the phenotype observed in MARCO-deficient mice is indeed caused by the deficiency in MARCO. To this end, littermate mice issued from the crossing between heterozygous MARCO +/- mice should be compared to each other. C57BL/6J mice can first be crossed with MARCO-deficient mice in F0, and F1 heterozygous MARCO +/- mice should be crossed together to produce F2 MARCO +/+, MARCO +/- and MARCO -/- littermate mice that can be used for experiments.

We thank the reviewer for their comments. We recognise the concern of the reviewer but due to limited experimenter availability we are unable to undertake such a breeding programme to address this particular concern.

2) The use of mice in which AM, but not other cells, lack MARCO expression would demonstrate that the effect is indeed linked to AM. To this end, AM-deficient Csf2rb-deficient mice could be adoptively transferred with MARCO-deficient AM. In addition, the phenotype of MARCO-deficient mice should be restored by the adoptive transfer of wild-type, MARCO-expressing AM. Alternatively, bone marrow chimeras in which only the hematopoietic compartment is deficient in MARCO would be another option, albeit less specific for AM.

We recognise the concern of the reviewer. We have access to an AM cell line which we plan to use to do co-culture experiments with an ACE-expressing endothelial cell line. In this way we will test whether this effect is linked to AMs.

3) If the hypothesis of the authors is correct, then additional read-outs could be performed to reinforce their claims: levels of Angiotensin I would be lower in MARCO-deficient mice, levels of Antiotensin II would be higher in MARCO-deficient mice, Arterial blood pressure would be higher in MARCO-deficient mice, natremia would be higher in MARCO-deficient mice, while kaliemia would be lower in MARCO-deficient mice. Similar read-outs could also be performed in the models proposed in point 2).

We are prepared to measure blood electrolytes and blood pressure (via tail cuff method) in Marco-deficient and Marco-sufficient mice.

4) Co-culture experiments between MARCO-sufficient or deficient alveolar macrophages and lung endothelial cells, combined with the assessment of ACE expression, would allow the authors to evaluate whether the AM-expressed MARCO can directly regulate ACE expression.

To address this concern, we plan to do a co-culture experiment as outlined above.

Broadly, we thank the reviewers for taking the time to critically appraise this manuscript. The reviewers primary concern seems to be the lack of direct evidence of an effect of AMs on endothelial Ace expresion, which we plan to address as outlined above. We will adjust our conclusions as appropriate based on the results of the experiments outlined above.

Author Response

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

Our comments on the initial eLife assessment

“This study presents a useful inventory of the joint effects of genetic and environmental factors on psychotic-like experiences, and identifies cognitive ability as a potential underlying mediating pathway. The data were analyzed using solid and validated methodology based on a large, multi-center dataset. The claim that these findings are of relevance to psychosis risk and have implications for policy changes are partially supported by the results”

We sincerely appreciate the editor and reviewers for their valuable feedback and their willingness to accommodate our perspectives in the first revision. In this revision, the comments from the reviewers have allowed us to further improve our manuscript. Regarding the eLife assessment, we would like to discuss two points.

Firstly, regarding your point of our “findings are of relevance to psychosis risk…partially supported…”, we want to address that our study is closely related to psychosis risk. Childhood psychotic-like experiences (PLEs) are closely linked to psychotic risk and have been shown to increase the risk of general psychopathology, as mentioned in our Introduction and Discussion.

The reviewers asked for clearer differentiation between PLEs and schizophrenia, which we incorporated in this revision (line 100~111; line 419~430). So, this revised version now clearly points out that findings are relevant primarily to psychosis risk, and only partially relevant to schizophrenia risk.

Secondly, regarding “…implications for policy changes are partially supported…”, we have revised our study’s social contribution more clearly and specifically. Incorporating the comments, we have revised that our study offers an insight to the future studies by showing the importance of integrative approaches, considering multi-factorial neurocognition and psychopathology ranging from genes to environment (line 503~512), rather than offers direct policy implications.

Our collaboration with eLife and the reviewers has proven satisfactory and enriching. The community, coupled with the innovative system and culture established around eLife, has significantly advanced the progression of scientific research. We are privileged to contribute to this endeavor.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I am happy with the revisions provided by the authors and I think most of my concerns have been addressed satisfactorily. One remaining concern is the authors' conflation of PLEs and schizophrenia. They stated, for example, that it is necessary to adjust for schizophrenia PGS. Even though studies have found a statistical relationship between schizophrenia PGS and PLEs, this relationship is not very strong (although statistically significant) and other studies have found no relationship. Similarly, having PLEs increases the risk of developing psychosis, but that does not necessarily mean that this risk is substantial or specific. I think this needs more nuance in the manuscript and the term 'schizophrenia' should be used sparsely and very carefully as the paper has focused on PLEs. Otherwise, great work on the revisions, thank you.

Thank you for your comment on the use of PLEs and schizophrenia. We clearly understand the differences between the two and we made relevant corrections throughout the manuscript. In particular, we added that PLEs are not a direct predictor of schizophrenia and corrected any expressions that may imply that PLEs are closely related to schizophrenia in the Introduction.

“Psychotic-like experiences (PLEs), which are prevalent in childhood, indicate the risk of psychosis (van der Steen et al., 2019; Van Os & Reininghaus, 2016). Although they are not a direct precursor of schizophrenia, children reporting PLEs in ages of 9-11 years are at higher risk of psychotic disorders in adulthood (Kelleher & Cannon, 2011; Poulton et al., 2000). PLEs also point towards the potential for other psychopathologies including mood, anxiety, and substance disorders (van der Steen et al., 2019), are linked to deficits in cognitive intelligence (Cannon et al., 2002; Kelleher & Cannon, 2011) and show a stronger association with environmental risk factors during childhood than other internalizing/externalizing symptoms (Karcher, Schiffman, et al., 2021).

Maladaptive cognitive intelligence may act as a mediator for the effects of genetic and environmental risks on the manifestation of psychotic symptoms (Cannon et al., 2000; Keefe et al., 2006; Reichenberg et al., 2005).” (line 100~111)

We also revised any expressions that could be perceived as implying relevance to schizophrenia in the Discussion. “Prior research identifying the mediation of cognitive intelligence focused on either genetic (Karcher, Paul, et al., 2021) or environmental factors (Lewis et al., 2020) alone. Studies with older clinical samples have shown that cognitive deficit may be a precursor for the onset of psychotic disorders (Eastvold et al., 2007; Fett et al., 2020; Vorstman et al., 2015). Our study advances this by demonstrating the integrated effects of genetic and environmental factors on PLEs through the cognitive intelligence in 9-11 years old children. Such comprehensive analysis contributes to assessing the relative importance of various factors influencing children's cognition and mental health, and it can aid future studies designed for identifying health policy implications. Considering the directions and magnitudes of the effects, though the effects of PGS remain significant, aggregated effects of environmental factors account for much greater degrees on PLEs.” (line 419~430)

Reviewer #2 (Recommendations For The Authors):

I thank the authors for addressing most of my comments. I feel the manuscript has already greatly improved.

I have a few more comments.

1) Although I did not make this comment, I find the authors' reply to the following comment by Reviewer #1 unclear: Original comment 'I like that the assessment of CP (cognitive performance) and self-reports PLEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL (Child Behavior Checklist) were used and how did they correlate with the child-reported PLEs? And how was distress taken into account in the child self-reported PLEs measurement? Which PLEs measures were used?'

The authors' response refers to correlation coefficients, but I think Reviewer #1's inquiry was on more than these correlations.

Thank you for your concern. We think that this comment was referring to our previous manuscript submitted elsewhere. In our initial submission to eLife, we already added the details about the four items from the parent-reported CBCL and how distress was considered in the child self-reported PLEs measurement (Appendix S1, page 48).

2) Regarding the authors' reply that they have 'standardized the use of 'cognitive capacity' - I do not understand what this means. How exactly was this term standardized? In fact, I can find the term 'cognitive capacity' only once and it seemed to have been deleted from the manuscript. This is fine, but it doesn't clearly align with the statement that this term has been standardized.

We apologize for causing such confusion. What we meant was that throughout our revised manuscript, we used the term “cognitive phenotypes” instead of “cognitive capacity”.

3) Regarding my initial comment that 'it needs to be described how cognitive performance was defined in Lee 2018.' - I believe this is still not clarified. The authors write 'CP was measured as the respondent's score on cognitive ability assessments', but it remains unclear what exactly these assessments were.

Thank you for pointing this out. We added that “CP, measured as the respondent's score on cognitive ability assessments of general cognitive function and verbal-numerical reasoning, was assessed in participants from the COGENT consortium and the UK Biobank” (line 204~206).

4) Regarding the authors' reply to my comment 'In the 'Path Modeling' section, please explain what 'factors and components' concretely refer to. How is this different from a standard SEM with latent factors?'

I can see that the authors explained 'components' (=the weighted sum of observed variables), but please also add what you mean by 'factors' - and how these are different from 'components' (line 284). Furthermore, I don't think it is correct that SEMs can only model latent factors, but not components (=measured variables). I also cannot see how using a weighted sum of observed variables controls more effectively for bias in estimation than latent factors. However, even though I do have some knowledge on this method, I'm not an expert and would appreciate the authors, other reviewer and/or editor to weigh in on this point.

Thank you for pointing this out. We added that latent factors are indirectly measured indicators that explain the covariance among observed variables (line 263~271). We also added that standard SEM method using latent factors assumes that observed variables within each construct share a common underlying factor, but if this assumption is not met, then the standard SEM method cannot effectively control for biases. This is the reason why the IGSCA method, which addresses this limitation by allowing for use of both composite and latent factors as constructs.

“Standard SEM using latent factors (i.e., indirectly measured indicators that explain the covariance among observed variables) to represent indicators such as PGS or family SES relies on the assumption that observed variables within each construct share a common underlying factor. If this assumption is violated, standard SEM cannot effectively control for estimation biases. The IGSCA method addresses this limitation by allowing for the use of composite indicators (i.e., components)—defined as a weighted sum of observed variables—as constructs in the model, more effectively controlling bias in estimation compared to the standard SEM. During estimation, the IGSCA determines weights of each observed variable in such a way as to maximize the variances of all endogenous indicators and components.” (line 263~271)

5) I overall disagree with the authors' following statement 'It has been suggested from prior studies that these variables (PGS, family SES, neighborhood SES, positive family and school environment, and PLEs) are less likely to share a common factor', but I appreciate the authors' argument.

Thank you for your comment. To make clarify our statement in the manuscript, we changed the sentence to “Considering that the observed variables of the PGSs, family SES, neighborhood SES, positive family and school environment, and PLEs are evaluated as a composite index by prior research, the IGSCA method can mitigate bias more effectively by representing these constructs as components” (line 274~277).

6) Regarding 'genetic ethnicity': please describe your methods on how this was defined.

Genetic ethnicity was defined as the genetic ancestry of participants, which is included as one of observations in the original ABCD Study data. To avoid further confusion, we corrected ‘genetic ethnicity’ to ‘genetic ancestry’ throughout the manuscript.

7) Regarding 'a more direct genetic predictor of PLEs' - I still don't understand what the contrast is here. More direct than what else?

The description was unclear; we removed it from our manuscript.

8) Regarding the factor loadings in Figure 3: I don't understand how deprivation loads positively on 'low neighborhood SES', but poverty loads negatively. Shouldn't they both show the same direction of effect/loading on neighbourhood SES, while 'years of residency' should show the opposite direction (i.e., deprivation and poverty = risk, while years of residency = protective)? Are these unexpected loadings?

The authors did not yet respond to this point: 'Please also add the autocorrelations between the 3 PLE measures. I assume these were also modelled statistically, given the strong correlations between time points?' Were these correlations not modelled? Why not?

Figure 3B is still unclear. Was intelligence included here? What is the difference between Figure 3A and B? The legend suggests that 3B shows the indirect effects, but figure 3B looks like a direct effect, while 3A seem to show the indirect effect.

The reviewer’s confusion resulted from our incorrect description. The factor loadings of low neighborhood SES were marked incorrectly. The loading for ‘years of residence’ and ‘poverty’ should be switched: -0.3648 for ‘years of residence’ and +0.877 for ‘poverty’. This was a mistake when we were applying factor loadings in the Figure. We thank you for pointing this out.

We apologize for missing your point on autocorrelation. Adding autocorrelations between the three PLEs is unrelated to our research goal. In this paper, we investigated how genetic and environmental factors explain the variations in PLEs between participants, regardless of changes over time. Since we used PLEs of multiple follow-ups to ensure that the results are robust irrespective of the timing of PLE measurements, taking autocorrelation into account is not necessary.

The decision to add autocorrelation, which involves using the outcome variable at time (t-1) as a predictor for the outcome variable at time t, depends on the research focus. If your interest lies in explaining inter-individual variation in the rate of change in PLEs over a one-year period, then autocorrelation should be controlled for (typically, predictors measured at different time points are used in such cases). However, this was not the focus of this paper, which is why we did not apply autocorrelation in the SEM analysis.

We apologize for the confusion between Figure 3A and 3B. To clarify, we added titles in the figure images as “Direct effects” and “Indirect effects”. We also changed the legend as well.

“A. Direct pathways from PGS, high family SES, low neighborhood SES, and positive environment to cognitive intelligence and PLEs. Standardized path coefficients are indicated on each path as direct effect estimates (significance level *p<0.05). B. Indirect pathways to PLEs via intelligence were significant for polygenic scores, high family SES, low neighborhood SES, and positive environment, indicating the significant mediating role of intelligence.” (line 968~973)

Figure 3A shows direct effects: i.e., the coefficients of paths from PGS, family SES, neighborhood SES, and positive environment to intelligence and PLEs, as well as the coefficient of paths from intelligence to PLEs. This is why Figure 3A shows colored arrows starting from PGS, family and neighborhood SES, and positive environment towards intelligence and PLEs, as well as the arrows from intelligence to PLEs. On the other hand, in Figure 3B, the colored arrows staring from PGS, family and neighborhood SES, and positive environment goes through intelligence, and heads towards PLEs. This was meant to show that the indirect effects shown in Figure 3B indicate the specific effects of PGS, family SES, neighborhood SES, and positive environment on PLEs mediated by intelligence.

In short, Figure 3 can be seen as a diagram drawn from Table 2: direct effects of the genetic and environmental variables on intelligence and PLEs, and direct effects of intelligence on PLEs are shown in Figure 3A; indirect effects of genetic and environmental variables on PLEs mediated by intelligence are shown in Figure 3B.

9) Regarding Supporting Information tables: to make these more digestible, I suggest using Excel and adding one table per sheet with a clear title and legend, indicating what each table shows. For example, Table S1 has 9(?) different subsections, all called the same (Linear Mixed Model: Multiethnic). It is not clear how each subsection differs from the others. Separate tables in separate excel sheets might be easier.

Also, I think two decimal points might be good enough, enhancing readability of these tables.

Thank you for your suggestion. We moved the supplementary tables into an external Excel file, with each sheet showing different tables, as well as titles, legends, and clear subsections.

10) Regarding reporting exact p-values in Table 2: I don't understand. At the moment, categorical significance statements are reported. Were these not based on exact p-values (or how else was it decided if a finding was significant at a 0.05 (?) significance level).

Either remove the significance column completely (as p-values cannot be estimated due to non-normality) or specify exactly/clarify what this column shows and this was derived.

We apologize for the confusion. In Table 2, we checked the significance of each path using 95% confidence intervals with 5,000 bootstrapping iterations. Since 95% confidence intervals that does not include zero is equivalent to p-values below 0.05 significance level, we believe this is an appropriate alternative for reporting the significance of each path in the SEM model.

We specified the reason why we were not able to calculate exact p-values (clean copy: line 299~303). “As a trade-off for obtaining robust nonparametric estimates without distributional assumptions for normality, the IGSCA method does not return exact p-values (Hwang, Cho, Jung, et al., 2021). As a reasonable alternative, we obtained 95% confidence intervals based on 5,000 bootstrap samples to test the statistical significance of parameter estimates.”

Author Response

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

We would first like to thank the reviewers for their time and effort in their critical review of our manuscript, and appreciate the opportunity to address these comments. We thank the reviewers for appreciating that our experimental design is well crafted, and contributes to the broader understanding of dietary exercise recommendations for metabolic health and muscle development. We have revised the figures and text in accordance with the reviewer’s recommendations, and hope that they appreciate the revised version.

Reviewer #1:

1) A significant limitation of this study pertains to the absence of a detailed exploration into the mechanistic underpinnings of the interaction between high protein intake and resistance exercise at the molecular level. The authors should provide a comprehensive discussion on potential avenues or prospective research directions to address this gap in understanding.

We agree and have added some theories in the discussion on page 14.

2) Figure 4 and Figure 7 can be moved to supplementary and text in the description can be arranged accordingly to make a better flow of the story.

We agree with this suggestion and have made adjustments.

3) The authors have used a high protein diet (36% calorie from protein) and a low protein diet (7% calorie from protein) for this study. The authors should explain whether this mouse diet is practically comparable to the human's high protein (2% of BW) and low protein diet (less than 0.8% BW) or not. The high protein diet is comparable to a human diet of 180 grams of protein ((0.36x2000 calories)/4 calories per gram=180 g), which is in a range that some people consume, particularly bodybuilders and athletes. The low protein diet is equivalent to 35 grams of protein per day ((0.07x2000 calories)/4 calories/gram=35g), and a diet of just 7% protein is not recommended for humans per the Acceptable Macronutrient Distribution Range (AMDR) of 10-35% dietary protein set by the Institute of Medicine (IOM). We have addressed this on page 14.

4) The color coding of the error bar and lines does not match with the group description in almost every figure. Maybe the authors could choose more contrasting colors.

Thanks, we have adjusted the coloring of the error bars and lines in all figures.

5) In Figure 3C-E it seems like the number of biological samples is not consistent in the LP+WP group. If the authors have excluded any outlier from the analysis, that should be included in the methodology.

We did list outliers in the methodology in the statistics section (page 19): “Outliers were determined using GraphPad Prism Grubbs’ calculator (https://www.graphpad.com/quickcalcs/grubbs1/).”

Reviewer #2:

Very nice work! I do not have a whole lot to say in terms of experiments, analysis, or data to present other than what is in my public review (and you cannot really provide it as it was not in the experimental design). The manuscript is also very well written. My only question is about the following two sentences in the introduction:

"Both exercise and amino acids activate the mechanistic target of TOR (mTOR) protein kinase, which stimulates the protein synthesis machinery needed to stimulate skeletal muscle hypertrophy (Schiaffino et al., 2021). Therefore, The Academy of Nutrition and Dietetics recommends consuming 1.2-2.0 grams of protein per kg of body weight (BW) per day in physically active individuals (Thomas et al., 2016)." I am not sure how the second sentence follows from the first, so I am not convinced that "therefore" is the right adverb in the right place.

Thanks for pointing this out. We have added a clarifying transition to the text (page 3).

Author Response

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

Reviewer #1 (Public Review):

Summary:

This important study from Godneeva et al. establishes a Drosophila model system for understanding how the activity of Tif1 proteins is modified by SUMO. The authors nicely show that Bonus, like homologous mammalian Tif1 proteins, is a repressor, and that it interacts with other co-repressors Mi-2/NuRD and setdb1 in Drosophila ovaries and S2 cells. They also show that Bonus is SUMOylated by Su(var)2-10 on at least one lysine at its N-terminus to promote its interaction with setdb1. By combining nice biochemistry with an elegant reporter gene approach, they show that SUMOylation is important for Bonus interaction with setdb1, and that this SUMO-dependent interaction triggers high levels of H3K9me3 deposition and gene silencing. While there are still major questions of how SUMO molecularly promotes this process, this study is a valuable first step that opens the door for interesting future experimentation.

Major Point:

The RNAseq and ChIPseq data is not available. This is critical for the review of the paper and would help the readers and reviewers interpret the Bonus mutant phenotype and its mechanism of repressing genes.

The sequencing data have been deposited to the NCBI GEO archive. The accession number for all other RNA-seq and ChIP-seq data reported in this paper is GEO: GSE241375.

1) The author's conclusion that Bonus SUMOylation is "essential for its chromatin localization" is not supported by the data. Figure 5F shows less 3KR mutant in the chromatin fraction but there is still significant signal.

We appreciate the reviewer's feedback and agree that the term "essential" was not appropriate in this context. We have revised the manuscript to replace "essential" with "contributes to" to accurately reflect our findings.

2) The author's conclusion that Bonus is SUMOylated at a single site close to its N-terminus is not necessarily true. In several SUMO and Bonus blots throughout the paper (5B, 6C, S4A), there are >2 differentially migrating species that could represent more than one SUMO added to Bonus. While the single K20R mutation eliminates all of these species in Fig 5C, it is possible that K20R SUMOylation is required for additional SUMOylation events on other residues. One way to determine if Bonus is SUMOylated on multiple sites is to add recombinant SUMO protease to the extract and see if multiple higher molecular weight bands collapse into a single migrating species (implying multiple SUMOs) or multiple migrating species (implying something else is altering gel migration).

We appreciate the suggestion made by the reviewer. While we acknowledge the presence of occasional multiple bands in SUMO Western blots, the predominant pattern is the presence of unmodified Bon and a single additional band corresponding to SUMO-modified Bon. To investigate the possibility of multi-site SUMOylation, we performed requested experiment where we added SENP2 SUMO protease to the extract and checked Bon's SUMOylation. In the presence of NEM, we observed the unmodified form of Bon, as well as a single additional band representing a SUMO-modified form of Bon. Following SENP2 SUMO protease treatment, SUMOylation form of Bon was completely abolished in all samples, leaving only the unmodified Bon band (Extended Data Fig. 4D). This indicates that Bon is not SUMOylated on multiple sites and that the observed differential migration species likely result from other factors affecting gel migration.

3) The authors state that most upregulated genes in BonusGLKD are not highly enriched in H3K9me3. The heatmap in figure 3D is not an ideal presentation of this argument. The authors should show an example of what the signal on a highly enriched gene looks like for comparison. The authors also argue that because most upregulated genes in BonusGLKD are not highly enriched in H3K9me3, they must be indirectly repressed. Another possibility is that bonus-mediated H3K9me3 is only important (and present) during early nurse cell differentiation and is later lost and dispensable during the rapid endocycles. After bonus establishes repression though H3K9me3, it might be maintained through bonus-Mi2/Nurd, something else, or nothing at all. The authors could discuss this possibility or perform H3K9me3 ChIP during cyst formation and early nurse cell differentiation rather than in whole ovaries, which are enriched for later stages.

We thank the reviewer for their thoughtful comments and suggestions. In our revised manuscript we have included the tracks of gene that is highly enriched in H3K9me3 but remain unchanged upon Bon GLKD (Extended Data Fig. 3B). This addition allows for a visual comparison and better supports our argument that majority of genes upregulated in Bon GLKD are not enriched in H3K9me3 mark. We also appreciate the reviewer's suggestion regarding the potential temporal dynamics of Bon-mediated H3K9me3. It is indeed possible that Bon's role in establishing H3K9me3 might be more prominent during early nurse cell differentiation and less critical in later stages. We included discussion of this possibility in revised manuscript. To further explore it would be valuable to perform H3K9me3 ChIP during cyst formation and early nurse cell differentiation. However, given the limitations of our current resources and time limitations, we were unable to perform these experiments for the revised manuscript.

4) The BonusGLKD RNAseq analysis is underwhelming. The conclusion that "Bonus represses tissue-specific genes" has limited value. Every gene that is not expressed in ovaries is "tissue-specific." What subset of tissue-specific genes does Bonus repress? What common features do these genes have and how do they compare to other sets of tissue-specific genes, such as those reportedly repressed by setdb1, Polycomb proteins, small ovary, l(3)mbt, and stonewall (among others in female germ cells). Comparing these available data sets could help the authors understand the mechanism of Bonus repression and how BonusGLKD leads to sterility. The authors could also further analyze the differences between nos-Gal4 and MT-Gal4 to better understand why nos- but not MT-driven knockdown is sterile.

We appreciate the reviewer's feedback regarding the RNA-seq analysis and acknowledge the importance of identifying the specific subset of tissue-specific genes. The Figure 2C shows specific tissues where genes derepressed upon Bon GLKD are normally expressed. These are tissues/organs such as the head, digestive system, and nervous system. The reviewer's suggestion to compare our findings with existing datasets are valid and could indeed provide a more comprehensive understanding of Bon repression and its implications in female germ cells. However, many of the published datasets are based on mutant fly lines or use different GAL4 drivers to induce knockdowns, making direct comparisons challenging. We have conducted a preliminary analysis of available data, specifically nos-Gal4>SetDB1KD (GSE109852), and identified an overlap of 135 genes out of the 464 genes upregulated upon nos-Gal4>BonusKD with those affected by SetDB1 knockdown. We have included this result in the revised manuscript.

Main Study Limitations:

1) It is unclear which genes are directly vs indirectly regulated by bonus, which makes it difficult to understand Bonus's repressive mechanism. Several lines of experiments could help resolve this issue. 1) Bonus ChIPseq, which the authors mentioned was difficult. 2) RNAseq of BonusGLKD rescued with KR3 mutation. This would help separate SUMO/setdb1-dependent regulation from Mi-2 dependent regulation. Similarly, comparing differentially expressed genes in Su(var)2-10GLKD, setdb1GLKD, 3KR rescue, and MI-2 GLKD could identify overlapping targets and help refine how bonus represses subsets of genes through these different corepressors.

We appreciate the reviewer's suggestions and agree that discrimination between direct and indirect Bon targets should be the next step in understanding Bon repressive mechanism. We have previously attempted to determine Bon direct targets using ChIP-seq approach. However, despite our multiple efforts using both native Bon antibodies and GFP-tagged Bon fly lines, analysis of ChIP-seq data did not reveal specific enrichment indicating that Bon – similar to many other chromatin-bound proteins – are not amenable to ChIP. The recommendation for RNA-seq analysis of Bon GLKD rescued with the 3KR mutation is valuable, and we will certainly consider it for future investigations.

We compared differentially expressed genes in Su(var)2-10 GLKD and Mi-2 GLKD and found limited overlap: out of the 231 genes affected by Bon GLKD, 39 genes were affected in Mi-2 GLKD and 42 in Su(var)2-10 GLKD. We acknowledge the importance of understanding which genes are directly or indirectly regulated by Bon and the potential for further experiments to address this question.

2) The paper falls short in discussing how SUMO might promote repression. This is important when considering the conservation (of lack thereof) of SUMOylation sites in Tif1 proteins in distantly related animals. One piece of data that was not discussed is the apparent localization of SUMOylated bonus in the cytoplasmic fraction of the blot in Figure 5F. Su(var)2-10 is mostly a nuclear protein, so is bonus SUMOylated in the nucleus and then exported to the cytoplasm? Also, setdb1 is a nuclear protein, so it is unlikely that the SUMOylated bonus directly interacts with setdb1 on target genes. Together with Fig 5E (unSUMOylatable Bonus aggregates in the nucleus), one could make a model where SUMO solubilizes bonus (perhaps by disassembling aggregates) and indirectly allows it to associate with setdb1 and chromatin. It is also important to note that in Figure 5I, the K3R mutation appears to lessen but not eliminate Bonus interaction with setdb1. This data again disfavors a model where SUMO establishes an interaction interface between setdb1 and Bonus. To determine which form of Bonus interacts with setdb1, the authors could perform a setdb1 pulldown and monitor the SUMOylation state of coIPed Bonus through mobility shift. If mostly unSUMOylated bonus interacts with setdb1, and SUMO indirectly promotes Bonus interaction with setdb1 (perhaps by disassembling Bonus aggregates), then the precise locations of Bonus SUMOylation sites could more easily shift during evolution, disfavoring the author's convergent evolution hypothesis.

We appreciate the reviewer's valuable feedback. Regarding the observation of SUMOylated Bon in the cytoplasmic fraction in Figure 5F, we recognize its significance. This finding has prompted us to consider a model in which SUMOylation may play a role in translocating Bon from the nucleus to the cytoplasm, potentially influencing interactions with SetDB1 and chromatin indirectly. Furthermore, Figure 5I which shows only a partial reduction in Bon-SetDB1 interaction with the 3KR mutation, suggests that SUMO may not be the primary mediator of this interaction. We recognize the need for further investigations to clarify SUMO's exact role in this context. In response to the reviewer's suggestion, we conducted SetDB1 pulldown experiments in S2 cells. The results reveal that indeed SetDB1 primarily interacts with unmodified Bon which is by far more abundant compared to SUMOylated form (Extended Data Fig. 5C). We think this experiment presents certain technical challenges, as the signal for Bon, when used as prey in co-IP experiments, is relatively faint, making it inherently difficult to detect the lower levels of SUMO-modified Bon. Additionally, in revised manuscript we have added new result of determining Bon interactors in ovary using mass-spec analysis, which showed that SetDB1 associates with wild-type, but not SUMO-deficient Bon. While our data support the idea that SUMO may contribute to Bon solubilization, possibly by disassembling aggregates, thereby indirectly facilitating its association with SetDB1 and chromatin, we acknowledge that the precise mechanism remains unclear.

Reviewer #2 (Public Review):

Summary:

The authors analyze the functions and regulation of Bon, the sole Drosophila ortholog of the TIF1 family of mammalian transcriptional regulators. Bon has been implicated in several developmental programs; however, the molecular details of its regulation have not been well understood. Here, the authors reveal the requirement of Bon in oogenesis, thus establishing a previously unknown biological function for this protein. Furthermore, careful molecular analysis convincingly established the role of Bon in transcriptional repression. This repressor function requires interactions with the NuRD complex and histone methyltransferase SetDB1, as well as sumoylation of Bon by the E3 SUMO ligase Su(var)2-10. Overall, this work represents a significant advance in our understanding of the functions and regulation of Bon and, more generally, the TIF1 family. Since Bon is the only TIF1 family member in Drosophila, the regulatory mechanisms delineated in this study may represent the prototypical and important modes of regulation of this protein family. The presented data are rigorous and convincing. As discussed below, this study can be strengthened by a demonstration of a direct association of Bon with its target genes, and by analysis of the biological consequences of the K20R mutation.

Strengths:

1. This study identified the requirement for Bon in oogenesis, a previously unknown function for this protein.
2. Identified Bon target genes that are normally repressed in the ovary, and showed that the repression mechanism involves the repressive histone modification mark H3K9me3 deposition on at least some targets.
3. Showed that Bon physically interacts with the components of the NuRD complex and SetDB1. These protein complexes are likely mediating Bon-dependent repression.
4. Identified Bon sumoylation site (K20) that is conserved in insects. This site is required for repression in a tethering transcriptional reporter assay, and SUMO itself is required for repression and interaction with SetDB1. Interestingly, the K20-mutant Bon is mislocalized in the nucleus in distinct puncta.
5. Showed that Su(var)2-10 is a SUMO E3 ligase for Bon and that Su(var)2-10 is required for Bon-mediated repression.

Weaknesses:

The study would be strengthened by demonstrating a direct recruitment of Bon to the target genes identified by RNA-seq. Given that the global ChIP-seq was not successful, a few possibilities could be explored. First, Bon ChIP-qPCR could be performed on the individual targets that were functionally confirmed (e.g. rbp6, pst). Second, a global Bon ChIP-seq has been reported in PMID: 21430782 - these data could be used to see if Bon is associated with specific targets identified in this study. In addition, it would be interesting to see if there is any overlap with the repressed target genes identified in Bon overexpression conditions in PMID: 36868234.

We greatly appreciate the reviewer's suggestion to demonstrate the direct recruitment of Bon to the target genes. As described in our answer to reviewer #1, we attempted to determine Bon direct targets using ChIP-seq approach using both native Bon antibodies and GFP-tagged Bon fly lines. However, analysis of ChIP-seq data did not reveal specific enrichment. Similarly, Bon ChIP-qPCR on individual targets showed the same results suggesting that Bon – similar to many other chromatin-bound proteins – are not amenable to ChIP protocol, at least in standard conditions. To further explore this issue, we have analyzed results of a global Bon ChIP-seq reported in PMID: 21430782. We did not find Bon binding to individual targets, but even more importantly, we did not see clear Bon enrichment elsewhere in the genome confirming a conclusion that Bon targets on chromatin cannot be determined by ChIP. Additionally, we explored the possibility of overlap between target genes repressed by Bon in our study and those observed under Bon overexpression conditions in PMID: 36868234. While we did identify 41 genes in common, it's important to note that the datasets are derived from different tissues (pupal eyes vs. ovaries), making direct comparison problematic.

The second area where the manuscript can be improved is to analyze the biological function of the K20R mutant Bonus protein. The molecular data suggest that this residue is important for function, and it would be important to confirm this in vivo.

We appreciate the reviewer's suggestion to analyze the biological function of the K20R mutant Bon protein. While we acknowledge that we did not use single-site K20R mutant for in vivo experiments, we demonstrated that the mutant with the three-residue substitution (3KR) is incapable of inducing repression (Figure 5G). Given that other experiments consistently showed that K20 is the primarily SUMOylation site, this result supports the conclusion that K20 SUMOylation plays an important role in Bon-mediated transcriptional silencing.

Reviewer #1 (Recommendations for The Authors):

Make the RNAseq and ChIPseq data publicly available!

The sequencing data have been deposited to the NCBI GEO archive. The accession number for all other RNA-seq and ChIP-seq data reported in this paper is GEO: GSE241375.

Reviewer #2 (Recommendations for The Authors):

It would be interesting to identify the biological basis of aberrant ovary development in Bon depletion conditions. Previous studies (e.g. PMID: 11336699) suggested that Bon loss of function clones are cell lethal, and the developmental defects in oogenesis presented in the current study offer an opportunity to delve more into the causes of cell loss, e.g. by showing that the cells die via apoptosis.

Thank you for your valuable suggestion. In response to your comment, we performed a TUNEL assay to investigate whether germ cells in nos-Gal4>BonusKD ovaries undergo apoptosis. Our results indeed indicate that germ cells in these ovaries exhibit apoptosis, as evidenced by the TUNEL signal (Extended Data Fig. 1C). This information has been included in the revised manuscript to provide insights into the biological basis of aberrant ovary development in Bon depletion conditions.

The K20 residue could also be ubiquitinated. This possibility could at least be discussed, particularly given the presence of the RING Ub ligase domain in Bon that might potentially perform self-ubiquitination.

Indeed, the possibility that Bon can be ubiquitinated is a valid consideration. We have explored this possibility. We did not detect any signals with the Ubiquitin antibody in both wild-type Bon immunoprecipitant and triple-mutant [3KR] ovaries (in which K20 is also mutated) (Extended Data Fig. 4C). This suggests that K20 is more likely responsible for Bon SUMOylation rather than ubiquitination. We appreciate the reviewer's suggestion and have included this information into the revised manuscript.

Author Response

We very much appreciate all the reviewers’ positive feedback and additional comments and suggestions for this manuscript!

In this provisional reply, we’d like to quickly address only one selected key point, for which we have already collected relevant experimental data:

Reviewer 1 suggests that ‘it would have been more rigorous for the authors to independently reproduce the kinetics reported for nsp8/9 using their specific experimental conditions.’ We absolutely agree with this and have already carried out these kinetic experiments while our paper was under review. We have now measured kinetic parameters for cleavage of the nsp8/9 peptide in our own hands under the same conditions as we used for nsp4/5 and TRMT1. We measured kcat and KM values of 0.019 +/- 0.002 s-1 and 40 +/- 7.5 µM, respectively, for nsp8/9 cleavage; these data are very much in line with the previously reported values from MacDonald et al (kcat = 0.013 +/- 0.001 s-1, KM = 36 +/- 6.0 µM) that we used for comparison in Figure 4 and listed in Table S2. We will add our own measured kinetic values for nsp8/9 in the next version of our manuscript, but wanted to report these numbers as soon as possible, because this further supports and validates our claim that the human TRMT1 sequence is cleaved at a similar rate to the known nsp8/9 viral polypeptide cleavage site.

We will provide a detailed, point-by-point reply to all reviewer comments accompanying the forthcoming revised manuscript, in which we intend to have new and updated data and additional MD simulations that directly address key questions raised by the reviewers.

Author Response

We thank the reviewers for their suggestions in improving the manuscript. We are currently working on a formal revision and plan to submit a revised manuscript in the near future. However, we would be remiss, if we did not address concerns regarding the conceptual merits of the paper. Below we speak to major points of note that address select reviewer comments and the eLife assessment of our manuscript.

eLife assessment:

However, the strength of evidence is incomplete due to the concern that larval contraction is a result of chilling the nervous system and muscles, which causes spreading depolarization and mechanical contraction of the body, rather than an active sensorimotor response to cold.

Reviewer #3:

The scientific premise is that a full body contraction in larvae that are exposed to noxious cold is a sensorimotor behavioral pathway. This premise is, to start with, questionable. A common definition of behavior is a set of "orderly movements with recognizable and repeatable patterns of activity produced by members of a species (Baker et al., 2001)." In the case of nociception behaviors, the patterns of movement are typically thought to play a protective role and to protect from potential tissue damage.

Does noxious cold elicit a set of orderly movements with a recognizable and repeatable pattern in larvae? Can the patterns of movement that are stimulated by noxious cold allow the larvae to escape harm? Based on the available evidence, the answer to both questions is seemingly no.

We thank the reviewer for their questions and clarify, here. Exposure to cold temperatures does elicit a recognizable and repeatable pattern of behavior across multiple strains, including both wildtype and genetic control strains (w1118, Oregon R) and numerous control conditions that have been previously published (Himmel et al., 2021, Himmel et al., 2023, Patel et al., 2022, Turner et al., 2016, Turner et al., 2018, Tenedini et al., 2019). Our initial publication on Drosophila cold nociception demonstrated a variety of cold-evoked behavior responses including head and/or tail raising of the larva as well as contraction behavior. These behaviors were repeatedly observed in assays involving either local cold stimulation with a cold probe or global cold stimulation on a cold plate. Head and/or tail raise behaviors are consistent with behavior that displaces the larval body from the cold surface, however, exposure to increasingly colder temperatures leads to an increasing level of cold-evoked contraction (CT) responses which result in a reduction of larval area (Turner et al., 2016). Presumably, increasing the level of CIII md neuron activation leads to greater activation of downstream circuitry. We previously performed optogenetic dose response assays to further clarify the increased prevalence CT response to strong noxious cold stimuli and investigated how CIII md neurons discriminate between innocuous touch and noxious cold stimuli. Here, we found that lower-level activation of CIII md neurons lead to predominantly touch-evoked behaviors whereas high-level activation led predominantly to cold-evoked responses (Turner et al., 2016). These analyses were coupled with stimulus-evoked calcium imaging, which revealed that touch-evoked Ca2+ levels were significantly lower than cold-evoked Ca2+ levels (Turner et al., 2016).

In this manuscript, we confirm our previously published findings that neural silencing of CIII md neurons with either tetanus toxin expression or impairing action potential propagation results impaired cold-evoked CT responses (Turner et al., 2016, Turner et al., 2018). However, neural silencing of CIII md neurons did not eliminate cold-evoked CT responses. We interpret this finding as evidence that some component of cold-evoked CT response may be due to cold-induced muscle contraction. Furthermore, in this manuscript, we implicate the requirement of chordotonal (Ch) neurons in cold-evoked CT and demonstrate cold-evoked Ca2+ increases in Ch neurons. Furthermore, neural silencing of multiple sensory neuron types (CIII + Ch or CIII + CII) resulted in greater deficits in cold-evoked behaviors (Turner et al., 2016). Thus, the noxious cold stimulus is detected by multiple peripheral sensory neurons and inhibiting neural activity in CIII md neurons alone cannot eliminate cold-evoked CT responses.

In this manuscript and in several other publications, studies have shown that optogenetic activation of CIII md neurons, or CIII neurons plus CII neurons or Ch neurons elicits CT-like responses (Hwang et al., 2007, Shearin et al., 2013, Turner et al., 2016). Conversely, optogenetic stimulation of CIII md neurons knocked down for paralytic, the α-subunit of voltage-gated sodium channel, did not elicit blue light-evoked CT responses due to impaired action potential propagation. These analyses collectively indicate that CIII md neuron activation is sufficient for eliciting CT-like responses. Additionally, we have previously published electrophysiological recordings of CIII md neurons under cold exposure. To address potential confounds of cold-induced muscle contraction on cold-induced electrical activity of CIII md neurons, we performed these analyses on de-muscled fillets revealing that CIII neural activity is not dependent upon muscles in response to cold. Exposure to noxious cold stimuli results in temperature-dependent increases in CIII neuron firing pattern consisting of both bursting and tonic firing (Himmel et al., 2021, Himmel et al., 2023, Maksymchuk et al., 2022, Patel et al., 2022, Himmel et al., 2022, Maksymchuk et al., 2023).

Reviewer #3:

Can the patterns of movement that are stimulated by noxious cold allow the larvae to escape harm?

We were similarly curious about the neuroethological and/or protective implications of cold-evoked behaviors. In Drosophila larvae, noxious mechanical stimuli-evoked body rolling allows for lateral escape from predatory wasp (Hwang et al., 2007). Reducing the overall surface area that is exposed to cold (e.g., huddling behavior) serves as a protective strategy in many species (Canals et al., 1997, Contreras, 1984, Gilbert et al., 2006, Vickery and Millar, 1984, Hayes et al., 1992). Low temperatures can be fatal to poikilotherms (e.g., insects), however, many species have evolved the ability to cold acclimate thereby increasing their cold tolerance. To explore the potential evolutionary benefit of CIII-mediated contraction response to cold, we previously published work revealing a neural basis for cold acclimation in Drosophila larvae implicating these neurons (Himmel et al., 2021). We demonstrated that cold-evoked CT behavior is evolutionarily conserved across 11 different drosophilid species and that other cold-induced behaviors (e.g., tail raise) were also observed. Furthermore, drosophilid species adapted to rapid temperature swings were more likely to retain the ability to locomote even at lower temperatures (Himmel et al., 2021). Next, we elucidated the role of CIII md neurons in cold acclimation. Silencing CIII md neurons resulted in the inability to cold acclimate. We additionally investigated roles of Ch or CII md neurons, which alone did not inhibit the ability of larvae to cold acclimate. However, combinatorial silencing of CIII with CII or Ch neurons resulted in an inability to cold acclimate but did not obviously increase baseline cold tolerance. We explored how developmental exposure to noxious cold temperature impacts CIII md neuron cold-evoked firing pattern. Electrophysiological analyses revealed that cold acclimation results in hypersensitization in CIII md neurons (Himmel et al., 2021). Lastly, developmental optogenetic activation of CIII md neurons led to increased cold tolerance. Therefore, CIII md neurons are necessary and sufficient for cold tolerance and our collective evidence demonstrate that CIII-mediated cold nociception constitutes a peripheral neural basis for Drosophila larval cold acclimation (Himmel et al., 2021).

Reviewer #3:

It should be noted that this actuator drives very strong activation, and other studies with milder optogenetic stimulation of Class III neurons have shown that these cells produce behavioral responses that resemble gentle touch responses (Tsubouchi et al 2012 and Yan et al 2013)…The latter makes the reported Calcium responses to cold difficult to interpret in light of the fact that the strong muscle contractions driven by cold may actually be driving mechanosensory responses in these cells (ie through deformation of the mechanosensitive dendrites)…. Are the cIII calcium signals still observed in a preparation where cold induced muscle contractions are prevented?”

We agree with the reviewer that mild activation of CIII md neurons results in gentle touch-like responses. In this manuscript, and other previously published work, it has been shown that optogenetic activation of CIII neurons, or CIII neurons and other sensory neurons, using a variety of optogenetic actuators (ChR2, ChETA, and CsChrimson) promotes bilateral contraction of the larval body along the anterior-posterior axis (Shearin et al., 2013, Hwang et al., 2007, Meloni et al., 2020, Turner et al., 2016, Patel and Cox, 2017, Patel et al., 2022, Himmel et al., 2023).

As described above, in our initial publication documenting larval cold nociception in Drosophila, we investigated how CIII md neurons discriminate multimodal stimuli to elicit stimulus relevant behavioral responses. We reported that increased activation of CIII md neurons results in cold-evoked behaviors, where lower activation results in touch-evoked behaviors. Subsequent, calcium analyses revealed greater stimulus-evoked calcium response to noxious cold and milder calcium response to gentle touch (Turner et al., 2016).

Though we have not performed cold-evoked Ca2+ imaging of CIII md neurons in larval preparations without muscles, we have recorded electrical responses of CIII md neurons in the absence of muscle contractions using de-muscled larvae fillets to analyze cold-evoked firing patterns of CIII md neurons (Himmel et al., 2021, Himmel et al., 2022, Himmel et al., 2023, Patel et al., 2022, Maksymchuk et al., 2022, Maksymchuk et al., 2023). These studies demonstrate the cold-evoked CIII neural activity is not dependent upon muscles.

Reviewer #3:

A major weakness of the study is that none of the second or third order neurons (that are downstream of CIII neurons) are found to trigger the CT behavioral responses even when strongly activated with the ChETA actuator (Figure 2 Supplement 2). These findings raise major concerns for this and prior studies and it does not support the hypothesis that the CIII neurons drive the CT behaviors.”

We conducted extensive screening of interneuron populations post-synaptically connected to CIII neurons in an effort to identify post-synaptic partners that were sufficient to trigger CT response. Much to our surprise, we were unable to find any individual neuron type or driver line that was sufficient to elicit a CT response. However, we provide substantial supporting evidence for our co-activation experiments including neural silencing, EM connectivity and calcium imaging. We also report necessity for the reported second/third order neurons in cold-evoked behavioral responses, where inhibiting neural activity resulted in reduced cold-evoked behavior. Second/third order neurons also exhibit cold-evoked calcium responses. Lastly, we also report CIII-evoked (using optogenetics) increases in calcium response in downstream post-synaptic neurons.

Previously published literature investigating CIV md neuron circuitry has implicated downstream neurons that are not sufficient to elicit rolling behavior upon activation. In CIV md neuron circuit dissection, select neurons are reported as acting downstream of CIV md neurons that require additional circuit components in order to execute rolling behavior. For example, A00c neuron activation alone does not lead to rolling behavior, however, co-activation of A00c and Basin-4 neurons facilitates rolling response (Ohyama et al., 2015). Similarly, co-activation of Basin-1 and Basin-4 neurons significantly enhance rolling probability relative to Basin-4 alone (Ohyama et al., 2015). Further, DnB neurons require Goro command neuron activity to promote rolling behavior (Burgos et al., 2018). Thus, there is precedent for co-activation requirements to elicit robust behavioral output in sensorimotor circuits and we employed a similar strategy after we discovered that activation of second or third order neurons alone did not elicit CT response.

Reviewer #3:

Later experiments in the paper that investigate strong CIII activation (with ChETA) in combination with other second and third order neurons does support the idea activating those neurons can facilitate body-wide muscle contractions. But many of the co-activated cells in question are either repeated in each abdominal neuromere or they project to cells that are found all along the ventral nerve cord, so it is therefore unsurprising that their activation would contribute to what appears to be a non-specific body-wide activation of muscles along the AP axis. Also, if these neurons are already downstream of the CIII neurons the logic of this co-activation approach is not particularly clear.”

We agree with the reviewer’s comment that various cell-types that were investigated are repeated in every abdominal neuromere, however, only select post-synaptic neurons (Basin 1-4, DnB, mCSI, and Chair neurons) are segmentally repeated in every abdominal segment. Conversely, other projection and ascending neurons we investigated (A09e, A00c, A05q, Goro, TePn04/05, and A08n) are not segmentally repeated in every section. We used connectome evidence to guide our experiments on populations of neurons to explore in cold-evoked behavior and as alluded to above our co-activation approach was driven by the observation that an individual subpopulation of connected interneurons was not found to be sufficient to elicit CT behavior. That said, it does not change the findings that inhibition of neural activity in these subpopulations impairs cold-evoked behavior, nor does it change the observation that connected interneurons exhibit cold-evoked Ca2+ responses that can also be observed with optogenetic activation of CIII neurons. Reviewer #3: “The authors argument that the co-activation studies support "a population code" for cold nociception is a very optimistic interpretation of a brute force optogenetics approach that ultimately results in an enhancement of a relatively non-specific body-wide muscle convulsion.” Many studies exploring circuit bases of behavior have applied large-scale optogenetic, including co-activation strategies, or silencing screens to identify circuit components involved in specific behaviors under investigation. We employed similar methods in our circuit-based dissection and our conclusions are not solely based upon optogenetic analyses.

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Author Response

Reviewer #1 (Public Review):

Summary: The current study reports a cryo-EM structure of MFS transporter MelB trapped in an inward-facing state by a conformationally selective nanobody. The authors compare this structure to previously-resolved crystal structures of outward-facing MelB. Additionally, the authors report H/D exchange/ mass spec experiments that identify accessible residues in the protein.

Strengths: The authors overcame very significant technical challenges to solve the first inward-facing structure of the small, model MFS transporter MelB by cryo-EM. The use of conformation-trapping nanobodies (which had been reported previously by this group) is particularly nice.

We appreciate reviewer #1’s positive comments.

Weaknesses: Maps and coordinates were not provided by the authors, which presents a gap in this assessment.

We didn’t know specific requests for maps & coordinates during the initial submission but will provide them per request.

The authors highlight the use of HDX experiments as a measurement of protein conformational dynamics. However, this experiment does not measure the conformational dynamics of the transporter, since in these experiments exchange is not initiated by ligand addition or another trigger. The experiment instead measures the accessibility of different residues, and of course, a freely-exchanging sodium bound transporter would have more exchangeable positions than when a conformation-trapping nanobody is bound. It is not clear what new mechanistic information this provides, since this property of the nanobody has already been established.

We thank you for your comment. We will address your and reviewer 2’s similar questions later.

Based on the evidence presented, it is somewhat speculative that the structure represents the EIIa-bound regulatory state.

We believe that have presented convincing evidence obtained by ITC and gel-filtration chromatography to support this statement. The effects of Nb725 or EIIAGlc on MelB functions are similar: little change in Na+ binding, little change in Nb725 or EIIAGlc binding in the absence or presence of the EIIAGlc or Nb725, but a great reduction in sugar-binding affinity (sFigs. 2&3; tables 1&2; published two papers in J. Biol. Chem. 2014; 289: 33012-33019 and 2023; 299:104967). To make it clear, we will add the related data from the two JBC papers into the table 2. Nb725 and EIIAGlc can concurrently bind to MelBSt (sFigs. 2&3; tables 1&2). Further, we will provide a new figure to show that a complex composed of all three proteins can be isolated by gel-filtration chromatography. We have also established this finding with another Nb733 from the same family (JBC, 2023; 299:104967). However, given the EIIAGlc-bound structure has not been resolved yet, we will tune down the related argument.

Reviewer #2 (Public Review):

Summary: In this manuscript, Hariharan and colleagues present an elegant study regarding the mechanistic basis of sugar transport by the prototypical Na+-coupled transporter MelB. The authors identified a nanobody (Nb 725) that reduces melibiose binding but not Na+ binding. In vitro (ITC) experiments suggest that the conformation targeted by this nanobody is different from the published outward-open structures. They go on to solve the structure of this other conformational by cryo-EM using the Nanobody grafted with a fiducial marker and enhancer and, as predicted, capture a new conformation of MelB, namely the inward-open conformation. Through MD simulations and ITC measurements, they demonstrate that such state has a reduced affinity for sugar but that Na+ binding is mostly unaffected. A detailed observation and comparison between previously published structures in the outward-open conformation and this new conformational intermediate allows to strengthen and develop the mobile barrier hypothesis underpinning sugar transport. The conformational transition to the inward-facing state leads to the formation of a barrier on the extracellular side that directly affects the amino acid arrangement of the sugar binding site, leading to a decreased affinity that drives the direction of transport. In contrast, the Na+ binding remains the same. This structural data is complemented with dynamic insights from HDX-MS experiments conducted in the presence and absence of the Nb. These measurements highlight the overall protective effect of nanobody binding, consistent with the stabilization of one conformational intermediate.

Strengths: The experimental strategy to isolate this elusive conformational intermediate is smart and well-executed. The biochemical and biophysical data were obtained in a lipid system (nanodiscs), which allows dismissing questions about detergent induced artefacts. The new conformation observed is of great interest and allows to have a better mechanistic understanding of ion-coupled sugar transport. The comparison between the two structures and the mobile barrier mechanism hypothesis is convincingly depicted and tested.

We appreciate the reviewer’s insightful understanding of our novel findings and the associated explanations on the cation-coupled symport mechanisms.

Weaknesses: This is excellent experimental work. My recommendations stem mostly from concerns regarding the interpretation of the observed results. In particular, I am somewhat puzzled by the important role the authors give to the regulatory protein EIIa with little structural or biophysical data to back up their claims. The hypothesis that the conformation captured by the Nb is physiologically and functionally equivalent to that caused by EIIa binding is definitely a worthy hypothesis, but it is not an experimental result. Evidence in support could include a structure with EIIa bound. Since it does not bind at the same location as the Nb, it seems feasible. Or, the authors could have performed HDX-MS in the presence of EIIa to determine if the effect is similar to that of Nb_725 binding. In the absence of these experiments, discussion about EIIa should be limited. Along the same lines, I find it misleading to put in the abstract a sentence such as "It is the first structure of a major facilitator superfamily (MFS) transporter with experimentally determined cation binding, and also a structure mimicking the physiological regulatory state of MelB under the global regulator EIIAGlc of the glucose-specific phosphoenolpyruvate:phosphotransferase system." None of this is supported by the experimental work presented in this article: the Na+ is modelled (with great confidence, but still) and whether this structure mimics the physiological state of MelB bound to EIIa is not known. The results of the paper are strong and interesting enough per se, and there is no need to inflate them with hypothesis that belongs to the discussion section.

As stated in the response to reviewer 1, we believe that we presented strong data to argue for a structure mimicking the physiological regulatory state of MelB. The only missing data is the lack of the structure determination of the EIIA-bound state. We will change the title and tune down the related discussions in a new version.

Regarding our statement in our abstract that “It is the first structure of a major facilitator superfamily (MFS) transporter with experimentally determined cation binding”, we believe that our claim is supported by the resolved Na+ binding in the cryoEM structure. So far, to our knowledge, there was no experimentally determined cation on its canonical binding site reported yet.

I also note that the HDX-MS experiments do not distinguish between two conformational states, but rather an ensemble of states vs one state.

We will address both reviewers 1 and 2 together. We agree with your comments and we compared the one (inward) state and ensembles of (predominantly outward) states. A lot of published data have demonstrated that the WT MelBSt predominantly populates outward-facing states, especially in the presence of Na+. The major differences in HDX-MS between the inward-facing state in the presence of the Nb and the outward-facing ensembles in the absence of the Nb should be related to the conformational changes between the inward- and outward-facing states, but not quantitatively. The type of measurements we performed do not contain information on the rates of conformational changes, but this study identified the dynamics regions involved in this conformational switch.

Reviewer #3 (Public Review):

Summary: The manuscript authored by Lan Guan and colleagues reveals the structure of the cytosol-facing conformation of the MelB sodium/Li coupled permease using the nab-Fab approach and cryoEM for structure determination. The study reveals the conformational transitions in the melB transport cycle and allows understanding the role of sugar and ion specificities within this transporter.

Strengths: The study employs a very exciting strategy of transferring the CDRS of a conformation specific nano body to the nab-fab system to determine the inward-open structure of MelB. The resolution of the structure is reasonable enough to support the major conclusions of the study. This is overall a well-executed study.

Thank you for your positive comments.

Weaknesses: The authors seem to have mixed up the exothermic and endothermic aspects of ITC binding in their description. Positive heats correspond to endothermic heat changes in ITC and negative heat changes correspond to exothermic heats. The authors seem to suggest the opposite.

This is consistently observed throughout the manuscript.

All of our ITC data are correctly presented. Our data were collected from the NanoITC (TA instruments, Inc), which directly measures the heat release/enthalpic changes and projects exotherm with positive values. This is in contrast to the MicroCal device, which detects heat changes through voltage compensation and exotherm is depicted with negative values. We will further emphasize this in related figure legends.

Author Response

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

Reviewer #1:

This manuscript describes a set of four passage-reading experiments which are paired with computational modeling to evaluate how task-optimization might modulate attention during reading. Broadly, participants show faster reading and modulated eye-movement patterns of short passages when given a preview of a question they will be asked. The attention weights of a Transformerbased neural network (BERT and variants) show a statistically reliable fit to these reading patterns above-and-beyond text- and semantic-similarity baseline metrics, as well as a recurrent-networkbased baseline. Reading strategies are modulated when questions are not previewed, and when participants are L1 versus L2 readers, and these patterns are also statistically tracked by the same transformer-based network.

I should note that I served as a reviewer on an earlier version of this manuscript at a different venue. I had an overall positive view of the paper at that point, and the same opinion holds here as well.

Strengths:

• Task-optimization is a key notion in current models of reading and the current effort provides a computationally rigorous account of how such task effects might be modeled

• Multiple experiments provide reasonable effort towards generalization across readers and different reading scenarios

• Use of RNN-based baseline, text-based features, and semantic features provides a useful baseline for comparing Transformer-based models like BERT

Thank you for the accurate summary and positive evaluation.

Weaknesses:

1) Generalization across neural network models seems, to me, somewhat limited: The transformerbased models differ from baseline models in numerous ways (model size, training data, scoring algorithm); it is thus not clear what properties of these models necessarily supports their fit to human reading patterns.

Thank you for the insightful comment. To dissociate the effect of model architecture and the effect of training data, we have now compared the attention weights across three transformer-based models that have the same architecture but different training data/task: randomized (with all model parameters being randomized), pretrained, and fine-tuned models. Remarkably, even without training on any data, the attention weights in randomly initialized models exhibited significant similarity to human attention patterns (Figure. 3A). The predictive power of randomly initialized transformer-based models outperformed that of the SAR model. Through subsequent pre-training and fine-tuning, the predictive capacity of the models was further elevated. Therefore, both model architecture and the training data/task contribute to human-like attention distribution in the transformer models. We have now reported this result:

“The attention weights of randomly initialized transformer-based models could predict the human word reading time and the predictive power, which was around 0.3, was significantly higher than the chance level and the SAR (Fig. 3A, Table S1). The attention weights of pre-trained transformerbased models could also predict the human word reading time, and the predictive power was around 0.5, significantly higher than the predictive power of heuristic models, the SAR, and randomly initialized transformer-based models (Fig. 3A, Table S1). The predictive power was further boosted for local but not global questions when the models were fine-tuned to perform the goal-directed reading task (Fig. 3A, Table S1).”

In addition, we reported how training influenced the sensitivity of attention weights to text features and question relevance. As shown in Figure 4AB, attention in the randomized models were sensitive to text features across all layers. After pretraining, the models exhibited increased sensitivity to text features in the shallow layers, and decreased sensitivity to text features in deep layers. Subsequent finetuning on the reading comprehension task further attenuates the encoding of text features in deep layers but strengthens the sensitivity to task-relevant information.

2) Inferential statistics are based on a series of linear regressions, but these differ markedly in model size (BERT models involve 144 attention-based regressor, while the RNN-based model uses just 1 attention-based regressor). How are improvements in model fit balanced against changes in model size?

Thank you for pointing out this issue. The performance of linear regressions was evaluated based on 5-fold cross-validation, and the performance we reported was the performance on the test set. To match the number of parameters, we have now predicted human attention using the average of all heads. The predictive power of the average head was still significantly higher than the predictive power of the SAR model. We have now reported this result in our revised manuscript:

“For the fine-tuned models, we also predict the human word reading time using an unweighted averaged of the 144 attention heads and the predictive power was 0.3, significantly higher than that achieved by the attention weights of SAR (P = 4 × 10-5, bootstrap).”

Also, it was not clear to me how participant-level variance was accounted for in the modeling effort (mixed-effects regression?) These questions may well be easily remedied by more complete reporting.

In the previous manuscript, the word reading time was averaged across participants, and we did not consider the variance between participants. We have now analyzed eye movements of each participant and used the linear mixed effects model to test how different factors affected human word reading time to account for participantslevel and item-level variances.

“Furthermore, a linear mixed effect model also revealed that more than 85% of the DNN attention heads contribute to the prediction of human reading time when considering text features and question relevance as covariates (Supplementary Results).”

“Supplementary Methods To characterize the influences of different factors on human word reading time, we employed linear mixed effects models [5] implemented in the lmerTest package [6] of R. For the baseline model, we treated the type of questions (local vs. global; local = baseline) and all text/task-related features as fixed factors, and considered the interaction between the type of questions and these text/taskrelated features. We included participants and items (i.e., questions) as random factors, each with associated random intercepts…”

Supplementary Results The baseline mixed model revealed significant fixed effects for question type and all text/task-related features, as well as significant interactions between question type and these text/task-related features (Table S7). Upon involving SAR attention, we observed a statistically significant fixed effect associated with SAR attention. When involving attention weights of randomly initialized BERT, the mixed model revealed that most attention heads exhibited significant fixed effects, suggesting their contributions to the prediction of human word reading time. A broader range of attention heads showed significant fixed effects for both pre-trained and fine-tuned BERT.

3) Experiment 1 was paired with a relatively comprehensive discussion of how attention weights mapped to reading times, but the same sort of analysis was not reported for Exps 2-4; this seems like a missed opportunity given the broader interest in testing how reading strategies might change across the different parameters of the four experiments.

Thank you for the valuable suggestion. We have now also characterized how different reading measures, e.g., gaze duration and counts or rereading, were affected by text and task-related features in Experiments 2-4.

For Experiment 2: “For local questions, consistent with Experiment 1, the effects of question relevance significantly increased from early to late processing stages that are separately indexed by gaze duration and counts of rereading (Fig. S9A, Table S3).”

For Experiment 3: “For local questions, the layout effect was more salient for gaze duration than for counts of rereading. In contrast, the effect of word-related features and task relevance was more salient for counts of rereading than gaze duration (Fig. S9B, Table S3).”

For Experiment 4: “Both the early and late processing stages of human reading were significantly affected by layout and word features, and the effects were larger for the late processing stage indexed by counts of rereading (Fig. S9C, Table S3).”

4) Comparison of predictive power of BERT weights to human annotations of text relevance is limited: The annotation task asked participants to chose the 5 "most relevant" words for a given question; if >5 words carried utility in answering a question, this would not be captured by the annotation. It seems to me that the improvement of BERT over human annotations discussed around page 10-11 could well be due to this arbitrary limitation of the annotations.

Thank you for the insightful comment. We only allowed a participant to label 5 words since we wanted the participant to only label the most important information. As the reviewer pointed out, five words may not be enough. However, this problem is alleviated by having >26 annotators per question. Although each participant can label up to 5 words, pooling the results across >26 annotators results in nonzero relevance rating for an average 21.1 words for local questions and 26.1 words for global question. More important, as was outlined in Experimental Materials, we asked additional participants to answer questions based on only 5 annotated keywords. The accuracy for question answering were 75.9% for global questions and 67.6% for local questions, which was close to the accuracy achieved when the complete passage was present (Fig. 1B), suggesting that even 5 keywords could support question answering.

5) Abstract ln 35: This concluding sentence didn't really capture the key contribution of the paper which, at least from my perspective, was something closer to "we offer a computational account of how task optimization modulates attention during reading"

p 4 ln 66: I think this sentence does a good job capturing the main contributions of this paper

Thanks for your suggestion. We have modified our conclusion in Abstract accordingly.

6) p 4 ln 81: "therefore is conceptually similar" maybe "may serve a conceptually similar role"

We have rewritten the sentence.

“Attention in DNN also functions as a mechanism to selectively extract useful information, and therefore attention may potentially serve a conceptually similar role in DNN.”

7) p. 7 ln 140: "disproportional to the reading time" I didn't understand this sentence

Sorry for the confusion and we have rewritten the sentence.

“In Experiment 1, participants were allowed to read each passage for 2 minutes. Nevertheless, to encourage the participants to develop an effective reading strategy, the monetary reward the participant received decreased as they spent more time reading the passage (see Materials and Methods for details).”

8) p 8 ln 151: This was another sentence that helped solidify the main research contributions for me; I wonder if this framing could be promoted earlier?

Thank you for the suggestion and we have moved the sentence to Introduction.

9) p. 33: I may be missing something here, but I didn't follow the reasoning behind quantifying model fit against eye-tracking measures using accuracy in a permutation test. Models are assessed in terms of the proportion of random shuffles that show a greater statistical correlation. Does that mean that an accuracy value like 0.3 (p. 10 ln 208) means that 0.7 random permutations of word order led to higher correlations between attention weights and RT? Given that RT is continuous, I wonder if a measure of model fit such as RMSE or even R^2 could be more interpretable.

We have now realized that the term “prediction accuracy” was not clearly defined and have caused confusion. Therefore, in the revised manuscript, we have replaced this term with “predictive power”. Additionally, we have now introduced a clear definition of “prediction power” at its first mention in Result:

“…the predictive power, i.e., the Pearson correlation coefficient between the predicted and real word reading time, was around 0.2”

The permutation test was used to test if the predictive power is above chance. Specifically, if the predictive power is higher than the 95 percentile of the chancelevel predictive power estimated using permutations, the significant level (i.e., the p value) is 0.05. We have explained this in Statistical tests.

10) p. 33: FDR-based multiple comparisons are noted several times, but wasn't clear to me what the comparison set is for any given test; more details would be helpful (e.g. X comparisons were conducted across passages/model-variants/whatever)

Sorry for missing this important information. We have now mentioned which comparisons are corrected,

“…Furthermore, the predictive power was higher for global than local questions (P = 4 × 10-5, bootstrap, FDR corrected for comparisons across 3 features, i.e., layout features, word features, and question relevance)…”

Reviewer #2:

In this study, researchers aim to understand the computational principles behind attention allocation in goal-directed reading tasks. They explore how deep neural networks (DNNs) optimized for reading tasks can predict reading time and attention distribution. The findings show that attention weights in transformer-based DNNs predict reading time for each word. Eye tracking reveals that readers focus on basic text features and question-relevant information during initial reading and rereading, respectively. Attention weights in shallow and deep DNN layers are separately influenced by text features and question relevance. Additionally, when readers read without a specific question in mind, DNNs optimized for word prediction tasks can predict their reading time. Based on these findings, the authors suggest that attention in real-world reading can be understood as a result of task optimization.

The research question pursued by the study is interesting and important. The manuscript was well written and enjoyable to read. However, I do have some concerns.

We thank the reviewer for the accurate summary and positive evaluation.

1) In the first paragraph of the manuscript, it appears that the purpose of the study was to test the optimization hypothesis in natural tasks. However, the cited papers mainly focus on covert visual attention, while the present study primarily focuses on overt attention (eye movements). It is crucial to clearly distinguish between these two types of attention and state that the study mainly focuses on overt attention at the beginning of the manuscript.

Thank you for pointing out this issue. We have explicitly mentioned that we focus on overt attention in the current study. Furthermore, we have also discussed that native readers may rely more on covert attention so that they do not need to spend more time overtly fixating at the task relevant words.

In Introduction:

“Reading is one of the most common and most sophisticated human behaviors [16, 17], and it is strongly regulated by attention: Since readers can only recognize a couple of words within one fixation, they have to overtly shift their fixation to read a line of text [3]. Thus, eye movements serve as an overt expression of attention allocation during reading [3, 18].”

In Discussion:

“Therefore, it is possible that when readers are more skilled and when the passage is relatively easy to read, their processing is so efficient so that they do not need extra time to encode task-relevant information and may rely on covert attention to prioritize the processing of task-relevant information.”

2) The manuscript correctly describes attention in DNN as a mechanism to selectively extract useful information. However, eye-movement measures such as gaze duration and total reading time are primarily influenced by the time needed to process words. Therefore, there is a doubt whether the argument stating that attention in DNN is conceptually similar to the human attention mechanism at the computational level is correct. It is strongly suggested that the authors thoroughly discuss whether these concepts describe the same or different things.

Thank you for bringing up this very important issue and we have added discussions about why human and DNN may generate similar attention distributions. For example, we found that both DNN and human attention distributions are modulated by task relevance and word properties, which include word length, word frequency, and word surprisal. The influence of task relevance is relatively straightforward since both human readers and DNN should rely more on task relevant words to answer questions. The influence of word properties is less apparent for models than for human readers and we have added discussions:

For DNN’s sensitivity to word surprisal:

“The transformer-based DNN models analyzed here are optimized in two steps, i.e., pre-training and fine-tuning. The results show that pre-training leads to text-based attention that can well explain general-purpose reading in Experiment 4, while the fine-tuning process leads to goal-directed attention in Experiments 1-3 (Fig. 4B & Fig. 5A). Pre-training is also achieved through task optimization, and the pre-training task used in all the three models analyzed here is to predict a word based on the context. The purpose of the word prediction task is to let models learn the general statistical regularity in a language based on large corpora, which is crucial for model performance on downstream tasks [21, 22, 33], and this process can naturally introduce the sensitivity to word surprisal, i.e., how unpredictable a word is given the context.”

For DNN’s sensitivity to word length:

“Additionally, the tokenization process in DNN can also contribute to the similarity between human and DNN attention distributions: DNN first separates words into tokens (e.g., “tokenization” is separated into “token” and “ization”). Tokens are units that are learned based on co-occurrence of letters, and is not strictly linked to any linguistically defined units. Since longer words tend to be separated into more tokens, i.e., fragments of frequently co-occurred letters, longer words receive more attention even if the model pay uniform attention to each of its input, i.e., a token.”

3) When reporting how reading time was predicted by attention weights, the authors used "prediction accuracy." While this measure is useful for comparing different models, it is less informative for readers to understand the quality of the prediction. It would be more helpful if the results of regression models were also reported.

Sorry for the confusion. The prediction accuracy was defined as the correlation coefficient between the predicted and actual eye-tracking measures. We have now realized that the term “prediction accuracy” might have caused confusion. Therefore, in the revised manuscript, we have replaced this term with “predictive power”. Additionally, we have now introduced a clear definition of “prediction power” at its first mention in Result:

“…the predictive power, i.e., the Pearson correlation coefficient between the predicted and real word reading time, was around 0.2”

4) The motivations of Experiments 2 and 3 could be better described. In their current form, it is challenging to understand how these experiments contribute to understanding the major research question of the study.

Thank you for pointing out this issue. In Experiments 1, different types of questions were presented in separate blocks, and all the participants were L2 reader. Therefore, we conducted Experiments 2 and 3 to examine how reading behaviors were modulated when different types of questions were presented in a mixed manner, or when participants were L1 readers. We have now clarified the motivations:

“In Experiment 1, different types of questions were presented in blocks which encouraged the participants to develop question-type-specific reading strategies. Next, we ran Experiment 2, in which questions from different types were mixed and presented in a randomized order, to test whether the participants developed question-type-specific strategies in Experiment 1.”

“Experiments 1 and 2 recruited L2 readers. To investigate how language proficiency influenced task modulation of attention and the optimality of attention distribution, we ran Experiment 3, which was the same as Experiment 2 except that the participants were native English readers.”

Reviewer #3:

This paper presents several eyetracking experiments measuring task-directed reading behavior where subjects read texts and answered questions.

It then models the measured reading times using attention patterns derived from deep-neural network models from the natural language processing literature.

Results are taken to support the theoretical claim that human reading reflects task-optimized attention allocation.

STRENGTHS:

1) The paper leverages modern machine learning to model a high-level behavioral task (reading comprehension). While the claim that human attention reflects optimal behavior is not new, the paper considers a substantially more high-level task in comparison to prior work. The paper leverages recent models from the NLP literature which are known to provide strong performance on such question-answering tasks, and is methodologically well grounded in the NLP literature.

2) The modeling uses text- and question-based features in addition to DNNs, specifically evaluates relevant effects, and compares vanilla pretrained and task-finetuned models. This makes the results more transparent and helps assess the contributions of task optimization. In particular, besides finetuned DNNs, the role of the task is further established by directly modeling the question relevance of each word. Specifically, the claim that human reading is predicted better by task-optimized attention distributions rests on (i) a role of question relevance in influencing reading in Expts 1-2 but not 4, and (ii) the fact that fine-tuned DNNs improve prediction of gaze in Expts 1-2 but not 4.

3) The paper conducts experiments on both L2 and L1 speakers.

We thank the reviewer for the accurate summary and positive evaluation.

WEAKNESSES:

1) The paper aims to show that human gaze is predicted the the DNN-derived task-optimal attention distribution, but the paper does not actually derive a task-optimal attention distribution. Rather, the DNNs are used to extract 144 different attention distributions, which are then put into a regression with coefficients fitted to predict human attention. As a consequence, the model has 144 free parameters without apparent a-priori constraint or theoretical interpretation. In this sense, there is a slight mismatch between what the modeling aims to establish and what it actually does.

Regarding Weakness (1): This weakness should be made explicit, at least by rephrasing line 90. The authors could also evaluate whether there is either a specific attention head, or one specific linear combination (e.g. a simple average of all heads) that predicts the human data well.

Thank you for pointing out this issue. One the one hand, we have now also predicted human attention using the average of all heads, i.e., the simple average suggested by the reviewer. The predictive power of the average head was still significantly higher than the predictive power of the SAR model. We have now reported this result in our revised manuscript.

“For the fine-tuned models, we also predict the human word reading time using an unweighted averaged of the 144 attention heads and the predictive power was 0.3, significantly higher than that achieved by the attention weights of SAR (P = 4 × 10-5, bootstrap).”

On the other hand, since different attention weights may contribute differently to the prediction of human reading time, we have now also reported the weights assigned to individual attention head during the original regression analysis (Fig. S4). It was observed that the weight was highly distributed across attention head and was not dominated by a single head.

Even more importantly, we have now rephrased the statement in line 90 of the previous manuscript:

“We employed DNNs to derive a set of attention weights that are optimized for the goal-directed reading task, and tested whether such optimal weights could explain human attention measured by eye tracking.”

Furthermore, in Discussion, we mentioned that:

“Furthermore, we demonstrate that both humans and transformer-based DNN models achieve taskoptimal attention distribution in multiple steps… Similarly, the DNN models do not yield a single attention distribution, and instead it generates multiple attention distributions, i.e., heads, for each layer. Here, we demonstrate that basic text features mainly modulate the attention weights in shallow layers, while the question relevance of a word modulates the attention weights in deep layers, reflecting hierarchical control of attention to optimize task performance. The attention weights in both the shallow and deep layers of DNN contribute to the explanation of human word reading time (Fig. S4).”

2) While Experiment 1 tests questions from different types in blocks, and the paper mentions that this might encourage the development of question-type-specific reading strategies -- indeed, this specifically motivates Experiment 2, and is confirmed indirectly in the comparison of the effects found in the two experiments ("all these results indicated that the readers developed question-typespecific strategies in Experiment 1") -- the paper seems to miss the opportunity to also test whether DNNs fine-tuned for each of the question-types predict specifically the reading times on the respective question types in Experiment 1. Testing not only whether DNN-derived features can differentially predict normal reading vs targeted reading, but also different targeted reading tasks, would be a strong test of the approach.

Regarding Weakness (2): results after finetuning for each question type could be reported.

Thank you for the valuable suggestion. We have now fine-tuned the models separately based on global and local questions. The detailed fine-tuning parameters employed in the fine-tuning process were presented in Author response table 1.

Author response table 1.

The hyperparameter for fine-tuning DNN models with specific question type.

The fine-tuning process yielded a slight reduction in loss (i.e., the negative logarithmic score of the correct option) on the validation set. Specifically, for BERT, the loss decreased from 1.08 to 0.96; for ALBERT, it decreased from 1.16 to 0.76; for RoBERTa, it went down from 0.68 to 0.54. Nevertheless, the fine-tuning process did not improve the prediction of reading time (Author response image 1). A likely reason is that the number of global and local questions for training is limited (local questions: 520; global questions: 280), and similar questions also exist in RACE dataset that is used for the original fine tuning (sample size: 87,866). Therefore, a small number of questions can significantly change the reading strategy of human readers but using these questions to effectively fine-tune a model seems to be a more challenging task.

Author response image 1.

Fine-tuning based on local and global questions does not significantly modulate the prediction of human reading time. Lighter-color symbols show the results for the 3 BERT-family models (i.e., BERT, ALBERT, and RoBERTa) and the darker-color symbols show the average over the 3 BERT-family models. trans_fine: model fine-tuned based on the RACE dataset; trans_local: models additionally fine-tuned using local questions; trans_global: models additionally fine-tuned using global questions.

3) The paper compares the DNN-derived features to word-related features such as frequency and surprisal and reports that the DNN features are predictive even when the others are regressed out (Figure S3). However, these features are operationalized in a way that puts them at an unfair disadvantage when compared to the DNNs: word frequency is estimated from the BNC corpus; surprisal is derived from the same corpus and derived using a trigram model. The BNC corpus contains 100 Million words, whereas BERT was trained on several Billions of words. Relatedly, trigram models are now far surpassed by DNN-based language models. Specifically, it is known that such models do not fit human eyetracking reading times as well as modern DNN-based models (e.g., Figure 2 Dundee in: Wilcox et al, On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior, CogSci 2020). This means that the predictive power of the word-related features is likely to be underestimated and that some residual predictive power is contained in the DNNs, which may implicitly compute quantities related to frequency and surprisal, but were trained on more data. In order to establish that the DNN models are predictive over and above word-related features, and to reliably quantify the predictive power gained by this, the authors could draw on (1) frequency estimated from the corpora used for BERT (BookCorpus + Wikipedia), (2) either train a strong DNN language model, or simply estimate surprisal from a strong off-the-shelf model such as GPT-2.

This concern does not fundamentally cast doubt on the conclusions, since the authors found a clear effect of the task relevance of individual words, which by definition is not contained in those baseline models. However, Figure S3 -- specifically Figure S3C -- is likely to inflate the contribution of the DNN model over and above the text-based features.

Thank you for pointing out these issues. Following the valuable suggestion of the reviewer, we have now 1) computed word frequencies based on BookCorpus and Wikipedia and 2) calculated word surprisal using GPT-2.

“The word features included word length, logarithmic word frequency estimated based on the BookCorpus [62] and English Wikipedia using SRILM [68], and word surprisal estimated from GPT-2 Medium [69].”

These recalculated word frequency and surprisal are correlated with the original measures (word frequency: 0.98; surprisal: 0.59), and the updated results are also closely aligned with those reported in the previous manuscript.

Others:

1) How does the statistical modeling take into account that measures are repeated both within the items (same texts read by different subjects) and within the subjects (some subject read multiple texts)? I only see the items-level repetition be addressed in line 715-721 in comparing between local and global questions, but not elsewhere. The standard approach in the literature on human reading times (e.g. the Wilcox et al paper mentioned above, or ref. 44) is to use mixed-effects regression with appropriate random effects for items and subjects. The same question applies to the calculation of chance accuracy (line 702-709), which is done by shuffling words within a passage. Relatedly, how exactly was cross-validation (line 681) calculated? On the level of subjects, individual words, trials, texts, ...?

Thank you for raising up this issue. In the previous manuscript, the word reading time was averaged across participants. The cross-validation was conducted on the level of texts (i.e., passages). Following the valuable suggestion, we have now separately analyzed each participant and applied the linear mixed effects models.

“Furthermore, a linear mixed effect model also revealed that more than 85% of the DNN attention heads contribute to the prediction of human reading time when considering text features and question relevance as covariates (Supplementary Results).”

“Supplementary Methods To characterize the influences of different factors on human word reading time, we employed linear mixed effects models [5] implemented in the lmerTest package [6] of R. For the baseline model, we treated the type of questions (local vs. global; local = baseline) and all text/task-related features as fixed factors, and considered the interaction between the type of questions and these text/taskrelated features. We included participants and items (i.e., questions) as random factors, each with associated random intercepts…”

Supplementary Results The baseline mixed model revealed significant fixed effects for question type and all text/task-related features, as well as significant interactions between question type and these text/task-related features (Table S7). Upon involving SAR attention, we observed a statistically significant fixed effect associated with SAR attention. When involving attention weights of randomly initialized BERT, the mixed model revealed that most attention heads exhibited significant fixed effects, suggesting their contributions to the prediction of human word reading time. A broader range of attention heads showed significant fixed effects for both pre-trained and fine-tuned BERT.

2) I could not find any statement about code availability (only about data availability). Will the source code and statistical analysis code also be made available?

We have added the code availability statement.

“The code is now available at https://github.com/jiajiezou/TOA.”

3) The theoretical claim, and some basic features of the research, are quite similar to other recent work (Hahn and Keller, Modeling task effects in human reading with neural network-based attention, Cognition, 2023; cited with very little discussion as ref 44), which also considered task-directed reading in a question-answering task and derived task-optimized attention distributions. There are various differences, and the paper under consideration has both weaknesses and strengths when compared to that existing work -- e.g., that paper derived a single attention distribution from task optimization, but the paper under consideration provides more detailed qualitative analysis of the task effects, uses questions requiring more high-level reasoning, and uses more state-of-the-art DNNs.

The paper would benefit from being more explicit about how the work under review provides a novel angle over Ref 44 (Hahn and Keller, Cognition, 2023).

Thanks for bringing up this issue. We have now incorporated a more comprehensive discussion that compare the current study with the recent work conducted by Hahn and Keller:

“When readers read a passage to answer a question that can be answered using a word-matching strategy [45], a recent study has demonstrated that the specific reading goal modulates the word reading time and the effect can be modeled using a RNN model [46]. Here, we focus on questions that cannot be answered using a word-matching strategy (Fig. 1B) and demonstrate that, for these challenging questions, attention is still modulated by the reading goal but the attention modulation cannot be explained by a word-matching model (Fig. S3). Instead, the attention effect is better captured by transformer models than an advanced RNN model, i.e., the SAR (Fig. 3A). Combining the current study and the study by Hahn et al. [46], it is possible that the word reading time during a general-purpose reading task can be explained by a word prediction task, the word reading time during a simple goal-directed reading task that can be solved by word matching can be modeled by a RNN model, while the word reading time during a more complex goal-directed reading task involving inference is better modeled using a transformer model. The current study also further demonstrates that elongated reading time on task-relevant words is caused by counts of rereading and further studies are required to establish whether earlier eye movement measures can be modulated by, e.g., a word matching task.”

4) In Materials&Methods, line 599-636, specifically when "pretraining" is mentioned (line 632), it should be mentioned what datasets these DNNs were pretrained on.

We have now mentioned this in the revised manuscript:

“The pre-training process aimed to learn general statistical regularities in a language based on large corpora, i.e., BooksCorpus [62] and English Wikipedia…”

Author Response

Reviewer 1 (Public Review)

Summary: The authors have made a novel and important effort to distinguish and include different sources of active deformations for fitting C elegans embryo development: cyclic muscle contrac- tions and actomyosion circumferential stresses. The combination and synchronisation of both contributions are, according to the model, responsible for different elongation rates, and can in- duce bending and torsion deformations, which are a priori not expected from purely contractile forces. The model can be applied to other growth processes in initially cylindrical shapes.

Strengths: The model allows us to fit and deduce specific growth patterns, frequencies, and lo- cations of contractions that yield the observed axial elongation during the 240 min of the studied process.

The deformation gradient is decomposed according to muscle and actomyosin activity, which can be distinguished and quantified. An energy-transferring process allows for the retrieval of the nec- essary permanent deformations that embryo development requires.

Weaknesses: Despite the completeness of the model, the explanation of the methodology needs to be improved. Parameters and quantities are not always explained in the main text and are intro- duced on some occasions in an ordered manner. This makes the comprehension and deduction of methodology difficult. There are some minor comments that are listed below. The most important points are:

How are the authors sure that there is a torsional deformation? Without tracking the muscle fibers, bending with respect to different angles for different Zs may yield a shape similar to the one in Figure 6E. Furthermore, it is unclear why the model yields torsion deformation. If material points of actomyosin rings do not change in reference configuration, no helicoidal growth should be happening.

Our torsional deformations were obtained computationally, and the results are plotted in Figure 6 according to our formalism. In our approach, the torsional deformation results from the interaction between the vertical muscles and the circumferential actin network: the muscles bend the cylinder and the bending modifies the direction of the actin fibers, as demonstrated in the experiment.

-The triple decomposition 𝐹 = 𝐹𝑒 ⋅ 𝐺𝑖 ⋅ 𝐺0 seems to complicate the expressions of growth and requires the use of angles alpha and beta due to the initial deformation 𝐺0. Why not use a simpler decomposition 𝐹 = 𝐹𝑒 ⋅ 𝐺, where 𝐺 contains all contributions from actomyosin and muscle contrac- tions in a material frame? This would avoid considering angles alpha and beta.

𝐺0 represents the active strain during the early elongation stage and 𝐺𝑖 during the late elongation stage respectively. Such a decomposition which is not mandatory, allows a better un- derstanding. In addition, due to the late elongation stage, both muscle and actin networks must be considered, and their orientation changes with deformation. Therefore, it is clearer and simpler to express the active strain in terms of alpha and beta angles.

The section "Energy transformation and Elongation" is unclear. Indeed, stresses need to relax, oth- erwise, the removal of muscle and actin activity would send the embryo back to its initial state. How- ever, the rationale behind the energy transfer is not explained. Authors seem to impose 𝑊𝑐 = 𝑊𝑟, and from this deduce the necessary actin contraction after muscle relaxation. Why should energy be maintained when muscle relaxes? Which mechanism physically imposes this energy transfer? Muscle contraction could indeed induce elongation if traction forces at the opposite side of the contracting muscle relax. In fact, an alternative approach for obtaining stress relaxation and axial elongation would be converting part of the elastic deformation 𝐹𝑒 to a permanent deformation 𝐹𝑝.

In this section, we do assume that all the energy accumulated by the muscle contrac- tions will be converted into the energy necessary for elongation, and as our estimate in the article shows, 𝑊𝑐 is indeed greater than 𝑊𝑟, indicating that a significant fraction of 𝑊𝑐 is converted into dissipation and friction, but also into the reorganization of the actin cables. Indeed, elongation of the cylinder induces a significant reduction in the experimentally observed and also in the actin cable density. However, this reduction in cable density is not observed experimentally. Thus, elon- gation requires a reorganization of the actin network, which is part of the energy consumption and which explains the existence of a permanent deformation 𝐹𝑝.

Self contact is ignored. This may well be a shape generator and responsible for bending deforma- tions. The convoluted shape of the embryo in the confined space deserves at least commenting on this limitation of the model.

Thank you for your suggestion. We have considered the effect of contact between C. elegans and the eggshell in the energy dissipation section but we also agree that the self-contact of the worm in confinement will be important. Here, we focus mainly on active filaments: actomyosin and muscle, and we restrict ourselves to a cylindrical shell that is far from the embryo.

Reviewer 2 (Public Review)

Summary

During C. elegans development, embryos undergo elongation of their body axis in the absence of cell proliferation or growth. This process relies in an essential way on periodic contractions of two pairs of muscles that extend along the embryo’s main axis. How contraction can lead to extension along the same direction is unknown.

To address this question, the authors use a continuum description of a multicomponent elastic solid. The various components are the interior of the animal, the muscles, and the epidermis. The different components form separate compartments and are described as hyperelastic solids with different shear moduli. For simplicity, a cylindrical geometry is adopted. The authors consider first the early elongation phase, which is driven by contraction of the epidermis, and then late elongation, where contraction of the muscles injects elastic energy into the system, which is then released by elongation. The authors get elongation that can be successfully fitted to the elongation dynamics of wild-type worms and two mutant strains.

Strengths

The work proposes a physical mechanism underlying a puzzling biological phenomenon. The framework developed by the authors could be used to explain phenomena in other organisms and could be exploited in the design of soft robots.

Weaknesses

1) This reviewer considers that the quality of the writing is poor. Because of this the main result of this work, how elongation is achieved by contraction, remains unclear to me. In the opinion of this reviewer, the work is not accessible to a biologist. This is a real pity because the findings are potentially of great interest to developmental biologists and engineers alike.

We regret that, despite a general introduction and a number of figures, the work does not seem accessible to biologists.

2) The authors assume that the embryo is elastic throughout all stages of development. Is this assumption appropriate? In my opinion, the authors need to critically discuss this assumption and provide justification. Would this still be true for the adult? If so could the adult relax back to the state prior to elongation? The embryo should be able to do that, if the contractility of the epidermis were sufficiently reduced, right?

Soft tissues are elastic, the modeling of soft tissues, even with large deformations, is now well established. The difference between a worm embryo and an adult is first of all the quality of the tissues, their low degree of heterogeneity, the weakness of the muscles and the absence of bones. As for the question of complete relaxation of the stresses, the fact that different components are attached to each other limits complete relaxation. We keep our fingerprints and cortical undula- tions, although they originate from an elastic instability that occurs in fetal life. It never disappears.

The authors impose strains rather than stress. Since they want to understand the final deformation, I find this surprising. Maybe imposing strain or stress is equivalent, but then you should discuss this.

Perhaps, the referee has in mind the question of active strain versus active stress and is concerned about the representation of biological forces such as those produced by actomyosin or muscle. In fact, both exist in morphoelasticity and are, of course, related. Usually, the choice is dictated by the simplicity of deriving quantitative results for comparison with experiments.

4) Does your mechanism need 4 muscle strands or would 2 be sufficient?

First, the 4 muscle strands are consistent with real C. elegans structures, and second, although we assume that two muscles on the same side contract simultaneously, their size and position affect the deformation results. Also, the time period we consider is just before the worm hatches. After that, the worm has to slide on the ground. So efficient muscles are needed.

5) It is sometimes hard to understand, whether the authors are talking about the model or the worm.

It will be corrected in the new version.

Author Response

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

The authors thank the reviewers for their thoughtful and constructive comments. We address each comment below and have uploaded a revised manuscript.

Public Reviews

1) One key point that could use further clarification is how to interpret densities in the reconstruction that do overlap with the template. If the omitted regions can be reliably reconstructed, and the density is smooth throughout, it implies the detected particles are not only (mostly) true positives but also their poses must be essentially correct. Therefore, why cannot the entire reconstruction be trusted, including portions overlapping with the template? In the "Future applications" section, the authors state that in order to obtain a reconstruction that is entirely devoid of template bias, it would be necessary to successively omit parts of the template structure through its entirety. I wonder if that is really necessary and if the presented approach of omitting template portions could be better framed as a "gold-standard" validation procedure.

Our assumption is indeed that the entire reconstruction can be trusted if the omitted features are faithfully reproduced in the reconstruction. We have added a sentence in the discussion to clarify this. However, we think that assessing template bias will still require the omit test (see also our reply below). Also, as discussed in the manuscript, there is likely a little bias left, even if it is not directly visible in the reconstruction. Therefore, if the goal is an entirely unbiased reconstruction, the only way will be to successively omit parts of the template structure throughout the template.

2) In other words, given the compelling evidence provided by the reconstructions in the omitted areas, I find it hard to imagine how the procedure would be "hallucinating" features in the rest of the structure, as the entire reconstruction depends on the same pose and defocus parameters. A possible experiment to test this hypothesis would be to go the opposite way, deliberately adding an unrealistic feature to the bait and checking whether it comes up in the reconstruction, while at the same time checking how it behaves in omitted parts.

Template bias might be generated in different ways. A common situation is the presence of noise, which causes biased deviations of the best template match from their “true” match that would just align the target signal to the template. Another type of bias may occur when there is a mismatch between the template and the detected target. The target may still be detected if there is sufficient structural overlap with the template. Since there might not be a clear “correct” alignment of a mismatching target to the template, the best alignment may again be biased, generating artificial density in the reconstruction. This second case may produce bias that is more pronounced in the mismatching regions. The different origins of bias will have to be investigated more thoroughly in another study. For the present study, however, we maintain that unless there is some assessment of bias in a given location, one cannot completely rule out bias based on the absence of it elsewhere in the reconstruction.

3) When assessing their approach to in situ data (the yeast ribosome), it is intriguing to see that the resolution downgraded from 3.1 to 8 Å when refinement of the particle poses against the current reconstruction was attempted. The authors do provide some possible explanations, such as the reduced signal of the reconstruction at high resolution and the crowded background, but it leaves one to wonder if this means that a 3.1 Å reconstruction could never be obtained from these data by conventional single-particle analysis procedures.

The refinement results with our in situ data do indeed appear to be limited to low resolution when using the conventional single-particle pipeline and software. It might be possible to improve refinement by introducing certain priors, filters and masking functions that are optimized for the increased background and spectral properties of in situ data. Also, we have not tested all available software, and some might perform better than others. It is worth noting that in a different study using our data, by Cheng et al (2023) and cited in our manuscript, the resolution of the refined reconstruction using different software was ~7 Å resolution, i.e., close to what we report here. Finally, refinement of the detected targets against a high-resolution template does work but since it involved the template, we regard this as part of the template matching process.

4) Furthermore, in the section "Quantifying template bias", the authors make the intriguing statement that there can still be some overfitting of noise even in true positives. I understand this overfitting would occur in the form of errors in the pose and defocus estimation, but a clarification would be helpful.

We have added a sentence in the Discussion to clarify where this bias may come from.

5) In the Discussion, the claim that "it is not necessary to use tomography to generate high-resolution reconstructions of macromolecular complexes in cells" is a misconception, at least in part. As demonstrated in works by the same group and others (https://doi.org/10.1016/j.xinn.2021.100166, https://doi.org/10.1038/s41467-023-36175-y, https://doi.org/10.1038/s41586-023-05831-0), 2D imaging of native cellular environments does offer a faster and better way to obtain high-resolution reconstructions compared to tomography. However, tomography provides the entire 3D context of the macromolecules, such as their localization to membranes and the cellular architecture, which can be readily visualized in a tomogram even at low resolution, so methods for structure determination from tilt series data such as subtomogram averaging remain of paramount importance. Most likely, a combination of 2D and 3D imaging approaches will be necessary to retrieve both the highest structural resolution and their cellular context to address biological questions.

We agree and have modified our statement accordingly.

6) The "Materials and Methods" section lacks a description of transmission electron microscopy data collection.

We are sorry for this oversight and have added these details.

7) Finally, the preprint version of this work posted on bioRxiv (https://doi.org/10.1101/2023.07.03.547552) contains the following competing interests statement, which is missing from the submitted version: "The authors are listed as inventors on a closely related patent application named "Methods and Systems for Imaging Interactions Between Particles and Fragments", filed on behalf of the University of Massachusetts."

This is correct. The statement was missing in the first version of the uploaded manuscript and was added after consultation with the eLife editorial office.

8) Quantification of the amount of model bias is then performed using omit maps, where every 20th residue is removed from the template and corresponding reconstructions are compared (for those residues) with the full-template reconstructions. As expected, model bias increases with lower thresholds for the picking. Some model bias (Omega=8%) remains even for very high thresholds. The authors state this may be due to overfitting of noise when template-matching true particles, instead of introducing false positives. Probably, that still represents some sort of problem. Especially because the authors then go on to show that their expectation of the number of false positives does not always match the correct number of false positives, probably due to inaccuracies in the noise model for more complicated images. This may warrant further in-depth discussion in a revised manuscript.

We have added further thoughts regarding the mismatch between expected and actual number of false positives in the Discussion section. A full understanding of the issue likely requires further study, which is currently underway.

9) The authors evaluate the effect of high-resolution 2D template matching on template bias in reconstructions, and provide a quantitative metric for overfitting. It is an interesting manuscript that made me reevaluate and correct some mistakes in my understanding of overfitting and template bias, and I'm sure it will be of great use to others in the field. However, its main point is to promote high-resolution 2D template matching (2DTM) as a more universal analysis method for in vitro and, more importantly, in situ data. While the experiments performed to that end are sound and well-executed in principle, I fail to make that specific conclusion from their results.

We do not see 2DTM as a more universal analysis method for in vitro and in situ data, but as simply as another method that can be used. We have added a sentence in the introduction to clarify this.

10) The authors correctly point out that overfitting is largely enabled by the presence of false-positives in the data set. They go on to perform their in situ experiments with ribosomes, which provide an extremely favorable amount of signal that is unrealistic for the vast majority of the proteome. This seems cherry-picked to keep the number of false-positives and false-negatives low. The relationship between overfitting/false-positive rate and the picking threshold will remain the same for smaller proteins (which is a very useful piece of knowledge from this study). However, the false-negative rate will increase a lot compared to ribosomes if the same high picking threshold is maintained. This will limit the applicability of 2DTM, especially for less-abundant proteins.

The reviewer is correct that the lower SNR of smaller targets poses a fundamental limit to 2DTM. We have stated this in previous studies and have added a sentence in the introduction of the current manuscript to clarify this.

11) I would like to see an ablation study: Take significantly smaller segments of the ribosome (for which the authors already have particle positions from full-template matching, which are reasonably close to the ground-truth), e.g. 50 kDa, 100 kDa, 200 kDa etc., and calculate the false-negative rate for the same picking threshold. If the resulting number of particles does plummet, it would be very helpful to discuss how that affects the utility of 2DTM for non-ribosomes in situ.

The suggested ablation study is a good idea and was reported by Rickgauer et al (2020), cited in our manuscript. We added our own analysis for this dataset in Figure 4-figure supplement 1 and show the proportion of LSUs detected as a function of template mass, indicating detection limit of ~300 kDa. We also added a note in the Results section to explain that the threshold we use to limit false positives means that there are also false negatives, with a rate that depends on their molecular mass.

12) Another point of concern is the dramatic resolution decrease to 8 A after multiple iterations of refinement against experimental reconstructions described in line 159. Was this a local search from the poses provided by 2DTM, or something more global? While this is not a manifestation of overfitting as the authors have conclusively shown, I think it adds an important point to the ongoing "But do we really need tomograms, or can we just 2D everything?" debate in the field, which is also central to the 2D part of 2DTM. Reaching 8 A with 12k ribosome particles would be considered a rather poor subtomogram averaging result these days. Being in the "we need tilt series to be less affected by non-Gaussian noise" camp myself, I wonder if this indicates 2D images are inherently worse for in situ samples. If they are, the same limitations would extend to template matching. In that case, shouldn't the authors advocate for 3DTM instead of 2DTM? It may not be needed for ribosomes, but could give smaller proteins the necessary edge.

We have extensively discussed the advantages and disadvantages of both tomography and 2DTM (Lucas et al, 2021) and think it is not useful to talk in terms of “better” and “worse”. Instead, each technique has its areas of application, and we maintain that a combination of the two may give the best results. The limitation of 8 Å does not apply to reconstructions aligned against high-resolution templates, as demonstrated in the present study. Regarding noise models, there is also need for these in 3DTM, as explained in recent publications: Maurer et al (2023), bioRxiv, doi.org/10.1101/2023.09.06.556487; Cruz-León et al (2023), bioRxiv, doi.org/10.1101/2023.09.05.556310; Chaillet et al (2023), Int. J. Mol. Sci. 24, 13375.

13) Right now, this study is also an invitation to practitioners who do not understand the picking threshold used here and cannot relate it to other template-matching programs to do a lot of questionable template matching and claim that the results are true because templates are "unoverfittable". I think such undesirable consequences should be discussed prominently.

We have added a discussion of this point in the Discussion section.

Recommendations for the authors

1) Lines 58-59: What does "nominally untilted" mean? Has the lamella pre-tilt (milling angle) been taken into account or not? If yes, how?

The lamella milling angle was not taken into account, so there is a tilt built into the sample of about 8° that was not compensated for by a counter-tilt of the microscope goniometer. We have added a note to explain this in the text of the manuscript.

2) Lines 113-114: A brief explanation of the threshold calculation method from Rickgauer et al, 2017 to achieve an expected false positive rate of one per micrograph would be helpful here.

We describe the equation for estimating the false discovery rate later in the manuscript. We have added a note in the text to point the reader to the relevant section of the manuscript.

3) For consistency, it would be interesting to include a plot of the SNR peaks found by 2DTM in the in situ dataset, that could be directly compared to Figure 1 - figure supplement 1B.

We have added this to Figure 2 - figure supplement 1A-C, to directly compare to Figure 1 – figure supplement 1A-C.

4) Showing model-map FSC curves between the density retrieved from the omitted areas and their respective models would provide further evidence not only that they are correct but to what extent.

An FSC calculation would be challenging for small regions, such as side chains and drugs, due to masking artifacts. Moreover, the model was built into an in vitro determined map and was not fit into the in vivo map calculated here. Therefore, deviations between the map and model may reflect differences between the two conditions and may not reflect the agreement of the map to the in vivo structure.

5) Lines 128-130: The figure references are wrong. Here, Figure 1B should probably be Figure 1A (or 1B), and Figure 1C clearly refers to Supplementary Figure 1F (FSC curve).

We have corrected the incorrect figure references.

6) Line 125: Wrong figure reference, Figure 1A here refers to Supplementary Figure 1B (cross-correlation peaks).

We have corrected the incorrect figure references.

7) I haven't been able to find mention of code availability in the manuscript. Given that it is a major outcome of the study, I think it should be provided.

The code is available from the cisTEM repository, github.com/timothygrant80/cisTEM, and an executable version of the program measure_template_bias has been posted for download on the cisTEM webpage, cistem.org. We have added a note in the Methods section to point the readers to these resources.

8) Line 50: "An additional complication of subtomogram averaging for in situ imaging is the selection of valid targets" - This is not specific to subtomogram averaging, but to in situ samples.

We agree and have updated the text to reflect this.

9) Line 77: "if this is true for high-resolution features, which are more susceptible to noise overfitting" - This is not intuitive to me. High-resolution features require more information to be overfitted with a constant set of model parameters, thus making their overfitting harder.

The reviewer is correct that there is more information at high resolution, partially compensating for the low SNR. However, the overall refinement behavior is still dominated by overfitting at high resolution, as we have demonstrated in an earlier publication in Stewart & Grigorieff (2004), Ultramicroscopy 102, 67–84.

10) Line 316: "Baited reconstruction is substantially faster and a more streamlined" - To back this and other similar statements, it would be helpful if the authors provided some time measurements for the execution of their potentially very computationally expensive search.

The current implementation of 2DTM requires 45 GPU hours per template per K3 image to search 13 defocus planes. However, for a comparison, the manual work for annotation, as well as additional processing to align and classify sub-tomograms to generate high resolution averages should also be considered in this comparison. These are highly project-dependent and can exceed the time required for 3DTM manifold. We have clarified this in our Discussion section.

11) Line 319: "We expect focused classification to identify sub-populations to further improve the resolution" - How would this work if refining the 2D data without a high-resolution template resulted in significantly worse resolution even for a ribosome? Or is this meant to be done with prior knowledge of every state?

Classification can be done using existing single particle software. To avoid alignment errors, as described above, particle alignment angles and shifts are fixed during classification. This leaves only the particle occupancy per class to be refined, which appears to lead to good classification. We have added a brief note to explain this strategy. However, since this is not shown in this manuscript, we have not added a more extensive discussion of particle classification.

12) Line 354: "without requiring manual intervention or expert knowledge" - Previous expert knowledge was arguably provided in the form of a high-resolution structure.

We agree with the reviewer and have clarified our statement.

Author Response

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

Reviewer #1 (Public Review)

Summary:

Huang and colleagues present a method for approximation of linkage disequilibrium (LD) matrices. The problem of computing LD matrices is the problem of computing a correlation matrix. In the cases considered by the authors, the number of rows (n), corresponding to individuals, is small compared to the number of columns (m), corresponding to the number of variants. Computing the correlation matrix has cubic time complexity , which is prohibitive for large samples. The authors approach this using three main strategies:

1. they compute a coarsened approximation of the LD matrix by dividing the genome into variant-wise blocks which statistics are effectively averaged over;

2. they use a trick to get the coarsened LD matrix from a coarsened genomic relatedness matrix (GRM), which, with time complexity, is faster when n << m;

3. they use the Mailman algorithm to improve the speed of basic linear algebra operations by a factor of log(max(m,n)). The authors apply this approach to several datasets.

Strengths:

The authors demonstrate that their proposed method performs in line with theoretical explanations.

The coarsened LD matrix is useful for describing global patterns of LD, which do not necessarily require variant-level resolution.

They provide an open-source implementation of their software.

Weaknesses:

The coarsened LD matrix is of limited utility outside of analyzing macroscale LD characteristics. The method still essentially has cubic complexity--albeit the factors are smaller and Mailman reduces this appreciably. It would be interesting if the authors were able to apply randomized or iterative approaches to achieve more fundamental gains. The algorithm remains slow when n is large and/or the grid resolution is increased.

Thanks for your positive and accurate evaluation! We acknowledge the weakness and include some sentences in Discussion.

“The weakness of the proposed method is obvious that the algorithm remains slow when the sample size is large or the grid resolution is increased. With the availability of such as UK Biobank data (Bycroft et al., 2018), the proposed method may not be adequate, and much advanced methods, such as randomized implementation for the proposed methods, are needed.”  

Reviewer #2 (Public Review)

Summary:

In this paper, the authors point out that the standard approach of estimating LD is inefficient for datasets with large numbers of SNPs, with a computational cost of , where n is the number of individuals and m is the number of SNPs. Using the known relationship between the LD matrix and the genomic- relatedness matrix, they can calculate the mean level of LD within the genome or across genomic segments with a computational cost of . Since in most datasets, n<<m, this can lead to major computational improvements. They have produced software written in C++ to implement this algorithm, which they call X-LD. Using the output of their method, they estimate the LD decay and the mean extended LD for various subpopulations from the 1000 Genomes Project data.

Strengths:

Generally, for computational papers like this, the proof is in the pudding, and the authors appear to have been successful at their aim of producing an efficient computational tool. The most compelling evidence of this in the paper is Figure 2 and Supplementary Figure S2. In Figure 2, they report how well their X- LD estimates of LD compare to estimates based on the standard approach using PLINK. They appear to have very good agreement. In Figure S2, they report the computational runtime of X-LD vs PLINK, and as expected X-LD is faster than PLINK as long as it is evaluating LD for more than 8000 SNPs.

Weakness:

While the X-LD software appears to work well, I had a hard time following the manuscript enough to make a very good assessment of the work. This is partly because many parameters used are not defined clearly or at all in some cases. My best effort to intuit what the parameters meant often led me to find what appeared to be errors in their derivation. As a result, I am left worrying if the performance of X-LD is due to errors cancelling out in the particular setting they consider, making it potentially prone to errors when taken to different contexts.

Thanks for you critical reading and evaluation. We do feel apologize for typos, which have been corrected and clearly defined now (see Eq 1 and Table 1). In addition, we include more detailed mathematical steps, which explain how LD decay regression is constructed and consequently finds its interpretation (see the detailed derivation steps between Eq 3 and Eq 4).

Impact:

I feel like there is value in the work that has been done here if there were more clarity in the writing. Currently, LD calculations are a costly step in tools like LD score regression and Bayesian prediction algorithms, so a more efficient way to conduct these calculations would be useful broadly. However, given the difficulty I had following the manuscript, I was not able to assess when the authors’ approach would be appropriate for an extension such as that.

See our replies below in responding to your more detailed questions.

Reviewer #1 (Recommendations For The Authors)

There are numerous linguistic errors throughout, making it challenging to read.

It is unclear how the intercepts were chosen in Figure S2. Since theory only gives you the slopes, it seems like it would make more sense to choose the intercept such that it aligns with the empirical results in some way.

Thanks for your critical evaluation. We do feel apologize some typos, and we have read it through and clarify the text as much as possible. In addition, we included Table 1, which introduces mathematical symbols of the paper.

In Figure S2, the two algorithms being compared have different software implementations, PLINK vs X-LD. Their real performance not only depended on the time complexity of the algorithms (right-side y-axis), but also how the software was coded. PLINK is known for its excellent programming. If we could have programmed as well as Chris Chang, the performance of X-LD should have been even better and approach the ratio m/n. However, even under less skilled programming, X-LD outperformed plink.

Reviewer #2 (Recommendations For The Authors):

Thank you for the chance to review your manuscript. It looks like compelling work that could be improved by greater detail. Providing the level of detail necessary may require creating a Supplementary Note that does a lot of hand-holding for readers like me who are mathematically literate but who don’t have the background that you do. Then you can refer readers to the Supplement if they can’t follow your work.

We fix the problems and style issues as possible as we can.

Regarding the weakness section in the public review, here are a few examples of where I got confused, though this list is not exhaustive.

1) Consider Equation 1 (line 100), which I believe must be incorrect. Imagine that g consists of two SNPs on different chromosomes with correlation rho. Then ell_g (which is defined as the average squared elements of the correlation matrix) would be

ell_g = 1/4 (1 + 1 + rho^2 + rho^2) = (1+rho^2)/2.

But ell_1=1 and ell_2=1 and ell_12=rho^2 (The average squared elements of the chromosome-specific correlation matrices and the cross-chromosome correlation matrix, respectively). So

sum(ell_i)+sum(ell_ij) = 1 + 1 + rho^2 + rho^2 = (1+rho^2)*2.

I believe your formulas would hold if you defined your LD values as the sum of squared correlations instead of the mean, but then I don’t know if the math in the subsequent sections holds. I think this problem also holds for Eq 2 and therefore makes Eqs 3 and 4 difficult to interpret.

Thanks for your attentive review and invaluable suggestions. We acknowledge the typo in calculating the mean in Eq 1, resulting in difficulties in understanding the equations. We sincerely apologize for this oversight. To address this issue and ensure clarity in the interpretation of Eq 3 and Eq 4, we have provided more detailed explanations (see the derivation between Eq 3 and Eq 4).

2) I didn’t know what the parameters are in Equation 3. The vector ell needs to be defined. Is it the vector of ell_i for each chromosomal segment i? I’m also confused by the definition of m_i, which is defined on line 113 as the “SNP number of the i-th chromosome.” Do the authors mean the number of SNPs on the i-th chromosomal segment? If so, it wasn’t clear to me how Eq 2 and Eq 3 imply Eq 4. Further, it wasn’t clear to me why E(b1) quantifies the average LD decay of the genome. I’m used to seeing plots of average LD as a function of distance between SNPs to calculate this, though I’m admittedly not a population geneticist, so maybe this is standard. Standard or not, readers deserve to have their hands held a bit more through this either in the text or in a Supplementary Note.

Thanks for your insightful feedback. When we were writing this paper, our actually focus was Eq 3 and to establish the relationship between chromosomal LD and the reciprocal of the length of chromosome (Fig 6A) – which was surrogated by the number of SNPs, the correlation between ell_i and 1/m_i.

We asked around our friends who are population geneticists, who anticipated the correlation between chromosomal LD (ell) and 1/m. The rationale simple if one knows the very basis of population genetics. A long chromosome experiences more recombination, which weakens LD for a pair of loci. In particular, for a pair of loci D_t=D_0 (1-c)^t. D_t the LD at the t generation, D_0 at the 0 generation, and c the recombination fraction. As recombination hotspots are nearly even distributed along the genome, such as reported by Science 2019;363:eaau8861, the chromosome will be broken into the shape in Author response image 1 (Fig 1C, newly added). Along the diagonal you see tight LD block, which will be vanished in the further as predicted by D_t equation, and any loci far away from each other will not be in LD otherwise raised by such as population structure. Ideally, we assume the diagonal block of aveage size of m×m and average LD of a SNP with other SNPs inside the diagonal block (red) is l_u; and, in contrast, off-diagonal average LD (light red) to be l_uv. This logic is hidden but employed in such as ld score regression and prs refinement using LD structure.

Author response image 1.

But, how to estimate chromosomal LD (ell), which is overwhelming as our friends said! So, the Figure 6A is logically anticipated by a seasoned population geneticist, but has never been realized because of is nightmare. Often, those signature patterns should have been employed as showcases in releasing new reference data, such as HapMap. However, to our knowledge, this signature linear relationship has never been illustrated in those reference data.

If you further test a population geneticist, if any chromosome will deviate from this line (Fig 6A)? The answer most likely will be chromosome 6 because of the LD tight HLA region. However, it is chromosome 11 because of its most completed sequenced centromere. Chr 11 is a surprise! With T2T sequenced population, Chr 11 will not deviate much. We predict!

However, we suspect whether people appreciate this point, we shift our focus to efficient computation of LD—which is more likely understood. We acknowledge the lack of clarity in notation definitions and the absence of the derivation for the interpretation of b1 and b0 for LD decay regression. So, we have added a table to provide an explanation of the notation (see the Table 1) and provided additional derivations, which explained how LD decay regression was derived (see the derivation between Eq 3 and Eq 4). Figure 1C provides illustration for the underlying assumption under LD.

The technique to bridge Eq 2~3 to Eq 4 is called “building interpretation”. It once was one of the kernel tasks for population genetics or statistical genetics, and a classical example is Haseman-Elston regression (Behavior Genetics, 1972, 2:3-19). When it is moving towards a data-driven style, the culture becomes “shut up, calculate”. Finding interpretation for a regression is a vanishing craftmanship, and people often end up with unclear results!

3) In line 135, it’s not clear to me what is meant by . If it is , then wouldn’t the resulting matrix be a matrix of zeros since is zero everywhere except the lower off-diagonal? So maybe it is ? But then later in that line, you say that the square of this matrix is the sum of several terms of the form . Are these the scalar elements of the G matrix? But then the sum is a scalar, which can’t be true since is a matrix.

Thanks for your attentive review. We indeed confused the definition of matrices and their elements, and should refer to the stacked off-diagonal elements of matrix . So, is a vector for variable – the relationship between sample i and j. We assume the reviewer use R software, then corresponds to mean .

See the text between Eq 5 and Eq 6.

“We extract two vectors , which stacks the off-diagonal elements of , and , which takes the diagonal elements of .”

In addition, , so the ground truth is that , but not zero.

To clarify these math symbols, we replace G with K, so as to be consistent with our other works (see Table 1).

To derive the means and the sampling variances for and , the Eq 7 can be established by some modifications on the Delta method as exampled in Appendix I of Lynch and Walsh’s book (Lynch and Walsh, 1998). We added this sentence near Eq 7 in the main text.

Author Response

Reviewer #1:

We thank Reviewer #1 for their review of our manuscript.

Reviewer #1, comment #1: “The authors of this manuscript are from the Canadian, public interest open-science company YCharos.”.

It is important to state that none of the authors work for YCharOS. The YCharOS company has created an open ecosystem consisting of antibody manufacturers, knockout cell lines providers, academics, granting agencies and publishers. The Antibody Characterization Group (participating authors are affiliated to the Department of Neurology and Neurosurgery, Structural Genomics Consortium, The Montreal Neurological Institute, McGill University) works in collaboration with YCharOS to have access to commercial antibodies and knockout cell lines donated by YCharOS’ manufacturer partners.

Reviewer #1, comment #2: In regard to ZENODO antibody characterization reports prepared by this group, Reviewer #1 wrote: “While the results are convincing, they could be more accessible. In the current format, researchers have to download reports for each target and look through all images to identify the most useful antibodies from the images. The reports I reviewed did not draw conclusions on performance. A searchable database that returns validated antibodies for each application seems necessary.”

After careful consideration and consultation with YCharOS industry partners, we decided not to rate the performance of the antibodies tested. It was determined that antibody selection is best left to the user, who should analyze all parameters, including the type of antibody to be chosen (recombinant-monoclonal, recombinant-polyclonal, monoclonal), the species used to generate the antibody, the species predicted to react with the antibody, performance in a specific application, antigen sequences, and antibody cost.

Reviewer #1, comment #3: “A key question is to what extent off-target binding was predictable from the WBs provided by the manufacturers. Thus, how often did the authors find multiple bands when the catalogue image showed a single band and vice versa?”

In many cases, the antibodies were tested on cell lines other than those used by the manufacturers. Given that protein expression is specific to each line, we can't answer this question properly.

Reviewer #1, comment #4: “Cross-reactive proteins will generally not be detected when blots are stained with an antibody reactive with a different epitope than the one used for IP. Possible solutions to overcome this limitation such as the use of mass spectrometry as readout should be discussed (Nature Methods volume 12, pages 725- 731 (2015)”.

Our protocols only inform whether an antibody can capture the intended target, without any evaluation of the extend to the capture of unwanted, cross-reactive proteins. Thus, our data can only be used to aid in selection of the best performing antibodies for IP – our data does not inform profiling of non-specific interactions.

IP/mass spec is an excellent approach for evaluating antibody performance for IP, and authors on this manuscript are experts in proteomics and recognize the importance of this methodology. We have considered implementing IP/mass in our platform. However, there are limitations, such as the cost of the approach and the difficulty of detecting smaller proteins or proteins with a certain amino acid composition (high presence of Cys, Arg or Lys). Fundamentally, we have decided to focus on throughput relative to details in this regard.

Reviewer #1, comment #5: “Performance in immunofluorescence microscopy was performed on cells that were fixed in 4% paraformaldehyde and then permeabilized with 0.1% Triton-X100. It seems reasonable to assume that this treatment mainly yields folded proteins wherein some epitopes are masked due to cross-linking. The expectation is therefore that results from IP are more predictive for on-target binding in IF than are WB results (Nature Methods volume 12, pages725-731 (2015). It is therefore surprising that IP and WB were found to have similar predictive value for performance in IF (supplemental Fig. 3). It would be useful to know if failure in IF was defined as lack of signal, lack of specificity (i.e. off-target binding) or both. Again, it is important to note the IP/western protocol used here does not test for specificity.”

The assessment of antibody performance is biased by how antibodies were originally tested by suppliers. Manufacturers primarily validate their antibody by WB. Thus, most antibodies immunodetect their intended target for WB. Thus, in retrospect, we tested a biased pool of antibodies that detect linear epitopes. Still, we observed that a large cohort of antibodies show specificity for their target across all three applications or for specific combinations of applications. This slightly challenges the idea that antibodies are fit-for-purpose reagents and can recognize either linear or native epitopes - a significant number of antibodies can specifically detect both types of epitope.

Reviewer #1, comment #6: “The authors report that recombinant antibodies perform better than standard monoclonals/mAbs or polyclonal antibodies. Again, a key question is to what extent this was predictable from the validation data provided by the manufacturers. It seems possible that the recombinant antibodies submitted by the manufacturers had undergone more extensive validation than standard mAbs and polyclonals”.

Our antibody manufacturing partners indicated that the recombinant antibodies are more recent products and have been more extensively characterized relative to standard polyclonal or monoclonal antibodies.

The main message is that recombinant antibodies can be used in all applications once validated. Although recombinant antibodies are available for many proteins, the scientific community is not adopting these renewable regents as we believe it should. We hope that the data provided will encourage scientists to adopt recombinant technologies when available to improve research reproducibility.

Reviewer #1, comment #7: “Overall, the manuscript describes a landmark effort for systematic validation of research antibodies. The results are of great importance for the very large number of researchers who use antibodies in their research. The main limitations are the high cost and low throughput. While thorough testing of 614 antibodies is impressive and important, the feasibility of testing hundreds of thousands of antibodies on the market should be discussed in more detail.”

We thank the reviewer for this comment. One of our challenges is to increase the platform's throughput to succeed in our mission to characterize antibodies for all human gene products. We will continue to test antibodies using protocols agreed upon with our partners, commonly used in the laboratory, to ensure that ZENODO reports can serve as a guide to the wider community.

In terms of development our marketing efforts have been substantially accelerated by our new partnership with the journal F1000. We have begun to convert our reports into peer-reviewed papers (20 ZENODO reports were converted into F1000 articles). This conversion allows researchers to find our work via PubMed, and easily cite any study. Producing peer-reviewed articles also further enhances the credibility of our research and our project as a whole: https://f1000research.com/ycharos

Colleagues have published a letter to Nature explaining the problem and our technology platform: (Kahn, et al., Nature, 2023, DOI: https://doi.org/10.1038/d41586-023-02566-w).

This project has been presented worldwide, with a presence at major antibody conferences, such as the annual Antibody Validation meeting in Bath (PSM attended the meeting in September 2023). The authors are organizing a sponsored mini-symposium on antibody validation at the next American Society for Cell Biology (ASCB) meeting in December 2023 (Boston, USA): https://plan.core- apps.com/ascbembo2023/event/6fb928f06b0d672e088c6fa88e4d77fb

Colleagues have prepared petitions addressed to various governmental organizations (US, Canada, UK) to support characterization and validation of renewable antibodies: https://www.thesgc.org/news/support- characterization-and-validation-renewable-antibodies.

Reviewer #2

We thank Reviewer #2 for the review of the antibody characterization reports we have uploaded to ZENODO. A manuscript describing the full standard operating procedures of the platform, which has been used in all reports is in preparation, and should be available on a preprint server before the end of the year. Our protocols were reviewed and approved by each of YCharOS' manufacturer partners. Moreover, a recent editorial describes the platform used here and gives advice on how to interpret the data: https://doi.org/10.12688/f1000research.141719.1)

Reviewer #2, comment #1: “A discussion of how the working concentrations of antibodies are selected and validated is required. Based on the dilutions described in the reports, it seems that dilutions suggested by the manufacturer were used - For LRRK2 it seems that antibody concentrations ranging from 0.06 to over 5 µg/ml for WB were used. Often commercial antibody comes in a BSA-containing buffer making it hard to validate the concentration of the antibody claimed by the manufacturer”.

The concentration recommended by the manufacturer is our starting point. For WB, when the signal is at the level of detectability, we will repeat with a ~5-10 fold increase in antibody concentration. For >80% of the antibody tested, the use of the recommended concentration led to the detection of bands (specific or not to the target protein).

Reviewer #2, comment #2: “In the authors' experience are the manufacturer's concentrations reliable? Additionally, if the information regarding applications provided by the manufacturers is unreliable how do the authors suggest working concentrations for antibodies to be assessed”?

We do not evaluate the concentration of antibodies internally. In the immunoprecipitation experiments, we use 2.0 µg of antibody for each IP, based on the concentration provided by the manufacturers. On Ponceau staining of membranes, we can observe the heavy and light chains of the primary antibodies used, giving an indication of the amount of antibodies added to the cell lysate. In most cases, the intensity of the heavy and light chains is comparable.

Reviewer #2, comment #3: “We understand that it would not be feasible to test every antibody at different concentrations, but this is an issue that should at least be mentioned. An antibody might be put in the wrong performance category solely because of the wrong concentration being used. Ie if an excellent antibody is used at too high a concentration, it may detect non-specific proteins that are not seen at lower dilutions where the antibody still picks up the desired antigen well”.

We agree with Reviewer #2, we do not use an optimal concentration for all tested antibodies. As mentioned previously, the concentration recommended by the manufacturer is our starting point. By testing multiple antibodies side-by-side against a single target protein, we can generally identify one or more specific and selective antibodies. We leave it to users of our reports to optimize the antibody concentration to suit their experimental needs.

Reviewer #2, comment #4: “Do the authors check different WB conditions ie 2h primary antibody with BSA or milk vs. overnight at 4 degrees with BSA or Milk”?

All primary antibodies are always tested in milk overnight at 4 degrees. The overnight incubation is convenient in the timeline of the protocol. All protocols were agreed upon after careful consultation with our partners.

Reviewer #2, comment #5: “Do the authors provide detailed WB protocols that include the description of the electrophoresis and type of gels used, transfer buffer and transfer method and time used, and conditions for all the primary and secondary blotting including times, buffers and dilutions of all antibodies and other reagents”?

This information is included in all ZENODO reports.

Reviewer #2, comment #6: “Do the authors discuss detection approaches- we have noticed for some antibodies there are significant different results using LICOR, ECL and other detection methods, with certain especially weaker antibodies preferring ECL-based methods”.

We only use ECL-based methods.

Reviewer #2, comment #7: “For IPs the amount of antibody needed can also vary-for some we can use 1 microgram or less, but for others, we need 5 to 10 micrograms. The amount of antibody needed to get maximal IP should be stated”.

We use 2.0 ug of antibodies and we have found this to be adequate for lower abundance proteins (e.g. Parkin - https://zenodo.org/records/5747356) and higher abundance proteins (e.g. PRDX6 - https://zenodo.org/records/4730953). Abundance is based on PaxDb.com. For Parkin and PRDX6, we were able to enrich the expected target in the IP and observe depletion in the unbound fraction. Optimization of the IP conditions is left to the antibody users.

Reviewer #2, comment #8: “Doing IPs with commercial antibodies can be very expensive or infeasible if many micrograms are needed especially if only packages of 10 micrograms for several hundred dollars are provided”.

This is a major advantage of the side-by-side comparison: the reader is free to choose between high-performance antibodies from different manufacturers, with varying antibody costs. We also work in partnership with the Developmental Studies Hybridoma Band (DSHB), which supplies antibodies on a cost recovery basis.

Reviewer #2, comment #9: “For IPs it is important to determine the percentage of antigen that is depleted from the supernatant for each IP. We think that this should be calculated and recorded in the Zenodo data. Some antibodies will only IP 10% of antigen whereas others may do 50% and others 80-90%. One rarely sees 100% depletion. For IPs the buffer detergent and salt concentration might also strongly influence the degree of IP and therefore these should be clearly stated”.

In Box 1, we define criteria of success. For IP, “under the conditions used, a successful primary antibody immunocaptures the target protein to at least 10% of the starting material”. Colleagues have written an editorial on how to interpret and analyze antibody performance https://f1000research.com/articles/12-1344).

The cell lysis buffer is a critical reagent when considering IP experiments. We use a commercial buffer consisting of 25 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40 and 5% glycerol (Thermo Fisher, cat. #87787). This buffer is efficient to extract the target proteins we have studied thus far.

Reviewer #2, comment #10: “Whether antibodies cross-react with human, mouse and other species of antigens is always a major question. It is always good to test human and mouse cell lines if possible. If antibodies cross-react in WB, in the authors' experience will they also cross-react for IF and IP”?

The authors started this initiative by focusing on the 20,000 human proteins, defining an end point. We and our collaborators found that most of the cherry-picked selective antibodies for WB for human proteins, which manufacturers claim react with the murine version of the target proteins, were selective for murine tissue lysates.

Indeed, poorly performing antibodies in WB mostly failed IF and IP. However, selective antibodies for IF or specific for IP were generally (>90%) selective for WB.

Reviewer #2, comment #11: “Cell lines express proteins at vastly different levels and it is possible that the selected cell line does not express the antigen or expresses it at very low levels - this could be a reason for wrongly assessing an antibody not working. It would be useful to use cell lines in which MS data has defined the copy number of protein per cell and this figure could be included in the antibody data if available. This MS data is available for the vast majority of commonly used cells”.

We agree with Reviewer #2 that MS data are useful for target protein selection. At the moment, our approach using transcriptomic data provided on DepMap.org proved to be a successful mechanism for cell line selection. We have identified a specific antibody for WB for each target, enabling the validation of expression in the cell line selected.

For some protein targets, the parental line corresponding to the only commercial or academic knockout line available has weak protein expression. We thus needed to generate a KO clone in a second cell line background with high expression, and indeed found that some antibodies which failed in the first commercial line were successful in the new higher-expressing line (e.g CHCHD10 - https://zenodo.org/records/5259992).

Reviewer #2, comment #12: “Some proteins are glycosylated, ubiquitylated or degraded rapidly making them hard to see in WB analysis”.

We used the full gel/membrane length when analyzing antibody performance by WB. Indeed, proteins can show different isoforms and molecular weights compared to that based on amino acid sequence (e.g. SLC19A1 -https://zenodo.org/records/7324605).

Reviewer #2, comment # 13: “We have occasionally had proteins that appear unstable when heated with SDS- sample buffer before WB. For these, we still use SDS-Sample buffer but omit the heating step. I often wonder how necessary the heating step is”.

For WB, samples are heated to 65 degrees, then spun to remove any precipitate.

Reviewer #2, comment # 14: “For IF the methods by which cells are fixed and stained, and the microscope and settings, can significantly influence the final result. It would be important to carefully record all the methods and the microscope used”.

We agree with Reviewer #2 that many parameters influence antibody performance for imaging purposes. We are progressively implementing the OMERO software to monitor any experimental parameters and information (metadata) about the microscope itself.

Reviewer #2, comment # 15: “How do the authors recommend antibodies are stored? These should be very stable, but I have had reports from the lab that some antibodies become less good when stored and others that recommend storing at 4 degrees”.

Antibodies are aliquoted to avoid freeze-thaw cycles and stored at -20 degrees. If it is recommended to store antibodies at 4 degrees, we add glycerol to a final concentration of 50% and store them at -20 degrees.

Reviewer #2, comment # 16: “Would other researchers not part of the authors' team, be able to add their own data to this database validating or de-validating antibodies? This would rapidly increase the number of antibodies for which useful data would be available for. It would be nice to greatly expand the number of antibodies being used in research and this is not feasible for a single team to undertake”.

Yes! We believe that only a community effort can resolve the antibody liability crisis. We partner with the Antibody Registry (antibodyregistry.org - led by co-author Anita Bandrowski). In the Registry, each antibody is labelled with a unique identifier, and third-party validation information can be easily tagged to any antibody. Antibody users are invited to upload information about an antibody they have characterized into the Registry.

Author Response

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

Thank you for your consideration and insightful comments on our article.

We have gone through all the reviewers' comments and addressed all their questions and concerns point by point.

As per their recommendation, we have amended our manuscript by providing more information about the experimental procedure and statistical analysis followed, and removed some analyses with a reduced number of imaging sessions. In addition, as a Resource and Tools article, the claim of our paper has been adjusted to a proof-of-concept paper showing robust and reliable preliminary results. In the meantime, we have provided 3 new Supplementary Figures, including one showing data from all individual animals.

Reviewer #1 (Public Review):

The authors apply a new approach to monitor brain-wide changes in sensory-evoked hemodynamic activity after focal stroke in fully conscious rats. Using functional ultrasound (fUS), they report immediate and lasting (up to 5 days) depression of sensory-evoked responses in somatosensory thalamic and cortical regions.

Strengths: This a technically challenging and proof-of-concept study that employs new methods to study brain-wide changes in sensory-evoked neural activity, inferred from changes in cerebral blood flow. Despite the minor typos/grammatical errors and small sample size, the authors provide compelling images and rigorous analysis to support their conclusions. Overall, this was a very technically difficult study that was well executed. I believe that it will pave the way for more extensive studies using this methodological approach. Therefore I support this study and my recommendations to improve it are relatively minor in nature and should be simple for the authors to address.

Weaknesses: The primary weakness of this paper is the small sample sizes. Drawing conclusions based on the small sham control group (n=2) or 5-day stroke recovery group (n=2), is rather tenuous. One way to alleviate some uncertainty with regard to the conclusions would be to state in the discussion that the findings (ie. loss of thalamocortical function after stroke) are perfectly consistent with previous studies that examined thalamocortical function after stroke. The authors missed some of these supporting studies in their reference list (see PMID: 28643802, 1400649). A second issue that can easily be resolved is their analysis of the 69 brain regions. This seems like a very important part of the study and one of the primary advantages of employing efUS. As presented, I had difficulty seeing the data. I think it would be worthwhile to expand Fig 3 (especially 3C) into a full-page figure with an accompanying table in the Supplementary info section describing the % change in CBF for each brain region.

Other Recommendations for the authors:.

• Since there is variability in spreading depolarizations, was there any trend in the relationship between # SD's and ischemic volume? I know there are few data points but a scatterplot might be of interest.

• For statistical comparisons of 'response curves' in Fig 3 and 4, what exactly was the primary dependent measure: changes in peak amplitude (%) or area under the curve?

• There are several typos and minor grammatical errors in the manuscript. Some editing is recommended.

We thank the reviewer for the comments and suggestion, we have adapted our message to a proof-of-concept paper showing robust and reliable preliminary results. We also thank the reviewer for pointing out important references that support our observation and have added them to our article. We have provided a supplementary full-page version of the current Figure 3C (see Supplementary Figure 3).

Regarding the recommendations, we strongly agree that it would be of interest to link SDs and ischaemia, but unfortunately this can't be done because our experimental design, i.e. narrow cranial window and single static plane, does not allow brain-wide quantification of ischemic volume. This would be possible either by scanning the brain or by using a matrix array (also discussed in the manuscript).

For statistical analysis of the hemodynamic response curves, we have adapted them to compare the area under the curve (AUC). In addition, we have provided a new Supplementary Figure 4 showing the associated values and statistics.

We have edited typos and errors.

Reviewer #2 (Public Review):

Brunner et al. present a new and promising application of functional ultrasound (fUS) imaging to follow the evolution of perfusion and haemodynamics upon thrombotic stroke in awake rats. The authors leveraged a chemically induced occlusion of the rat Medial Cerebral Artery (MCA) with ferric chloride in awake rats, while imaging with fUS cerebral perfusion with high spatio and temporal resolution (100µm x 110µm x 300µm x 0.8s). The authors also measured evoked haemodynamic response at different timepoints following whisker stimulation.

As the fUS setup of the authors is limited to 2D imaging, Brunner and colleagues focused on a single coronal slice where they identified the primary Somatosensory Barrel Field of the Cortex (S1BF), directly perfused by the MCA and relay nuclei of the Thalamus: the Posterior (Po) and the Ventroposterior Medial (VPM) nuclei of the Thalamus. All these regions are involved in the sensory processing of whisker stimulation. By investigating these regions the authors present the hyper-acute effect of the stroke with these main results:

• MCA occlusion results in a fast and important loss of perfusion in the ipsilesional cortex.

• Thrombolysis is followed by Spreading Depolarisation measured in the Retrosplenial cortex.

• Stroke-induced hypo-perfusion is associated with a significant drop in ipsilesional cortical response to whisker stimulation, and a milder one in ipsilesional subcortical relays.

• Contralesional hemisphere is almost not affected by stroke with the exception of the cortex which presents a mildly reduced response to the stimulation.

In addition, the authors demonstrate that their protocol allows to follow up stroke evolution up to five days post-induction. They further show that fUS can estimate the size of the infarcted volume with brilliance mode (B-mode), confirming the presence of the identified lesional tissue with post-mortem cresyl violet staining.

Upon measuring functional response to whisker stimulation 5 days after stroke induction, the authors report that:

• The ipsilesional cortex presents no response to the stimulation

• The ipsilesional thalamic relays are less activated than hyper acutely

• The contralesional cortex and subcortical regions are also less activated 5d after the stroke.

These observations mainly validate the new method as a way to chronically image the longitudinal sequelae of stroke in awake animals. However, the potentially more intriguing results the authors describe in terms of functional reorganization of functional activity following stroke appear to be preliminary, and underpowered ( N = 5 animals were imaged to describe hyper-acute session, and N = 2 in a five day follow-up). While highly preliminary, the research model proposed by the author (where the loss of the infarcted cortex induces reduces activity in connected regions, whether by cortico-thalamic or cortico-cortical loss of excitatory drive), is interesting. This hypothesis would require a greatly expanded, sufficiently powered study to be validated (or disproven).

We thank the reviewer for the careful and accurate description of our work. We have addressed all the comments, recommendations and concerns raised by providing details of the experimental procedure and statistical analysis followed, and by removing some analyses associated with a reduced number of imaging sessions (at d5, n=2).

Reviewer #3 (Public Review):

The authors set out to demonstrate the utility of functional ultrasound for evaluating changes in brain hemodynamics elicited acutely and subacutely by the middle cerebral artery occlusion model of ischemic stroke in awake rats.

Functional ultrasound affords a distinct set of tradeoffs relative to competing imaging modalities. Acclimatization of rats for awake imaging has proven difficult with most, and the high quality of presented data in awake rats is a major achievement. The major weakness of the approach is in its being restricted to single-slice acquisitions, which also complicates the registration of acquisition across multiple imaging sessions within the same animal. Establishing that awake imaging represents an advancement in relation to studies under anesthesia hinges upon the establishment of the level of stress experienced by the animals in the course of imaging, i.e., requires providing data on the assessment of stress over the course of these long imaging sessions. This is particularly significant given how significant a stressor physical restraint has been established to be in rodent models of stress. Furthermore, assessment of the robustness of these measurements is of particular significance for supporting the wide applicability of this approach to preclinical studies of brain injury: the individual animal data (effect sizes, activation areas, kinetics) should thus be displayed and the statistical analysis expanded. Both within-subject, within/across sessions, and across-subjects variability should be evaluated. Thoughtful comments on the relationship between power doppler signal and cerebral blood volume are important to include and facilitate comparisons to studies recording other blood volume-weighted signals. Finally, the contextualization of the observations with respect to other studies examining acute and subacute changes in brain hemodynamics post focal ischemic stroke in rats is needed. It is also quite helpful, for establishing the robustness of the approach, when the statistical parametric maps are shown in full (i.e. unmasked).

We would like to thank the reviewer for the comments, recommendations and concerns he/she/they raised. We have addressed all the points to clarify our article and make it more relevant and informative for readers.

Reviewer #2 (Recommendations For The Authors):

The work described by Brunner et al is primarily a methodological paper, with potentially interesting, yet not robust enough, novel biological insight into the mechanisms of stroke. Nonetheless, the method employed is interesting and potentially well-validated.

General comments/suggestions

1- One potential concern I have is related to the relatively low sample size used, with n=5 for the main results and only n=2 for the follow-up after 5d. I am not sure much can be generalized using only two animals in any research study and this N = 2 dataset should probably be removed entirely from the study. Moreover, I found the statistical methods used were only superficially described, which prevented me from assessing whether the results reported by the authors are biologically relevant or not (including some significant differences in rCBV well below 1% estimated over two individuals).

We fully agree with the reviewer’s comment and balanced our claim by considering this work as a proof-of-concept on brain imaging of multiple aspects of stroke hemodynamics (ischemia, spreading depolarization-like events, cortico-thalamic functions) in awake head-fixed rats. Therefore, we attenuated our message along the entire manuscript to prevent misunderstanding and over statement (e.g., Lines 356, 441, 455), we also remove statistics from the analysis at d5 post-stroke, see Figure 4 and associated paragraph from Line 356.

2- Based on their investigations, the authors propose a model where the loss of infarcted cortex induces reduced activity in connected regions, whether by cortico-thalamic or cortico-cortical loss of excitatory drive. This is an intriguing framework but this hypothesis would require a more complete, well-powered study to be substantiated.

I think a clear recognition of the fact that these findings are just preliminary and not validated should be more explicitly reported. I also marginally note here that these results are in contrast with previous reports from the same team where occlusion of the MCA induced increased response to whisker stimulation in anaesthetised rats. These contradictory findings are not discussed in this manuscript.

As mentioned above, we explicit more on the proof-of-concept proposed in this work as well as clearly stating on the preliminary aspect of the findings described in this work. As mentioned above, we attenuated our message along the entire manuscript to prevent misunderstanding and over statement (e.g., Lines 348, 433, 447), we also remove statistics from the analysis at d5 post-stroke, see figure 4 and associated paragraph from Line 348.

We thanks the reviewer for pointing out the missing link with our previous work performed under anesthesia. We therefore provided a discussion point on this contradictory finding (Line 441).

3- In a previous study from the same group perfusion was imaged in 3D either by means of a motorized probe or by using a 2D matrix arrays. It would be interesting to discuss why a 2D approach was chosen in this study over those previous methods.

Indeed, brain-wide coverage would be of great interest in such experiment context. As mentionned by the reviewer, two strategies can be used:

• One can scan the brain using a motorized probe as performed for different purposes by Sieu et al., Nature Methods, 2015; Hingot, Brodin et al., Theranostics 2020; Macé et al., Neuron 2019 and also by our group in Sans-Dublanc, Chrzanowska et al., Neuron, 2022; Brunner et al. Frontiers in Neuroscience 2022 and Brunner et al., JCBFM 2023. (This list of publication is not exhaustive).

• A second approach aims at using a 2D matrix array to capture functions at brain-wide scale. So far, this strategy has been employed in a couple of studies (Rabut et al., Nature Methods, 2019 and Brunner, Grillet et al., Neuron, 2020).

The strategy consisting of scanning (manually or using a motor) strongly limits investigation on brain functions, as performing an accurate covering of the functional regions requires an extensive and time-consumming scanning: brain functions must be addressed several time to capture a reliable and robust signal for all the brain section scanned (see Brunner et al., 2022). Unfortunately, this strategy prevents us to accurately capture other brain hemodynamics like the dynamic of the ischemia or the spreading depolarization event.

On the other hand, the volumetric functional ultrasound imaging (vfUSI) would be suited for brain-wide coverage capturing large-scale brain functions (see Brunner, Grillet et al. Neuron 2020) and hemodynamic events (see Rabut et al., Nature Methods, 2019) but at the cost of the resolution, frame rate and larger cranial window. Unfortunately, this technology was not available when this work was conducted.

Such experimental opportunities have been suggested at the end of the manuscript: “To overcome such limitation, one can extend the size of the cranial window to allow for larger scale imaging either by sequentially scanning the brain27,28,31,32,59,69,71,72, or by using the recently developed volumetric fUS which provides whole-brain imaging capabilities in anesthetized73 and awake rats30.“

4- Overall the registration scheme seems suboptimal which ultimately questions the specificity of the findings in thalamic regions. It would be interesting to validate this procedure, especially the probe repositioning five days after the stroke.

Positioning was not difficult part of this experiment. First, all head posts were implanted in the same position relative to the skull references bregma and lambda. Second, the head fixation ensures the same placement of the headpost for all animals. Finally, fine adjustement of the ultrasound probe position were done using a micromanipulator by finding key landmarks from the µDoppler image. In practice, minimal adjustements were needed to find back the same imaging plane. We provide additional information about the positionning in the Materials and Methods section.

New text – Line 126: “Positionning.

The mechanical fixation of the head-post ensures an easy and repeatabe positionning of the ultrasound probe across imaging session. The ultrasound probe is indeed fixed to a micromanipulator enabling light adjustements To find the plane of interest (containing both S1BF and thalamic relays: bregma - 3.4mm), we used brain landmarks (e.g., surface of the brain, hippocampus, superior sagittal sinus, large vessels). Note that as the headpost was carefully placed in the same position relative to the skulls landmarks (bregma and lambda), the position of the region of interest was minimal across animals.”

Second, at d5 post-stroke, we positionned the ultrasound probe over the imaging window as described in the Materials and Methods section and use brain landmarks from baseline/post-stroke image to maximize the position of brain image. We better detail the procedure followed.

Original text: “First, we used the vascular markers and the shape of the hippocampus31,32 to find back the coronal cross-section imaged during the pre-stroke session. Five days after the MCA occlusion,….”

New text – Line 360 :“Five days after the MCA occlusion, we first placed the ultrasound probe over the imaging window and adjusted its position (using micromanipulator) to find back the recording plane from Pre-Stroke session using Bmode (morphological mode) and µDoppler imaging using brain vascular landmarks (i.e., vascular patterns, brain surface and hippocampus34,35; see Figure 2B).”

More detailed questions/comments/suggestions

Methods

ARRIVE methodology

• Point 2b: sample size is not adequately explained, especially the use of n = 2 animals for 5d follow up

We have explicited the sample size by adding a short paragraph at the beginning of the Results section. We also make the Supplementary Table 1 more accurate. New text – Line 239: “Animals

Report on animal use, experimentation, exclusion criteria can be found in Supplementary Table 1. Rat#1 was excluded after the control session as the imaging window was too anterior to capture both cortical and thalamic responses. Ra#2 was excluded as hemodynamic responses were inconsistent during baseline (pre-stroke) period. Rat#3 showed early post-stroke reperfusion and was excluded from stroke analysis, the control session (pre-stroke) from Rat#3 was analyzed.”

• Point 7: statistical methods: The quantification used to assess significant differences in stimulation traces is poorly described.

We have amended the Materials and Methods section about statistics and provided Supplementary Figure 4.

New text – Line 221: “Activated brain regions were detected from hemodynamic response time-courses using GLM followed by t-test across animals as proposed in Brunner, Grillet et al.,34. The area under the curve (AUC) from hemodynamic response time-courses was computed for individual trials in S1BF, VPM and Po regions, for all the periods of the recording and for all rats included in this work. AUC were compared and analysed using a non-parametric Kruskal-Wallis test corrected for multiple comparison using a Dunn’s test. Tests were performed using GraphPad Prism 10.0.1. “

Functional Ultrasound Imaging acquisition

• References 26 and 28 imply 2.5Hz and 2Hz acquisition rates, respectively. Why does the same method result in a 1.25Hz acquisition rate here? Can you confirm the same spatial resolution in these conditions?

The spatial resolution is independent of the temporal resolution (frame rate). The spatial resolution depends on the resolution of the compound image and the temporal resolution is given by the number of compound images to generate a single Doppler image (exposure time). By increasing the number of compound images, the frame rate decreases while increasing the signal to noise ratio and sensistivity. For some work, a pause between 2 frames is used (mostly due to technical limitations in the software (processing time , or execution of a real-time display/processing by the user), however this reduces the frame rate.

Author response table 1.

Comparing with the sequences used in references 26 and 28, we have the following timing parameters

In this work, we decided to reduce the frame rate to have less images but with higher SNR. The 0.3s were added by technical considerations in this specific implementation.

New text – Line 158:“ To obtain a single vascular image we acquired a set of 250 compound images in 0.5s, an extra 0.3s pause is included between each image to have some processing time to display the images for real-time monitoring of the experiment. “

Activity Maps

• How is the use of a 40s window motivated?

The 40s window has been choosen to better compare hemodynamic responses to either left or right whisker stimulation and centered the period of interest on the start of the stimulation. Original text:” Pre- and post-stroke recordings are reshaped in shorter 40-s sessions, i.e., 50 frames, …”

New text – Line 206:“ Pre- and post-stroke recordings are reshaped in 40-s sessions, i.e., 50 frames, centered on the start of the stimulation (at 20s), …”

• I think the manuscript would benefit from the use of an established, event-based GLM for activity mapping.

We thank the reviewer for this suggestion, here we used a z-score for activity mapping that is largerly established in the neuroimaging realm.

• The statistical thresholds used should account for multiple comparisons.

We have amended the Materials and Methods section, and figure captions about statistics and provided Supplementary Figure 4.

Statistical analyses

• Overall this section is only superficially described, and lacks detailed information.

We have amended the Materials and Methods section about statistics and provided Supplementary Figure 4.

New text – Line 221 : “Activated brain regions were detected from hemodynamic response time-courses using GLM followed by t-test across animals as proposed in Brunner, Grillet et al.,34. The area under the curve (AUC) from hemodynamic response time-courses was computed for individual trials in S1BF, VPM and Po regions, for all the periods of the recording and for all rats included in this work. AUC were compared and analysed using a non-parametric Kruskal-Wallis test corrected for multiple comparison using a Dunn’s test. Tests were performed using GraphPad Prism 10.0.1. “

• Are average rCBV changes referred to in the 40s window?

The rCBV changes are referring to the pre-stimulation baseline. We have modified the text accordingly (Line 206).

• Were normality and variance equality requirements verified in the group with n=2?

Based on reviewers comment’s on the limited amount of recording at 5d, we have decided to remove this statistical analysis. The manuscript, figure and caption were corrected accordingly.

• There is no method for cresyl violet staining

We thank the review for highlighting this omission. We have provided a paragraph in the Materials & Methods section detailling the histology procedure – Line 228:

“Histopathology

Rats were killed 24hrs after the occlusion for histological analysis of the infarcted tissue. Rats received a lethal injection of pentobarbital (100mg/kg i.p. Dolethal, Vetoquinol, France). Using a peristaltic pump, they were transcardially perfused with phosphate-buffered saline followed by 4% paraformaldehyde (Sigma-Aldrich, USA). Brains were collected and post-fixed overnight. 50-μm thick coronal brain sections across the MCA territory were sliced on a vibratome (VT1000S, Leica Microsystems, Germany) and analyzed using the cresyl violet (Electron Microscopy Sciences, USA) staining procedure (see Open Lab Book for procedure). Slices were mounted with DPX mounting medium (Sigma-Aldrich, USA) and scanned using a bright-field microscope.”

Results 1: Real time imaging of stroke induction in awake rats

• Why is the window so narrow in the anteroposterior direction?

The imaging window was defined based on the brain regions investigated in this work, meaning the primary somatosensory cortex (S1BF) and the ventroposterior medial thalamic relay (VPM). From Paxinos atlas, a position of interest is located at Bregma -3.4mm. The cranial window was performed accordingly, and restricted couple of mm to avoid non-needed procedure and brain exposure. We added a new sentence in the Materials & Methods section – Line 116: “This cranial window aims to cover bilateral thalamo-cortical circuits of the somatosensory whisker-to-barrel pathway.”

• What validation was employed for the habituation protocol? Are animals stressed by the procedure? Do you have cortisol data to show? Ar animal weights throughout the procedure?

The habituation protocol employed in this work follows recommandations from the expert in the field and peers (Martin et al., Journal of Neuroscience Methods, 2002; Martin et al., Neuroimage 2006; Topchiy et al., Behav Brain Res 2009). We have amended the corresponding paragraph in the Materials & Methods section detailling the habituation procedure:

Original text: “Body restraint and head fixation.

Rats were habituated to the workbench and to be restrained in a sling suit (Lomir Biomedical inc, Canada), progressively increasing the restraining period from minutes to hours33,34. After the headpost implantation (see below), rats were habituated to be head-fixed while restrained in the sling. The period of fixation was progressively increased from minutes to hours. Water and food gel (DietGel, ClearH2O, USA) were provided along the habituation session. Once habituated, the cranial window for imaging was performed as described below (Figure 1A-C).”

New text - Line 90:“ Body restraint and head fixation.

The body restraint and head fixation procedures are adapted from published protocols and setup dedicated for brain imaging of awake rats39–41. Rats were habituated to the workbench and to be restrained in a sling suit (Lomir Biomedical inc, Canada) by progressively increasing restraining periods from minutes (5mins, 10mins, 30mins) to hours (1 and 3hrs) for one or two weeks. The habituation to head-fixation started by short (5 to 30s) and gentle head-fixation of the headpost between fingers. The headpost was then secured between clamps for fixation periods progressively increased following the same procedure as with the sling. For both body restraint and head fixation, the initial struggling and vocalization diminished over sessions. Water and food gel (DietGel, ClearH2O, USA) were provided for all body restraint and head-fixation habituation sessions. Once habituated, the cranial window for imaging was performed as described below (Figure 1A-C).”

• The observation of contralateral oligemia is based only on RSG traces.

We provided contralesional perfusion changes for all regions in Supplementary Figure 1.

• The spatial and temporal distribution of Bmode measured hyperechogenicity is surprising and should be discussed. Reference 29 describes for instance non-overlap with an area of hypo-perfusion. Overlap between hypo-perfused and infarct volumes should be systematically investigated and coregistered with histology. Moreover, reference 40, while using a different model, presents hyperechogenicity at 5h.

The B-mode images in Figure 2B are presented as an illustration of the potential morphological changes detected at different timepoint. However, our study focuses on functional responses and not on the evolution of the morphological changes. Indeed, this Bmode images remain difficult to interpret as they show a structural reorganization at the level of the ultrasound scatterers which has not been directly linked with tissue infarction, oedema, orother histological conditions.

Regarding the reference 40, the authors found an hyper-echogenicity at 5h a time window is not covered by our protocol. In reference 29, we indeed detailed a mismatch between the µDoppler images and histopathology. As suggested by the reviewer, seeking for other potential mismatchs/overlaps between Bmode/µDoppler and histopathology is an interesting field on investigation, but remains out of the scope of this work.

Results 3: Delayed alteration of the somatosensory thalamocortical pathway

• These results are underpowered and as such should probably be removed entirely from the paper (or substantiated with greater Ns of animals). Based on reviewers comment’s on the limited amount of recording at 5d, we have decided to remove this statistical analysis. The manuscript, figure and caption were corrected accordingly.

• If I am not mistaken, reference 28 describes a protocol for awake mouse imaging, and thereby does not introduce any hippocampal landmark allowing effective positioning of the probe.

We thanks the reviewer for this comment. While not used in the figure detailling image registration in reference 28, step 42 (page 17) from the protocol mentions the use of hippocampal landmark to position of the imaged brain to the atlas. The hippocampal landmark is also used in Brunner et al., JCBFM 2023, we have added this reference which is more appropriate to this work (i.e., rat model, digitalized paxinos atlas, linear ultrasound transducer).

• Significant difference in ispsilesional VPM with post-stroke period looks spurious.

We have amended the Materials and Methods section about statistics and provided Supplementary Figure 4.

Discussion:

The sentence "might result from the direct loss of the excitatory corticothalamic feedback to the VPM" should be moderated in the absence of electrophysiology support. Such a decrease could be explained by reduced perfusion due to the challenge.

The reviewer is right and we believe the tense used in the sentence already balance the claim. However, we clarified on how such result could be better validated.

Original text: “Further work will need to dissect the complex and long-lasting post-stroke alterations of the functional whisker-to-barrel pathway, including at the neuronal level, as fUS only reports on hemodynamics as a proxy of local neuronal activity27,28,60,66–68“

New text – Line 445: “Therefore, further studies will be needed to accurately dissect the complex and long-lasting post-stroke alterations of the functional whisker-to-barrel pathway, including at the neuronal level by direct electrophysiology recordings and imaging, as fUS only reports on hemodynamics as a proxy of local neuronal activity30,31,63,74–76.“

Figure 2

• Panel B would be more informative if presented as an average.

The aim of this figure is to show the raw data of a typical case. Averaging µDoppler images wouldn’t be illustrative as individual vessels will not be visible anymore. Because the vessels are in different positions from one animal to another, an average image would be blurred.

• Panel C lacks contralateral S1BF trace.

We have provided contralesional perfusion changes for all regions in Supplementary Figure 1.

• Methods for detection of SDs refer to non-peer-reviewed reference 29, where SD is defined as 50% over baseline level. What is the actual threshold/method used to define a SD in this study?

We better detailled this procedure in the Materials & Methods section - Line 195: “The detection of hemodynamic events associated with spreading depolarizations (SDs) was performed based on the temporal analysis of the rCBV signal in the retrosplenial granular (RSGc) and dysgranular (RSD) cortices of the left hemisphere (ipsi-lesional). SDs were defined as transient increase of rCBV signal (+25%) detected with a temporal delay of <10 frames (i.e., 8secs) between the two regions of interest, validating both the hyperemia and spreading features of hemodynamic events associated with spreading depolarizations.”

• For panel F, a measure of variance would be more suited to show stereotypic profile across animals as the number of SDs varies between animals.

Figure 2F indeed shows the average profile of hemodynamic events associated with spreading depolarizations (black line) with the variance (95% confidence interval error bands in gray). We have adjusted the corresponding figure caption to make this information more clear.

Figure 3

• The exact stimulation employed is not clear as the methods describe a 1.33 min delay between two whisker pad stimulations, but the figure reports 40s. The description is thereby ambiguous. We thank the reviewer for pointing out this potiential confusion which allowed us to correct a mistake

• The effective delay between two stimulations delivered to the whisker pads is 40 seconds

• The effective delay between two stimulations delivered to the same whisker pad is 80 seconds from start to start or 75 seconds from end to start.

The text was amended accordingly in line 144: “Thus, the effective delay between two stimulations delivered to the same whisker pad is 80 seconds from start to start.“

• In panel B the choice of colormap and transparency for template overlay is not explained and is confusing given the employed threshold of 1.6. Which mask was used to overlay the activation map on the template? Why black color to represent a supposedly significant difference?

We thank the reviewer for pointing out this potiential confusion. We have adjusted the colormap in Figures 3 and 4.

• The pre-stroke thalamic response is clearly localized in VPM for left stimulation, while it overlaps VPM and Po for the right stimulation. This questions the accuracy of the employed registration scheme and consequently the choice of these ROIs, which appear quite small as compared to the resolution and this positioning precision.

We see the point of the reviewer, here the apparent difference because the brain is slighly tilted. By adjusting the angle for both activity maps (see Author response image 1) we confirm that both maps are very similar including the for activated areas VPM and Po.

Author response image 1.

• It would be interesting to see the same activation maps for all animals in supplementary.

We have provided the Supplementary Figure 5 that contains both ipsilateral and contralateral responses to whiskers stimulation (from both left and right pads) for all trials and all rats included in this work.

• Looking at panel C, more cortical regions seem to respond to the stimulation above S1BF.

The reviewer is right and we have indeed mentioned this point several times in the original manuscript in:

• the result section: “We also detected significant increase of activity in S2, AuD, Ect (*p<0.0001) and PRh (p<0.001) cortices and VPL nucleus (**p<0.01; the list of acronyms is provided in Supplementary Table 2), brain regions receiving direct efferent projections from the S1BF45,48,49, VPM or Po nuclei50–52.”

• the caption of Figure 4: “S1BF, S2, AuD, VPM, VPL and Po regions are brain regions significatively activated (all pvalue<0.01; GLM followed by t-test.”

• the conclusion section : “Functional responses to mechanical whisker stimulation were detected in several regions relaying the information from the whisker to the cortex, including the VPM and Po nuclei of the thalamus, and S1BF, the somatosensory barrel-field cortex. Responses were also observed in the S2 cortex involved in the multisensory integration of the information43,44,61, the auditory cortex as it receives direct efferent projection from S1BF45,61, and the VPL nuclei of the thalamus connected via corticothalamic projections45.“

• It would be interesting to see bilateral traces as supplementary figures.

We have provided the Supplementary Figure 5 that contains both ipsilateral and contralateral responses to whiskers stimulation (from both left and right pads) for all trials and all rats included in this work.

• In both panels C and D, n=5 is reported, but methods state the use of 7 animals. Please clarify how animals have been used in the different studies

We have clarified the report on animal use and amended the Supplementary Table 1 accordingly.

• In Panel D, the 95% CI intervals seem particularly narrow. Might this be the result of considering multiple trials as independent events? A GLM analysis would avoid this statistical fallacy.

We have provided the Supplementary Figure 5 that contains both ipsilateral and contralateral responses to whiskers stimulation (from both left and right pads) for all trials and all rats included in this work. The statistical analysis has been adjusted (see Materials and Methods) and completed with a Supplementary Figure 4

Figure 4 - See comments above for Figure 3

We have adjusted the Figure 3 accordingly to reviewer’s suggestions

Reviewer #3 (Recommendations For The Authors):

1) Introduction: Given the emphasis on the awake state, it would be helpful to note that a significant portion of strokes occur during sleep - as well as comment on its hemodynamic difference with respect to an awake state.

We agree with the reviewer on the remark that some strokes occur during sleep phase. However, here the awake state, which has been poorly addressed in the litterature, is opposed to anesthesia a condition largerly used to investigate brain functions after stroke. We added a point and corresponding references about wake-up stroke, see Line 49.

2) The effects of anesthetics on stroke are quite variable and the literature data on the topic is rather divergent: it would be helpful for the introduction to reflect the large level of discord in the literature and the wide-ranging mechanisms of action of different anesthetics.

We thank the reviewer for this comment. We have completed our original sentence in the introduction to better reflect the various effects of anesthetics on stroke, see Line 50

3) The reference list (14-17) to other studies of brain hemodynamic changes post ischemic stroke is egregiously short. Please expand. Similarly, the list of citations to other functional ultrasound rodent studies in the literature (23-24) is misleading: other groups have published similar work and ought to be cited.

We thank the reviewer for this comment and added complementary references. However, we believe that the references 14-17 pointed by the reviewer are not only refering to brain hemodynamic changes but mostly on network and function as stated in the manuscript. Regarding references on fUS (23-24) mentioned by the reviewer, we did not limited our citation on functional ultrasound imaging to those 2 articles but on 15+ from 4 different research groups.

4) It would be helpful if the authors used "spreading depolarization" the way it has been utilized in the many decades of research on them in the literature, namely, as waves of hyper/hypoactivity in the electrophysiological signals. Please use a distinct term to refer to waves of changes in the hemodynamic state.

We have amended the terminology used in the manuscript. “Spreading depolarization” has been replaced by “hemodynamic events associated with spreading depolarizations” or similar.

5) Why is this investigation restricted to male rats?

As a proof of concept, we did not performed experiments in female rats. We agree that further investigation would require a gender mix. We added a line in the discussion.

New text – Line 455:” Finally, it is important to note that this proof-of-concept work did not specifically focus the impact of sex dimorphism on the stroke or early behavioral outcomes following the insult that would greatly enhance the translational value of such preclinical stroke study80.”

6) Were the animals tested during their active phase? If not, why not, and what are the implications of testing their responses during the sleep phase?

We think there is a misunderstanding here as we investigated brain functions in awake head-fixed rats. Therefore, the sleep/active phases were not investigated neither mentioned in the manuscript.

7) How is the level of stress monitored/established?

In this work, we followed established procedure used to reduce stress and disconfort of the rats all along the experiment. The procedure used is now better detailled in the Materials and Methods section. However, the level of stress was not monitored, and would be of interest to considere in future experiments.

8) What are the sequelae of stress on brain hemodynamics, especially given 1-4 hour long sessions.

This is a good remark. While we cannot state on how the stress impacts brain hemodynamics, the data extracted show that hemodynamics reponse functions were stable and robust over hour-long recording (see control and pre-stroke sessions in Supplementary Figure 5).

9) How is the animal prepared for stroke induction? In general, the methodological steps surrounding animal handling and preparation are exceedingly terse.

We provided more details about the handling and preparation of the rats in the Materials and Methods section.

Original text: “Body restraint and head fixation.

Rats were habituated to the workbench and to be restrained in a sling suit (Lomir Biomedical inc, Canada), progressively increasing the restraining period from minutes to hours33,34. After the headpost implantation (see below), rats were habituated to be head-fixed while restrained in the sling. The period of fixation was progressively increased from minutes to hours. Water and food gel (DietGel, ClearH2O, USA) were provided along the habituation session. Once habituated, the cranial window for imaging was performed as described below (Figure 1A-C).”

New text - Line 90:“ Body restraint and head fixation.

The body restraint and head fixation procedures are adapted from published protocols and setup dedicated for brain imaging of awake rats39–41. Rats were habituated to the workbench and to be restrained in a sling suit (Lomir Biomedical inc, Canada) by progressively increasing restraining periods from minutes (5mins, 10mins, 30mins) to hours (1 and 3hrs) for one or two weeks. The habituation to head-fixation started by short (5 to 30s) and gentle head-fixation of the headpost between fingers. The headpost was then secured between clamps for fixation periods progressively increased following the same procedure as with the sling. For both body restraint and head fixation, the initial struggling and vocalization diminished over sessions. Water and food gel (DietGel, ClearH2O, USA) were provided for all body restraint and head-fixation habituation sessions. Once habituated, the cranial window for imaging was performed as described below (Figure 1A-C).”

10) What is the reproducibility of the chemo-thrombotic model timeline? What are its limitations?

We have provided more information on the chemo-thrombotic model and its limitations in the discussion section to discuss

New text – Line 402:” However, to adequatly and efficiently occlude the vessel of interest, removing a piece of skull remains required. As mentioned in the report on animal use, one rat was excluded from the analysis as the MCA spontaneously reperfuses, thus dropping the success rate of such model.”

11) What is the motivation behind the 5-days post stroke timepoint selection?

In addition to demonstrating the feasability of imaging brain functions at different timepoint following the ischemia, the motivation to performed this delayed session was to capture functional diaschisis which is known to occur few days after the initial insult. More recurrent imaging sessions covering a longer post-stroke period would be of high interest to better capture the impact of ischemia on both the brain hemodynamics and functions.

12) How predictive is hyperacute hemodynamics imaging of the long-term outcome?

We thanks the reviewer for this question, that remains of major interest in the stroke realm. However, the prediction of long-term outcome would require to capture brain hemodynamic at larger scale as performed in Hingot et al., Theranostics 2020 and Brunner et al. JCBFM 2023, a coverage not accessible with the imaging window proposed in this work.

13) It would be greatly reassuring if the authors presented the statistical parametric maps without masking regions of interest (eg Fig3B).

We thank the reviewer for pointing out this potential confusion. In the first version of the figure, the colormap used of activity maps was indeed non optimal. Therefore, we i) adjusted the colormap used in Fig 3 and 4 and ii) provided non-thresholded z-score maps for all rats in Supplementary Figure 5.

14) Fig 3C is hard to make out.

We provided a full page version of the Figure 3C in Supplementary Figure 3.

15) Figs 3,4 should incorporate box and whisker plots of data across all rats scatter plots of individual animal data.

We are not sure which kind of data the reviewer wants to have displayed here. However, we have provided the Supplementary Figure 5 that contains both ipsilateral and contralateral responses to whiskers stimulation (from both left and right pads) for all trials and for individual animal included in this work.

16) The final panels in Figures 3,4 would more tellingly include the plots of the linear models fitted.

Based on all reviewers’ comments, we have adjusted and clarified the statistical analysis performed (see Materials and Method) and completed with a Supplementary Figure 4.

17) The frame rate calculations are not adding up unless averaging and pauses are included so some more details should be stated. Are tilted plane waves averaged before compounding as in prior publications?

The angles are averaged 6 times before compounding to reduce signal to noise ration and there is a pause of 0.3s between each Doppler image. See also question “Functional Ultrasound Imaging acquisition” from reviewer 2. We also provided supplementary and key information about the sequence used in this work.

We have provided complementary information in the manuscript:

Original text:” The ultrasound sequence generated by the software is the same as in Macé et al.,26 and Brunner, Grillet et al., Briefly, the ultrafast scanner images the brain 140 with 5 tilted plane-waves (-6°, -3°, +0.5°, +3°, +6°) at a 10-kHz frame rate. The 5 plane-wave images are added to create compound images at a frame rate of 500Hz. Each set of 250 compound images is 142 filtered to extract the blood signal. Finally, the intensity of the filtered images is averaged to obtain a 143 vascular image of the rat brain at a frame rate of 1.25Hz. Then, the acquired images are processed with a dedicated GPU architecture, displayed in real-time for data visualization, and stored for subsequent off-line analysis.”

New text – Line 146:” The ultrasound sequence generated by the software is adapted from Macé et al.31 and Brunner, Grillet et al.34 Ultrafast images of the brain were generated using 5 tilted plane-waves (-6°, -3°, +0.5°, +3°, +6°). Each plane wave is repeated 6 times and the recorded echoes are averaged to increase the signal to noise ration. The 5 plane-wave images are added to create compound images at a frame rate of 500Hz. To obtain a single vascular image we acquired a set of 250 compound images in 0.5s, an extra 0.3s pause is included between each image to have some processing time to display the images for real-time monitoring of the experiment. The set of 250 compound images has a mixed information of blood and tissue signal. To extract the blood signal we apply a low pass filter (cutt off 15Hz) and an SVD filter that eliminates 20 singular values. This filter aims to select all the signal from blood moving with an axial velocity higher than ~1mm/s. To obtain a vascular iimage we compute the intensity of the blood signal i.e., Power Doppler image. This image is in first approximation proportional to the cerebral blood volume26,28. Overall, this process enables a continious acquisition of power Doppler images at a frame rate of 1.25Hz during several hours.”

18) Ultrasound data processing: The filtering process should have more description. It would be highly instructive to explain that the power Doppler signal is being used and comment clearly on its relationship to blood volume, commenting on stalled flow mircrovessels/RBC-devoid micrrovessels, and considerations of vessel orientation.

The compound image has a mixed information of blood and tissu signal. To extract the blood signal, we applied a low pass filter (cutt off 15Hz) and an SVD filter that eliminates 20 singular values. This filter selects all the signal from blood moving with an axial velocity higher than ~1mm/s. To obtain a vascular iimage we compute the intensity of the blood signal (Power Doppler image). This power Doppler image is in first approximation proportional to the cerebral blood volume.

These information have been added in the Materials and Methods section of the manuscript.

19) Does the SVD processing have the same cut off (20 singular values) as in prior publications as a standard value, or is that adjusted for each study? There are enough minor differences between sequences that these details are uncertain. Do the overall hemodynamics measurements (Fig 2) include all data acquired, or do they exclude the whisker stimulation events, and if so, how long of a window is excluded? The explanation of the activity maps should be rephrased e.g. "... recordings are segmented in shorter 40-s time windows encompassing the whisker stimulation trials..."

We agree that these details are important, all these information have been added to the manuscript

• SVD processing: We eliminate 20 singular values as in cited studies.

• Sequence: we have included more details about the sequence.

• Processing: all data during the whisker stimulation is used.

• We have rephrased the explanation about the activity maps.

20) Discuss the methodology behind histological data shown in Fig. 1.

We thank the review for highlighting this omission. We have provided a paragraph in the Materials & Methods section detailling the histology procedure (Line 228):

“Histopathology

Rats were killed 24hrs after the occlusion for histological analysis of the infarcted tissue. Rats received a lethal injection of pentobarbital (100mg/kg i.p. Dolethal, Vetoquinol, France). Using a peristaltic pump, they were transcardially perfused with phosphate-buffered saline followed by 4% paraformaldehyde (Sigma-Aldrich, USA). Brains were collected and post-fixed overnight. 50-μm thick coronal brain sections across the MCA territory were sliced on a vibratome (VT1000S, Leica Microsystems, Germany) and analyzed using the cresyl violet (Electron Microscopy Sciences, USA) staining procedure (see Open Lab Book for procedure). Slices were mounted with DPX mounting medium (Sigma-Aldrich, USA) and scanned using a bright-field microscope

Author Response

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

We were pleased with seeing our work published as a Reviewed Preprint online so swiftly. Now, we would like to take the opportunity to include our responses to the comments made by the reviewers into the Reviewed Preprint and also submit a revised version of the manuscript, in which we have incorporated and addressed the reviewers’ comments.

We believe that our revisions have significantly improved the quality of the manuscript. Specifically, we have described our results more precisely and explained certain decisions that were made in the analysis pipeline more clearly. For example, Figure 4 was improved substantially, by incorporating a schematic representation of how ERP traces were extracted from neural data. Furthermore, we have added three paragraphs in the Discussion where we elaborate on 1) the two observed interaction effects between attention and drug condition, 2) the relation between behavioral, computational, and neural effects, and 3) the statistical robustness of our findings. As such, we believe our interpretation of the results and their robustness now more faithfully represents our observations.

Moreover, we have incorporated the Supplementary Information and Figures, initially presented as a separate section of the manuscript, to the main manuscript and its accompanying supplementary figures. Thereby, the structure of the paper now better follows the eLife format. As a result, some of the previously included supplementary figures are now described in text of the main manuscript.

Reviewer #1 comments:

In the results section on page 6, the authors conclude that "Attention and ATX both enhanced the rate of evidence accumulation towards a decision threshold, whereas cholinergic effects were negligible." I believe "negligible" is wrong here: the corresponding effects of donepezil had p-values of .09 (effect of donepezil on drift rate), .07 (effect of donepezil on the cue validity effect on drift rate) and .09 (effect of donepezil on non-decision time), and were all in the same direction as the effects of atomoxetine, and would presumably have been significant with a somewhat larger sample size. I would say the effects of donepezil were "in the same direction but less robust" (or at the very least "less robust") instead of "negligible".

We agree with the reviewer that ‘negligible’ may not properly capture the effects of DNP on DDM parameter estimates. Although we do feel that caution is warranted in interpreting the effects of DNP on computational parameter estimates, we have now described these effects in line with the reviewer’s suggestion: in the same direction as the effects of ATX, but not (or less) statistically robust.

"In the results section on page 8, the authors conclude that "Summarizing, we show that drug condition and cue validity both affect the CPP, but they do so by affecting different features of this component (i.e. peak amplitude and slope, respectively)." This conclusion is a bit problematic for two reasons. First, drug condition had a significant effect not only on peak amplitude but also on slope. Second, cue validity had a significant effect not only on slope but also on peak amplitude. It may well be that some effects were more significant than others, but I think this does not warrant the authors' conclusion.

Indeed, we observed that cue validity affected both CPP peak amplitude and slope and some effects were more significant than others. As such, we agree with the reviewer that the conclusion that cue validity and drug condition affect different features of the CPP was too strongly formulated. We have changed this statement in the manuscript to reflect the observed data pattern more appropriately. We would however like to point out that this does not undermine our main conclusion. Spatial attention and drug condition showed only limited interaction effects in terms of behavior and neural data and their effects on occipital activity were separable in terms of timing and spatial profile. Therefore, our conclusion that catecholamines and spatial attention jointly shape perceptual decision-making remains valid.

In the discussion section on page 11, the authors conclude that "First, although both attention and catecholaminergic enhancement affected centro-parietal decision signals in the EEG related to evidence accumulation (O'Connell et al., 2012; Twomey et al., 2015), attention mainly affected the build-up rate (slope) whereas ATX increased the amplitude of the CPP component (Figure 3D-F)." As I wrote above, I believe it is not correct that "attention mainly affected the build-up rate or slope", given that the effect of cue-validity on CPP slope was also significant. Also, while the authors' data do support the conclusion that ATX increased the amplitude and not the slope of the CPP component, a previous study in humans found the opposite: ATX increased the slope but did not affect the peak amplitude of the CPP (Loughnane et al 2019, JoCN, https://pubmed.ncbi.nlm.nih.gov/30883291). Although the authors cite this study (as from 2018 instead of 2019), they do not draw attention to this important discrepancy between the two studies. I encourage the authors to dedicate some discussion to these conflicting findings.

We thank the reviewer for spotting this error, we cited the preprint version (from 2018) of Loughnane and colleagues and not the published JoCN paper (from 2019). We have changed this in the updated version of the manuscript. We further thank the reviewer for asking about this interesting discrepancy between our observation that ATX increased CPP peak amplitude in absence of slope effects and the observation by Loughnane et al. (2019, JoCN) that ATX increased CPP slope, but not amplitude. We first would like to point out that the peak amplitude effect in Loughnane et al. (2019) was in the same direction as our reported effect, with numerically higher peak amplitudes for ATX compared to PLC (Figure 2A – right panel in Loughnane et al., 2019). However, as their omnibus main effect of drug condition on CPP peak amplitude was not significant, they did not provide statistics for a pairwise comparison of ATX and PLC in terms of CPP peak amplitude, which makes it hard to compare the effects directly. Regardless, Loughnane et al. (2019) did observe an effect on CPP slope, whereas we did not. Speculatively, this difference could be related to the behavioral tasks that were used in both studies. Below we have added a new paragraph from the Discussion in which we elaborate on this more.

In Discussion, page 15:

Here, we demonstrated that response accuracy and response speed are differentially represented in the CPP, with correct vs. erroneous responses resulting in a higher slope and peak amplitude, whereas fast vs. slow responses are only associated with increased slopes (Figure 3A-B). Speculatively, the specific effect of any (pharmacological) manipulation on the CPP may depend on task-setting. For example, Loughnane et al. (2019) used a visual task on which participants did not make many errors (hit rate>98%, no false alarms), whereas we applied a task in which participants regularly made errors (roughly 25% of all trials). Possibly, the effects of ATX from Loughnane et al. (2019) in terms of behavior (RT effect, not accuracy/d’) and CPP feature (slope effect, not peak) may therefore have been different from the effects of ATX we observed on behavior (d’ effect, not RT) and CPP feature (peak effect, not slope). Regardless, when we compared subjects with high and low drift rates (Figure 3C), we observed that both CPP slope and CPP peak were increased for the high vs. low drift group (independent of the drug or attentional manipulation). This indicates that both CPP slope and CPP peak were associated with drift rate from the DDM. Clearly, more work is needed to fully understand how evidence accumulation unfolds in neural systems, which could consequently inform future behavioral models of evidence accumulation as well.

On page 12 and page 14 the authors suggest a selective effect of ATX on tonic catecholamine activity, but to my knowledge the exact effects of ATX on phasic vs. tonic catecholamine activity are unknown. Although microdialysis studies have shown that a single dose of atomoxetine increases catecholamine concentrations in rodents, it is unknown whether this reflects an increase in tonic and/or phasic activity, due to the limited temporal resolution of microanalysis. Thus, atomoxetine may affect tonic and/or phasic catecholamine activity, and which of these two effects dominates is still unknown, I think.

We agree with the reviewer that the direct effects of ATX on tonic versus phasic catecholaminergic activity are not clear as initially stated in the manuscript. Equally problematic, previous work has demonstrated that changes in tonic neuromodulation shape evoked neuromodulatory discharge (Aston-Jones & Cohen, 2005, Annu. Rev. Neurosci; Knapen et al., 2016, PLoS ONE). As such, any effect of ATX on tonic neuromodulatory drive would probably have affected phasic catecholaminergic responses as well, although this claim will have to be experimentally addressed. We think that because of the close relation between tonic and phasic neuromodulation, it may indeed be better to refrain from the simplistic interpretation that ATX (and DNP) solely and specifically affects tonic neuromodulation. We have used more neutral language in that regard in the updated version of the manuscript, for example by only mentioning elevated neuromodulator levels (not specifying tonic or phasic). Moreover, we have extended a part of our previous Discussion, to elaborate this issue in more detail. An excerpt of this paragraph, consisting of previous and newly added text, can be seen below.

In Discussion, page 14:

In contrast with recent work associating catecholaminergic and cholinergic activity with attention by virtue of modulating prestimulus alpha-power shifts (Bauer et al., 2012; Dahl et al., 2020, 2022) and attentional cue-locked gamma-power (Bauer et al., 2012; Howe et al., 2017), the current work shows that the effects of neuromodulator activity are relatively global and non-specific, whereas the effects of spatial attention are more specific to certain locations in space. Our findings are, however, not necessarily at odds with these previous studies. Most recent work associates phasic (event-related) arousal with selective attention (for reviews see: Dahl et al., 2022; Thiele & Bellgrove, 2018). For example, cue detection in visual tasks is known to be related to cholinergic transients occurring after cue onset (Howe et al., 2017; Parikh et al., 2007). Contrarily, in our work we aimed to investigate the effects of increased baseline levels of neuromodulation by suppressing the reuptake of catecholamines and the breakdown of acetylcholine throughout cortex and subcortical structures. Tonic and phasic neuromodulation have previously been shown to differentially modulate behavior and neural activity (de Gee et al., 2014, 2020, 2021; McGinley et al., 2015; McGinley, Vinck, et al., 2015; van Kempen et al., 2019). Note, however, that it is difficult to investigate causal effects of tonic neuromodulation in isolation of changes in phasic neuromodulation, mostly because phasic and tonic activity are thought to be anti-correlated, with lower phasic responses following high baseline activity and vice versa (Aston- Jones & Cohen, 2005; de Gee et al., 2020; Knapen et al., 2016). As such, pharmacologically elevating tonic neuromodulator levels may have resulted in changes in phasic neuromodulatory responses as well. Concurrent and systematic modulations of tonic (e.g. with pharmacology) and phasic (e.g. with accessory stimuli; Bruel et al., 2022; Tona et al., 2016) neuromodulator activity may be necessary to disentangle the respective and interactive effects of tonic and phasic neuromodulator activity on human perceptual decision-making.

Reviewer #2 comments:

The main weakness of the paper lies in the strength of evidence provided, and how the results tally with each other. To begin with, there are a lot of significance tests performed here, increasing the chances of false positives. Multiple comparison testing is only performed across time in the EEG results, and not across post-hoc comparisons throughout the paper. In and of itself, it does not invalidate any result per se, but it does colour the interpretation of any results of weak significance, of which there are quite a few. For example, the effect of Drug on d' and subsequent post-hoc comparisons, also effect of ATX on CPP amplitude and others.

We agree with the reviewer that the statistical evidence for some of the results presented in this study is limited. This issue mostly concerns the effects of the pharmacological manipulation (effects of attention were strong and robust), which is unfortunately often the case given the high inter-individual variability in responses to pharmaceutical agents. We have added a paragraph to the Discussion in which we discuss this limitation of the current study. Furthermore, we discuss our findings in the context of previous work, thereby showing that - although not always robust- most of the reported drug effects were in the direction that could be expected based on previous literature. We have pasted that paragraph below.

In Discussion, pages 16:

Although the effects of the attentional manipulation were generally strong and robust, the statistical reliability of the effects of the pharmacological manipulation was more modest for some comparisons. This may partly be explained by high inter-individual variability in responses to pharmaceutical agents. For example, initial levels of catecholamines may modulate the effect of catecholaminergic stimulants on task performance, as task performance is supposed to be optimal at intermediate levels of catecholaminergic neuromodulation (Cools & D’Esposito, 2011). While acknowledging this, we would like to highlight that many of the observed effects of ATX were in the expected direction and in line with previous work. First, pharmacologically enhancing catecholaminergic levels have previously been shown to increase perceptual sensitivity (d’) (Gelbard-Sagiv et al., 2018), a finding that we have replicated here. Second, methylphenidate (MPH), a pharmaceutical agent that elevates catecholaminergic levels as well, has been shown to increase drift rate as derived from drift diffusion modeling on visual tasks (Beste et al., 2018) in line with our ATX observations. Third, a previous study using ATX to elevate catecholaminergic levels observed that ATX increased CPP slope (Loughnane et al., 2019). Although in our case ATX increased the CPP peak and not its slope, this provide causal evidence that centro-parietal ERP signals related to sensory evidence accumulation are modulated by the catecholaminergic system (Nieuwenhuis et al., 2005). Fourth, we observed that elevated levels of catecholamines affected stimulus driven occipital activity relatively late in time and close to the behavioral response, which resonates with previous observations (Gelbard-Sagiv et al., 2018). Finally, ATX had robust effects on physiological responses (heart rate, blood pressure, pupil size), cue-locked ERP signals and oscillatory power dynamics in the alpha-band, leading up to stimulus presentation. We concur, however, that more work is needed to firmly establish how (various forms of) attention and catecholaminergic neuromodulation affect perceptual decision-making.

The lack of an overall RT effect of Drug leaves any DDM result a little underwhelming. How do these results tally? One potential avenue for lack of RT effect in ATX condition is increased drift rate but also increased non-decision time, working against each other. However, it may be difficult to validate these results theoretically.

As the reviewer remarks, an increase in performance/d’ in absence of any RT effects can be algorithmically explained by a combination of increased drift rate and prolonged non-decision time. This is indeed what we observed for ATX. Non-decision time is generally thought to reflect the time necessary for stimulus encoding and motor execution and as such is seen as separate from the evidence-accumulation decision process. We deem it possible that ATX simultaneously prolonged stimulus encoding/motor execution (reflected in changes in non-decision time) and fastened evidence accumulation (reflected in changes in drift rate). Although our neural data did not provide evidence for this claim, previous work has demonstrated that increased baseline (pupil-linked) arousal/neuromodulation is associated with a decreased build-up rate of a neural signal associated with motor execution (β-power over motor cortex, Van Kempen et al., 2019, eLife), potentially linking increased non-decision time under ATX to slowing down of motor execution processes. The same authors also report relationships between baseline (pupil-linked) arousal/neuromodulation and activity over occipital and centroparietal cortices, respectively associated with sensory processing and sensory evidence accumulation, suggesting that baseline neuromodulation may affect all stages leading up to a decision (sensory processing, evidence accumulation and motor execution). Note also that the attentional manipulation seems to simultaneously increase drift rate and shorten non-decision time in our case, as one would expect (Figure 2E, Figure 2 – Supplements 4&5).

There is an interaction between ATX and Cue in terms of drift rate, this goes against the main thesis of the paper of distinct and non-interacting contributions of neuromodulators and attention. This finding is then ignored. There is also a greater EDAN later for ATX compared to PLA later in the results, which would also indicate interaction of neuromodulators and attention but this is also somewhat ignored.

There are indeed some interesting interaction effects between ATX and spatial attention (cue), as pointed out by the reviewer. However, we did also observe striking differences in the effects of ATX and attention on stimulus-locked occipital activity (in timing and spatial specificity) as well as independent (main) effects on CPP amplitude and pre-stimulus alpha power. Therefore, throughout the paper we tried to carefully describe the effects of attention and ATX as largely independently and jointly modulating perceptual decision-making, while at the same time highlighting the interaction effects that we observed, where present. We have highlighted the effects the reviewer refers to even more explicitly in a separate paragraph that we added to the discussion, pasted below.

In Discussion, page 13-14:

We did observe two striking interaction effects between the catecholaminergic system and spatial attention. First, effects of attention on drift rate were increased under catecholaminergic enhancement (Figure 2D). Although this interaction effect was not reflected in CPP slope/peak amplitude, this does suggest that catecholamines and spatial attention might together shape sensory evidence accumulation in a non-linear manner. Second, the amplitude of the cue-locked early lateralized ERP component (resembling the EDAN) was increased under ATX as compared to PLC. The underlying neural processes driving the EDAN ERP, as well as its associated functions, have been a topic of debate. Some have argued that the EDAN reflects early attentional orienting (Praamstra & Kourtis, 2010) but others have claimed it is mere a visually evoked response and reflects visual processing of the cue (Velzen & Eimer, 2003). Thus, whether this effect reflects a modulation of ATX on early attentional processes or rather a modulation of early visual responses to sensory input in general is a matter for future experimentation.

The CPP results are somewhat unclear. Although there is an effect of ATX on drift rate algorithmically, there is no effect of ATX on CPP slope. On the other hand, even though there is no effect of DNP on drift rate, there is an effect of DNP on CPP slope. Perhaps one may say that the effect of DNP on drift rate trended towards significance, but overall the combination of effects here is a little unconvincing. In addition, there is an effect of ATX on CPP amplitude, but how does this tally with behaviour? Would you expect greater CPP amplitude to lead to faster or slower RTs? The authors do recognise this discrepancy in the Discussion, but discount it by saying the relationship between algorithmic and CPP parameters in terms of DDM is unclear, which undermines the reasoning behind the CPP analyses (and especially the one correlating CPP slope with DDM drift rate).

We thank the reviewer for pointing out this dissociation of drug effects in terms of the algorithmic (DDM) and neural (CPP) ‘implementations’ of the evidence accumulating process underlying perceptual decisions. We have added a new paragraph to the discussion where we interpret the effects of ATX on the neural and algorithmic levels of evidence accumulation. Below we have pasted that paragraph:

In Discussion, page 14-15:

We reported attentional and neuromodulatory effects on algorithmic (DDM, Figure 2) and neural (CPP, Figure 3) markers of sensory evidence accumulation. Recent work has started to investigate the association of these two descriptors of the accumulation process, aiming to uncover whether neural activity over centroparietal regions reflects evidence accumulation, as proposed by computational accumulation-to-threshold models (Kelly & O’Connell, 2015; O’Connell et al., 2018; O’Connell & Kelly, 2021; Twomey et al., 2015). Currently, the CPP is often thought to reflect the decision variable, i.e. the (unsigned) evidence for a decision (Twomey et al., 2015), and consequently its slope should correspond with drift rate, whereas its amplitude at any time should correspond with the so-far accumulated evidence. As -computationally- the decision is reached when evidence crosses a decision bound (the threshold), it may be argued that the peak amplitude of the CPP (roughly) corresponds with the decision boundary. This seems to contradict our observation that 1) ATX modulated drift rate, but not CPP slope and 2) ATX did not modulate boundary separation, but did modulate CPP peak. Note, however, that previous studies using pharmacology or pupil-linked indexes of (catecholaminergic) neuromodulation have also demonstrated effects on both CPP peak (van Kempen et al., 2019) and CPP slope (Loughnane et al., 2019).

The posterior component effects are problematic. The main issue is the lack of clarification of and justification for the choice of posterior component. The analysis is introduced in the context of the target selection signal the N2pc/N2c, but the component which follows is defined relative to Cue, albeit post-target. Thus this analysis tells us the effect of Cue on early posterior (possibly) visual ERP components, but it is not related to target selection as it is pooled across target/distractor. Even if we ignore this, the results themselves wrt Drug lack context. There is a trending lower amplitude for ATX at later latencies at temporo-parietal electrodes, and more positive for DNP, relative to PLA. Is this what one would expect given behaviour? This is where the issue of correct component identification becomes critical in order to inform any priors on expected ERP results given behaviour.

We thank the reviewer for raising this issue with the occipital ERP analysis, allowing us to clarify our decisions regarding the analyses and our interpretations of the results. First, the selection of electrodes was based on, and identical to, previous studies investigating lateralized target selection signals in visual tasks containing bilateral visual stimuli (Loughnane et al., 2016; Newman et al., 2017; Papaioannou & Luck, 2020; van Kempen et al., 2019). Second, the ERPs were defined relative to both the direction of the cue as well as the location of the target. As cue direction and target location were not always congruent (cue validity=80%), we could adopt a 2x2 (cue direction x stimulus identity) design for our ERP analyses (we are ignoring drug condition for explanation purposes). For example, for validly cued target trials we extracted two ERP traces: 1) from the hemisphere contralateral to both the cue and the target stimulus (representing processing of cued target stimulus) and 2) from the hemisphere ipsilateral to the cue and the target stimulus (representing processing of non-cued noise stimulus). However, for invalidly cued trials, ERP traces were extracted from 3) the hemisphere contralateral to cue direction and ipsilateral to the target stimulus (reflecting processing of cued noise stimuli) as well as 4) from the hemisphere ipsilateral to cue direction but contralateral to the target stimulus (reflecting processing of non-cued target stimuli). By defining our ERPs as such, we were able to gauge effects of cue direction (reflecting general shifts in attention), stimulus identity (reflecting target vs. noise selection processes) and their interaction (reflecting cue validity) on activity over occipito-temporal activity. Third, we did not pool data (across target/noise stimuli) for statistical analyses, but only for visualization purposes. To clarify how we extracted ERP traces, we have changed Figure 4 substantially. The updated figure now contains a schematic of how these four distinct ERP traces (cue x stimulus identity) were extracted from neural activity. Moreover, for clarity sake, we now show all 12 ERP traces (3x2x2, drug condition x cue direction x stimulus identity) as well as the three main effects that we observed after performing a 3x2x2 repeated measures (rm)ANOVA over time.

We observed robust (cluster-corrected) effects of cue direction (not validity) on early occipital activity (Fig. 4C – left panel) and of stimulus identity (target/noise) and drug condition on later occipital activity (Fig. 4C – middle and right panel). These results crucially highlight the different temporal (early/late) and spatial (lateralized/not lateralized) profiles of cue, target and drug effects on occipital activity. Moreover, we observed a specific order of drug effects on late occipital activity (DNP>PLC>ATX). The behavioral relevance of this pattern of effects remains elusive. Although the effects of drug condition coincide in time with those of target selection (i.e. when activity contralateral and ipsilateral to the target stimulus was different), the effects of drug were bilateral, meaning that occipito-temporal activity related to the processing of the target (task-relevant) stimulus and non-target (task-irrelevant) stimulus was equally modulated by these pharmaceutical agents. One might argue that these effects show that neither ATX nor DNP modulated the signal-to-noise ratio (SNR), a feature that describes how well relevant stimulus information (signal) can be discerned from irrelevant information (noise). Although it may be tempting to extrapolate this finding to behavior, by suggesting that on the basis of these drug effect neither ATX nor DNP could have modulated d’ (behavioral measure describing how well signal is separated from noise), we would like to point out that our behavioral task specifically concerned a discrimination task about the (orientation of the) target stimulus in which the difference between signal and noise was only relevant for localization purposes and thus has a less direct relation with task performance. As such it is difficult to grasp how the modulation of late occipito-temporal activity by ATX and DNP relates to their behavioral effects. Moreover, the bilateral effect of both ATX and DNP also suggests an absence of interaction effects between drug conditions and visuo-spatial attention, as the effects of ATX/DNP were similar across all cue and target identity conditions.

Author Response

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

Reviewer #1 (Public Review):*

The manuscript by Hariani et al. presents experiments designed to improve our understanding of the connectivity and computational role of Unipolar Brush Cells (UBCs) within the cerebellar cortex, primarily lobes IX and X. The authors develop and cross several genetic lines of mice that express distinct fluorophores in subsets of UBCs, combined with immunocytochemistry that also distinguishes subtypes of UBCs, and they use confocal microscopy and electrophysiology to characterize the electrical and synaptic properties of subsets of so-labelled cells, and their synaptic connectivity within the cerebellar cortex. The authors then generate a computer model to test the possible computational functions of such interconnected UBCs.

Using these approaches, the authors report that:

1) GRP-driven TDtomato is expressed exclusively in a subset (20%) of ON-UBCs, defined electrophysiologically (excited by mossy fiber afferent stimulation via activation of UBC AMPA and mGluR1 receptors) and immunocytochemically by their expression of mGluR1.

2) UBCs ID'd/tagged by mCitrine expression in Brainbow mouse line P079 are expressed in a similar minority subset of OFF-UBCs defined electrophysiologically (inhibited by mossy fiber afferent stimulation via activation of UBC mGluR2 receptors) and immunocytochemically by their expression of Calretinin. However, such mCitrine expression was also detected in some mGluR1 positive UBCs, which may not have shown up electrophysiologically because of the weaker fluorophore expression without antibody amplification.

This is correctly stated with the exception that the P079 mouse line itself expresses mCitrine. The Brainbow mouse line was used in the connectivity study by crossing it to the GRP-Cre or Calretinin-Cre lines.

3) Confocal analysis of crossed lines of mice (GRP X P079) stained with antibodies to mGluR1 and calretinin documented the existence of all possible permutations of interconnectivity between cells (ON-ON, ON-OFF, OFF-OFF, OFF-ON), but their overall abundance was low, and neither their absolute nor relative abundance was quantified.

They were certainly rare to observe using our approaches, but we reasoned that the densities of such connections are not possible to estimate accurately. Please see discussion below.

4) A computational model (NEURON ) indicated that the presence of an intermediary UBC (in a polysynaptic circuit from MF to UBC to UBC) could prolong bursts (MF-ON-ON), prolong pauses (MF-ON-OFF), cause a delayed burst (MF-OFF- OFF), cause a delayed pause (MF-OFF-ON) relative to solely MF to UBC synapses which would simply exhibit long bursts (MF-ON) or long pauses (MF-OFF).

The authors thus conclude that the pattern of interconnected UBCs provides an extended and more nuanced pattern of firing within the cerebellar cortex that could mediate longer-lasting sensorimotor responses.

The cerebellum's long-known role in motor skills and reflexes, and associated disorders, combined with our nascent understanding of its role in cognitive, emotional, and appetitive processing, makes understanding its circuitry and processing functions of broad interest to the neuroscience and biomedical community. The focus on UBCs, which are largely restricted to vestibular lobules of the cerebellum reduces the breadth of likely interest somewhat. The overall design of specific experiments is rigorous and the use of fluorophore expressing mouse lines is creative. The data that is presented and the writing are clear. However, the overall experimental design has issues that reduce overall interpretation (please see specific issues for details), which combined with a lack of thorough analysis of the experimental outcomes severely undermines the value of the NEURON model results and the advance in our understanding of cerebellar processing in situ (again, please see specific issues for details).

Specific issues:

1) All data gathered with inhibition blocked. All of the UBC response data (Fig. 1) was gathered in the presence of GABAAR and Glycine R blockers. While such an approach is appropriate generally for isolating glutamatergic synaptic currents, and specifically for examining and characterizing monosynaptic responses to single stimuli, it becomes problematic in the context of assaying synaptic and action potential response durations for long-lasting responses, and in particular for trains of stimuli, when feed-forward and feed-back inhibition modulates responses to afferent stimulation. That is, even for single MF stimuli, given the >500ms duration of UBC synaptic currents, there is plenty of time for feedback inhibition from Golgi cells (or feedforward, from MF to Golgi cell excitation) to interrupt AP firing driven by the direct glutamatergic synaptic excitation. This issue is compounded further for all of the experiments examining trains of MF stimuli. Beyond the impact of feedback inhibition on the AP firing of any given UBC, it would also obviously reduce/alter/interrupt that UBC's synaptic drive of downstream UBCs. This issue fundamentally undermines our ability to interpret the simulation data of Vm and AP firing of both the modeled intermediate and downstream UBC, in terms of applying it to possible cerebellar cortical processing in situ.

The goal of Figure 1 was to determine the cell types of labeled UBCs in transgenic mouse lines, which is determined entirely by their synaptic responses to glutamate (Borges-Merjane and Trussell, 2015). Thus, blocking inhibition was essential to produce clear results in the characterization of GRP and P079 UBCs. While GABAergic/glycinergic feedforward and feedback inhibition is certainly important in the intact circuit, it was not our intention, nor was it possible, to study its contribution in the present study. Leaving inhibition unblocked does not lead to a physiologically realistic stimulation pattern in acute brain slices, because electrical stimulation produces synchronous excitation and inhibition by directly exciting Golgi cells, rather than their synaptic inputs. The main inhibition that UBCs receive that are crucial to determining burst or pause durations is not via GABA/glycine, but instead through mGluR2, which lasts for 100-1000s of milliseconds. The main excitation that drives UBC firing is mGluR1 and AMPA, which both last 100-1000s of milliseconds. Thus, these large conductances are unlikely to be significantly shaped by 1-10 ms IPSCs from feedforward and feedback GABA/glycine inhibition. Recent studies that examined the duration of bursting or pausing in UBCs had inhibition blocked in their experiments, presumably for the reasons outlined above (Guo et al., 2021; Huson et al., 2023).

In Author response image 1 is an example showing the synaptic currents and firing patterns in an ON UBC before and after blocking inhibition. The GABA/glycinergic inhibition is fast, occurs soon after the stimuli and has little to no effect on the slow inward current that develops after the end of stimulation, which is what drives firing for 100s of milliseconds.

Author response image 1.

Example showing small effect of GABAergic and glycinergic inhibition on excitatory currents and burst duration. A) Excitatory postsynaptic currents in response to train of 10 presynaptic stimuli at 50 Hz before (black) and after (Grey) blocking GABA and glycine receptors. The slow inward current that occurs at the end of stimulation is little affected. B) Expanded view of the synaptic currents evoked during the train of stimuli. GABA/glycine receptors mediate the fast outward currents that occur immediately after the first couple stimuli. C) Three examples of the bursts caused by the 50 Hz stimulation in the same cell without blocking GABA and glycine receptors. D) Three examples in the same cell after blocking GABA and glycine receptors.

2) No consideration for the involvement of polysynaptic UBCs driving UBC responses to MF stimulation in electrophysiology experiments. Given the established existence (in this manuscript and Dino et al. 2000 Neurosci, Dino et al. 2000 ProgBrainRes, Nunzi and Mugnaini 2000 JCompNeurol, Nunzi et al. 2001 JCompNeurol) of polysynaptic connections from MFs to UBCs to UBCs, the MF evoked UBC responses established in this manuscript, especially responses to trains of stimuli could be mediated by direct MF inputs, or to polysynaptic UBC inputs, or possibly both (to my awareness not established either way). Thus the response durations could already include extension of duration by polysynaptic inputs, and so would overestimate the duration of monosynaptic inputs, and thus polysynaptic amplification/modulation, observed in the NEURON model.

We are confident that the synaptic responses shown are monosynaptic for several reasons. UBCs receive a single mossy fiber input on their dendritic brush, and thus if our stimulation produces a reliable, short-latency response consistent with a monosynaptic input, then there is not likely to be a disynaptic input, because the main input is accounted for by the monosynaptic response. In all cells included in our data set, the fast AMPA receptor-mediated currents always occurred with short latency (1.24 ± 0.29 ms; mean ± SD; n = 13), high reliability (no failures to produce an EPSC in any of the 13 GRP UBCs in this data set), and low jitter (SD of latency; 0.074 ± 0.046 ms; mean ± SD; n = 13). These measurements have been added to the results section. In some rare cases, we did observe disynaptic currents, which were easily distinguishable because a single electrical stimulation produced a burst of EPSCs at variable latencies. Please see example in Author response image 2. These cases of disynaptic input, which have been reported by others (Diño et al., 2000; Nunzi and Mugnaini, 2000; van Dorp and De Zeeuw, 2015) support the conclusion that UBCs receive input from other UBCs.

Author response image 2.

Example of GRP UBC with disynaptic input. Three examples of the effect of a single presynaptic stimulus (triangle) in a GRP UBC with presumed disynaptic input. Note the variable latency of the first evoked EPSC, bursts of EPSCs, and spontaneous EPSCs.

3) Lack of quantification of subtypes of UBC interconnectivity. Given that it is already established that UBCs synapse onto other UBCs (see refs above), the main potential advance of this manuscript in terms of connectivity is the establishment and quantification of ON-ON, ON-OFF, OFF-ON, and OFF-OFF subtypes of UBC interconnections. But, the authors only establish that each type exists, showing specific examples, but no quantification of the absolute or relative density was provided, and the authors' unquantified wording explicitly or implicitly states that they are not common. This lack of quantification and likely small number makes it difficult to know how important or what impact such synapses have on cerebellar processing, in the model and in situ.

As noted by the reviewer, the connections between UBCs were rare to observe. We decided against attempting to quantify the absolute or relative density of connections for several reasons. A major reason for rare observations of anatomical connections between UBCs is likely due to the sparse labeling. First, the GRP mouse line only labels 20% of ON UBCs and we are unable to test whether postsynaptic connectivity of GRP ON UBCs is the same as that of the rest of the population of ON UBCs that are not labeled in the GRP mouse line. Second, the Brainbow reporter mouse only labels a small population of Cre expressing cells for unknown reasons. Third, the Brainbow reporter expression was so low that antibody amplification was necessary, which then limited the labeled cells to those close to the surface of the brain slices, because of known antibody penetration difficulties. Therefore, we refrained from estimating the density of these connections, because each of these variables reduced the labeling to unknown degrees and we reasoned that extrapolating our rare observations to the total population would be inaccurate.

A paper that investigated UBC connectivity using organotypic slice cultures from P8 mice suggests that 2/3 of the UBC population receives UBC input, based on the observation that 2/3 of the mossy fibers did not degenerate as would be expected after 2 days in vitro if they were severed from a distant cell body (Nunzi and Mugnaini, 2000). It remains to be seen if this high proportion is due to the young age of these mice or is also the case in adult mice. Even if these connections are indeed rare, they are expected to have profound effects on the circuit, as each UBC has multiple mossy fiber terminals (Berthie and Axelrad, 1994), and mossy fiber terminals are estimated to contact 40 granule cells each (Jakab and Hamori, 1988). We have added a comment regarding this point to the discussion.

4) Lack of critical parameters in NEURON model.

A) The model uses # of molecules of glutamate released as the presumed quantal content, and this factor is constant. However, no consideration of changes in # of vesicles released from single versus trains of APs from MFs or UBCs is included. At most simple synapses, two sequential APs alters release probability, either up or down, and release probability changes dynamically with trains of APs. It is therefore reasonable to imagine UBC axon release probability is at least as complicated, and given the large surface area of contact between two UBCs, the number of vesicles released for any given AP is also likely more complex.

B) the model does not include desensitization of AMPA receptors, which in the case of UBCs can paradoxically reduce response magnitude as vesicle release and consequent glutamate concentration in the cleft increases (Linney et al. 1997 JNeurophysiol, Lu et al. 2017 Neuron, Balmer et al. 2021 eLIFE), as would occur with trains of stimuli at MF to ON-UBCs.

A) The model produces synaptic AMPA and mGluR2 currents that reproduce those we recorded in vitro. We did not find it necessary to implement changes in glutamate release during a train as the model was fit to UBC data with the assumption that the glutamate transient did not change during the train. If there is a change in neurotransmitter release during a train, it is therefore built into the model, which has the advantage of reducing its complexity. UBCs are a special case where the postsynaptic currents are mediated mostly by the total amount of transmitter released. Most of the evoked current occurs tens to hundreds of milliseconds after neurotransmitter release and is therefore much more sensitive to total release and less sensitive to how it is released during the train. Author response image 3 shows the effect of reducing the amount of glutamate released by 10% on each stimulus in the model. Despite a significant change in the pattern of neurotransmitter release, as well as a reduction in the total amount of glutamate, the slow EPSC still decays over the course of hundreds of milliseconds.

Author response image 3.

Effect of short-term depression of neurotransmitter release. A) The top trace shows the glutamate transient that drives the AMPA receptor model used in our study. No change in release is implemented, although the slow tail of each transient summates during the train. The bottom trace shows the modeled AMPA receptor mediated current. B) In this model the amount of glutamate released is reduced by 10% on each stimulus. The duration of the slow AMPA current that develops at the end of stimulation is similar, despite a profound change in the pattern of neurotransmitter exposure.

B) The detailed kinetic AMPA receptor model used here accurately reproduces desensitization, and in fact recovery from desensitization is what mediates the slow ON UBC current. This AMPA receptor is a 13-state model, including 4 open states with 1-4 glutamates bound, 4 closed states with 1-4 glutamates bound, 4 desensitized states with 1-4 glutamates bound, and 5 closed states with 0-4 glutamates bound. The forward and reverse rates between different states in the model were fit to AMPA receptor currents recorded from dissociated UBCs and they accurately reproduced the ON UBC currents evoked by synaptic stimulation in our previous work (Balmer et al., 2021).

5) Lack of quantification of various electrophysiological responses. UBCs are defined (ON or OFF) based on inward or outward synaptic response, but no information is provided about the range of the key parameter of duration across cells, which seems most critical to the current considerations. There is a similar lack of quantification across cells of AP duration in response to stimulation or current injections, or during baseline. The latter lack is particularly problematic because, in agreement with previous publications, the raw data in Fig. 1 shows ON UBCs as quiescent until MF stimulation and OFF UBCs firing spontaneously until MF stimulation, but, for example, at least one ON UBC in the NEURON model is firing spontaneously until synaptically activated by an OFF UBC (Fig. 11A), and an OFF UBC is silent until stimulated by a presynaptic OFF UBC (Fig. 11C). This may be expected/explainable theoretically, but then such cells should be observed in the raw data.

To address this reasonable concern of a general lack of quantification of electrophysiological responses we have added data characterizing the slow inward and outward currents evoked by synaptic stimulation in GRP and P079 UBCs in the results section and in new panels in Figure 1. We report the action potential pause lengths in P079 UBCs and burst lengths in ON UBCs in the results section. However, we favor the duration of the currents to the length of burst and pause, because the currents do not depend on a stable resting membrane potential, which is itself difficult to determine in intracellular recordings of these small cells. We have added peak times and decay time constants of the slow inward and outward currents in ON and OFF UBCs in the results section and have added new panels to figure 1.

In a series of recent publications that focused on UBC firing, the authors argue that cell-attached recordings are necessary to determine accurately the burst and pause lengths, as well as spontaneous firing rates (Guo et al., 2021; Huson et al., 2023). (The trade-off of these extracellular recordings is that the monosynaptic nature of the input is nearly impossible to confirm.) Spontaneous firing rates were variable within both GRP and P079 UBCs from silent to firing regularly or in bursts, as previously reported for UBCs (Kim et al., 2012; van Dorp and De Zeeuw, 2015). For clarity, we chose to model the GRP UBCs as silent unless receiving synaptic input and P079 UBCs as active unless receiving synaptic input. As the reviewer suggests, we have observed UBCs firing in the patterns similar to those shown in the model UBCs that have input from a spontaneously active presynaptic UBC. In Author response image 4 are some examples.

Author response image 4.

Examples of UBCs that receive spontaneous input. A) Three ON UBCs that had spontaneous EPSCs, suggesting the presence of an active presynaptic UBC. B) Two OFF UBCs that had spontaneous outward currents.

Reviewer #2 (Public Review):

In this paper, the authors presented a compelling rationale for investigating the role of UBCs in prolonging and diversifying signals. Based on the two types of UBCs known as ON and OFF UBC subtypes, they have highlighted the existing gaps in understanding UBCs connectivity and the need to investigate whether UBCs target UBCs of the same subtype, different subtypes, or both. The importance of this knowledge is for understanding how sensory signals are extended and diversified in the granule cell layer.

The authors designed very interesting approaches to study UBCs connectivity by utilizing transgenic mice expressing GFP and RFP in UBCs, Brainbow approach, immunohistochemical and electrophysiological analysis, and computational models to understand how the feed-forward circuits of interconnected UBCs transform their inputs.

This study provided evidence for the existence of distinct ON and OFF UBC subtypes based on their electrophysiological properties, anatomical characteristics, and expression patterns of mGluR1 and calretinin in the cerebellum. The findings support the classification of GRP UBCs as ON UBCs and P079 UBCs as OFF UBCs and suggest the presence of synaptic connections between the ON and OFF UBC subtypes. In addition, they found that GRP and P079 UBCs form parallel and convergent pathways and have different membrane capacitance and excitability. Furthermore, they showed that UBCs of the same subtype provide input to one another and modify the input to granule cells, which could provide a circuit mechanism to diversify and extend the pattern of spiking produced by mossy fiber input. Accordingly, they suggested that these transformations could provide a circuit mechanism for maintaining a sensory representation of movement for seconds.

Overall, the article is well written in a sound detailed format, very interesting with excellent discovery and suggested model, however, I have some comments/suggestions that may help to improve this manuscript:

• The discovery of UBCs innervating each other and their own subtypes, suggesting the presence of feed-forward networks in the cerebellum, is an incredibly fascinating and exciting finding followed by an intriguing model by authors. However, it is worth considering an alternative model as well. I acknowledge that visualizing such interactions using current tools and methods can be challenging ("The approaches used here were not able to determine the existence of networks of more than 2 UBCs connected one after the other. If present, 3 or more UBCs in series could extend and transform the input in even more dramatic ways. The temporal diversity that UBC circuits generate may underlie the flexibility of the cerebellum to coordinate movements over a broad range of behaviors."). Therefore, if this is the case in which more than 2 UBCs connected one after the other, then an alternative model PERHAPS resembles the basal nuclei, with its direct and indirect circuits, can be considered (maybe a type of circular model). The basal nuclei circuits are also regulated by modulators such as D1 dopamine receptors in the direct pathway, causing depolarization, and D2 dopamine receptors in the indirect pathway, resulting in hyperpolarization upon dopamine activation. This approach could involve using computational models to gain insight into potential alternatives within this pathway (may be a future direction).

Thank you for this suggestion to consider the potentially similar circuit interactions in the basal nuclei. We will certainly investigate this further as we move forward with modeling the feed-forward networks in the cerebellum.

• GRP UBCs are more densely distributed in lobes VI-IX, while P079 UBCs are more densely distributed in the dorsal leaflet of lobe X in sagittal sections. While the cerebellum is well known for its characteristic stripy pattern, are UBC distributions the same in coronal/transverse section?

UBCs of different types, based on their expression of specific proteins, have overlapping but somewhat distinct distributions in coronal sections. The densities of calretinin-expressing UBCs are higher within Zebrin II positive zones and form sagittal stripes, whereas the densities of mGluR1-expressing and PLCb4-expressing UBCs vary less but are in their highest densities at the midline (Chung et al., 2009; Sekerkova et al., 2014). The difference noted by the reviewer between the dorsal and ventral leaflets of lobe X are the most distinct that we know of in the GRP and P079 populations.

• The extension of the axons from both subtypes of UBCs show they are long enough to pass several UBCs and even projections are directed toward the white matter (e.g. Fig 9A), suggesting targeting the UBCs or granule cells in other lobules. Is it suggesting UBCs connectivity between different lobules (perhaps longitudinal connectivity)? Is there any observation or information in coronal/transverse section to visualize mediolateral connectivity?

This is certainly worth exploring in future work. UBCs have been reported to project their axons into and across the white matter (Diño et al., 2000). To our knowledge, whether UBCs project their axons out of one lobule and into another has not been examined.

• The limitation in identifying networks involving more than two sequentially connected UBCs was briefly noted. I suggest including a paragraph describing limitations and discussing the implications of the findings would enhance the overall impact of the research and broaden our understanding of cerebellar function.

• It is a pity that there is no clear conclusion to the discussion of this very interesting study. I suggest providing the key points as a conclusion.

Thank you for these suggestions. Limitations and implications are included throughout the discussion section and we feel that the summary figure and significance statement now sufficiently convey the key conclusions of the study.

• Please make the correction in Figure 2A by relabeling it as IXa, IXb, and IXc to correct the typographical error.

Fixed

• I recommend rotating Figure 7A to align its orientation with the other figures for consistency.

Fixed

Reviewer #1 (Recommendations For The Authors):

Minor comments that should be addressed for clarity:

1) In the NEURON model, why was the reversal potential for the leak conductance and Gmax for Ih different for the two types of UBCs. Relatedly, why is Erev for GABAB -95mV if Ek is -90mV?

The h-current (Ih) was estimated from a hyperpolarizing current step in both cell types and these data have been added to the result section and as a panel in Figure 1. The conductance of Ih in the model cells were adjusted accordingly, with OFF UBCs having ~3 times that of ON UBCs and approximated the measured voltage sag, as we now describe in the methods section. The reversal potential of the model mGluR2 current (which is based on a model of GABAB) has been fixed.

2) Line 69 justification for their dual genetic approach is a bit too strong: "Paired recordings not possible". It may be difficult, but it is certainly possible.

Reworded

3) Confusing wording, only one stat for two parameters? Line 93: These currents were produced by both mGluR1 and AMPA receptors, as they were blocked by their antagonists JNJ16259685 and GYKI53655, respectively (92.86% {plus minus} 3.25; paired t-test; P=0.0066; n = 9; 95 mean {plus minus} SEM) (Fig 1D-E).

Reworded

References

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Author Response

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

Cook, Watt, and colleagues previously reported that a mouse model of Spinocerebellar ataxia type 6 (SCA6) displayed defects in BDNF and TrkB levels at an early disease stage. Moreover, they have shown that one month of exercise elevated cerebellar BDNF expression and improved ataxia and cerebellar Purkinje cell firing rate deficits. In the current work, they attempt to define the mechanism underlying the pathophysiological changes occurring in SCA6. For this, they carried out RNA sequencing of cerebellar vermis tissue in 12-month-old SCA6 mice, a time when the disease is already at an advanced stage, and identified widespread dysregulation of many genes involved in the endo-lysosomal system. Focusing on BDNF/TrkB expression, localization, and signaling they found that, in 7-8 month-old SCA6 mice early endosomes are enlarged and accumulate BDNF and TrkB in Purkinje cells. Curiously, TrkB appears to be reduced in the recycling endosomes compartment, despite the fact that recycling endosomes are morphologically normal in SCA6. In addition, the authors describe a reduction in the Late endosomes in SCA6 Purkinje cells associated with reduced BDNF levels and a probable deficit in late endosome maturation.

We would like to thank the reviewers for their careful reading of the paper, their feedback has helped us to add information and experiments to the paper that enhance the clarity of the findings.

Strengths:

The article is well written, and the findings are relevant for the neuropathology of different neurodegenerative diseases where dysfunction of early endosomes is observed. The authors have provided a detailed analysis of the endo-lysosomal system in SCA6 mice. They have shown that TrkB recycling to the cell membrane in recycling endosomes is reduced, and the late endosome transport of BDNF for degradation is impaired. The findings will be crucial in understanding underlying pathology. Lastly, the deficits in early endosomes are rescued by chronic administration of 7,8-DHF.

We thank the reviewers for their positive feedback on this work.

Weaknesses:

The specificity of BDNF and TrkB immunostaining requires additional controls, as it has been very difficult to detect immunostaining of BDNF. In addition, in many of the figures, the background or outside of Purkinje cell boundaries also exhibits a positive signal.

We agree with the reviewers that the performance of the BDNF and TrkB antibodies is an important concern. We have ourselves had difficulties with the performance of many antibodies and the images in this paper are the result of many years of optimization. We have therefore added further detail about the antibody optimization to the methods section of this paper, and have carried out new staining experiments with additional controls. We have added 2 new figure panels in supplementary figures 3 and 4 to demonstrate these tests.

In the case of anti-BDNF antibodies, we have tested several antibodies and staining protocols and found that in our hands, the only antibody that reliably stained BDNF with a good signal to noise ratio was the one used in this paper (abcam ab108319). Even for this antibody, the staining was greatly enhanced by the use of a heat induced epitope retrieval (HIER) step, which allowed the visualization of BDNF within intracellular structures such as endosomes. When we quantified the intensity of this staining in our previous paper, the results were in agreement with those from a BDNF ELISA used to measure levels of BDNF in the cerebellar vermis of WT and SCA6 mice (Cook et al., 2022), which corroborates these results. As the staining was carried out in tissue sections and not dissociated cells, we also see positive signal from the BDNF staining outside of the Purkinje cells, since BDNF acts on cell-surface receptors and is thus released into the extracellular space around cells (Kuczewski et al., 2008) and is detectable in the extracellular matrix (Lam et al., 2019) and presynaptic terminals around neurons (Camuso et al., 2022; Choo et al., 2017). This is in contrast to studies that image BDNF mRNA with in-situ hybridization, which labels BDNF mRNA predominantly found in cells, and cannot tell us about sub-cellular or extracellular localization of BDNF protein. Together, these factors explain why we observe staining that is not cell- limited, but extends into the space around the cells of interest.

We have added an additional supplemental figure to demonstrate the importance of using HIER when staining slices with anti-BDNF (Supplementary figure 3). We tested HIER protocols that involved heating the slices to 95°C in a variety of buffers. The buffers tested were sodium citrate buffer (10 mM sodium citrate, 0.05% Tween 20, pH 6), Tris buffer (10mM TBS, 0.05% Tween 20, pH 10), EDTA buffer (1mM EDTA, 0.05% Tween 20, pH 8) and neutral PBS. The PBS produced the best result, enhancing the staining of both anti-BDNF and anti-EEA1 antibodies (Supplementary figure 3). Therefore all slices stained using those antibodies were heated to 95°C in PBS using a heat block or thermocycler for 10 minutes, then allowed to cool before staining proceeded.

The antibody we use (abcam ab108319) has been used in hundreds of other publications, including Javed et al., 2021 who ectopically expressed BDNF and noted colocalization between the antibody staining and the GFP tag of the BDNF construct, and Lejkowska et al., 2019 who overexpressed BDNF and saw a dramatic increase in antibody staining as well. The colocalization between ectopically expressed BDNF and the antibody in these studies demonstrates the specificity of the antibody.

However, to further validate antibody specificity we used liver tissue as a negative control. In liver tissue from rodents and humans, the majority of the liver contains negligible levels of BDNF (Koppel et al., 2009; Vivacqua et al., 2014), see also the Human Protein Atlas. The exception is some cholangiocytes: epithelial cells that express BDNF at high levels (Vivacqua et al., 2014). We obtained liver tissue from a WT mouse that was undergoing surgery for an unrelated project and fixed and processed the tissue as we did for brain tissue (outlined in methods section). As we would expect, most of the cells in the liver showed BDNF immunoreactivity that was comparable to background levels (Supplementary figure 3). Interestingly, we were also able to detect sparse highly BDNF-positive cells in the liver, presumed cholangiocytes (Supp. Fig. 3). This pattern of liver BDNF expression is as predicted in the literature, and thus acts as a control for our antibody. We therefore believe that in our hands this antibody is able to stain BDNF with an appropriate degree of specificity.

We also carried out staining experiments using a second anti-TrkB antibody that we had previously used to detect TrkB via Western bloing. We carried out immunohistochemistry as previously described using tissue sections from a WT mouse. The staining with the two different antibodies was carried out at the same time and all other reagents were kept constant. We found that both antibodies labelled TrkB in a similar pattern of localization, including in the early endosomes of the Purkinje cells (Supplementary figure 4). The second antibody however did have a lower signal to noise ratio and so we believe that the original anti-TrkB antibody used in this manuscript (EMD Millipore ab9872) is optimal for staining cerebellar tissue sections in our hands.

One important concern about the conclusions is that the RNAseq experiment was conducted in 12-month- old SCA6 mice suggesting that the defects in the endo-lysosomal system may be caused by other pathophysiological events and, likewise, the impairment in BDNF signaling may also be indirect, as also noted by the authors. Indeed, Purkinje cells in SCA6 mice have an impaired ability to degrade other endocytosed cargo beyond BDNF and TrkB, most likely because of trafficking deficits that result in a disruption in the transport of cargo to the lysosomes and lysosomal dysfunction.

We agree with the reviewers that the defects in the endo-lysosomal system may be caused by other events occurring in the course of disease progression. As mentioned by the reviewers, we have noted this possibility in the text. Detailed investigation into the sequence of events and the root causes of signaling disruption in SCA6 merits future study and we aim to address this in future work. We have expanded this explanation in the text.

Moreover, the beneficial effects of 7,8-DHF treatment on motor coordination may be caused by 7,8-DHF properties other than the putative agonist role on TrkB. Indeed, many reservations have been raised about using 7,8-DHF as an agonist of TrkB activity. Several studies have now debunked (Todd et al. PlosONE 2014, PMID: 24503862; Boltaev et al. Sci Signal 2017, PMID: 28831019) or at the very least questioned (Lowe D, Science 2017: see Discussion: https://www.science.org/content/blog-post/those-compounds-aren-t- what-you-think-they-are Wang et al. Cell 2022 PMID: 34963057). Another interpretation is that 7,8-DHF possesses antioxidant activity and neuroprotection against cytotoxicity in HT-22 and PC12 cells, both of which do not express TrkB (Chen et al. Neurosci Lett 201, PMID: 21651962; Han et al. Neurochem Int. 2014, PMID: 24220540). Thus, while this flavonoid may have a beneficial effect on the pathophysiology of SCA6, it is most unlikely that mechanistically this occurs through a TrkB agonistic effect considering the potent anti-oxidant and anti-inflammatory roles of flavonoids in neurodegenerative diseases (Jones et al. Trends Pharmacol Sci 2012, PMID: 22980637).

We thank the reviewers for raising this important point. We have noted in our previous paper (Cook et al., 2022) that 7,8-DHF may not be acting as a TrkB agonist in SCA6 mice, and are in agreement that other explanations are possible. We have now added information to the text of this paper to highlight this possibility. We did show in our previous paper that 7,8-DHF administration activates Akt signaling in the cerebellum of SCA6 mice, a signaling event that is known to take place downstream of TrkB activation. Additionally, 7,8-DHF treatment led to the increase of TrkB levels in the cerebellum of SCA6 mice (Cook et al., 2022), implicating TrkB in the mechanism of action, even if mechanistically, this is not via direct TrkB activation alone. However, even if the mechanism is currently incompletely explained, we believe that 7,8- DHF remains a valuable treatment strategy for SCA6. We have tried to rewrite the Discussion to highlight what we think is the most important takeaway: that 7,8-DHF can rescue endosomal and other deficits in SCA6, even if we do not currently know the full mechanism of action. We have therefore amended the text to add more detail about other potential explanations for the mechanism of action of 7,8-DHF.

References

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Choo M, Miyazaki T, Yamazaki M, Kawamura M, Nakazawa T, Zhang J, Tanimura A, Uesaka N, Watanabe M, Sakimura K, Kano M. 2017. Retrograde BDNF to TrkB signaling promotes synapse elimination in the developing cerebellum. Nat Commun 8:195. doi:10.1038/s41467-017-00260-w

Cook AA, Jayabal S, Sheng J, Fields E, Leung TCS, Quilez S, McNicholas E, Lau L, Huang S, Watt AJ. 2022. Activation of TrkB-Akt signaling rescues deficits in a mouse model of SCA6. Sci Adv 8:3260. doi:10.1126/sciadv.abh3260

Javed S, Lee YJ, Xu J, Huang WH. 2021. Temporal dissection of Rai1 function reveals brain-derived neurotrophic factor as a potential therapeutic target for Smith-Magenis syndrome. Hum Mol Genet 31:275–288. doi:10.1093/HMG/DDAB245

Koppel I, Aid-Pavlidis T, Jaanson K, Sepp M, Pruunsild P, Palm K, Timmusk T. 2009. Tissue-specific and neural activity-regulated expression of human BDNF gene in BAC transgenic mice. BMC Neurosci 10:68. doi:10.1186/1471-2202-10-68

Kuczewski N, Porcher C, Ferrand N, Fiorentino H, Pellegrino C, Kolarow R, Lessmann V, Medina I, Gaiarsa JL. 2008. Backpropagating action potentials trigger dendritic release of BDNF during spontaneous network activity. J Neurosci 28:7013–7023. doi:10.1523/JNEUROSCI.1673-08.2008

Lam D, Enright HA, Cadena J, Peters SKG, Sales AP, Osburn JJ, Soscia DA, Kulp KS, Wheeler EK, Fischer NO. 2019. Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuron-glia co-culture on a multi-electrode array. Sci Rep 9. doi:10.1038/s41598- 019-40128-1

Lejkowska R, Kawa MP, Pius-Sadowska E, Rogińska D, Łuczkowska K, Machaliński B, Machalińska A. 2019. Preclinical Evaluation of Long-Term Neuroprotective Effects of BDNF-Engineered Mesenchymal Stromal Cells as Intravitreal Therapy for Chronic Retinal Degeneration in Rd6 Mutant Mice. Int J Mol Sci 2019, Vol 20, Page 777 20:777. doi:10.3390/IJMS20030777

Vivacqua G, Renzi A, Carpino G, Franchitto A, Gaudio E. 2014. Expression of brain derivated neurotrophic factor and of its receptors: TrKB and p75NT in normal and bile duct ligated rat liver. Ital J Anat Embryol 119:111–129. doi:10.13128/IJAE-15138

Author Response

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

We thank the reviewers and editor for their thoughful and careful evaluation of our manuscript. We appreciate your time and effort and have incorporated many of these suggestions to improve our revised manuscript.

Reviewer #1 (Public Review):

Summary: Cullinan et al. explore the hypothesis that the cytoplasmic N- and C-termini of ASIC1a, not resolved in x-ray or cryo-EM structures, form a dynamic complex that breaks apart at low pH, exposing a C-terminal binding site for RIPK1, a regulator of necrotic cell death. They expressed channels tagged at their N- and C-termini with the fluorescent, non-canonical amino acid ANAP in CHO cells using amber stop-codon suppression. Interaction between the termini was assessed by FRET between ANAP and colored transition metal ions bound either to a cysteine reactive chelator attached to the channel (TETAC) or metal-chelating lipids (C18-NTA). A key advantage to using metal ions is that they are very poor FRET acceptors, i.e. they must be very close to the donor for FRET to occur. This is ideal for measuring small distances/changes in distance on the scales expected from the initial hypothesis. In order to apply chelated metal ions, CHO cells were mechanically unroofed, providing access to the inner leaflet of the plasma membrane. At high pH, the N- and C- termini are close enough for FRET to be measured, but apparently too far apart to be explained by a direct binding interaction. At low pH, there was an apparent increase in FRET between the termini. FRET between ANAP on the N-and Ctermini and metal ions bound to the plasma membrane suggests that both termini move away from the plasma membrane at low pH. The authors propose an alternative hypothesis whereby close association with the plasma membrane precludes RIPK1 binding to the C-terminus of ASIC1a.

Strengths: The findings presented here are certainly valuable for the ion channel/signaling field and the technical approach only increases the significance of the work. The choice of techniques is appropriate for this study and the results are clear and high quality. Sufficient evidence is presented against the starting hypothesis.

Weaknesses: I have a few questions about certain controls and assumptions that I would like to see discussed more explicitly in the manuscript.

My biggest concern is with the C-terminal citrine tag. Might this prevent the hypothesized interaction between the N- and C-termini? What about the serine to cysteine mutations? The authors might consider a control experiment in channels lacking the C-terminal FP tag.

While it is certainly possible that the C-terminal citrine tag is preventing the hypothesized interaction between the intracellular termini, there are a few things that mitigate (but not eliminate) this concern. First, previous work looking at the interaction between the intracellular termini used FPs on both the N- and C-termini and concluded that in fact there is an interaction (PMID:31980622). Our channels have only a single FP, and we use a higher resolution FRET approach. Second, we aVach our citrine tag with a 11-residue linker, allowing for enhanced flexibility of the region and hopefully allowing for more space for an interaction that was posited to be between the very proximal part of the C-terminus (near the membrane and away from the tag) and the untagged N-terminus. Third, we previously showed that Stomatin, a much larger protein than the NTD, could bind the distal C-terminus of rASIC3 with a large fluorescent protein connected by the same linker on the C-terminus. In the case of Stomatin, the interaction involved the residues at the distal portion of the C-terminus close to the bulky FP. Interestingly, while we did not publish this, without this flexible linker, Stomatin could not regulate the channel and likely did not bind.

Despite this, we agree that this is possible and have added a statement in our limitations section explicitly saying this.

Figure 2 supplement 1 shows apparent read-through of the N-terminal stop codons. Given that most of the paper uses N-terminal ANAP tags, this figure should be moved out of the supplement. Do Nterminally truncated subunits form functional channels? Do the authors expect N-terminally truncated subunits to co-assemble in trimers with full-length subunits? The authors should include a more explicit discussion regarding the effect of truncated channels on their FRET signal in the case of such co-assembly.

The positions that show readthrough (E6, L18, H515) were not used in the study. We eliminated them largely on the basis of these westerns. We elected to put the bulk of the blots in the supplement simply because of how many there were. We believe this is the best compromise. It allows us to show representative blots for all our positions without making an illegible figure with 7 blots.

The N-terminally truncated subunits would create very short peptides that are not able to create functional channels. A premature stop at say E8 would create a 7-mer. Our longest N-terminal truncation would only create a protein of 32 amino acids. These don’t contain the transmembrane segments and thus cannot make functional channels.

As the epitope used for the western blots in Figure 2 and supplements is part of the C-terminal tag, these blots do not provide an estimate of the fraction of C-terminally truncated channels (those that failed to incorporate ANAP at the stop codon). What effect would C-terminally truncated channels have on the FRET signal if incorporated into trimers with full-length subunits?

Alternatively, C-terminally truncated subunits would be able to form functional channels because they contain the full N-terminus, the transmembrane domains, the extracellular domain and a portion of the C-terminus. We don’t think this is a major contaminant to our experiments. The only two C-terminal ANAP positions we use are 464 and 505. In each of these cases, they are only used for memFRET. The ones that do not contain ANAP are essentially “invisible” to the experiment. Since we are measuring their proximity to the membrane, having some missing should not maVer. However, there is some chance that truncations in some subunits could allosterically affect the position of the CT in other subunits. We have added a discussion of this in the manuscript.

Some general discussion of these results in the context of trimeric channels would be helpful. Is the putative interaction of the termini within or between subunits? Are the distances between subunits large enough to preclude FRET between donors on one subunit and acceptor ions bound on multiple subunits?

Thank you for this comment. We did not directly test whether the distances are within or between subunits. We considered using a concatemer to do this, however, the concatemeric channels do not express particularly well. Then, UAA incorporation hurts the expression as well. It was unlikely we would be able to get sufficient expression for tmFRET.

However, the Maclean group has previously tested this using FRET between concatenated subunits and determined that FRET is stronger within than between subunits. We have updated the manuscript to reflect a more thorough discussion of our results in the context of their trimeric assembly.

The authors conclude that the relatively small amount of FRET between the cytoplasmic termini suggests that the interaction previously modeled in Rosetta is unlikely. Is it possible that the proposed structure is correct, but labile? For example, could it be that the FRET signal is the time average of a state in which the termini directly interact (as in the Rosetta model) and one in which they do not?

The proposed RoseVa model does not include the reentrant loop of the channel, so it is probable that this model would change if it were redone to include this new feature of the channel.

However, we do discuss the limitation of FRET as a method that measures a time average that is weighted towards closest approach in our discussion section. The termini are most certainly dynamic and it is possible that spend some time in close proximity. Given that FRET is biased towards closest approach, we actually think this strengthens our argument that the termini don’t spend a great deal of time in complex. In addition, our MST data suggests that the termini do not bind. We have added some commentary on this to the discussion section for clarity.

Reviewer #2 (Public Review):

Summary:

The authors use previously characterised FRET methods to measure distances between intracellular segments of ASIC and with the membrane. The distances are measured across different conditions and at multiple positions in a very complete study. The picture that emerges is that the N- and C-termini do not associate.

Strengths:

Good controls, good range of measurements, advanced, well-chosen and carefully performed FRET measurements. The paper is a technical triumph. Particularly, given the weak fluorescence of ANAP, the extent of measurements and the combination with TETAC is noteworthy.

The distance measurements are largely coherent and favour the interpretation that the N and C terminus are not close together as previously claimed.

Weaknesses:

One difficulty is that we do not have a positive control for what binding of something to either N- or Cterminus would look like (either in FRET or otherwise).

We acknowledge that this is a challenge for the approach. Having a positive control for binding would be great but we are not sure such a thing exists. You could certainly imagine a complex between two domains where each label (ANAP and TETAC) are pointed away from one other (giving comparatively modest quenching) or one where they are very close (giving comparatively large quenching), both of which could still be bound. This is essentially a less significant version of the problem with using FPs to measure proximity…they are not very good proxies for the position of the termini. These small labels are certainly beVer proxies but still not perfect. Our conclusion here is based more on the totality of the data. We tried many combinations and saw no sign of distances closer than ~ 20A at resting pH. We think the simplest explanation is that they are not close to one another but we tried to lay out the limitations in the discussion.

One limitation that is not mentioned is the unroofing. The concept of interaction with intracellular domains is being examined. But the authors use unroofing to measure the positions, fully disrupting the cytoplasm. Thus it is not excluded that the unroofing disrupts that interaction. This should be mentioned as a possible (if unlikely) limitation.

Thank you for your comment. We discuss unroofing as a potential limitation because it exposes both sides of the plasma membrane to changes in pH. We have updated this section to include acknowledgement of the possibility that unroofing disrupts the interaction via washout of other critical proteins.

Reviewer #3 (Public Review):

Summary: The manuscript by Cullinan et al., uses ANAP-tmFRET to test the hypothesis that the NTD and CTD form a complex at rest and to probe these domains for acid-induced conformational changes. They find convincing evidence that the NTD and CTD do not have a propensity to form a complex. They also report these domains are parallel to the membrane and that the NTD moves towards, and the CTD away, from the membrane upon acidification.

Strengths:

The major strength of the paper is the use of tmFRET, which excels at measuring short distances and is insensitive to orientation effects. The donor-acceptor pairs here are also great choices as they are minimally disruptive to the structure being studied.

Furthermore, they conduct these measurements over several positions with the N and C tails, both between the tails and to the membrane. Finally, to support their main point, MST is conducted to measure the association of recombinant N and C peptides, finding no evidence of association or complex formation.

Weaknesses:

While tmFRET is a strength, using ANAP as a donor requires the cells to be unroofed to eliminate background signal. This causes two problems. First, it removes any possible low affinity interacting proteins such as actinin (PMID 19028690). Second, the pH changes now occur to both 'extracellular' and 'intracellular' lipid planes. Thus, it is unclear if any conformational changes in the N and CTDs arise from desensitization of the receptor or protonation of specific amino acids in the N or CTDs or even protonation of certain phospholipid groups such as in phosphatidylserine. The authors do comment that prolonged extracellular acidification leads to intracellular acidification as well. But the concerns over disruption by unroofing/washing and relevance of the changes remain.

We acknowledge that unroofing is a limitation of our approach and noted it in the discussion. However, we have updated the section to include the possibility that the act of unroofing and washing could also disrupt the potential interaction between the intracellular domains as well as between these domains and other intracellular proteins. This was the best approach we could use to address our questions and it required that we unroof the cells. However, we look forward to future studies or new techniques that do not require the unroofing of the cells.

The distances calculated depend on the R0 between donor and acceptor. In turn, this depends on the donor's emission spectrum and quantum yield. The spectrum and yield of ANAP is very sensitive to local environment. It is a useful fluorophore for patch fluorometry for precisely this reason, and gating-induced conformational changes in the CTD have been reported just from changes in ANAP emission alone (PMID 29425514). Therefore, using a single R0 value for all positions (and both pHs at a single position) is inappropriate. The authors should either include this caveat and give some estimate of how big an impact changes spectrum and yield might have, or actually measure the emission spectra at all positions tested.

This is a reasonable concern and one we considered. Measuring the quantum yield would be quite difficult. However, we have measured spectra at a number of positions and see a relatively minimal shik in the peak. Most positions peak between 481 and 484nm. If you calculate the difference in R0 using theoretical spectra with a blue shik of 20nm, the difference in R0 is only ~1.5A. A shik of 20nm is on the higher side of anything we have seen in the literature (PMID 30038260) and since even with that large a shik, the difference is minimal we do not think measuring spectra for each position would impact the overall conclusions presented. As you noted, though, the quantum yield also changes. Assuming a change in yield from 0.22 to 0.47, the largest we found reported in the literature (PMID:29923827) , the R0 would increase by 2A. This same paper showed that the blue shiked position was the one with the higher extinction coefficient so these changes would be working in opposition to one another making the difference in R0 even smaller. It is important to note, that while tmFRET is a much more powerful measure of distance than standard FRET, these distances, as you point out, are quite challenging to measure precisely. Our conclusions are based less on the absolute distances and more on the observation that no positions show large quenching and that if there is any change upon acidification, it is in the wrong direction.

Overall, the writing and presentation of figures could be much improved with specific points mentioned in the recommendations for authors section.

See below.

The authors argue that the CTD is largely parallel to the plasma membrane, yet appear to base this conclusion on ANAP to membrane FRET of positions S464 and M505. Two positions is insufficient evidence to support such a claim. Some intermediate positions are needed.

We do not see in the paper where we suggest that the CTD is parallel. However, your point that we could try and determine if this was the case is correct. However, we aVempted to create several other CTD TAG mutants but struggled with readthrough and poor expression of these mutants so we opted to just include S464 and M505. Our point from these data is only that the distal CTD (505) must spend significant time near the membrane to explain our FRET data.

Upon acidification, NTD position Q14 moves towards the plasma membrane (Figure 8B). Q14 also gets closer to C515 or doesn't change relative to 505 (Figures 7C and B) upon acidification. Yet position 505 moves away from the membrane (Figure 8D). How can the NTD move closer to the membrane, and to the CTD but yet the CTD move further from the membrane? Some comment or clarification is needed.

This is a reasonable question and one that is hard to definitively answer. Our goal here was to test the hypothesis that the termini are bound at rest. Mapping the precise positions of the termini is difficult for reasons we will enumerate in the question that asks why we didn’t make a model. There are potentially multiple explanations but the easiest one would be that the CTD could move away from the membrane but closer to Q14, for instance, if the distal termini, say, rotated towards the NTD. This would move 505 closer and have no impact on whether or not the NTD and CTD moved away or toward the membrane.

Reviewer #1 (Recommendations For The Authors):

Minor concerns

The authors show the spectrum of ANAP attached to beads and use this spectrum to calculate R0 for their FRET measurements. Peak ANAP fluorescence is dependent on local environment and many reports show ANAP in protein blue-shiked relative to the values reported here. How would this affect the distance measurements reported?

This is an important point. See above for the answer.

Could the lack of interaction between the N- and C-terminal peptides in Figure 7 arise from the cysteine to serine mutations or lack of structure in the synthetic peptides. How were peptide concentrations measured/verified for the experiment?

It is possible that cysteine to serine mutations could prevent the interaction. It is also possible that these peptides are not capable of adopting their native fold without the presence of the plasma membrane or due to being synthetically created. However, the termini are thought to be largely unstructured. We received these peptides in lyophilized form at >95% purity and resuspended to our desired stock concentration (3 mM C-terminus, 1 mM N-terminus). Even if our concentration was off, we see no signs of interaction up to quite a high concentration.

How was photobleaching measured for correcting the data?

We executed several mock experiments at various TAG positions using either pH 8 and pH 6, where we performed the experiments as usual but with a mock solution exchange when we would normally add the metal. We normalized the L-ANAP fluorescence to the first image and averaged together these values for pH 8 and pH 6. We then corrected using Equation 2 in the manuscript..

We have updated the methods to include how we adjusted for bleaching.

The authors may wish to make it more explicit that their Zn2+ controls also preclude the possibility that a changing FRET signal between ANAP and citrine may affect their data.

Thank you for this comment. We agree, it would strengthen the manuscript to include this statement. We have now included this.

It might be useful to the reader if the authors could include (as a supplement) plots of their data (like in Figure 6), in which FRET efficiency has been converted to distance.

We considered this idea as well but felt like showing the actual data in the figures and the distances in a table would be best.

Figure 5D is mentioned in the text before any other figures. This is unconventional. Could this panel be moved to Figure 1 or the mention moved to later?

Changed

western blot is not capitalized.

Changed.

Figure 1, the ANAP structure shown is the methyl ester, which is presumably cleaved before ANAP is conjugated to the tRNA. The authors may wish to replace this with the free acid structure.

This is a fair point. We originally used the methyl ester structure to indicate the version of ANAP we chose to use. However, you are correct that the methyl ester is cleaved before conjugation to the tRNA. We replaced the methyl ester with the free acid structure to clarify this.

Figures 1 and 4 should have scale bars for the images.

Scale bars have been added to figures 1, 4, and 5.

In Figure 3, the letters in the structures (particularly TETAC) are way too small. Please increase the font size.

Changed

In Figure 3 and Figure 3 supplement 1, the axes are labeled "Absorbance (M-1cm-1)." Absorbance is dimensionless. The authors are likely reporting the extinction coefficient.

Thank you for catching this. We adjusted the axes to extinction coefficient.

In Figures 5 B and C, it might be clearer if the headers read "Initial, +Cu2+/TETAC, DTT" rather than "Initial, FRET, Recovery."

Changed

The panel labels for Figure 8 seem to be out of order.

Changed

The L for L-ANAP should be rendered, by convention, in small caps.

This is a good example of learning something new from the review process. This is the first I have ever heard of small caps. We can find no other papers that use small caps for L-ANAP so I am not 100% sure what convention this is referring to and don’t want to change the wrong thing in the paper. We are happy to change if the editorial staff at eLife agree but have lek this for now.

Reviewer #2 (Recommendations For The Authors):

With so many distances measured, why was not even a basic structural model attempted?

We certainly considered it, but a number of things lead us to conclude that it might imply more certainty about the structure of these termini than we hope to give. 1) Given that the FRET is a time average of positions, these distance constraints would not do much constraining. 2) Given that the termini are likely unstructured and flexible this makes the problem in 1 worse. 3) There is no structural information to use as a starting point for a model. 4) The flexibility of the linkers for each FRET pair also introduces uncertainty. This can, in theory, be modeled as they do in EPR but all of this together made us decide not to do this. What we hope readers take home, is the overall picture of the data is not consistent with the original RIPK1 hypothesis.

Maybe it would be good to draw a band on the graphs in Figure 6 for the FRET signal expected for interaction (and thus, disfavoured by these data). This would at least give context.

We agree this could be helpful, but it is not so easy to do. What distance would we choose? We could put a line at ~5Å (the model predicted distance). As we noted above, a number of distances could be compatible with an interaction. However, we think it’s unlikely that if a complex was formed that none of our measurements would show a distance closer than 20Å at rest and that an unbinding event would then lead to a decrease in distance. This, to us, is the take home message.

Minor points:

"Aker unroofing the cells, only fluorescence associated with the "footprint", or dorsal surface, of the cell membrane is lek behind."

The authors use dorsal and ventral in this section to describe parts of an adherent cell. But in the first instance, they remove the dorsal part of the cell, and then in this phrase, the dorsal part is lek behind....I am a bit confused.

Thank you for pointing out this mistake, we have fixed this. It is indeed the ventral surface lek behind.

"bind at rest an" - and?

Changed

"One previous study used a different approach to try and map the topography of the intracellular termini of ASIC1a comparable to our memFRET experiments." I think a citation is due.

Citation added

"great deal of precedent" even if this result is from my own lab, I would prefer that the authors note that it's one study from one lab! I think best just to delete "great deal of".

“Great deal of” deleted

I think the column "Significance" in the tables is unnecessary when the P value is given.

Thank you for this suggestion. We agree and have made the change.

Figure 7a Q14TAG has a clearly bimodal distribution at pH 8. What could be the meaning of this result? The authors do not mention it that I could find. Perhaps there is no meaning. The authors should state what they think is (or is not) going on.

This is a good question and we don’t have a good answer. It appears to be experimental variability. The data from the “low fret” in this experimental condition all came from the same days. So something was different that day. We considered that they might be outliers to exclude but thought showing all of our data was the beVer path. We reperformed the ANOVA here separating out the “outlier” day and nothing of substance changed. Both populations were still different with P value less than 0.001.

Typo: Lumencore

Changed

Maybe just a matter of taste but the panel created with Biorender in Figure 8 is not attractive and depicts the channel differently to in Figure 5D, which is again different from Figure 1A. Surely one advantage of using computer-generated artwork could be to have consistency.

We agree and have used the same cartoon for all of our images with the one exception being the schematics that are just meant to show the positions that are present in each bar graph.

Figure 4A was squashed to fit (text aspect ratio is wrong).

Fixed

Reviewer #3 (Recommendations For The Authors):

Citrine is used to report incorporation. Yet citrine has a strong tendency to dimerize (PMID 27240257). Did the authors use mCitrine or just Citrine? This is quite important in interpreting their data.

Thank you for pointing out this important distinction. We use mCitirine which we have added to the methods.

The manuscript has numerous instances of imprecise language. For example, page 10, last para, first line, "previous studies have looked at..." or page 7, final paragraph "tell a similar story". Related, the figures could be much better. For example, in Figure 1, where the authors depict the anap chemical in red, as opposed to the blue one might expect of a blue emiqng fluorophore. In figure 6, ANAP is also in red with the quenching group in green. This is opposite to how one typically thinks of FRET with the warmer color being the acceptor not the donor. Moreover, the pH 6 condition is also colored the same shade of red as the ANAP. Labels of Cys positions would again be useful here. In Figure 3, the heteroatoms of TETAC and C18-NTA are very small and difficult to see. It would also be good to label these structures, and the spectra below, so the reader can tell at a glance without looking at the caption, what the structures and spectra arise from. Also, how are the absorption spectra normalized? This is not discussed in the methods. The lack of attention to presentation mars an otherwise nice study.

Thank you for these points. We have made modifications to the manuscript to address these comments.

Abstract, second last line "Aker prolonged acidification, ...", 'prolonged' could be interpreted as 'it takes a while for the domain to move' or 'the movement only happens aker a while'. This not what the authors intend to convey. Consider modifying to just 'Aker acidification,'

We updated the main text to indicate that prolonged acidification is intended to describe acidification that occurs over the minutes timescale.

Pdf page 6, bottom para on Anap incorporation not altering channel function: What is meant by 'steady state pH dependence of activation'? This implies the authors applied a pH stimulus, then waited until equilibrium was achieved ie. until desensitization was complete and measured the current at that point. It seems more likely they simply applied different pH stimuli and measured the peak response and that the use of 'steady state' here is a typo.

We removed the phrase steady state.

Same section, controls of electrophysiology allude to 485, 505 and 515 ANAP-containing channels. In fact, the authors have no way of determining what fraction (if any) of the pH evoked currents arise from channels containing Anap in those positions versus from simply having a translation stop but still functioning. This should be mentioned.

This is correct. We cannot be sure the CTD TAG positions are not a mixture of ANAP-containing channels and truncations. See above for why we do not think this a big concern for the FRET experiments. Functionally, though, you are correct that we cannot tell. We now mention this in the paper.

Methods, the abbreviation for SBT should be defined somewhere.

Added.

Methods, unroofing section, middle paragraph, the authors use nM not nm to list wavelengths of light.

Changed.

Figure 3C-D: There's an unexpected blip in the Anap emission spectra at ~500 nm. Are the grating efficiency of the spectrograph and quantum efficiency of the camera accounted for in these spectra?

This is a good question. The data are not corrected for either camera efficiency or grating efficiency. We don’t have easy access to the actual data (although we can see a pdf version of each). There is a liVle blip in the grating efficiency graph that could partly explain the blip in our spectra.

Figure 5C, were recovery experiments routinely done? If so, would be good to show more than n = 1 in the plot to get an idea of reproducibility.

Recovery experiments were done in every experiment but are not shown for simplicity. We have included all FRET and recovery data for position Q14TAG-C469 at pH 6 in figure 5C to show reproducibility of our FRET and recovery data.

Table 1, considering adding a Δ distance column (pH 8 versus 6) so the magnitude of changes are more easily seen.

This is a reasonable suggestion but we decided not to include a Δ distance column. The data are whole numbers and people can easily determine the Δ distance. We felt that including that column would bring too much focus on what we think are preVy small changes. Our hope is that readers take away that the data are not consistent with complex formation between the determine and focus less on absolute distances.

Figure 7A, Q14tag pH 8 condition has a quite a bit of spread and, likely, two populations. These data, as well as G11, are unlikely to be parametric and hence ANOVA is inappropriate. A normality test, and likely Kruskal-Wallis test is called for.

Aker testing for normality, the data for Q14TAG C485 pH8 are non-normally distributed. However, a Kruskal Wallis is a non-parametric test for a one-way ANOVA and not applicable here. We separated the data out into population 1 and 2 and repeated the two-way ANOVA statistical test. When Q14TAG pH 8 is split into 2 populations, the statistics hardly change. When the data is not separated, Q14TAG pH 8 relative to pH 6 has a p-value <0.0001. When the 2 populations are separated, both populations relative to Q14TAG pH 6 still have a p-value of <0.0001.

Author Response

eLife assessment

This paper by Aitchison and colleagues describes nanobody neutralizing and binding activity against various SARS-CoV-2 variants of concern. The findings are important in that the described nanobodies may have broad therapeutic relevance against current and future variants of concern and may be able to avoid significant resistance. The claims are incomplete: while the study is well-executed and uses a nice balance of biochemical and cellular assays, the efficacy of the proposed nanobody library against VOCs is not completely supported as IC50 values appear to increase against newer variants and are higher than previously used therapeutic bNAbs, animal data showing in vivo efficacy is lacking, and protection against future possible variants is not proven.

This manuscript is a follow-up of our previous eLife manuscript “Highly synergistic combinations of nanobodies that target SARS-CoV-2 and are resistant to escape” https://elifesciences.org/articles/73027 where we described an “impressive collection of hundreds of new nanobodies binding SARS-CoV-2 spike by combining in vivo antibody affinity maturation and proteomics. [Editor’s evaluation]”. As a follow-up this submission extends the findings of our previous eLife publication and thus focuses on how our repertoire functions in the context of a rapidly evolving SARS-CoV-2 virus, relying on the established methodologies and approaches of the original paper. We explore how nanobody functions have been influenced by the emergence of SARS-CoV-2 variants containing extensive mutations in spike protein, which largely reduced the usefulness of therapeutic monoclonal antibody therapeutics. Our findings show that while some nanobodies lost efficacy in binding to and neutralizing these evolved spikes, a surprising number of nanobodies retained their binding and neutralization activity. This is an important finding, because these efficacious nanobodies target regions that appear rarely targetable by monoclonal antibodies. We also provide experimental validation of the importance of the interplay between binding and neutralization in synergy experiments, where even weakened binding still contributed to strongly enhancing the neutralization.

Reviewer #1 (Public Review):

Summary:

In this manuscript, Ketaren, Mast, Fridy et al. assessed the ability of a previously generated llama nanobody library (Mast, Fridy et al. 2021) to bind and neutralize SARS-CoV-2 delta and omicron variants. The authors identified multiple nanobodies that retain neutralizing and/or binding capacity against delta, BA.1 and BA.4/5. Nanobody epitope mapping on spike proteins using structural modeling revealed possible mechanisms of immune evasion by viral variants as well as mechanisms of cross-variant neutralization by nanobodies. The authors additionally identified two nanobody pairs involving non-neutralizing nanobodies that exhibited synergy in neutralization against the delta variant. These results enabled the refinement of target epitopes of the nanobody repertoire and the discovery of several pan-variant nanobodies for further preclinical development.

Strengths:

Overall, this study is well executed and provides a valuable framework for assessing the impact of emerging SARS-CoV-2 variants on nanobodies using a combination of in vitro biochemical and cellular assays as well as computational approaches. There are interesting insights generated from the epitope mapping analyses, which offer possible explanations for how delta and omicron variants escape nanobody responses, as well as how some nanobodies exhibit cross-variant neutralization capacity. These analyses laid out a clear path forward for optimizing these promising next-gen therapeutics, particularly in the face of rapidly emerging SARS-CoV-2 variants. This work will be of interest to researchers in the fields of antibody/nanobody engineering, SARS-CoV-2 therapeutics, and host-virus interaction.

Weaknesses:

A main weakness of the study is that the efficacy statement is not thoroughly supported. While the authors comprehensively characterized the neutralizing ability of nanobodies in vitro, there is no animal data involving mice or hamsters to demonstrate the real protective efficacy in vivo. Yet, in the title and throughout the manuscript, the authors repeatedly used phrases like "retains efficacy" or "remains efficacious" to describe the nanobodies' neutralization or binding capacities.

This claim is not well supported by the data and underestimates the impact of variants on the nanobodies, especially the omicron sublineages. For example, the authors showed that S1-RBD-15 had a ~100-fold reduction in neutralization titer against Omicron, with an IC50 at around 1 uM. This is much higher than the IC50 value of a typical anti-ancestral RBD nanobody reported in the previous study (Mast, Fridy et al. 2021). In fact, the authors themselves ascribe nanobodies with an IC50 above 1 uM as weak neutralizers. And there were many in the range of 0.1-1 uM.

Furthermore, many nanobodies selected for affinity measurement against BA.4/5 had no detectable binding.

Without providing in vivo protection data or including monoclonal antibodies that are known to be efficacious against variants in the in vitro assays as a benchmark, it is difficult to evaluate the efficacy just with the IC50 values.

We respectfully disagree with the reviewer on several aspects of this critique.

As to our use of the word efficacy - the quality of being successful in producing an intended result; effectiveness - we were specific to nanobody binding and in vitro neutralization of the variant spike proteins tested in the manuscript. Indeed, our manuscript made no claim of efficacy outside of this intended meaning. However, to prevent misinterpretation we will modify the final paragraph of our introduction to state explicitly that the nanobody repertoire retains efficacy in binding and neutralizing variants of spike. The final paragraph of the Introduction will include the following:

“Here, we demonstrate that a subset of our previously published repertoire of nanobodies, generated against spike from the ancestral SARS-CoV-2 virus (Mast, Fridy et al. 2021), retains binding and in vitro neutralization efficacy against circulating variants of concern (VoC), including omicron BA.4/BA.5.”

We agree that in vivo neutralization data would be an important complement to the in vitro binding and neutralization data. Experiments along these lines are ongoing, but are not considered part of a follow-up to our original paper where in vivo data were not included.

We disagree with the Reviewer that “This claim is not well supported by the data and underestimates the impact of variants on the nanobodies, especially the omicron sublineages.” As we specifically state: “In comparison, groups I, I/II, I/IV, V, VII, VIII and the anti-S2 nanobodies contained the majority of omicron BA.1 neutralizers, though here the neutralization potency of many nanobodies was decreased compared to wild-type. This decrease in neutralization potency largely correlates with the accumulation of omicron BA.1 specific mutations throughout the RBD, which likely alters the epitope-binding site of these nanobodies, weakening their interaction with BA.1 spike (Fig. 1B). (emphasis added)”

Naturally, we expected that some of our nanobodies would lose the ability to bind BA.4/BA.5. This enabled us to determine which areas on spike remained susceptible to our nanobodies. We show that 10/29 nanobodies tested retained binding to BA.4/5. We did not test our entire repertoire, just a subset was selected for. We stated the following:

“Of the nanobodies that neutralized both delta and omicron BA.1, representatives from each of the nanobody epitope groups were selected for SPR analysis, where S1 binders with mapped epitopes that neutralized one or both variants well, were prioritized.”

Reviewer #2 (Public Review):

Summary:

Interest in using nanobodies for therapeutic interventions in infectious diseases is growing due to their ability to bind hidden or cryptic epitopes that are inaccessible to conventional immunoglobulins. In the present study, the authors were posed (sic) to characterize nanobodies derived from the library produced earlier with the Wuhan strain of SARS-CoV-2, map their epitopes on SARS-CoV-2 spike protein, and demonstrate that some nanobodies retain binding and even neutralization against antigenically distant Variants of Concern (VOCs) that are currently circulating.

Strengths:

The authors demonstrate that some nanobodies - despite being obtained against the ancestral virus strain - retain high affinity binding to antigenically distant SARS-CoV-2 strains. This is despite the majority of the repertoire losing binding. Although limited to only two nanobody combinations, the demonstration of synergy in virus neutralization between nanobodies targeting different epitopes is compelling.

We thank the Reviewer for this positive summary of the strengths of our study. In our previous work, we applied stringent criteria for the down-selection of nanobodies based on their affinity and diversity, as elaborated on in https://elifesciences.org/articles/73027. The current dataset is a further judiciously curated subset, featuring 41 nanobodies chosen to represent and inform on the 10 structurally mapped epitope groups that we initially identified. This subset is but the tip of an iceberg. For each nanobody demonstrating high-affinity binding and neutralization, we possess multiple sequence variants, offering alternative avenues for investigation. Moreover, our repertoire has since been further elaborated by use of a yeast display library (Cross et al., 2023 JBC) providing additional nanobodies capable of targeting the same epitopes. Our findings presented here, thus serve as a heuristic, enabling us to distill the much larger repertoire into manageable and informative clusters of data. We will modify our manuscript to be more explicit of these facts.

Weaknesses:

The authors imply that nanobodies that retain binding/neutralization of early Omicron sublineages will be active against currently circulating and future virus strains. Unfortunately, no reasoning for such a conclusion nor data supporting this prediction are provided.

The nanobodies we propose to retain binding to current and emerging omicron sublineages at the time (Fig. 4) are those that still bind to omicron BA.1, BA.4/5. The structures of XBB and BQ.1 are not divergent enough from these aforementioned omicron sublineages in the regions we propose our nanobodies retain binding (Fig. 4) to result in loss of binding. Thus, we hypothesize that the epitopes where these nanobodies bind or are predicted to bind (outlined in black (Fig. 4)), represent regions on spike vulnerable to nanobody intervention. Importantly, we also now have further experimental data to support our predictions that these nanobodies in Fig. 4 will retain binding (see plot in Author response image 1). We will provide additional data and complements to key figures to help illustrate this in the revised manuscript.

Author response image 1.

Author Response

In this paper, we examine the behavioral context that generates foraging decisions at the boundaries of food patches in the nematode C. elegans. By analyzing animal locomotion at high spatial and temporal resolution, we identify discrete behavioral responses to encountering the edge of a food patch that can be understood as a decision: either to remain inside the food patch or to leave it. We find that the decision to leave a food patch is associated with increased behavioral arousal that unfolds on long and short timescales. The coupling of increased arousal to lawn leaving decisions is preserved across genetic, neuronal, and environmental manipulations that alter global arousal levels. However, genetic inactivation of a set of chemosensory neurons disrupts the coupling of arousal and lawn leaving, revealing a potential site of integration between internal signals and external sensation that governs foraging.

We appreciate the reviewers’ thoughtful engagement with this work. In addition to modifications in the text to address minor concerns and ambiguities, we have conducted new analyses and made text and figure edits to strengthen or explain our conclusions. We have also investigated possible confounding explanations to our interpretation of the data.

In newly added analysis, we show that increased arousal does not result in increased proximity to the lawn boundary, which would be a trivial reason why roaming animals leave more than dwelling ones (new Figure 2-Supplement 1E).

We also addressed the concern that classifying the brief speed acceleration motif as a roaming state would inflate the apparent coupling of roaming to leaving. By measuring the duration of roaming states prior to leaving, we in fact found the opposite: roaming states that precede leaving are slightly longer than other roaming states, not short acceleration events (new Figure 2-Supplement 4).

The reviewers also asked reasonable questions about variability between batches of experiments. In particular, reviewers pointed out high levels of roaming in wild type controls accompanying npr-1 mutants. Indeed, the simultaneously-tested wild type animals roamed more than usual in this experiment (Fig. 4C,K) and less than usual in other panels (Fig. 4A,B,I,J) in these small datasets. There is more to do here, but the results support the general point that roaming and leaving are correlated in several neuromodulatory mutants that regulate roaming. We have included a new sentence in the Figure 4 legend to draw the reader’s attention to the potential limitations of these results, and to explicitly state that results should not be compared across panels. Similarly, there is more to be done to understand tax-4, as we did not test all tax-4-expressing sensory neurons for their effects on roaming and leaving.

In private comments, reviewers also asked about experimental design and statistics and were concerned that certain assays conducted on just a few days may not represent independent experiments. We have updated the Methods section to improve the description of the behavioral experiments, including more information about the behavioral chambers and imaging conditions. We note that for all experiments we tested all relevant genotypes in the same batches and days, enabling comparisons of experimental animals with matched controls conducted at the same time.

Reviewers asked us to compare our results to those generated by Rhoades, et al. (2019) and Cermak, et al. (2020). To the best of our knowledge, our results are fully consistent with those studies. The study by Rhoades and co-authors is primarily concerned with behavioral slowing upon first encountering a food patch, and thus does not include data regarding roaming or lawn leaving (Rhoades et al., 2019). As we mention in the text, we were initially surprised that tph-1 did not eliminate regulation of roaming by feeding, but there are straightforward explanations (redundant transmitters, other neurons). tph-1 did have a significant, albeit small, effect. The study by Cermak and co-authors presents an alternative Hidden Markov Model that uses whole animal postures to segment on-food behavior into 9 states including 8 dwelling states and a single roaming state (Cermak et al., 2020); we refer to this analysis in the discussion. Cermak’s paper and ours differ in experimental conditions, the behaviors measured, and the models used to analyze them. The animals in the Cermak paper are exposed to a large bacterial lawn of uniform density, whereas animals in our study are recorded on small bacterial lawns with thick edges. The analysis tools also differ in their use of animal posture (Cermak only) and autoregressive dynamics (our work only). Further studies of the neurons and molecules involved may help to fully harmonize these models.

References

Cermak, N., Yu, S.K., Clark, R., Huang, Y.C., Baskoylu, S.N., and Flavell, S.W. (2020). Whole-organism behavioral profiling reveals a role for dopamine in statedependent motor program coupling in C. Elegans. Elife 9, 1–34.

Rhoades, J.L., Nelson, J.C., Nwabudike, I., Yu, S.K., McLachlan, I.G., Madan, G.K., Abebe, E., Powers, J.R., Colón-Ramos, D.A., and Flavell, S.W. (2019). ASICs Mediate Food Responses in an Enteric Serotonergic Neuron that Controls Foraging Behaviors. Cell 176, 85-97.e14.

Author Response

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

Reviewer 1

We now make clear throughout the manuscript that our proposition, holding the fast cassette as central to control over powerful movements governed by the PMn, remains a hypothesis. However, we provide additional rationale for our thinking that this is the case based on functional distinctions between the PMns and SMns. Both reviewers 1 and 2 also questioned why so few synaptic and ion channel genes are seen for the SMn type. As pointed out by the reviewer, the idea that small differences in birthdates between Mn types seems like an unlikely explanation and was removed. Now, we better develop the idea that the low levels of expression of both ion channel and synaptic genes in SMns are consistent with the finding from electrophysiology that point to greatly lowered levels of transmitter release, compared to PMns. Additionally, for the purpose of identifying all synaptic and ion channel genes shared equally between Mn types, we re-examined the transcriptome. Figure 7A & B now reflect all genes in these two categories detected above threshold in PMn and SMn types, and not just examples.

Reviewer 2

We have added cell types in mammalian circuits shown to express the ion channel cassette members. Examples include the calyx of Held in the auditory circuit and the cerebellar Purkinje neurons. As we show with zebrafish PMn these mammalian neurons form fast, reliable circuits. In these cases, it is noteworthy that our proposal is the first to link all three as functional partners in fast AP firing and high-fidelity synaptic transmission. The suggestion that pancreatic cells would be represented in our data is deemed highly unlikely as our technique separated out the spinal cords prior to dissociation. Finally, as suggested, we added the disclaimer that we can not exclude the possibility that clusters sharing both glia and neuronal markers may represent cell doublets. Other minor corrections were all made.

Reviewer 3

First, we agree that the role of PMns is not restricted to escape behavior. They have been shown to participate in the highest speed of swimming as well. We have made this clear throughout the paper.

Second, we are at odds with this reviewer over the Type I and Type II V2a recruitment during high speed swimming. We agree that both V2a types of interneurons are involved in high speed swimming and likely escape, as both directly innervate the PMns, as pointed out by the reviewer in Figure 2c of Menelaou and McLean 2019. However, the reviewer interprets Figure 2c to show that Type I, not Type II, V2a is more highly recruited over the range of higher swimming speeds whereas we conclude just the opposite. These data, along with other papers we cited, have been firmed up in the text to support a central role played by Type II.

Third, the reviewer recommends we remove Figures 6b and 6c relating to our two newly discovered SMn markers, fox1b and alcamb. Our data shown in Figure 6a shows that these markers label SMn somas in two distinct layers along the dorsal-ventral axis in the spinal cord. The reviewer objects to Figures 6b and 6c which compare the location of our two markers to the distributions of two well studied SMn labeling transgenic lines, islet:GFP and gata2:GFP. The correspondence is not absolute but suggests that the fox1b labels islet SMns and alcamb labels the gata2 SMns. In the previous version of the paper, we suggested that this correspondence might further signal different dorsal-ventral projections. This suggestion was based solely on reports that islet and gata2 transgenic lines preferentially label SMns with different projections. We do not view this particular point as important and in light of the controversy surrounding these projections, as noted by the reviewer, we removed all reference to the subject of muscle target areas. We focus instead, on our finding of two new markers that label different dorsal ventral soma layers which MAY correspond to previously described SMn types. This reasoning is made clear in the manuscript and, because of its potential importance, we elected to retain Figures 6b and 6c as a call for future testing.

The reviewer makes other suggestions that were all incorporated. The CoLo estimates indeed were too high, as questioned by the reviewer, because, early on, we inadvertently counted two clusters rather than the single cluster that was later authenticated. This has been corrected to reflect 1.1% in Table 1. The evx1 and evx2 data have been added to Figure 4C. Nomenclature is corrected for KA neurons. We make clear that the axonal projections for CoLo were made with mCherry expression not the in-situ label. The Hayashi reference was added.

Author Response

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

Reviewer #1 (Recommendations for The Authors)

MAJOR CONCERNS

1) Not addressed, but perhaps relevant, is that most of the postembryonic fish growth results from stem cells located in the ciliary marginal zone that make new neurons and Muller glia throughout the fish's life. Thus, Muller cell heterogeneity may result from the central to the peripheral gradient of Muller glial cell maturation.

1a. Müller glial cell heterogeneity needs to be confirmed using, for example, in situ hybridization studies with gene-specific probes identified in the scRNAseq that distinguish these 2 populations. An additional approach could be the use of transgenic lines harboring tagged endogenous or transgene that reflects the promoter activity of the Muller glia subtypespecific gene.

We thank the reviewer for the insightful comments and agree on the importance to substantiate the Müller glia heterogeneity in our manuscript. Our study is not the only study that provides evidence for Müller glia heterogeneity. In particular, we would like to refer to a recent publication (Krylov et al., 2023). Using single cell RNA sequencing, Krylov et al. detect Müller glia heterogeneity in the uninjured retina, as well as upon selective, genetic ablation of distinct subtypes of photoreceptors (e.g. long and short wavelength sensitive cones, as well as rods). They observe six distinct clusters of quiescent Müller glia that show differential spatial distribution along the dorsal/ventral retinal axis. For instance, they report a ventral quiescent Müller glia population that shares some marker genes (aldh1a3, rdh10a, smoc1) with our nonreactive Müller glia 2 (cluster 2, supplementary files 1 and 2). Moreover, the authors report that Müller glia located at different positions along the dorsal/ventral axis exhibit distinct patterns of pcna upregulation as well as subsequent re-activation upon photoreceptor ablation. We have added the supportive information from Krylov et al. in the discussion section (lines: 781-789) of our manuscript.

2) Most interesting, but also least substantiated, is the authors' report of 2 different quiescent Muller glial cell populations in the uninjured retina and 2 different reactive Muller cell populations in the injured retina. If these populations exist independently of each other, it would be important to investigate if they differentially impacted retina regeneration.

2a. CRISPR knockdown in F0 of factors thought to be involved in specific Müller glia-derived progenitor trajectories would be important to lend some functional significance to the data.

We fully agree with the reviewer that addition of functional data would enrich the manuscript with valuable information. However, we don´t believe that the suggested CRISPR knockdown of selected genes in F0 animals (also known as crispants) represents a suitable approach. Crispants have been used successfully to investigate genetic contributions in embryonic-tolarval stages (the first few days) of zebrafish development, as injection of multiple gRNAs targeting the same gene is sufficient to achieve a bi-allelic knockout of the gene of up to 90% (Kroll et al., 2021). However, unless both alleles of the target gene(s) is/are mutated already early on with nearly 100%, it is unlikely that the gRNA inactivation would work equally well during subsequent development into adult stages (several months later, and after exponential growth and volume increase of the animal). Even if biallelic inactivation in the crispants does work early on, it remains unclear whether and how crispants survive to adulthood, which will be necessary in order to address gene function in the context of retina regeneration. Moreover, since we observe that the genetic events during adult retina regeneration are highly similar to the events during retina development, we would rather expect the crispants already display developmental phenotypes, which would further hamper the study of potential regenerationspecific phenotypes in adult animals. We are convinced that only ‘clean’ conditional gene inactivation studies will be suitable to address the impact of Müller glia and derived progenitor trajectories on retina regeneration. In this respect, we have recently developed the new conditional Cre-Controlled CRISPR mutagenesis system (Hans et al., Nature Comm 2021). We are currently establishing stable lines to enable controlled and specific gene inactivation, but have only obtained preliminary results so far; the final analysis will take much more time and is, therefore, beyond the scope of this work.

3) The discussion should be modified to relate the data here presented with those described in Hoang et al., 2020.

We followed the suggestions of the reviewer and compared our single cell RNA sequencing dataset to that described in Hoang et al., 2020. As one might expect, the comparison between the two datasets showed similarities but also significant differences due to the different experimental set-ups. We show the results of this comparison in additional main (new Figure 9) and supplementary figures (new Figure 9-figure supplement 1). In order to compare our newly obtained scRNAseq dataset of MG and MG-lineage-derived cells of the regenerating zebrafish retina to the previously published dataset of light-lesioned retina (Hoang et al., 2020), we employed the ingestion method (Scanpy, https://scanpy-tutorials.readthedocs.io/en/latest/ integrating-data-using-ingest.html) and mapped the clusters identified by Hoang and colleagues to our clusters (new Figure 9). While we applied a short-term lineage tracing strategy and only sequenced the enriched population of FAC-sorted MG and MG-derived cells of the regenerating zebrafish retina, Hoang and colleagues sequenced all retinal cells in the light-lesioned retina. Consequently, comparison between the two datasets uncovered similarities, but also significant differences, due to the different experimental set-ups (Figure 9A). Consistently, the cluster annotated as resting MG in Hoang et al. mapped to clusters annotated as non-reactive MG 1 and 2 in our dataset (new Figure 9B). The cluster annotated as activated MG in Hoang et al. mapped to clusters annotated as reactive MG 1 and 2, as well as to the cluster with hybrid identity of MG/progenitors in our dataset. Interestingly, some cells annotated as activated MG in Hoang et al. mapped also to neurogenic progenitor 2 and 3 clusters in our dataset (Figure 9B). The cluster annotated as progenitors in Hoang et al. mapped to the progenitor area in our dataset, which included neurogenic progenitors 2, 3 as well as photoreceptor and horizontal cell precursors (new Figure 9B). Finally, retinal ganglion cells, cones, GABAergic amacrine cells and bipolar cells annotated in Hoang et al. perfectly mapped to retinal ganglion cells, cone, amacrine and bipolar cells in our dataset (new Figure 9B). While we did not detect a mature horizontal cell cluster, Hoang and colleagues annotated a horizontal cell cluster, which cells mapped to reactive MG 2, MG/progenitors 1 and part of progenitors 3 in our dataset (new Figure 9B). Moreover, Hoang and colleagues annotated rod photoreceptors that mapped to progenitors 3, photoreceptor precursors, red and blue cones, horizontal cell precursors and bipolar cells in our dataset (new Figure 9B). Finally, due to the different cell isolation protocol, Hoang and colleagues annotated additional cell clusters that did not map to any cluster in our more selective dataset, and included oligodendrocytes, pericytes, retinal pigmented epithelial cells as well as vascular/endothelial cells (new Figure 9B). Next, we selected representative marker genes per cluster from our scRNAseq dataset and checked their expression in the dataset by Hoang and colleagues (Figure 9-figure supplement 1). The dot plot showing the expression of selected gene candidates per cluster further corroborated the large overlap between clusters annotated in the present study with those annotated in the study by Hoang and colleagues. These novel comparisons to the data of Hoang et al. are now included in the resubmitted version, and are described and discussed in an additional paragraph in the results (lines: 482-517) as well as discussion (lines: 766-807) sections.

MINOR CONCERNS

1) Fig 1C is difficult to interpret. I am also confused by the color coding which is not presented in the figure legend - why 3 shades of red and two of blue? Please define each (for example, what's the difference between red, purple, and light red in the 6dpl panel?). What are the white areas outlined by blue and red circles/cells (looks like a topography plot)? It appears that there is a fairly large amount of pcna:EGFP expression in the uninjured retina - what are these cells?

We have replaced Figure 1C with a better one and rephrased/extended the explanation of the figure in the results (lines: 192-195). Figure 1C depicts contour plots, which represent the relative frequency of data. Each contour line encloses an equal percentage of events (that is, cells), and contour lines that are closely packed indicate a high concentration of events. In flow cytometry, contour plots are used to represent highly frequent events, as this kind of plots are independent on sample size.

Concerning the observed pcna:EGFP expressing cells in the uninjured retina, we interpret them as proliferating cells coming from the ciliary marginal zone and from Müller glia of the central retina, which represent progenitors and Müller glia that have re-entered the cell cycle to generate rod progenitors, respectively. Consistent with that, we observe pcna:EGFPpositive cells in the ciliary marginal zone as well as central retina using immunofluorescence, as shown in Figure 1-figure supplement 1.

2) Results, lines 186-188 are not presented clearly: EGFP+ cells may persist for some time after they leave the cell cycle, so stating EGFP+ cells are proliferating may not be correct. How long does PCNA promoter activity and EGFP expression remain after Muller cells exit the cell cycle? mCherry+/EGFP- cells may be non-reactive Muller glia or reactive Muller glia that have not entered the cell cycle. It seems likely that Muller glia start reprogramming before undergoing cell division.

We agree with the reviewer that EGFP persists for some time after the cells have left the cell cycle, which we actually describe and use to benefit in our study. We do not know for how long exactly the pcna promoter is active within the cell cycle, but EGFP is known to have a half-life of approximately 24 hours (Li et al., 1998). Even though we cannot make a statement about EGFP persistence in Müller glia, we note that previous reports (Lahne et al., 2015; Nagashima et al., 2013; Nelson et al., 2013; Thummel et al., 2008) and our study (Figure 3-figure supplement 2) show PCNA at the protein level in Müller glia cells between 24 and 48 hpl, including our sampled 44 hpl time point (lines: 69-73). We also agree with the reviewer that Müller glia will become reactive to the injury most likely prior (lines: 67-69) to activation of the pcna promoter, meaning that Müller glia are EGFP-negative at this time point due to the immature status of EGFP after translation. However, we are confident that our data also comprises this cell state (early phase of Müller glia activation) because we sampled proliferating (EGFP- and mCherry-double positive cells) as well as non-proliferating Müller glia (mCherry-only positive cells) at all time points (lines: 213-215 and Figure 1C). We interpret that the early phase of Müller glia activation corresponds to Müller glia transitioning from a nonreactive to a reactive state. With respect to our UMAP, we map this cell state in cluster 1 localizing to the top left part of the cluster, abutting cluster 3, the reactive Müller glia 1 (Figure 2B).

3) I am concerned by the observation that microglia were identified by scRNAseq as a contaminating cell population. Since FACS was based on gfap:mCherry expression, why did microglia end up in the mix? Also, what are the ‘...low-quality cells expressing many ribosomal transcripts...’ and why, if they are low-quality cells, did they pass the sequencing quality control as stated on lines 208-209?

The reviewer is right that microglia should actually not end up in the sample when using the gfap:mCherry line. However, microglia always displayed a certain level of autofluorescence in our experimental set-up (possibly because they may have ingested some cell debris), which may have contributed to their presence in the FACS samples. In contrast to the reviewer, we were not concerned about this ‘contamination’, because the microglia could be easily identified and sorted out using bioinformatics. This is supported by the presented supplementary figure in which microglia separate from the core of clusters containing Müller glia and Müller gliaderived cells in the UMAP of the full dataset (Figure 2-figure supplement 1).

We also acknowledge that ‘low quality cells’ is not an appropriate term for cells in the cluster expressing ribosomal mRNAs at high levels, as ribosomal enrichment actually does not give any information concerning their quality. We referred to them as ‘low quality’ because the enrichment in ribosomal transcripts masks their identity considerably. To correct this, we now renamed cells in this cluster descriptively as ‘ribosomal gene-enriched’ cells (Figure 2-figure supplement 1, line: 226).

4) Fig. 2: please list in the text or fig legend the specific genes used to identify each cell cycle state. Why is cluster 3 considered a reactive Muller population when expressing S phase markers and PCNA? These features seem to distinguish cluster 3 from 4 and may suggest cluster 3 is a progenitor population. Further explanation is necessary to understand the assignments.

Information about the specific genes used to identify each cell cycle state is provided in the paragraph “Bioinformatic analysis” (lines: 925-934) in the Materials and Methods section. We considered listing all the markers in either the results or the figure legends as well, but decided against it, as it impairs readability in our opinion. Nevertheless, we have now highlighted also in the results (line: 261) that the list of cell cycle markers is available in the Materials and Methods section.

We understand the reviewer´s point that cluster 3 represents progenitors and not Müller glia, when PCNA expression is considered as a sole marker of progenitors or of Müller glia reprogrammed to a progenitor state (Hoang et al., 2020). However, we disagree with this view for three reasons. First, although PCNA is used as a marker of Müller glia reprogrammed to a progenitor state and of progenitors in Hoang et al., 2020, it should be noted that PCNA-positive, Müller glia cells are present in the central retina already in uninjured conditions, when regeneration-associated, Müller glia-derived progenitors are not detectable. Second, cluster 3 is evident only at 44 hpl, a time point at which Müller glia cells are about to divide or have undergone their first and only cell division. In this regard, we would like to refer to the discussion about Müller glia and Müller glia-derived progenitors as distinct populations in Lenkowski and Raymond, 2014. Third, we have performed in situ hybridization for starmaker (stm), a marker gene highly specific for cells in cluster 3 (supplementary files 1 and 3), combined with immunohistochemistry for GFAP and PCNA. The results of the staining are depicted in a new Figure 3-figure supplement 2. In strong agreement with our sequencing results, we observe stm expression only at 44 hpl, whereas no signal is detected in the uninjured as well as 4 and 6 dpl retina (Figure 3- figure supplement 2). Virtually all stm-positive cells at 44 hpl are also PCNA- and GFAP-double positive cells displaying a clear Müller glia morphology (Figure 3- figure supplement 2). Hence, we interpret cells in cluster 3 as reactive Müller glia, indicating that pcna can be used as a marker of progenitors, but not exclusively of progenitors, prevalently at later stages. At 44 hpl, Müller glia express pcna in order to undergo asymmetric cell division giving rise to the renewed Müller glia and the multipotent progenitor that will continue dividing.

5) I am confused by the crlf1a scRNAseq data indicating it is associated with proliferating PCNA+ reactive Muller glia Cluster 3 and PCNA- reactive Muller glia Cluster4 at 44 hpl (Fig. 3), yet in Fig. 4 crlf1a in situ signal is exclusively associated with proliferating Muller glia at 44 hpl. Why don't we observe the crlf1a+/PCNA- cell population?

We highlight that crlf1a expression is actually detected also at 4 dpl (Fig. 3). We also note that immunofluorescence in Fig 3. shows crlf1a mRNA and PCNA protein, whereas single cell RNA sequencing detects crlf1a and pcna transcripts. In this context, it is possible that crlf1a-, PCNAdouble positive cells detected at 4 dpl are still positive for the PCNA protein, but no longer express the pcna transcript. Double in situ hybridization for pcna and crlf1a would be needed to fully address whether crlf1a-positive cells are still pcna-positive at 4 dpl. It is also possible that crlf1a-, GFAP-double positive, PCNA-negative Müller glia are fewer and only masked in the crowd of crlf1a-, PCNA-double positive, GFAP-negative progenitors at 4 dpl (Raymond et al., 2006). We amended the discussion section with this information (lines: 634-654).

6) scRNAseq cluster 3 is a proliferating population that is assigned "reactive Muller glia", whereas cluster 5 is assigned Muller glia/progenitor and in the Discussion referred to as MG about to go or already underwent asymmetric division to generate a progenitor (lines 568-571). I don't understand why cluster 3 is not referred to as the one harboring reactive MG/progenitors that underwent or are undergoing asymmetric cell division - The timing is right, as are the markers.

We would like to refer the reviewer to the discussion in point 4, including the changes we introduced in the Materials and Methods (Lines 925-934). As mentioned above, we do not agree that PCNA alone represents an exclusive marker of progenitors, but is rather a marker of cells undergoing proliferation. Moreover, we note that Müller glia first and only division occurs between 31 and 48 hpl. Finally, as mentioned above, expression of stm is a unique marker for cluster 3, which is only evident at 44 hpl, but not of cluster 5, which is evident at 4 dpl.

It seems cluster 5 might better fit the amplifying progenitor stage where some MG markers are retained but diluted by cell division. Please clarify the reasoning behind the labeling of this cluster. It is not clear why this cluster has to contain self-renewed Muller glia - why wouldn't these Muller cells partition to quiescent MG clusters 1 and 2 or reactive Muller glia in clusters 3 and 4?

We partially agree with the reviewer that cluster 5 might better fit the amplifying progenitor state, and this is why we indicate this cluster as a “crossroad in the trajectory” in the discussion (lines: 613-631). However, we cannot entirely exclude that cells in cluster 5 contain selfrenewed Müller glia (differential gene expression analysis highlights glial markers too, see Figure 3A, supplementary file 6). Cells that we interpret as self-renewing Müller glia do not partition back to quiescent Müller glia (cluster 1 and 2) because they are on the way to be quiescent Müller glia again, yet they did not reach that point, maybe due to sampling reasons. Unfortunately, our short-term lineage tracing strategy ceases at 6 dpl. We also speculate in the discussion (lines: 679-682) that if we had sampled at later time points (e.g. at 14 dpl), we might have been able to detect the density of the cells in the glial area moving back to clusters 1 or 2 (cell density plots, Figure 2B).

I also have trouble understanding cluster 4's assignment. The Discussion states it represents cells at the crossroad of glial and neurogenic trajectory containing self-renewed Muller glia as well as first-born MG-derived progenitors. However, it is populated by cells after 44 hpl (Fig. 2B) which is when reactive Muller glia are detected and lacks proliferative markers.

We think that there is a misunderstanding here. We never refer to cluster 4 as a crossroad in the glial and neurogenic trajectory. We state that cluster 5 is actually the crossroad between the two trajectories (line 629). We further propose that self-renewed MG close the cycle via late reactive MG (cluster 4) and return into non-reactive Müller glia (clusters 1 and 2, red, dashed line in Figure 10) (now described in lines 631-633). The cell density plots support the direction of the cycle closing towards non-reactive Müller glia, in particular at 4 and 6 dpl (Figure 2B).

Might cluster 4 represent a population of reactive MG remaining at 4 dpl that never entered the cell cycle and therefore would be devoid of Muller glia-derived progenitors?

As stated in the manuscript, we actually think that marker expression as well as the cell density plots support our assignment of cluster 4 to represent self-renewed Müller glia closing the cycle to non-reactive Müller glia. Our assignment also fits well with the expected events following asymmetric cell division. However, as we cannot rule out the reviewer´s entire idea, we included the suggestion in the updated discussion (lines 651-654).

7) Results, lines 163-164; Please provide a reference for "..... consistent with the previously described....."

We thank the reviewer for this observation and we added the appropriate references (Fimbel et al., 2007; Lenkowski and Raymond, 2014; Thummel et al., 2008) in the updated version of the manuscript (lines: 171-172).

Reviewer #2 (Recommendations For The Authors):

Overall, this very thorough study provides interesting and unexpected results. The published data set will be useful for many subsequent studies. I have only a few remarks that the authors may consider discussing. Their cluster analysis revealed most of the expected cell clusters with some interesting surprises. One relates to photoreceptors where the authors describe well-separated clusters for red and green cones, while rods, UV and blue cones do not form clusters. For rods, this is discussed, but I miss a brief discussion on the "missing" cone subtypes.

We thank the reviewer for the insightful comments. It is correct that we indeed detect only red and blue cones, as indicated by their expression of red-sensitive opsin gene (opn1lw2) and the blue-sensitive opsin gene (opn1sw2), respectively. It is possible that missing cone subtypes are born later than 6 dpl. As the reviewer suggested, we amended the discussion and added information about the missing cone subtypes (lines: 724-726).

I am also intrigued by the two, quite separated amacrine cell clusters, while bipolar cells cluster in one cluster, without separation in (say) ON and OFF bipolar cells. This may also merit a discussion. What are their ideas on the small and quite separated amacrine cell cluster (cluster 14).

Bipolar cells in cluster 15 are very sparse in our dataset, with only 40 cells in total. Hence, the sample size might be too small to be separated into ON and OFF subtypes. Alternatively, cells might be still immature, as we use 6 dpl as our latest sampled time point. Concerning cells in cluster 14, we think they are starburst amacrine cells, as indicated by their simultaneous expression of gad1b and chata (Figure 8-figure supplement 2B), which is a characteristic feature of starburst amacrine cells in mouse (O´Malley et al., 1992). We added this observation in the discussion (lines: 706-712).

Author Response

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

The Authors wish to thank the Reviewers for their detailed and insightful comments. By properly addressing these critiques, we sincerely believe our finished product will be substantially improved and provide greater insight to the academic community.

Both Reviewers noted the importance of identifying the limitations of our study with particular emphasis on embedded implant heating due to switching gradient coils. Understanding the limitations of any model and/or simulation process is critical when adopting its use, especially when estimating the safety of embedded devices. For this reason, we have included the following text and corresponding references in our Discussion section:

While the workflow presented herein establishes a validated approach to estimate RF heating due to the presence of a passive implant within a human subject undergoing an MR procedure, certain limitations and proper use stipulations of this methodology should be identified. These include:

1) The approach of embedding a given passive implant must be carefully considered and supervised by an orthopaedic subject matter expert, preferably an orthopaedic surgeon. While the procedures described above focus on insertion and registration of an implant to make it numerically suitable for simulation, relevant anatomic and physiological considerations must also be addressed to ensure a physically realistic and appropriate result. This will enable a proper simulated fit and no empty spaces or unintended tissue deformations.

2) Temperature changes presented are due only to RF energy deposition. The results do not take into account the impact of low-frequency induction heating of metallic implants naturally caused by the switching gradient coils. Important work on this subject matter has recently been reported in [21],[22],[23],[24],[25],[26],[27]. Unless an orthopaedic implant has a loop path, heating due to gradient fields is typically less than heating due to RF energy deposition. The present testbed would be applicable to the induction heating of implants (and the expected temperature rise of nearby tissues), after switching from Ansys HFSS (the full wave electromagnetic FEM solver) to Ansys Maxwell (the eddy current FEM solver). Two examples of this kind have already been considered in [25],[45].

3) The procedures presented in this work have been based on the response of a single human model of advanced age and high morbidity.

4) Finally, validation was achieved using available published data [42]-[44] and relies upon the legitimacy and veracity of that data. Coil geometry, power settings, and other relevant parameters were taken explicitly from these sources and modeled to enable a faithful comparison.

Author Response

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

Reviewer #1 (Public Review):

Summary:

Heitmann et al introduce a novel method for predicting the potential of drug candidates to cause Torsades de Pointes using simulations. Despite the fact that a multitude of such methods have been proposed in the past decade, this approach manages to provide novelty in a way that is potentially paradigm-shifting. The figures are beautiful and manage to convey difficult concepts intuitively.

Strengths:

(1) Novel combination of detailed mechanistic simulations with rigorous statistical modeling

(2) A method for predicting drug safety that can be used during drug development (3) A clear explication of difficult concepts.

Weaknesses:

(1) In this reviewer's opinion, the most important scientific issue that can be addressed is the fact that when a drug blocks multiple channels, it is not only the IC50 but also the Hill coefficient that can differ. By the same token, two drugs that block the same channel may have identical IC50s but different Hill coefficients. This is important to consider since concentration-dependence is an important part of the results presented here. If the Hill coefficients were to be significantly different, the concentration- dependent curves shown in Figure 6 could look very different.

See our response below.

(2) The curved lines shown in Figure 6 can initially be difficult to comprehend, especially when all the previous presentations emphasized linearity. But a further issue is obscured in these plots, which is the fact that they show a two-dimensional projection of a 4dimensional space. Some of the drugs might hit the channels that are not shown (INaL & IKs), whereas others will not. It is unclear, and unaddressed in the manuscript, how differences in the "hidden channels" will influence the shapes of these curves. An example, or at least some verbal description, could be very helpful.

See our response below.

Reviewer #1 (Recommendations For The Authors):

The manuscript is generally well-written (with one important exception, see below). The manuscript can be improved with a few suggested modifications, ordered from most important to least important.

(1) In this reviewer's opinion, the most important scientific issue that the authors need to address is the fact that when a drug blocks multiple channels, it is not only the IC50 but also the Hill coefficient that can differ. By the same token, two drugs that block the same channel may have identical IC50s but different Hill coefficients. This is important to consider since concentration-dependence is an important part of the results presented here.

In a recent study (Varshneya et al, CPT PSP 2021 (PMID: 33205613)) they originally ran simulations with Hill coefficients of 1 for all the 4 drugs and 7 channels, then re-ran the simulations with differing Hill coefficients. The results were quantitatively quite different than what was originally obtained, even though the overall trends were identical. A look at the table provided in that paper's supplement shows that the estimated Hill coefficients range from 0.5 to 1.9, which is a pretty wide range.

In this case, I don't think the authors should re-run the entire analysis. That would require entirely too much work and potentially detract from the elegant presentation of the manuscript in its current form. Although I haven't looked at the Llopis-Lorente dataset recently, I doubt that reliable Hill coefficients have been obtained for all 105 drugs. However, the Crumb et al dataset (PMID: 27060526) does provide this information for 30 drugs.

Perhaps the authors could choose an example of two drugs that affect similar channels but with differences in the estimated Hill coefficients. Or even a carefully-designed hypothetical example could be of value. At the very least, Hill coefficients need to be mentioned as a limitation, but this would be stronger if it were coupled with at least some novel analyses.

We fixed the Hill coefficients to h=1 because there is no evidence for co-operative drug binding in the literature that would require coefficients other than one. There is also the practical matter that only 17 of the 109 drugs in the dataset have a complete set of Hill coefficients. We have revised the Methods (Drug datasets) to make these justifications explicit:

Lines 560-566: “… We also fixed the Hill coefficients at h = 1 because (i) there is no evidence for co-operative drug binding in the literature, and thus no theoretical justification for using coefficients other than one; (ii) only 17 of the 109 drugs in the dataset had a complete set of Hill coefficients (hCaL, hKr, hNaL, hKs) anyway. …”

Out of interest, we re-ran our analysis using only those n=17 drugs (Amiodarone, Amitriptyline, Bepridil, Chlorpromazine, Diltiazem, Dofetilide, Flecainide, Mibefradil, Moxifloxacin, Nilotinib, Ondansetron, Quinidine, Quinine, Ranolazine, Saquinavir, Terfenadine and Verapamil). When the Hill coefficients were fixed at h=1, the prediction accuracy was 88.2% irrespective of the dosage (Author response image 1). When we used the estimated (free) Hill coefficients, the prediction accuracy remained unchanged (88.2%) for all doses except the lowest (1x to 2x) where it dropped to 82.4%. We concluded that using the Hill coefficients from the dataset made little difference to the results.

Author response image 1.

(2) I initially had a hard time understanding the curved lines shown in Figure 6 when all the previous presentations emphasized linearity. After thinking for a while, I was able to get it, but there was a further issue that I still struggle with. That is the fact that the plots all show a two-dimensional projection of a 4-dimensional space. Some of the drugs might hit the channels that are not shown (INaL & IKs), whereas others will not. How will differences in the "hidden channels" influence the shapes of these curves? An example, or at least some verbal description, could be very helpful.

We omitted GKs and GNaL from Figure 6 because they added little to the story. Those “hidden” channels operate in the same manner as GKr and GNaL. They are shown in Supplementary Dataset S1. We have included more explicit references to the Supplementary in both the main text and the caption of Figure 6. We have also rewritten the section on ‘The effect of dosage on multi-channel block’ (lines 249-268) to better convey that the drug acts in four dimensions.

(3) I also struggled a bit with Figure 3 and the section "Drug risk metric." What made this confusing was the PQR notation on the figure and the equations represented as A and B. Can these be presented in a common notation, or can the relationship be defined?

We have replaced the PQR notation in Figure 3A with vector notation A and B to be consistent with the equations.

Also in Figure 3B, I was unclear about the units on the x-axis. Is each step (e.g. from 0 to 1) the same distance as a single log unit along the abscissa or ordinate in Figure 3A?

Yes it is. We have revised the caption for Figure 3B to explain it better.

(4) The manuscript manages to explain difficult concepts clearly, and it is generally wellwritten. The important exception, however, is that the manuscript contains far too many sentence fragments. These often occur when the authors explain a difficult concept, then follow up with something that is essentially "and this in addition" or "with the exception of this."

Lines 220-223: "In comparison, Linezolid is an antibacterial agent that has no clinical evidence of Torsades (Class 4) even though it too blocks IKr. Albeit less than it blocks ICaL (Figure 5A, right)."

Lines 242-245: "Conversely, Linezolid shifts the population 1.18 units away from the ectopic regime. So only 0.0095% of those who received Linezolid would be susceptible. A substantial drop from the baseline rate of 0.93%."

There are several others that I didn't note, so the authors should perform a careful copy edit of the entire manuscript.

Thank you. We have remediated the fragmented sentences throughout.

Reviewer #2 (Public Review):

Summary:

In the paper from Hartman, Vandenberg, and Hill entitled "assessing drug safety, by identifying the access of arrhythmia and cardio, myocytes, electro physiology", the authors, define a new metric, the axis of arrhythmia" that essentially describes the parameter space of ion channel conductance combinations, where early after depolarization can be observed.

Strengths:

There is an elegance to the way the authors have communicated the scoring system. The method is potentially useful because of its simplicity, accessibility, and ease of use. I do think it adds to the field for this reason - a number of existing methods are overly complex and unwieldy and not necessarily better than the simple parameter regime scan presented here.

Weaknesses:

The method described in the manuscript suffers from a number of weaknesses that plague current screening methods. Included in these are the data quality and selection used to inform the drug-blocking profile. It's well known that drug measurements vary widely, depending on the measurement conditions.

We agree and have added a new section to describe these limitations, as follows:

Lines 467-478: Limitations. The method was evaluated using a dataset of drugs that were drawn from multiple sources and diverse experimental conditions (LlopisLorente et al., 2020). It is known that such measurements differ prominently between laboratories and recording platforms (Kramer et al., 2020). Some drugs in the dataset combined measurements from disparate experiments while others had missing values. Of all the drugs in the dataset, only 17 had a complete set of IC50 values for ICaL, IKr, INaL and IKs. The accuracy of the predictions are therefore limited by the quality of the drug potency measurements.

There doesn't seem to be any consideration of pacing frequency, which is an important consideration for arrhythmia triggers, resulting from repolarization abnormalities, but also depolarization abnormalities.

It is true that we did not consider the effect of pacing frequency. We have included this in the limitations:

Lines 479-485: The accuracy of the axis of arrhythmia is likewise limited by the quality of the biophysical model from which it is derived. The present study only investigated one particular variant of the ORd model (O’Hara et al., 2011; KroghMadsen et al., 2017) paced at 1 Hz. Other models and pacing rates are likely to produce differing estimates of the axis.

Extremely high doses of drugs are used to assess the population risk. But does the method yield important information when realistic drug concentrations are used?

Yes it does. The drugs were assessed across a range of doses from 1x to 32x therapeutic dose (Figure 8A). The prediction accuracy at low doses is 88.1%.

In the discussion, the comparison to conventional approaches suggests that the presented method isn't necessarily better than conventional methods.

The comparison is not just about accuracy. Our method achieves the same results at greatly reduced computational cost without loss of biophysical interpretation. We emphasise this in the Conclusion:

Lines 446-465: Conclusion. Our approach resolves the debate between model complexity and biophysical realism by combining both approaches into the same enterprise. Complex biophysical models were used to identify the relationship between ion channels and torsadogenic risk — as it is best understood by theory. Those findings were then reduced to a simpler linear model that can be applied to novel drugs without recapitulating the complex computer simulations. The reduced model retains a bio-physical description of multi-channel drug block, but only as far as necessary to predict the likelihood of early after-depolarizations. It does not reproduce the action potential itself. Our approach thus represents a convergence of biophysical and simple models which retains the essential biophysics while discarding the unnecessary details. We believe the benefits of this approach will accelerate the adoption of computational assays in safety pharmacology and ultimately reduce the burden of animal testing.

In conclusion, I have struggled to grasp the exceptional novelty of the new metric as presented, especially when considering that the badly needed future state must include a component of precision medicine.

Safety pharmacology has a different aim to precision medicine. The former concerns the population whereas the latter concerns the individual. The novelty of our metric lies in reducing the complexity of multi-channel drug effects to a linear model that retains a biophysical interpretation.

Reviewer #2 (Recommendations For The Authors):

A large majority of drugs have more complex effects than a simple reduction and channel conductance. Some of these are included in the 109 drugs shown in Figure 7. An example is ranolazine, which is well known to have potent late sodium channel blocking effects - how are such effects included in the model as presented? I think at least suggesting how the approach can be expanded for broader applicability would be important to discuss.

Our method does consider the simultaneous effect of the drug on multiple ion channels, specifically the L-type calcium current (ICaL), the delayed rectifier potassium currents (IKr and IKs), and the late sodium current (INaL). In the case of ranolazine (class 3 risk), the dose-responses for all four ion channels, based on IC50s published in Llopis-Lorente et al. are given in Supplementary Dataset S1.

The response curves in Author response image 2 show that in this dataset, ranolazine blocks IKr and INaL almost equally - being only slightly less potent against IKr. There are two issues to consider here that potentially contribute to ranolazine being misclassified as pro-arrhythmic. First, the cell model is more sensitive to block of IKr than INaL. As a result, in the context of an equipotent drug, the prolonging effect of IKr block outweighs the balancing effect of INaL block, resulting in a pro-arrhythmic risk score. Second, the potency of IKr block in this dataset may be overestimated which in turn exaggerates the risk score. For example, measurements of ranolazine block of IKr from our own laboratory (Windley et al J Pharmacol Toxicol 87, 99–107, 2017) suggest that the IC50 of IKr is higher (35700 nM) than that reported in the LlopisLorente dataset (12000 nM). If this were taken into account, there would be less block of IKr relative to INaL, resulting in a safer risk score.

Author response image 2.

Author Response

The following is the authors’ response to the previous reviews

Reviewer #1 (Public Review):

Comments on the original submission:

Trypanosoma brucei undergoes antigenic variation to evade the mammalian host's immune response. To achieve this, T. brucei regularly expresses different VSGs as its major surface antigen. VSG expression sites are exclusively subtelomeric, and VSG transcription by RNA polymerase I is strictly monoallelic. It has been shown that T. brucei RAP1, a telomeric protein, and the phosphoinositol pathway are essential for VSG monoallelic expression. In previous studies, Cestari et al. (ref. 24) has shown that PIP5pase interacts with RAP1 and that RAP1 binds PI(3,4,5)P3. RNAseq and ChIPseq analyses have been performed previously in PIP5pase conditional knockout cells, too (ref. 24). In the current study, Touray et al. did similar analyses except that catalytic dead PIP5pase mutant was used and the DNA and PI(3,4,5)P3 binding activities of RAP1 fragments were examined. Specifically, the authors examined the transcriptome profile and did RAP1 ChIPseq in PIP5pase catalytic dead mutant. The authors also expressed several C-terminal His6-tagged RAP1 recombinant proteins (full-length, aa1300, aa301-560, and aa 561-855). These fragments' DNA binding activities were examined by EMSA analysis and their phosphoinositides binding activities were examined by affinity pulldown of biotin-conjugated phosphoinositides. As a result, the authors confirmed that VSG silencing (both BES-linked and MES-linked VSGs) depends on PIP5pase catalytic activity, but the overall knowledge improvement is incremental. The most convincing data come from the phosphoinositide binding assay as it clearly shows that N-terminus of RAP1 binds PI(3,4,5)P3 but not PI(4,5)P2, although this is only assayed in vitro, while the in vivo binding of full-length RAP1 to PI(3,4,5)P3 has been previously published by Cestari et al (ref. 24) already. Considering that many phosphoinositides exert their regulatory role by modulate the subcellular localization of their bound proteins, it is reasonable to hypothesize that binding to PI(3,4,5)P3 can remove RAP1 from the chromatin. However, no convincing data have been shown to support the author's hypothesis that this regulation is through an "allosteric switch".

Comments on revised manuscript:

In this revised manuscript, Touray et al. have responded to reviewers' comments with some revisions satisfactorily. However, the authors still haven't addressed some key scientific rigor issues, which are listed below:

1) It is critical to clearly state whether the observations are made for the endogenous WT protein or the tagged protein. It is good that the authors currently clearly indicate the results observed in vivo are for the RAP1-HA protein. However, this is not as clearly stated for in vitro EMSA analyses. In addition, in discussion, the authors simply assumed that the c-terminally tagged RAP1 behaves the same as WT RAP1 and all conclusions were made about WT RAP1.

There are two choices here. The authors can validate that RAP1-HA still retains RAP1's essential function as a sole allele in T. brucei cells (as was recommended previously). Indeed, HA-tagged RAP1 has been studied before, but it is the N-terminally HA-tagged RAP1 that has been shown to retain its essential functions. Adding the HA tag to the C-terminus of RAP1 may well cause certain defects to RAP1. For example, N-terminally HA-tagged TERT does not complement the telomere shortening phenotype in TERT null T. brucei cells, while C-terminally GFP-tagged TERT does, indicating that HA-TERT is not fully functional while TERT-GFP likely has its essential functions (Dreesen, RU thesis). Although RAP1-HA behaves similar to WT RAP1 in many ways, it is still not fully validated that this protein retains essential functions of RAP1. By the way, it has been published that cells lacking one allele of RAP1 behave as WT cells for cell growth and VSG silencing (Yang et al. 2009, Cell; Afrin et al. 2020, mSphere). In addition, although RAP1 may interact with TRF weakly, the interaction is direct, as shown in yeast 2-hybrid analysis in (Yang et al. 2009, Cell).

Alternatively, if the authors do not wish to validate the functionality of RAP1-HA, they need to add one paragraph at the beginning of the discussion to clearly state that RAP1-HA may not behave exactly as WT RAP1. This is important for readers to better interpret the results. In addition, the authors need to tune down the current conclusions dramatically, as all described observations are made on RAP1-HA but not the WT RAP1.

The results with RAP1-HA are consistent with previous knowledge of RAP1 interactions with telomeric proteins and DNA. Hence, the C-terminal HA-tagged RAP1 seems, by all measures, functional. Nevertheless, to make it clear for the reader, we added a note in the discussion, lines 244-246: “Although we showed that C-terminal HA-tagged RAP1 protein has telomeric localization (Cestari et al. 2015, PNAS) and interactions with other telomeric proteins (Cestari et al. 2019 Mol Cell Biol); we cannot rule out potential differences between HA-tagged and non tagged RAP1.”

For a similar reason, the current EMSA results truly reflect how C-terminally His6-tagged RAP1 and RAP1 fragments behave. If the authors choose not to remove the His6 tag, it is essential that they use "RAP1-His6" to refer to these recombinant proteins. It is also essential for the authors to clearly state in the discussion that the tagged RAP1 fragments bind DNA, but the current data do not reveal whether WT RAP1 binds DNA. In addition, the authors incorrectly stated that "disruption of the MybL domain sequence did not eliminate RAP1-telomere binding in vivo" (lines 165-166). In ref 29, deletion of Myb domain did not abolish RAP1-telomere association. However, point mutations in MybL domain that abolish RAP1's DNA binding activities clearly disrupted RAP1's association with the telomere chromatin. Therefore, the current observation is not completely consistent with that published in ref 29.

We stated in line 149-150 “…we expressed and purified from E. coli recombinant 6xHistagged T. brucei RAP1 (rRAP1)”. To clarify to the authors, we replaced rRAP1 with rRAP1-His throughout the manuscript and figures. As for the statement that “disruption of the MybL domain sequence did not eliminate RAP1-telomere binding in vivo" (lines 165-166).”. We removed the statement from the manuscript.

2) There is no evidence, in vitro or in vivo, that binding PI(3,4,5)P3 to RAP1 causes conformational change in RAP1. The BRCT domain of RAP1 is known for its ability to homodimerize (Afrin et al. 2020, mSphere). It is therefore possible that binding of PI(3,4,5)P3 to RAP1 simply disrupts its homodimerization function. The authors clearly have extrapolated their conclusions based on available data. It is therefore important to revise the discussion and make appropriate statements.

We did not state that PI(3,4,5)P3 causes RAP1 conformational changes. We discussed the possibility. We stated in lines 199-201: “PI(3,4,5)P3 inhibition of RAP1-DNA binding might be due to its association with RAP1 N-terminus causing conformational changes that affect Myb and MybL domains association with DNA.” This is a reasonable discussion, given the data presented in the manuscript.

Reviewer #2 (Public Review):

In this manuscript, Touray et al investigate the mechanisms by which PIP5Pase and RAP1 control VSG expression in T. brucei and demonstrate an important role for this enzyme in a signalling pathway that likely plays a role in antigenic variation in T. brucei. While these data do not definitively show a role for this pathway in antigenic variation, the data are critical for establishing this pathway as a potential way the parasite could control antigenic variation and thus represent a fundamental discovery.

The methods used in the study are generally well-controlled. The authors provide evidence that RAP1 binds to PI(3,4,5)P3 through its N-terminus and that this binding regulates RAP1 binding to VSG expression sites, which in turn regulates VSG silencing. Overall their results support the conclusions made in the manuscript. Readers should take into consideration that the epitope tags on RAP1 could alter its function, however.

There are a few small caveats that are worth noting. First, the analysis of VSG derepression and switching in Figure 1 relies on a genome which does not contain minichromosomal (MC) VSG sequences. This means that MC VSGs could theoretically be mis-assigned as coming from another genomic location in the absence of an MC reference. As the origin of the VSGs in these clones isn't a major point in the paper, I do not think this is a major concern, but I would not over-interpret the particular details of switching outcomes in these experiments.

We agree with the reviewer and thus made no speculations on minichromosomes. The data analysis must rely on the available genome, and the reference genome used is well-assembled with PacBio sequences and Hi-C data (Muller et al. 2018, Nature).

Another aspect of this work that is perhaps important, but not discussed much by the authors, is the fact that signalling is extremely poorly understood in T. brucei. In Figure 1B, the RNA-seq data show many genes upregulated after expression of the Mut PIP5Pase (not just VSGs). The authors rightly avoid claiming that this pathway is exclusive to VSGs, but I wonder if these data could provide insight into the other biological processes that might be controlled by this signaling pathway in T. brucei.

We published that the inositol phosphate pathway also plays a role in T. brucei development (Cestari et al. 2018, Mol Biol Cell; reviewed by Cestari I 2020, PLOS Pathogens)

Overall, this is an excellent study which represents an important step forward in understanding how antigenic variation is controlled in T. brucei. The possibility that this process could be controlled via a signalling pathway has been speculated for a long time, and this study provides the first mechanistic evidence for that possibility.

Reviewer #1 (Recommendations For The Authors):

Please see the public review for recommendations.1. It is critical to clearly state whether the observations are made for the endogenous WT protein or the tagged protein. It is good that the authors currently clearly indicate the results observed in vivo are for the RAP1-HA protein. However, this is not as clearly stated for in vitro EMSA analyses. In addition, in discussion, the authors simply assumed that the c-terminally tagged RAP1 behaves the same as WT RAP1 and all conclusions were made about WT RAP1.

There are two choices here. The authors can validate that RAP1-HA still retains RAP1's essential function as a sole allele in T. brucei cells (as was recommended previously). Indeed, HA-tagged RAP1 has been studied before, but it is the N-terminally HA-tagged RAP1 that has been shown to retain its essential functions. Adding the HA tag to the C-terminus of RAP1 may well cause certain defects to RAP1. For example, N-terminally HA-tagged TERT does not complement the telomere shortening phenotype in TERT null T. brucei cells, while C-terminally GFP-tagged TERT does, indicating that HA-TERT is not fully functional while TERT-GFP likely has its essential functions (Dreesen, RU thesis). Although RAP1-HA behaves similar to WT RAP1 in many ways, it is still not fully validated that this protein retains essential functions of RAP1. By the way, it has been published that cells lacking one allele of RAP1 behaves as WT cells for cell growth and VSG silencing (Yang et al. 2009, Cell; Afrin et al. 2020, mSphere). In addition, although RAP1 may interact with TRF weakly, the interaction is direct, as shown in yeast 2-hybrid analysis in (Yang et al. 2009, Cell).

Alternatively, if the authors do not wish to validate the functionality of RAP1-HA, they need to add one paragraph at the beginning of the discussion to clearly state that RAP1-HA may not behave exactly as WT RAP1. This is important for readers to better interpret the results. In addition, the authors need to tune down the current conclusions dramatically, as all described observations are made on RAP1-HA but not the WT RAP1.

The results with RAP1-HA are consistent with previous knowledge of RAP1 interactions with telomeric proteins and DNA. Hence, the C-terminal HA-tagged RAP1 seems, by all measures, functional. Nevertheless, to make it clear for the reader, we added a note in the discussion, lines 244-246: “Although we showed that C-terminal HA-tagged RAP1 protein has telomeric localization (Cestari et al. 2015, PNAS) and interactions with other telomeric proteins (Cestari et al. 2019 Mol Cell Biol); we cannot rule out potential differences between HA-tagged and non tagged RAP1.”

For a similar reason, the current EMSA results truly reflect how C-terminally His6-tagged RAP1 and RAP1 fragments behave. If the authors choose not to remove the His6 tag, it is essential that they use "RAP1-His6" to refer to these recombinant proteins. It is also essential for the authors to clearly state in the discussion that the tagged RAP1 fragments bind DNA, but the current data do not reveal whether WT RAP1 binds DNA. In addition, the authors incorrectly stated that "disruption of the MybL domain sequence did not eliminate RAP1-telomere binding in vivo" (lines 165-166). In ref 29, deletion of Myb domain did not abolish RAP1-telomere association. However, point mutations in MybL domain that abolish RAP1's DNA binding activities clearly disrupted RAP1's association with the telomere chromatin. Therefore, the current observation is not completely consistent with that published in ref 29.

We stated in lines 149-150 “…we expressed and purified from E. coli recombinant 6xHistagged T. brucei RAP1 (rRAP1)”. To clarify to the authors, we replaced rRAP1 with rRAP1-His throughout the manuscript text. As for the statement that “disruption of the MybL domain sequence did not eliminate RAP1telomere binding in vivo" (lines 165-166).”. We removed the statement from the manuscript.

2) There is no evidence, in vitro or in vivo, that binding PI(3,4,5)P3 to RAP1 causes conformational change in RAP1. The BRCT domain of RAP1 is known for its ability to homodimerize (Afrin et al. 2020, mSphere). It is therefore possible that binding of PI(3,4,5)P3 to RAP1 simply disrupts its homodimerization function. The authors clearly have extrapolated their conclusions based on available data. It is therefore important to revise the discussion and make appropriate statements.

We did not state that PI(3,4,5)P3 causes RAP1 conformational changes. We discussed the possibility. We stated in lines 199-201: “PI(3,4,5)P3 inhibition of RAP1-DNA binding might be due to its association with RAP1 N-terminus causing conformational changes that affect Myb and MybL domains association with DNA.” This is a reasonable discussion, given the data presented in the manuscript.

Author Response

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

We greatly appreciate the valuable and constructive review of our manuscript. The reviewers’ comments have helped us to improve the quality of the paper. Here we provide detailed responses to the reviewers’ comments and discuss the new experiments we performed.

Reviewer #1

Summary:

In this study, the authors generate a Drosophila model to assess disease-linked allelic variants in the UBA5 gene. In humans, variants in UBA5 have been associated with DEE44, characterized by developmental delay, seizures, and encephalopathy. Here, the authors set out to characterize the relationship between 12 disease-linked variants in UBA5 using a variety of assays in their Drosophila Uba5 model. They first show that human UBA5 can substitute all essential functions of the Drosophila Uba5 ortholog, and then assess phenotypes in flies expressing the various disease variants. Using these assays, the authors classify the alleles into mild, intermediate, and severe loss-of-function alleles. Further, the authors establish several important in vitro assays to determine the impacts of the disease alleles on Uba5 stability and function. Together, they find a relatively close correlation between in vivo and in vitro relationships between Uba5 alleles and establish a new Drosophila model to probe the etiology of Uba5-related disorders.

Strengths:

Overall, this is a convincing and well-executed study. There is clearly a need to assess disease-associated allelic variants to better understand human disorders, particularly for rare diseases, and this humanized fly model of Uba5 is a powerful system to rapidly evaluate variants and relationships to various phenotypes. The manuscript is well written, and the experiments are appropriately controlled.

Recommendations For The Authors:

1) It would seem of value to determine what tissue(s) the essential function of Uba5 resides. The authors nicely detail the expression of Uba5 in a subset of neurons and glia, and indicate it is expressed in a variety of other tissues. Null mutants are embryonic lethal, suggesting an essential function. From the mouse study cited, it appears Uba5 functions early in development in the hematopoietic system. The authors can express their UAS-Uba5 rescue construct using a variety of tissue-specific Gal4 lines to determine whether the essential function of Uba5 is in the nervous system or other tissues, which would be of interest in understanding key functions of Uba5.

We thank the reviewer for the suggestion. We tried to rescue the lethality of the Uba5 mutants by expressing human UBA5 reference protein in different tissues. We found that ubiquitous expression of UBA5 (da-GAL4 or act-GAL4) successfully rescues the lethality, however, expression of UBA5 in neurons (elav-GAL4), glia (repo-GAL4), or both neurons and glia does not. In addition, expression of UBA5 in fat body (SPARC-GAL4) or muscles (Mef2-GAL4) does not rescue the lethality either. These results suggest that Uba5 is required in multiple tissues in flies. These data are included in the revised manuscript.

2). Do intermediate Uba5 alleles impact synaptic function or growth? The etiology of the disease is linked with epilepsy and neurodevelopmental disorders, and the interesting parallels the authors note between Uba5 and Para expression indicate perhaps shared roles in neurons that drive firing activity. Together, these lines of evidence may suggest the Uba5 alleles may have possible impacts on synaptic growth, morphology, and/or function. It would be of interest to examine the larval neuromuscular junction and assess NMJ growth, morphology, and perform some basic electrophysiology to determine if there are any functional defects.

Following the reviewer’s suggestion, we tested the morphology of NMJs in the humanized flies. We did not observe any obvious changes in the number or size of the synaptic boutons caused by the Group II variants. Hence, we conclude that the Uba5 variants do not cause an obvious defect in synaptic growth. The results are included in the Figure S3.

More generally, can the authors comment on the expression pattern of Uba5? One might consider something like Uba5 to be a "housekeeping" gene and expressed/required in most if not all cell types. From the data presented in Fig. 2, it appears expression is more sparse, perhaps, as the authors point out, because of roles in mature neurons that actively fire (like Para). Are neuronal targets of Uba5 known, which might suggest key pathways it modulates?

We showed that Uba5 is broadly expressed in third instar larvae. FlyAtlas2 and FlyCellAtlas datasets show that Uba5 is broadly expressed but not in all the cells. In the larval CNS and adult brain, Uba5 is not expressed in all cells either. Hence, we cannot say Uba5 is a “housekeeping” gene. Regarding the neuronal targets of Uba5, we do not know which types of neurons express Uba5 and which pathways Uba5 modulates. This could be studied in the future.

3) Does strong overexpression of the various Uba5 alleles in otherwise wild-type flies cause any phenotypes? This might support possible antimorphic/dominant negative functions of some of the variants. Is it plausible that any of the alleles could impact oligomerization of Uba5?

We have not observed compromised viability or any obvious phenotype in flies overexpressing human reference UBA5 or UBA5 variants. So, our results do not support a dominant negative effect of any of the variants.

To our knowledge, people do not have sufficient knowledge on UBA5 dimerization to speculate on whether some variants could play a dominant negative role. There is one variant, V260M, that lies at the dimer interface. We showed that the V260M variant biochemically affects ATP binding as well as UFM1 activation, but we do not have evidence to support that it causes dominant negative effects by affecting UBA5 dimerization.

Minor points:

1) Page 5 line 45: It seems a reference is missing about the temperature dependence of Gal4 activity.

We apologize for the missing reference. We have incorporated a reference to PMID 25824290.

2) It might be of interest to assay the various transgenic rescue alleles at a higher temperature (say 29C) in addition to the nice work looking at 18/25C survival. Perhaps some of the alleles display temperature sensitivity at low (18) and high (29) temperatures.

We now include the survival rate data at 29C. The enzyme dead and severe LoF variants fail to rescue the lethality at 29C, while the mild (Group IA and IB) variants fully rescue. For the three Group II variants, the survival rate at 29C is higher than that at 25C and 18C. The results support the dosage sensitive effects of UBA5 overexpression, but do not support any variant to be temperature sensitive within this range.

Reviewer #2

Relative simplicity and genetic accessibility of the fly brain make it a premier model system for studying the function of genes linked to various diseases in humans. Here, Pan et al. show that human UBA5, whose mutations cause developmental and epileptic encephalopathy, can functionally replace the fly homolog Uba5. The authors then systematically express in flies the different versions of the gene carrying clinically relevant SNPs and perform extensive phenotypic characterization such as survival rate, developmental timing, lifespan, locomotor and seizure activity, as well as in vitro biochemical characterization (stability, ATP binding, UFM-1 activation) of the corresponding recombinant proteins. The biochemical effects are well predicted by (or at least consistent with) the location of affected amino acids in the previously described Uba5 protein structure. Most strikingly, the severity of biochemical defects appears to closely track the severity of phenotypic defects observed in vivo in flies. While the paper does not provide many novel insights into the function of Uba5, it convincingly establishes the fly nervous system as a powerful model for future mechanistic studies.

One potential limitation is the design of the expression system in this work. Even though the authors state that "human cDNA is expressed under the control of the endogenous Uba5 enhancer and promoter", it is in fact the Gal4 gene that is expressed from the endogenous locus, meaning that the cDNA expression level would inevitably be amplified in comparison. The fact that different effects were observed when some experiments were performed at different temperatures (18 vs. 25) is also consistent with this. While I do not think this caveat weakens the conclusions of this paper, it may impact the interpretation of future experiments that use these tools, and thus should be clearly discussed in the paper. Especially considering the authors argue that most disease variants of UBA5 are partial loss-of-functions, the amplification effect could potentially mask the phenotypes of milder hypomorphic alleles. If the authors could also show that the T2A-Gal4 expression pattern in the brain matches well with that of endogenous RNA or protein (e.g. using HCR-FISH or antibody), it would help to alleviate this concern.

We thank the reviewer for pointing out the issue.

Regarding the humanization strategy we used in the study, we agree that this is a binary system which could induce overexpression of the target protein. However, as the reviewer also points out, this temperature sensitive system also enables us to flexibly adjust the expression level of the target protein (PMIDs 34113007, 35348658, 36206744), which is especially useful to study partial LoF variants. In our study we have successfully compared the relevant allelic strength of most of the variants.

We agree with the reviewer that a masking effect may exist in our system due to its gene overexpression nature. However, we cannot conclude that this masking effect really affects the three Group IA variants in our tests. The three variants are mild LoF, which is supported by our biochemical assays. Individuals homozygous for one of the Group IA variants, p.A371T, do not have any obvious phenotype, which is also consistent with our findings in flies.

Regarding the expression pattern of the T2A-GAL4, the Bellen lab has generated T2A-GAL4 lines for more than 3,000 genes. The expression pattern of many GAL4 lines faithfully reflect the expression pattern of the endogenous genes, which has been shown in our previous publications (PMIDs 25824290, 29565247, 31674908).

Recommendations For The Authors:

As related to the expression pattern comment in the public review, I think the authors could also take advantage of Fly Cell Atlas or other available scRNA-seq atlases of the fly brain to present a much more detailed description of the Uba5 expression profile with minimal additional effort. If the cells that express it share other features or genes (other than the para that the authors mention), this could lead to further insights about the gene's neuronal or glial functions.

In response to the reviewer, we show the expression pattern of Uba5 documented in FlyCellAtlas and another adult brain single-cell RNA seq profile (PMID 29909982) in the revised manuscript.

In addition, one of the mutants (assuming the same one) is referred to as Leu254Pro in some parts of the manuscript while in some other parts (including tables 1-2) it is Lys254Pro.

We apologize for the mistakes. The variant should be Leu254Pro and we have made these corrections in the revised manuscript.

Reviewer #3

Summary:

Variants in the UBA5 gene are associated with rare developmental and epileptic encephalopathy, DEE44. This research developed a system to assess in vivo and in vitro genotype-phenotype relationships between UBA5 allele series by humanized UBA5 fly models and biochemical activity assays. This study provides a basis for evaluating current and future individuals afflicted with this rare disease.

Strengths:

The authors developed a method to measure the enzymatic reaction activity of UBA5 mutants over time by applying the UbiReal method, which can monitor each reaction step of ubiquitination in real time using fluorescence polarization. They also classified fruit fly carrying humanized UBA5 variants into groups based on phenotype. They found a correlation between biochemical UBA5 activity and phenotype severity.

Weaknesses:

In the case of human DEE44, compound heterozygotes with both loss-of-function and hypomorphic forms (e.g., p.Ala371Thr, p.Asp389Gly, p.Asp389Tyr) may cause disease states. The presented models have failed to evaluate such cases.

We agree with the reviewer that our current system has a limitation that it evaluates one variant at a time rather than any combination of variants. However, our biochemical data do show that the three Group IA variants are mild LoF variants rather than benign variants. One of these variants, p.A371T, does not cause any obvious phenotype in homozygous individuals, which is also consistent with our findings in flies. The modeling of variant combinations, especially the Group IA/Group III combinations could be carried out in future studies.

Recommendations For The Authors:

Figure 3G. Typo. "ContonS" should be replaced by "CantonS."

We apologize for the spelling mistake. We correct the typo in the revised manuscript.

Figure 5. The labels should be in uppercase instead of lowercase.

We correct the panel labels in the revised manuscript.

Figure 6A. Is the molecular weight of UBA5~UFM1 intermediate (99 kDa) in model Figure correct? In Fig. 6B, the molecular weight of UBA5~UFM1 intermediate seems to be 70-75 kDa.

Both are correct. The molecular weight depicted in the schematic of Figure 6A is based on the UBA5 dimer, which dissociates in the SDS-PAGE gel shown in Figure 6B. We have reconfigured the schematic to make this more apparent.

Figure. 6E, F, H, and I. The time points for quantification in these figures should be specified.

We apologize for the confusion. The details on data quantification are now more thoroughly explained in the Methods.

Author Response

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

Reviewer #1:

In this manuscript, the authors investigate differences between Tibetans and Han Chinese at altitude in terms of placental transcriptomes during full-term pregnancy. Most importantly, they found that the inter-population differentiation is mostly male-specific and the observed direction of transcriptional differentiation seems to be adaptive at high altitude. In general, it is of great importance and provides new insights into the functional basis of Tibetan high-altitude adaptations, which so far have been mostly studied via population genetic measures only. More specifically, I firmly believe that we need more phenotype data (including molecular phenotypes such as gene expression data) to fully understand Tibetan adaptations to high altitude, and this manuscript is a rare example of such a study. I have a few suggestions and/or questions with which I hope to improve the manuscript further, especially in terms of 1) testing if the observed DEG patterns are truly adaptive, and 2) how and whether the findings in this study can be linked to EPAS1 and EGLN1, the signature adaptation genes in Tibetans.

We appreciate the reviewer’s constructive comments. We have addressed these points and the details are discussed below.

Major Comments:

1) The DEG analysis is the most central result in this manuscript, but the discrepancy between sex-combined and sex-specific DEGs is quite mind-boggling. For those that were differentially expressed in the sex-specific sets but not in the sex-combined one, the authors suggest an opposite direction of DE as an explanation (page 11, Figure S5). But Figure S5A does not show such a trend, showing that down-regulated genes in males are mostly not at all differentially expressed in females. Figure S5B does show such a trend, but it doesn't seem to be a dominant explanation. I would like to recommend the authors test alternative ways of analysis to boost statistical power for DEG detection other than simply splitting data into males and females and performing analysis in each subset. For example, the authors may consider utilizing gene-by-environment interaction analysis schemes here biological sex as an environmental factor.

We agree with reviewer that the opposite direction of DEGs is likely only one of the possible explanations for the discrepancy between the sex-combined and the sex-specific DEGs. We have toned down the description of this point in the revised manuscripts.

Following the suggestion of reviewer, we performed a ANCOVA analysis to evaluate the variance explained by sex from the expression data. For each gene, univariate comparisons of the average of gene expression between Tibetans and Han Chinese were made by using the ANCOVA test in R aov function with sex as covariates: aov (Expression ~ Ethnicity + Fetal sex). We observed a significantly higher variance explained by sex than by ethnicity in six layers of the placenta (except for the CN layer) (Author response image 1). For example, in the UC layer, fetal sex can explain ~0.203 variance, while the ethnicity explains ~0.107 variance (P-value = 4.9e-4). These results suggest a significant contribution of fetal sex for the observed variance of gene expression, consist with the observed sex-biased DEG patterns.

Author response image 1.

The ANCOVA results of the seven layers of placenta. The scatter plot shows the comparison of the explained variance (y-axis) and significance (x-axis, denoted by –log10(P-value)) between ethnicity (dots in red) and fetal sex (dots in blue). Each dot represents an investigated gene, and only genes with P<0.05 in significance are shown in the plots. The table is the summary statistics of the ANCOVA analysis.

2) Please clarify how the authors handled multiple testing correction of p-values.

There were three analyses involving multiple testing in this study: 1) for the differential expression analysis, we obtained the multiple corrected p-values by Benjamini-Hochberg FDR (false discovery rate) procedure; 2) for the GO enrichment analysis, we calculated the FDR-adjusted q-values from the overall p-values to correct for multiple testing.

3) for the WGCNA analysis, considering the 12 traits were involved, including population, birth weight (BW), biparietal diameter (BPD), femur length (FL), gestation time (GT), placental weight (PW), placental volume (PLV), abdominal girth (AG), amniotic fluid maximcon depth (AFMD), amniotic fluid (AFI), fetal heart rate (FH) and fundal height (FUH). We calculated a Bonferroni threshold (p-value = 0.05/the number of independent traits) using the correlation matrix of the traits to evaluate the significant modules. We estimated the number of independent traits among the 12 investigated traits was 4 (Author response image 2). Therefore, we used a more stringent significant threshold p-value = 0.0125 (0.05/4) as the final threshold to correct the multiple testing brought by multiple traits in our WGCNA analyses. We have updated this section based on the new threshold.

Author response image 2.

The correlation matrix of 12 traits involved in the WGCNA analysis. The correlation coefficients larger than 0.2 (or smaller than -0.2) are regarded as significant correlation and marked in gradient colors.

3) The "natural selection acts on the placental DEGs ..." section is potentially misleading readers to assume that the manuscript reports evidence for positive selection on the observed DEG pattern between Tibetans and Han, which is not.

a) Currently the section simply describes an overlap between DEGs and a set of 192 genes likely under positive selection in Tibetans (TSNGs). The overlap is quite small, leading to only 13 genes in total (Figure 6). The authors are currently not providing any statistical measure of whether this overlap is significantly enriched or at the level expected for random sampling.

We understand the reviewer’s point that the observed gene counts overlapped between DEGs from the three sets (4 for female + male; 9 for male only and 0 for female only) with TSNGs should be tested using a statistical method. Therefore, we adopted permutation approach to evaluate the enrichment of the overlapped DEGs with TSNGs.

For each permutation, we randomly extracted 192 genes from the human genome, then overlapped with DEGs of the three sets (female + male; female only and male only) and counted the gene numbers. After 10,000 permutations, we constructed a null distribution for each set, and found that the overlaps between DEGs and TSNGs were significantly enriched in the “female + male” set (p-value = 0.048) and the “male only” set (p-value = 9e-4), but not in the “female only” set (p-value = 0.1158) (Author response image 3). This result suggests that the observed DEGs are significantly enriched in TSNGs when compared to random sampling, especially for the male DEGs. We added this analysis in the revised manuscript.

Author response image 3.

The distribution of 10,000 permutation tests of counts of the overlapped genes between DEGs and the 192 randomly selected genes in the genome. The red-dashed lines indicate the observed values based on the 192 TSNGs.

b) The authors are describing sets of DEGs that seem to affect important phenotypic changes in a consistent and adaptive direction. A relevant form of natural selection for this situation may be polygenic adaptation while the authors only consider strong positive selection at a single variant/gene level.

We agree with reviewer that polygenic adaptation might be a potential mechanism for DEGs to take effect on the adaptive phenotypes. Therefore, following the suggestion in the comment below, we conducted a polygenic adaptation analysis using eQTL information.

c) The manuscript is currently providing no eQTL information that can explain the differential expression of key genes. The authors can actually do this based on the genotype and expression data of the individuals in this study. Combining eQTL info, they can set up a test for polygenic adaptation (e.g., Berg and Coop; https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004412). This will provide a powerful and direct test for the adaptiveness of the observed DEG pattern.

Following the reviewer’s suggestion, we employed the PolyGraph (Racimo et al., 2018) tool to identify the signatures of polygenic selection in Tibetans using eQTL information. We conducted eQTL analysis for the seven layers, and collected a set of 5,251 eQTLs, covering the SNPs associated with gene expression with a significanct p-value < 5e-8. To obtain a list of independent eQTLs, we removed those SNPs in linkage disequilibrium (r2 > 0.2 in 1000 Genome Project). Finally, we obtained 176 independent eQTLs. At the same time, we generated a set of 1,308,436 independent SNPs of Tibetans as the control panel. The PolyGraph result showed that Tibetans have a clear signature of polygenic selection on gene expression (Bonferroni-correction p-value = 0.003) (Author response image 4).

We have added this result in the revised manuscript (Figure S4), and added a detailed description of polygenic adaption in the Methods section.

Author response image 4.

Polygraphs for the eQTLs that show evidence for polygenic adaptation in the five-leaf tree built using the allele frequency data of 1001 Tibetans (Zheng et al. 2023) and 1000 Genome Project. The colors indicate the marginal posterior mean estimate of the selection parameter for variants associated with the gene expression. r, q, s and v in the tree nodes refer to the nodes in terminal branches and internal branches. TBN, Tibetans; CHB, Han Chinese in Beijing; JPT, Japanese in Tokyo, Japan; CEU, Northern Europeans from Utah; YRI, Yoruba in Ibadan, Nigeria.

4) The manuscript is currently only minimally discussing how findings are linked to EPAS1 and EGLN1 genes, which show the hallmark signature of positive selection in Tibetans. In fact, the authors' group previously reported male-specific association between EPAS1 SNPs and blood hemoglobin level. Many readers will be intrigued to see a discussion about this point.

According to the reviewer’s suggestion, in the revised manuscript, we added a paragraph to discuss the relationship between our transcriptomic data and the two genes with strong selective signals, i.e. EPAS1 and EGLN1.

“As the gene with the strongest signal of natural selection in Tibetans, EPAS1 has been reported in numerus studies on its contribution to high altitude adaptation. In this study, we detected a significant expression reduction of EPAS1 in the Tibetan UC compared to the high-altitude Han. It was reported that the selected-for EPAS1 variants/haplotype were associated with lower hemoglobin levels in the Tibetan highlanders with a major effect (Beall et al., 2010; Peng et al., 2017), and the low hemoglobin concentration of Tibetans is causally associated with a better reproductive success (Cho et al., 2017). Therefore, we speculate that the selective pressure on EPAS1 is likely through its effect on hemoglobin, rather than directly on the reproductive traits. The down-regulation of EPAS1 in placentas likely reflects a blunted hypoxic response that may improve vasodilation of UC for better blood flow, and eventually leading to the higher BW in Tibetans (He et al., 2023). For EGLN1, another well-known gene in Tibetans, we detected between-population expression difference in the male UC layer, but not in other placental layers. Considering the known adaptation mechanism of EGLN1 is attributed to the two Tibetan-enriched missense mutations, the contribution of EGLN1 to the gene expression changes in the Tibetan UC is unexpected and worth to be explored in the future.”

Reviewer #2 (Public Review):

In this manuscript, the authors use newly-generated, large-scale transcriptomic data along with histological data to attempt to dissect the mechanisms by which individuals with Tibetan ancestry are able to mitigate the negative effects of high elevation on birth weight. They present detailed analyses of the transcriptomic data and find significant sex differences in the placenta transcriptome.

I have significant concerns about the conclusions that are presented. The analyses also lack the information necessary to evaluate their reliability.

The experimental design does not include a low elevation comparison and thus cannot be used to answer questions about how ancestry influences hypoxia responses and thus birthweight at high elevations. Importantly, because the placenta tissues (and trophoblasts specifically) are quickly evolving, there are a priori good reasons to expect to find population differences irrespective of adaptive evolution that might contribute to fetal growth protection. There are also significant details missing in the analyses that are necessary to substantiate and replicate the analyses presented.

Although the datasets are ultimately valuable as reference sets, the absence of low elevation comparisons for Tibetans and Han Chinese individuals undermines the ability of the authors to assess whether differences observed between populations are linked to hypoxia responses or variation in the outcomes of interest (i.e., hypoxia-dependent fetal growth restriction).

We understand the reviewer’s concern about the lack of low-altitude comparison. For the placenta transcriptomic data, actually, we previously studied the comparison of placenta from high-altitude Tibetans and low-altitude Han Chinese, including 63 placentas of Tibetans living at Lhasa (elevation: 3650m) and 14 placentas of Han in Kunming (elevation: 1800m) (Peng et al. 2017). The main finding was that in general, the expression profiles are similar between the high-altitude Tibetans and the low-altitude Han. In particular, most high-altitude Tibetans have a similar level of EPAS1 expression in the placenta as the lowlander Han Chinese, a reflection of Tibetans’ adaptation at altitude. In other words, (Peng et al. 2017). In this study, we observed a significant down-regulation of EPAS1 in the Tibetan UC when compared to Han Chinese living at the same high altitude. Therefore, the observed differences between Tibetans and Han Chinese placenta at high altitude are due to the adaptation of Tibetans.

For phenotypic data, we made a systematical comparison of reproductive outcomes in our previous studies (He et al., 2023; He et al., 2022). We proved that polygenic adaptation of reproduction in Tibetans tends to reduce the chance of preterm birth and eliminate the restriction on fetal development at high altitude. Compared to the high-altitude Han Chinese migrants, the high-altitude Tibetans exhibit a less birth weight reduction and infant mortality induced by hypoxia, similar with the lowland Han Chinese as reference.

In summary, although we cannot make combination analysis with our high-altitude data and the published low-altitude data because of batch effect and difference of sampling strategy, we obtained more supportive evidence for the adaptation of placenta expression regulation in Tibetans. To be objective, we have discussed the limitation of the lack of lowlander placenta data in the Discussion section.

The authors attempt to tackle this phenotypic association by looking for correlations between gene networks (WGCNA) and individual genes with birthweight and other measurements collected at birth. I have some reservations about this approach with only two groups (i.e., missing the lowland comparison), but it is further problematic that the authors do not present data demonstrating that there are differences in birthweight or any other traits between the populations in the samples they collected.

Throughout, I thus find conclusions about the adaptive value and hypoxia-responses made by the authors to be unsubstantiated and/or the data to be inadequate. There are also a gratuitous number of speculative statements about mechanisms by which differential gene expression leads to the protection of birthweight that are not evaluated and thus cannot be substantiated by the data presented.

As currently presented and discussed, these results thus can only be used to evaluate population differences and tissue-specific variation therein.

We understand the reviewer’s point that the observed differences of gene expression between Tibetan natives and Han immigrants living at high altitude might be explained by ancestral divergence, rather than hypoxia-associated response and genetic adaptation of native Tibetans.

Firstly, we conclude that Tibetans have a better reproductive outcome, not only based on the two highlander groups living at the same altitude, but also relied on the change direction compared to the lowland level. For example, we observed a significant higher BW in Tibetans than Han migrants in our dataset (35 Tibetans vs. 34 Han: p-value = 0.012) (Author response image 5), and in a larger dataset (He et al. 2023) (1,317 Tibetans vs. 87 Han: p-value = 1.1e-6), suggesting an adaptation of Tibetans because BW decreases with the increase of altitude. The logic was the same to the other traits. Following the suggestion of reviewer, we added these phenotype comparisons in the revised manuscripts. The detailed information of the investigated samples and the statistic results were also added as supplementary tables in the revised version.

For the WGCNA, we agree with the reviewer that the detected modules both showing significant correlation with population and other reproductive traits cannot be fully explained by adaptation of Tibetans. Therefore, we tuned down the description of this section and added other possible explanations, such as population differences, in the discussion.

Author response image 5.

Comparison of 11 reproductive traits between Tibetans and Han immigrants. (A) comparison based on the dataset of this study (35 Tibetans vs. 34 Han); (B) correlation between BW and altitude (left panel) and comparison analysis based on the larger sample size (the data were retrieved from (He et al., 2023)). Univariate comparisons of the average of each trait cross population were made by using the ANCOVA test in R aov function with fetal sex and maternal age as covariates.

There is also some important methodological information missing that makes it difficult or impossible to assess the quality of the underlying data and/or reproduce the analyses, further limiting the potential impact of these data:

1) Transcriptome data processing and analyses: RNA quality information is not mentioned (i.e., RIN). What # of reads are mapped to annotated regions? How many genes were expressed in each tissue (important for contextualizing the # of DE genes reported - are these a significant proportion of expressed genes or just a small subset?).

According to the reviewer’s suggestion, we added more information about transcriptome data processing and analyses in the revised Methods and Results:

“After RNA extraction, we assessed the RNA integrity and purity using 1% agarose gel electrophoresis. The RIN value of extracted RNA was 7.56 ± 0.71.”

“In total, 10.6 billion reads were mapped to the annotated regions, and 17,283 genes express in all the investigated placenta.”

“We identified 579 differentially expressed genes (DEGs) between Tibetans and Han, accounting for 3.4% of the total number of expressed genes.”

2) The methods suggest that DE analyses were run using data that were normalized prior to reading them into DESeq2. DESeq2 has an internal normalization process and should not be used on data that was already normalized. Please clarify how and when normalization was performed.

Actually, we made raw read count matrix as input file when conducting differential analysis using DESeq2, rather than using the normalized data. We have updated our description in the method section of the revised manuscript.

3) For enrichment analyses, the background gene set (all expressed genes? all genes in the genome? or only genes expressed in the tissue of interest?) has deterministic effects on the outcomes. The background sets are not specified for any analyses.

Actually, we utilized the genes expressed in placenta as the background gene set for enrichment analyses. The genes with more than two transcripts per million transcripts (TPM) were regarded as an expressed gene, which is commonly used criteria for RNA-seq data.

4) In the WGCNA analysis, P-values for correlations of modules with phenotype data (birthweight etc.) should be corrected for multiple testing (i.e., running the module correlation for each outcome variables) and p.adjust used to evaluate associations to limit false positives given the large number of correlations being run.

As we explained in response to comment#2 of Reviwer-1, we used a more stringent significant threshold of p-value = 0.0125 (0.05/4) as the final threshold to correct the multiple testing brought by multiple traits in the WGCNA analysis.

5) The plots for umbilical histological data (Fig 5 C) contain more than 5 points, but the use of replicate sections is not specified. If replicate sections were used, the authors should control for non-independence of replicate sections in their analyses (i.e., random effects model).

We did not use replicate sections. Figure 5C shows the umbilical artery intima and media. Because each human umbilical cord includes two umbilical arteries, the 5 vs. 5 individual comparison generates 10 vs. 10 umbilical artery comparison. To be clearer, we added an explanation in the revised manuscript.

On more minor notes:

There is significant and relevant published data on sex differences and hypoxia in rodents (see Cuffe et al 2014, "Mid- to late-term hypoxia in the mouse alters placental morphology, glucocorticoid regulatory pathways, and nutrient transporters in a sex-specific manner" and review by Siragher and Sferuzzi-Perro 2021, "Placental hypoxia: What have we learnt from small animal models?"), and historical work reporting sex differences in placental traits associated with high elevation adaptation in Andeans (series of publications by Moira Jackson in the late 1980s, reviewed in Wilsterman and Cheviron 2021, "Fetal growth, high altitude, and evolutionary adaptation: A new perspective").

We thank the reviewer for the constructive comments on literature review. We have cited and discussed them in the revised manuscript.

Reviewer #3 (Public Review):

More than 80 million people live at high altitude. This impacts health outcomes, including those related to pregnancy. Longer-lived populations at high altitudes, such as the Tibetan and Andean populations show partial protection against the negative health effects of high altitude. The paper by Yue sought to determine the mechanisms by which the placenta of Tibetans may have adapted to minimise the negative effect of high altitude on fetal growth outcomes. It compared placentas from pregnancies from Tibetans to those from the Han Chinese. It employed RNAseq profiling of different regions of the placenta and fetal membranes, with some follow-up of histological changes in umbilical cord structure and placental structure. The study also explored the contribution of fetal sex in these phenotypic outcomes.

A key strength of the study is the large sample sizes for the RNAseq analysis, the analysis of different parts of the placenta and fetal membranes, and the assessment of fetal sex differences.

A main weakness is that this study, and its conclusions, largely rely on transcriptomic changes informed by RNAseq. Changes in genes and pathways identified through bioinformatic analysis were not verified by alternate methods, such as by western blotting, which would add weight to the strength of the data and its interpretations. There is also a lack of description of patient characteristics, so the reader is unable to make their own judgments on how placental changes may link to pregnancy outcomes. Another weakness is that the histological analyses were performed on n=5 per group and were rudimentary in nature.

For the weakness raised by the reviewer, here are our responses:

(1) Considering that our conclusions largely rely on the transcriptomic data, we agree with reviewer that more experiments are needed to validate the results from our transcriptomic data. However, this study was mainly aimed to provide a transcriptomic landscape of high-altitude placenta, and to characterize the gene-expression difference between native Tibetans and Han migrants. The molecular mechanism exploration is not the main task of this study, and more validation experiments are warranted in the future.

(2) For the lack of description of patient characteristics, actually, we provided three level results on the placental changes of Tibetans: macroscopic phenotypes (higher placental weight and volume), histological phenotypes (larger umbilical vein walls and umbilical artery intima and media; lower syncytial knots/villi ratios) and transcriptomic phenotypes (DEG and differential modules). Combined with the previous studies, these placenta changes suggest a better reproductive outcome. For example, the placenta volume shows a significantly positive correlation with birth weight (R = 0.31, p-value = 2.5e-16), therefore, the larger placenta volume of Tibetans is beneficial to fetal development at high altitude. In addition, the larger umbilical vein wall and umbilical artery intima and media of Tibetans can explain their adaptation in preventing preeclampsia.

(3) For the sample size of histological analyses, we understand the reviewer’s concern that 5 vs. 5 samples are not large in histological analyses. This is because it was difficult to collect high-altitude Han placenta samples, and we only got 13 Han samples, from which we selected 5 infant sex matched samples.

References

Beall, C.M., Cavalleri, G.L., Deng, L.B., Elston, R.C., Gao, Y., Knight, J., Li, C.H., Li, J.C., Liang, Y., McCormack, M., et al. (2010). Natural selection on EPAS1 (HIF2 alpha) associated with low hemoglobin concentration in Tibetan highlanders. P Natl Acad Sci USA 107, 11459-11464.

Cho, J.I., Basnyat, B., Jeong, C., Di Rienzo, A., Childs, G., Craig, S.R., Sun, J., and Beall, C.M. (2017). Ethnically Tibetan women in Nepal with low hemoglobin concentration have better reproductive outcomes. Evol Med Public Health 2017, 82-96. He, Y., Guo, Y., Zheng, W., Yue, T., Zhang, H., Wang, B., Feng, Z., Ouzhuluobu, Cui, C., Liu, K., et al. (2023). Polygenic adaptation leads to a higher reproductive fitness of native Tibetans at high altitude. Curr Biol.

He, Y., Li, J., Yue, T., Zheng, W., Guo, Y., Zhang, H., Chen, L., Li, C., Li, H., Cui, C., et al. (2022). Seasonality and Sex-Biased Fluctuation of Birth Weight in Tibetan Populations. Phenomics 2, 64-71.

Peng, Y., Cui, C., He, Y., Ouzhuluobu, Zhang, H., Yang, D., Zhang, Q., Bianbazhuoma, Yang, L., He, Y., et al. (2017). Down-Regulation of EPAS1 Transcription and Genetic Adaptation of Tibetans to High-Altitude Hypoxia. Mol Biol Evol 34, 818-830.

Racimo, F., Berg, J.J., and Pickrell, J.K. (2018). Detecting Polygenic Adaptation in Admixture Graphs. Genetics 208, 1565-1584.

Author Response

We thank the reviewers and editorial team for their positive and thoughtful comments and recommendations for our paper. We will provide a detailed point-to-point response accompanying a revised version of our paper to carefully incorporate all the recommendations and clarify several confusing points. Here we provide a brief provisional response to summarize the key points.

1) Are the two factors in the enslavement patterns after stroke, changes in shape (loss of complexity) and magnitude (intrusion of flexor bias), dissociable? Our results show both a loss of shape (Fig. 5) and an increase of magnitude (Fig. 7) in enslavement patterns in the paretic hand. We agree with the reviewers that the key measures for these two factors, Angular (Cosine) and Euclidean Distances, are not mathematically orthogonal because, while Angular Distance is indeed only influenced by shape, Euclidean Distance is influenced by both magnitude and shape changes of the enslavement patterns. However, our LME results show that increased flexor bias in the paretic hand strongly predicts Euclidean Distance but not Angular Distance (Fig. 9), thereby suggesting that pattern shape change cannot be fully accounted for by flexor intrusion. This analysis was also recommended by Reviewer 1. In the revised version, we will further clarify the dissociation of the two components.

2) Can biomechanical factors be ruled out from the enslavement patterns in the paretic hand? We agree with the reviewers that resting hand posture measures alone cannot fully assess biomechanical factors, given that biomechanical constraints during action and abnormal postures due to neural loss after stroke were not captured in these measures. In the paper, however, we used three analyses to justify this point. In the first analysis, we showed that resting hand posture (Mount Distance and Mount Angle) could not account for the Biases in all groups (healthy, paretic, non-paretic). In the second analysis, we showed that resting hand posture could not account for Enslavement in all groups. In the third analysis, we showed that Biases in the non-paretic hand could not predict Biases or Enslavement in the paretic hand within the same patients. The third analysis was done based on the existing literature that secondary biomechanical change after stroke was likely not the major contributor in the hand impairment, where passive muscle stimulation could successfully evoke a similar level of fingertip forces in both stroke and control hands (Hoffmann et al. 2016) and median nerve stimulation could significantly reduce intrusion of finger flexion (Kamper et al. 2003). The resting hand posture and non-paretic hand biases would include both biomechanical and neural factors, but since none of these measures could predict enslaving patterns, we maintain that biomechanical factors would not be a contribution to the enslavement in the paretic hand.

3) Neural correlates of behavioral changes were not tested, therefore claims such as "low-level," "subcortical," and "top-down cortical" contributions are not fully justified. We agree with the reviewers, and we will clear references to these neural correlates from the text of the Results section in the revised version of the paper. These neural correlates will only be discussed in the Discussion section.

4) RDM construction for "by-Target Direction" was not clearly explained. We agree with the reviewer that the diagram in Fig. 4D was a little confusing. To construct these matrices, we analyzed differences in coactivation patterns of the non-instructed fingers when two fingers move in the same target direction. A cleaner pattern comparison should exclude both the two instructed fingers to be compared from the enslavement matrices. This will be clarified in the revised version.

References

Hoffmann, Gilles, Megan O. Conrad, Dan Qiu, and Derek G. Kamper. 2016. “Contributions of Voluntary Activation Deficits to Hand Weakness after Stroke.” Topics in Stroke Rehabilitation 23 (6): 384–92. https://doi.org/10.1179/1945511915Y.0000000023.

Kamper, D G, R L Harvey, S Suresh, and W Z Rymer. 2003. “Relative Contributions of Neural Mechanisms versus Muscle Mechanics in Promoting Finger Extension Deficits Following Stroke.” Muscle & Nerve 28 (3): 309–18. https://doi.org/10.1002/mus.10443.

Author Response

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

Editorial comments:

Comment 1 - Recommendations for the authors: please note that you control which revisions to undertake from the public reviews and recommendations for the authors.

We appreciate the feedback from the 3 Reviewers and Editor. We have enumerated each Reviewer comment and provide a detailed response. We endeavoured to include each suggestion into the revised manuscript. All changes in the manuscript are indicated in red font. In instances in which we respectfully disagree with the Reviewer, we have provided a fair rebuttal. We feel the comments from the Reviewers has significantly improved the clarity and quality of the manuscript.

Comment 2 - The revision process has demonstrated the value of your work, highlighting both its strengths and shortcomings. Importantly, it provides detailed and achievable suggestions for improving the current version of your contribution.

We thank the Reviewers and Editor for their time and expert input on our manuscript. We feel the suggestions from the Reviewers to address the shortcomings has resulted in a significantly improved manuscript.

Comment 3 - There is a general consensus among the reviewers on three key aspects. Firstly, the article would greatly benefit from a clearer layout of the experimental design and methodology, potentially including schematics to help readers comprehend the complexity and details of the study.

We appreciate the feedback from Reviewer 2 in particular. We have added a new schematic for Experiment 3 (see PUBLIC REVIEWS Reviewer #2 Comment 2). We have also revised the Results section by including subheadings and additional text to help explain the methods.

Comment 4 - Secondly, conducting a more comprehensive analysis of the available dataset, utilizing tools such as WGCNA to explore gene co-expression networks beyond specific genes, is recommended. Additionally, it is advised to exercise greater caution when discussing the limitations of the employed methods.

The suggestion for the WGCNA is excellent and very much appreciated. The revised manuscript includes WGCNA for both the MBH and pituitary gland. See Figures S3 & Table S6 and lines 166-182; 497-505).

Comment 5 - Thirdly, expanding the results section to create a more engaging narrative that guides readers through the numerous findings, and extending the discussion and conclusions to emphasize the ecological relevance of learning photoperiodic/seasonal responses and highlighting the presented model, would be valuable.

These were excellent suggestions that significantly improved the clarity and quality of the manuscript. The results section included several subheadings to help break up of the transitions across experiments. We have also significantly revised the introduction and discussion to include the ecological relevance and importance to consider sex as a factor in the interpretations.

Comment 6 - Finally, please pay close attention to the comment on the statistical analysis provided by Rev#2.

It is unclear why the Benjamini-Hochberg’s FDR analyses was suggested. The statistical test is a version of the Bonferroni test but is less stringent. We prefer to use conservative tests (i.e., Bonferroni correction). Moreover, the Bonferroni correction is the commonly used statistical tests in the field. To be consistent with the field and to be careful in our statistical approach, the revised manuscript did not change the post-hoc correction.

PUBLIC REVIEWS:

Reviewer #1:

Comment 1 - The authors investigated the molecular correlates in potential neural centers in the Japanese quail brain associated with photoperiod-induced life-history states. The authors simulated photoperiod to attain winter and summer-like physiology and samples of neural tissues at spring, and autumn life-history states, daily rhythms in transcripts in solstices and equinox, and lastly studies FSHb transcripts in the pituitary. The experiments are based on a series of changes in photoperiod and gave some interesting results. The experiment did not have a control for no change in photoperiod so it seems possible that endogenous rhythms could be another aspect of seasonal rhythms that lack in this study. The short-day group does not explain the endogenous seasonal response.

We thank the Reviewer for the fair assessment of the manuscript. The statement ‘the experiment did not have a control for no change in photoperiod’ is not clear to us. We think the Reviewer is arguing that prolonged constant photoperiod was not conducted to examine circannual timing in avian reproduction. The constant short photoperiod in Exp3 does provide the ability to examine the initial stages of interval timing. A different endogenous mechanism used by animals. The revised manuscript has clarified the different physiological responses.

Comment 2 - The manuscript would benefit from further clarity in synthesizing different sections. Additionally, there are some instances of unclear language and numerous typos throughout the manuscript. A thorough revision is recommended, including addressing sentence structure for improved clarity, reframing sentences where necessary, correcting typos, conducting a grammar check, and enhancing overall writing clarity.

We have incorporated the suggestions from both Reviewer 1 and Reviewer 2 that aimed to increase the clarity of the manuscript. We have provided detailed responses to each comment below and state how each comment was incorporated in the revised manuscript. We also had the manuscript reviewed by a colleague to help identify issues associated with sentence structure, grammar, and spelling.

Comment 3 - Data analysis needs more clarity particularly how transcriptome data explains different physiological measures across seasonal life-history states. It seems the discussion is built around a few genes that have been studied in other published literature on quail seasonal response. Extending results on the promotor of DEGs and building discussion is an extrapolating discussion on limited evidence and seems redundant.

A new statistical analysis (ie., WGCNA) was conducted to identify relations between photoperiod, physiology and transcripts. The focus on the few photoperiodic gene was kept in the discussion as the transcript expression is important to highlight the differences from the prevailing hypotheses and novel patterns of expression across seasonal timescales. See Figures S3 & Table S6 and lines 166-182; 497-505).

Comment 4 - Last, I wondered if it would be possible to add an ecological context for the frequent change in the photoperiod schedule and not take account of the endogenous annual response. Adding discussion on ecological relevance would make more sense.

This is an excellent suggestion. The introduction and discussion were substantially revised to include the ecological relevance.

Reviewer #2:

Comment 1 - This study is carefully designed and well executed, including a comprehensive suite of endpoint measures and large sample sizes that give confidence in the results. I have a few general comments and suggestions that the authors might find helpful.

We appreciate the Reviewers support for our manuscript. We have endeavoured to incorporate all suggestions in the revised manuscript.

Comment 2 - I found it difficult to fully grasp the experimental design, including the length of light treatment in the three different experiments (which appears to extend from 2 weeks up to 8 weeks). A graphical description of the experimental design along a timeline would be very helpful to the reader. I suggest adding the respective sample sizes to such a graphic, because this information is currently also difficult to keep track of.

We have created a new figure panel to address the Reviewer’s concern. See figure S4 panel ‘a’. The new schematic representation was designed to illustrate the similarity in experimental design used in Experiment 1 and Experiment 2. But clearly illustrates the extended short photoperiod manipulation (4 weeks and not 8 weeks). We added the sample sizes to initial drafts but felt the added text hindered the clarity of the schematic representation (particularly for Fig1a). The sample sizes for each experiment and treatment are provided in the raw data provided in the supplementary Table 1. For this reason, we have opted to not add the sample size to each diagram. We hope that the Reviewer will understand our perspective.

Comment 3 - The authors use a lot of terminology that is second nature to a chronobiologist but may be difficult for the general reader to keep track of. For example, what is the difference between "photoinducibility" and "photosensitivity"? Similarly, "vernal" and "autumnal" should be briefly explained at the outset, or maybe simply say "spring equinox" and "fall equinox."

This is a very helpful suggestion, and we thank the Reviewer. Two changes were made to the manuscript to address this comment. First, we revised the second introductory paragraph to describe the photoperiodic response and the terms used. Second, we have removed all reference to ‘vernal’ and replaced with ‘spring’. We opted to keep ‘autumn’ as the change to ‘fall’ did not provide the clarity of seasonal state in some statements (as fall is also used as a downward direction).

Comment 4 What was the rationale for using only male birds in this study? The authors may want to include a brief discussion on whether the expected results for females might be similar to or different from what they found in males, and why.

We agree with the Reviewer’s position that studies should include, or least describe, male and female biology. We have revised the text to address this comment. In the methods, we provide 2 sentences that state the photoperiodic response is the same for both male and females, and why males were selected. See lines (352-355). Then, in the discussion, we describe why females will be important to study how other supplementary environmental cues impact seasonal timing of reproduction. See lines (312-330; and 334-339).

Comment 5 - The authors used the Bonferroni correction method to account for multiple hypothesis testing of measures of testes mass, body mass, fat score, vimentin immunoreactivity and qPCR analyses in Study 1. I don't think Bonferroni is ever appropriate for biological data: these methods assume that all variables are independent of each other, an assumption that is almost never warranted in biology. In fact, the data show clear relationships between these endpoint measures. Alternatively, one might use Benjamini-Hochberg's FDR correction or various methods for calculating the corrected alpha level.

This concern is not clear to us. The Benjamini-Hochberg’s FDR is a slight modification of the Bonferroni correction. Moreover, the FDR is a less-stringent statistical test compared to the Bonferroni correction. We prefer to keep the Bonferroni approach to correct for multiple tests for two reasons. First, this test is commonly used in the field of chronobiology, and second, the Bonferroni correction is more conservative. We hope the Reviewer will appreciate our perspective to be consistent with the research field and higher stringency in our statistical approach.

Comment 6 - The graphical interpretations of the results shown in Figure 1n and Figure 3e, along with the hypothesized working model shown in Figure S5, might best be combined into a single figure that becomes part of the Discussion. As is, I do not think these interpretative graphics (which are well done and super helpful!) are appropriate for the Results section.

We appreciate the Reviewer’s suggestion. During the revision we developed a single figure to show the graphical representation for the respective experiments. Unfortunately, we found the single source to be very difficult to provide a clear description and overview of the findings. We feel that the interpretations, (admittedly unusual for Results section) are best placed in the respective figures that correspond to the different experiments.

Reviewer #3:

Comment 1a - It is well known that as seasonal day length increases, molecular cascades in the brain are triggered to ready an individual for reproduction. Some of these changes, however, can begin to occur before the day length threshold is reached, suggesting that short days similarly have the capacity to alter aspects of phenotype. This study seeks to understand the mechanisms by which short days can accomplish this task, which is an interesting and important question in the field of organismal biology and endocrinology.

We thank the Reviewer for their positive feedback.

Comment 1b - The set of studies that this manuscript presents is comprehensive and well-controlled. Many of the effects are also strong and thus offer tantalizing hints about the endo-molecular basis by which short days might stimulate major changes in body condition. Another strength is that the authors put together a compelling model for how different facets of an animal's reproductive state come "on line" as day length increases and spring approaches. In this way, I think the authors broadly fulfill their aims.

We thank the Reviewer for the positive support of our research and manuscript.

Comment 1c - I do, however, also think that there are a few weaknesses that the authors should consider, or that readers should consider when evaluating this manuscript. First, some of the molecular genetic analyses should be interpreted with greater caution. By bioinformatically showing that certain DNA motifs exist within a gene promoter (e.g., FSHbeta), one is not generating robust evidence that corresponding transcription factors actually regulate the expression of the gene in question. In fact, some may argue that this line of evidence only offers weak support for such a conclusion. I appreciate that actually running the laboratory experiments necessary to generate strong support for these types of conclusions is not trivial, and doing so may even be impossible. I would therefore suggest a clear admission of these limitations in the paper.

We agree with the Reviewer’s position. The transcription binding protein analyses was used as a means to identify potential factors involved in the regulation of transcript expression. We have written a new paragraph to address this comment. In the discussion, we that highlight the links between the well characterised circadian regulation of photoperiodic transcripts (e.g, D- & E-box elements and the photoperiodic control of TSHβ. We also indicate that our bioinformatic approach identified potentially new transcription binding motifs, and provide a clear admission and state that functional analyses are required to determine necessity of these pathways (e.g., MEF2). See lines 293-295.

Comment 2 - Second, I have another issue with the interpretation of data presented in Figure 3. The data show that FSHbeta increases in expression in the 8Lext group, suggesting that endogenous drivers likely act to increase the expression of this gene despite no change in day length. However, more robust effects are reported for FSHbeta expression in the 10v and 12v groups, even compared to the 8Lext group. Doesn't this suggest that both endogenous mechanisms and changes in day length work together to ramp up FSHbeta? The rest of the paper seemed to emphasize endogenous mechanisms and gloss over the fact that such mechanisms likely work additively with other factors. I felt like there was more nuance to these findings than the authors were getting into.

We agree with the Reviewer and a similar concern was raised by Reviewer 1. Our aim was to highlight that FSH expression increased in constant short photoperiod. We have revised the manuscript to address the concern raised by the Reviewer. We have added 2 sentences in the results to highlight the additive role of endogenous timing and photoperiodic effects on FSH expression (see lines 223-226). We have kept the text that describes endogenous increases in expression (e.g., FSH/GnRH) in response to short photoperiod in the manuscript as this observation is not influenced by long photoperiod.

Comment 3 - Third, studies 1 - 3 are well controlled; however, I'm left wondering how much of an effect the transitions in day length might have on the underlying molecular processes that mediate changes in body condition. While the changes in day length are themselves ecologically relevant, the transitions between day length states are not. How do we know, for example, that more gradual changes in day length that occur over long timespans do not produce different effects at the levels of the brain and body? This seemed especially relevant for study 3, where animals experience a rather sudden change in day length. I recognize that these experimental methods are well described in the literature, and they have been used by endocrinologists for a long time; nonetheless, I think questions remain.

There are two points raised in this comment. First, the effect of transition in day length on body condition. We are investigating the impact of photoperiodic transitions on body condition. The ongoing project has examined the changes in tissue lipid content and conducted transcriptomic analyses of multiple peripheral tissues involved in energy balance. Although we made an initial attempt to combine all the findings into a single manuscript, the large datasets resulted in an overwhelming manuscript that lacked clarity. Instead, we have opted for two manuscripts that focus on the respective physiological systems. Those data should be published shortly. We did expand the discussion by developing a single paragraph that focused on the pattern of POMC expression and changes in quail body mass and adipose tissue. See lines 300-311.

Second, the Reviewer raised the issue of more gradual changes in day length over longer timespans. The day length and duration of exposure selected was to replicate previously used photoperiod manipulations to ensure reproducibility in research programmes, and to reduce the impact of photoperiod history (see lines 367-369). The present manuscript is the first study in birds to examine multiple intervening (ie within the extreme long- and short-photoperiods) day length conditions and we feel this is a major and novel contribution to the field. We agree that other time points (e.g., 13L:11D), or quicker/longer timespans could provide additional insight into the molecular mechanisms that govern seasonal transitions in reproduction/energy balance. The question raised by the Reviewer requires the types of studies that use natural conditions from wild-caught animals (or semi-natural laboratory settings) and beyond the focus of the current manuscript.

Recommendations For The Authors:

Reviewer #1

Comment 1 - Abstract: Overall abstract needs more clarity in rationale, hypothesis, and result outcomes. How this study advances our knowledge in seasonal/ photoperiodic regulation of reproduction in birds. Particularly what knowledge gap FSHb results fill in.

We have substantially revised the abstract considering the Reviewer’s suggestions. The abstract has clarified the rationale, hypothesis and results outcomes. We have also added new introductory and concluding statements that place the work into a wider ecological context (as suggested below).

Comment 2 - In general the introduction needs more clarity and doesn't seem to cover the ecological relevance of learning photoperiodic/seasonal response.

We agree with the Reviewer the introduction could be improved. We have substantially revised the introduction with an aim to increase the clarity. This involved an addition on the ecological context, clarification of the photoperiodic states in birds, and a description of the general and specific objectives. Note we did not include an introduction to ‘learning’ of the photoperiodic response, as the term implies a cognitive component is involved which is incorrect. See lines (61-67, 71-74, 80-86, and 100-105).

Comment 3 - Line 58: What does the author mean by "future seasonal environment" Is it to introduce change in climate or future seasonal events? This sentence needs rephrasing and more clarity.

In response to Comment 2, we have revised the introductory paragraph and the sentence was removed from the text.

Comment 4 - Line 63: I would recommend the use of circannual rhythms with caution for the kind of experiments authors have proposed. The approach used here is beyond the scope of addressing circannual endogenous rhythms, which can be tested only independent of photoperiod change.

We agree with the Reviewer’s concern. The use of circannual rhythms was limited to the first paragraph (lines 56-63) only to introduce the concept of endogenous rhythmicity. We were careful to not use the term ‘circannual’ for the rest of the manuscript, as the Reviewer has indicated, would be inappropriate. We have retained the use of ‘endogenous program’ to refer to the molecular and physiological changes that can occur independent of photoperiod change (ie Experiment 3). In this case, the use of endogenous is appropriate as this form of timing adheres to an interval timer. We also provided a definition for interval timer and ecological examples to illustrate the difference between circannual rhythms and annual interval timer (see lines 71-74). We also reviewed the entire manuscript to ensure the distinction for the endogenous program was clear.

Comment 5 - Another aspect authors missed is that Quail is not an absolute photorefractory (Robinson and Follett, 1982).

We agree with the Reviewer that quail are not absolute photorefractory (but instead relative photorefractory). As our photoperiod manipulations do not address criterion 1, or criterion 2 of the avian photoperiodic response (MacDougall-Shackelton et al., 2009; see https://doi.org/10.1093/icb/icp048), we feel that adding the type of photorefractory response would be a distraction and reduce the clarity of the concepts/experimental design described in the manuscript.

Comment 6 - Line 223-234: "Chicks were raised under constant light and constant heat lamp". Constant photoperiod experienced during development raises concern on how this pretreatment would shape the adult seasonal response, which could be different in the seasonal response of birds raised in natural photoperiod. If this is correct, the results shown are not tenable for birds inhabiting the natural environment.

The light schedule used in our experiment is the most appropriate for laboratory reared chicks. The light schedule, use of an incubator and hatchery is commonly used in research laboratories. The procedure serves to increase the hatch rate and welfare of chicks. Undoubtedly there will be some early developmental programming effects on quail development. However, the gonadal response across all 3 experiments was consistent with the vast scientific literature on the avian photoperiodic response in both laboratory and wild birds. As the robust gonadal response clearly replicated previous studies, we are confident the results are tenable for birds inhabiting natural environments.

Comment 7 - Numerous studies done in mammals suggest that photoperiod experienced in the early life stage affects the circadian and seasonal response in adults (Ciarleglio et al., 2011, Perinatal photoperiod imprints the circadian clock, Nat Neurosceince; Stetson M., et al., 1986, Maternal transfer of photoperiodic information influences the photoperiodic response of prepubertal Djungarian hamsters).

We agree with the Reviewer that developmental programming in mammals is important for the photoperiodic response. However, there are vast differences between the avian and mammalian photoperiodic response. Critically, in mammals, the maternal transfer of information to the offspring is achieved via the melatonin hormone. Conversely, in birds, melatonin is not necessary, nor sufficient for photoperiodic time measurement (Juss et al., 1993; see https://doi.org/10.1098/rspb.1993.0121). It is not scientifically tenable to relate the mammalian and avian photoperiodic responses in adulthood based on early developmental programs. For this reason, we did not introduce or discuss developmental programming in our manuscript.

Comment 8 - Please give details on the month in which these birds were exposed to different short and long photoperiods. It is not clear in the method section. The birds experience long to short day transition and then back to long day in 16 weeks (~ 4 months). The annual cycle is ~12 months long in nature. Again, what is the ecological relevance of such an experimental paradigm. This could give some idea on photoperiodic response, but not on how the endogenous annual cycle would respond.

Birds were delivered in September 2019 and 2020. (We have added these details to the manuscript (see lines 351-352). We agree with the Reviewer that the ecological relevance of the experimental design is limited. Our focus was to use laboratory conditions and well characterised photoperiodic manipulations to examine the role of the environmental, initial predictive cue to time seasonal transitions in reproduction. The 2-week duration for each photoperiod state in Experiment 1 provides the ability to eliminate the impact of photoperiodic history (see lines 367-369; Stevenson et al., 2012a) and reduce the time necessary for the research project. As described above in Comment #4 – we did not examine the endogenous annual cycle – but instead focused on an endogenous interval timer. Experiment 3 was designed to best examine an endogenous interval timer.

Comment 9 - Line 251: "A jugular blood sample" Please rephrase this sentence and add 50 ul heparinized tubes

We thank the Reviewer for identifying this oversight. The text was changed accordingly.

Comment 10 - Line 259: The scale.....fat pads" - The sentence doesn't read correctly.

The sentence was revised accordingly.

Comment 11 - Line 274: Male.....six weeks. It is not clear from this sentence; what photoperiod birds were exposed to before transferring to 2 long days. Is it 16 or 14 LD.

The birds were held in 16L. The text has been revised accordingly.

Comment 12 - Line 276: It is not clear what is Home Office approved schedule 1. This may be a commonly used term for animal sacrifice protocol in UK and Europe. But it is not familiar jargon for the rest of the globe.

We apologise for the jargon. The text was revised to include the exact methods (decapitation followed by exsanguination).

Comment 13 - Line 277-284: Birds under SD for 4 weeks (8 Lext) is a bit confusing and particularly in the context of studying endogenous rhythm. Needs more clarity.

The text was revised to improve the clarity. The manuscript now states: ‘A subset of birds (n=6) was maintained in short day photoperiods for four more weeks (8Lext). This group of birds provided the ability to examine whether an endogenous increase in FSHβ expression would occur in constant short day photoperiod condition.’

Comment 14 - Line 322-323: Give RIN number (RNA integrity number) here which is a very common parameter to determine RNA degradation in RNAseq experiments. I guess, the MiniON is a portable sequencer and sequences one sample at a time. If this is true authors should consider any batch effect in sequencing and use it as a covariate in the model.

The RIN values from our extraction protocol reliably produce RIN values >9.0. The text now states: Isolated RNA reliably has RIN values >9.0 for both the mediobasal hypothalamus and pituitary gland. Our RIN values are well above the recommended 7.0 limit. The Reviewer is correct that MinION is portable, however, more than one sample can be run at a time. We stated in the text (lines 454-460) that birds were counterbalanced across Flow cells so that each sequencing run had 9 samples, one from each treatment group. Our counterbalancing approach and quality control steps prevented batch effects.

Comment 15 - Line 397-398: Adding quail or chicken-specific vimentin peptide pre-incubation with primary Ab will serve more confirming control. Omitting primary Ab doesn't address cross-reactive/ nonspecific binding issues.

We agree that a positive control (ie primary Ab) is the gold standard to support specificity of the antibody. Unfortunately, we have not found a supplier of the epitope for quail/chicken vimentin. We have conducted another in silico analysis an established that the sequences for the vimentin antibody is specific for vimentin. The next closest sequence alignment is only 68% for a protein that is not expressed in the brain. The immunoreactive pattern observed in our histology reproduces work from mammalian models in which the epitope is available. Therefore, we are confident that our immunoreactive signal for vimentin is specific. We have added the in silico analysis in the manuscript on lines 535-538.

Comment 16 - Line 430: Was the GLM model used for testing all variables? Running a statistical model to explain Differentially expressed genes, photoperiod, and physiological variables together will give a more conclusive outcome to explain the photoperiod effect and seasonal state.

A similar comment was raised by Reviewer 2. We have conducted a WGCNA analyses to examine the relationship between photoperiod, physiological variables and DEG. See Figures S3 & Table S6 and lines 166-182; 497-505).

Comment 17 - It is a bit unclear why the author used cherry-picking approach by talking about only a few genes that have been studied as key regulators of photoperiodic response in quail. What was the purpose of transcriptome? A better approach would have been to use a model to reduce the data (PCA) and explain the physiological response by regression against different PCs.

We agree with the Reviewer that other statistical approaches could be conducted, and other genes could be discussed. However, we focussed on the key regulators of the photoperiodic response in quail as these are the well characterised genes. It is important that our discussion focused on these transcripts as most do not conform to the predicted patterns of expression. We feel it is best that we keep the focus on these genes.

Comment 18 - TSHb result is inconsistent with past studies, where TSHb is the first responder gene on photoinduction. The author did not pay attention to explaining it further in the discussion.

We respectfully disagree with the Reviewer. Our results are consistent with past studies and show that TSHβ expression is a molecular marker of long day photoperiod. Our study does not examine photoinduction; which does not provide the ability to compare between our study and previous work (eg., Nakao et al., 2008; see doi: 10.1038/nature06738). We have revised the text in consideration of the concern raised by the Reviewer. The text now states ‘Previous reports established that TSHβ expression is significantly increased during the period of photoinducibility in quail (Nakao et al., 2008). Although the present study did not directly examine photoinduction, TSHβ expression was consistently elevated in long day photoperiod (i.e., 16L).’. (see lines 262-265).

Comment 19 - PRL result seems interesting and there could be more discussion in relation to the rise in PRL transcripts levels termination of breeding. Elaborating on PRL expression and breeding termination can add more information to the discussion.

This comment is not clear to us, and we would incorporate a clarified comment in a revised manuscript. The increased expression of prolactin does not occur during the termination of breeding. The increase in prolactin occurs during the vernal increase in photoperiod (ie 14L) but does not have a clear link with gonadal growth.

Comment 20 - Line 217-219: Based......respectively. Sounds like a big claim with less evidence.

We have removed the sentence from the discussion.

Comment 21 - Line 220-223: The .....Bird. The sentence is not clear about how this study would add to ecological studies. Need more clarity on the importance of such data.

The sentence was removed from the text.

Comment 22 - I think that it would be helpful to add a couple of caveats to provide more ecological context. First, the model is only based on males, and responses in females could be different.

We agree with the Reviewer there are undoubtedly sex differences in timing seasonal biology. However, the photoperiodic response (growth and regression) is similar in both males and females. Sex differences exist in response to supplementary environmental cues (e.g., temperature). Males were used in these studies as the gonadal response to changes in photoperiod manipulations are much larger compared to ovarian changes in females. The focus on males allows for fewer animals to be used in the experiments and greater statistical power. To address the Reviewers concern, we have added a paragraph in the discussion that describes the similarity in photoperiodic responses in males and females, and the importance of supplementary cues for full reproductive development in female birds. We also provide a couple sentences in the methods that describe the justification for only males in the present study. See lines (Methods 352-355; Discussion 312-330; and 334-339).

Comment 23 - Last, I wondered if it would be possible to add an ecological context for the frequent change in the photoperiod schedule and not take account of the endogenous annual response. Would the procedure simulate a similar kind of underlined molecular response for a bird under natural conditions responding to changing daylight cycles on an annual time frame?

The discussion was considerably revised to address the ecological relevance of the study, and findings. We have added a sentence at the beginning of the discussion to highlight that the laboratory-based approach and photoperiodic manipulations reliable replicate previous findings using semi-natural conditions (Robinson and Follett, 1982) (See lines 248-250). We have already reduced the focus on the endogenous annual response.

Reviewer #2:

Comment 1 - The writing is very terse and could benefit from a more narrating style, which would make it a lot easier for the reader to get through some of the very data-heavy text. Breaking up the Results with subheadings would also be helpful.

We appreciate the suggestion to add subheadings to the Results. We added 3 descriptive headings for each other studies conducted in the manuscript. We feel the added revision (e.g., ecological) has improved the narrative and made the manuscript accessible to the wider readership.

Comment 2 - The transcriptome analyses could be developed a bit more. First, using the limma package would allow the authors to apply a more complete model to the DEG analyses, which would likely be superior to EdgeR. Second, the authors may want to consider WGCNA or a similar approach to discover gene co-expression modules, and then examine whether any of the resulting module eigengenes co-vary with any morphological or physiological measures and/or vary rhythmically.

This is an excellent suggestion, and the new analyses was incorporated into the revised manuscript. Using the Langfelder and Horvath 2008 WCGNA package we conducted module-trait analyses to examine co-variation in our findings. These data are presented in Figure S# and lines 476-484. We agree that other DEG analyses would be useful; our main objectives was to use BioDare2.0 to identify rhythmic transcription in the seasonal transcriptomes. EdgR provides an excellent approach to identify transcripts and commonly used.

Comment 3 - In the Data and code availability statement (lines 226ff) the authors state that "all raw data are available in Extended data Table 1." However, they should be submitted to the GEO database or a similar public repository along with all relevant metadata. Also, and maybe I overlooked this, I did not see anywhere that the "R code used in Study 1 is freely available" (I was not sure what "the methods reference list" was supposed to refer to). Instead of stating that "the full R code used is available upon request" I suggest making all scripts available via GitHub or Dataverse, along with all non-omics data. The advantage of the latter platform is that a citable DOI is assigned to each upload.

The data are now available in the GEO database and can be accessed see GSE241775. We have added this information to the text. The R code is now provided as a Table S11 so that the reader can directly access the script.

Comment 4 - Line 191: Delete the extra "that"

We thank the Reviewer for identifying the oversight. We have revised the text accordingly.

Comment 5 - Line 24f: What does "pseudo-randomly" mean? Maybe "haphazardly" would be more appropriate here?

The term pseudo-randomly is used to describe the organized manner in which subjects are assigned to each treatment group. The aim is to ensure that a particular physiological variable, such as body mass, is evenly distributed across treatment groups. (Note although the term derived from the field of psychology). The aim is to reduce bias in the experiment due to an initial bias established when assigning treatment group. We are reluctant to replace pseudorandomly with haphazardly as the latter does not imply a logical organization. We have added text to help clarify the reason. The text now state: At the end of each photoperiodic treatment a subset of quail (n=12) body mass was used as a measure to pseudo randomly select birds for tissue collection and served to reduce the potential for unintentional bias.

Comment 6 - Figure 1e,j: The text indicates that 398 and 130 genes were "rhythmically expressed" in the MBH and pituitary, respectively, but considerably fewer genes are shown in the heatmaps in Figure 1e,j. How were these genes selected, and what was the rationale for doing so? Also, some autumnal and vernal expression patterns show some strong similarities (e.g., 16a and 16v in the MBH), which could be discussed. Consider showing the two heatmaps with the columns also hierarchically clustered in a supplementary figure.

We agree with the Reviewer that the full heatmap for the transcripts should be provided. The heat maps in Figure 1 are based on the transcripts with the most significant change; and were selected to provide a graphical representation that would be easily digested by the wide readership. We have created a new figure (ie. Fig. S1) that provides all the transcripts in heat maps for both the MBH and pituitary gland.

Reviewer #3:

Comment 1 I do not have too much to add to this section of my review. Broadly speaking, I would suggest that the authors address some of the concerns I highlight above, and integrate their thoughts into the paper more than they currently do. I think this is particularly important with respect to the limitations of many of the bioinformatic analyses.

We thank the reviewer for their input and time assessing the manuscript. We have revised the manuscript in many sections incorporating the suggestions by Reviewer 3 above, and Reviewers 1 and 2.

Comment 2 Some of the methods are also a little scant. For example, the qPCR analyses are not described in sufficient detail to replicate the study. What are the efficiencies? Were samples run in duplicate? What was the housekeeping control gene used? Was there only one, or were multiple housekeeping genes used?

We apologise for the oversight, the absence of information was a mistake that missed our previous early revisions. The revised manuscript includes all the requested information. Line 333 states that all samples were run in duplicate. The efficiency for each transcript was within the MIQE guidelines (indicated on line 342) and were within the 0.7 to 1.0 range. Actin and glyceraldehyde 3-phosphate dehydrogenase were used as the reference transcripts. The most stable reference transcript was used to calculate fold change in target gene expression (lines 343-345).

Author Response

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

eLife assessment

In this important paper, the authors report a link between brumation and tissue size in frogs, summarizing convincing evidence that extended brumation is associated with smaller brain size and increased investment in reproduction-related tissues. The research will be of broad interest to ecologists, evolutionary biologists, and those interested in global change biology. While the dataset involves significant field work and advanced statistical analyses, the manuscript would benefit from more explanation of the models, including why frogs are a good model in which to address these questions, and from general improvement in the structure and conciseness.

We highly appreciate your positive assessment and that you considered our paper important and convincing.

Reviewer #1 (Public Review):

The authors have conducted lots of field work, lab work and statistical analysis to explore the effect of brumation on individual tissue investments, the evolutionary links between the relative costly tissue sizes, and the complex non-dependent processes of brain and reproductive evolution in anuran. The topic fits well within the scope of the journal and the manuscript is generally written well. The different parameters used in the present study will attract a board readership across ecology, zoology, evolution biology, and global change biology.

Thank you for your positive and supporting feedback.

Reviewer #2 (Public Review):

The authors set out to show how hibernation is linked to brain size in frogs. If there were broader aims it is hard to decipher them. The authors present an extremely impressive dataset and a thorough set of cutting-edge analyses. However not all details are well explained. The main result about hibernation and brain size is fairly convincing, but it is hard to think of broader implications for this study. Overall, the manuscript is very confusing and hard to follow.

Thank you for your compliments on our paper. As for your concerns, we have greatly revised our paper and, as we hope, improved its clarity. We have also added a few sentences to the conclusions to draw attention to potentially broader implications. Specifically, we describe how the focal traits of our study may all be affected by climate change. Differential constraints in necessary investments could be one of several reasons for the varying resilience to climate change between species in the same habitat.

Reviewer #1 (Recommendations For The Authors):

There are no issues on the availability of data and code.

Thank you.

Line 15: in the author contribution section, it seems that C.L.M. and J.P.Y are not in the author list.

These two authors are not part of this study. This was a mistake.

Line 24: I don't think it is vital or logical to address or compare too much on birds or mammals, which are not the focused taxa of the present study. Instead, it is better to clarify the reason why frogs and toads are ideal model taxon to this study.

The reason for comparisons with birds and mammals was that all hypotheses related to the various trade-offs tested here had been developed in these taxa. One of the points of our paper was that these needed validation beyond the two taxa, in addition to being tested against one another (each prediction had been developed in a specific group and typically in isolation of all other hypotheses).

Line 25-26: as the authors are shooting for eLife, as a general journal, it is not essential to provide the detailed methods in the abstract. But I think the authors need to strengthen the novelty of the work in the field here.

The strength of our study was that all traits were measured directly in our species, including estimates of hibernation duration. Prior studies used various proxies, categorial classification or datasets assembled from multiple sources. To us, this seemed like a sufficiently important advance in the field to mention it, but considering the reviewer’s comment, we have now removed it.

Line 28: "protracted brumation reduces brain size and instead promotes reproductive investments", as a correlative study, it is much more precise to change this sentence to a similar description as "protracted brumation is negatively correlated with brain size but is positively correlated with reproductive investments" here and related statements throughout the whole text.

We agree that, strictly speaking, a path analysis can only point toward possible causality and not provide hard evidence as experimental manipulation might. The wording may have been a bit too strong here in our attempt to minimize wordiness and because all our analyses combined very strongly pointed in this direction. However, we have now changed this as suggested even though it now reads almost as if we had done no more than conducting a simple correlation. We have further paid attention to the wording of our interpretations throughout the paper.

Line 32-33: it needs a bigger ending linking your main findings with the implication in understanding species response to the sustained environment change.

We have reworded the ending of the abstract to: “Our results provide novel insights into resource allocation strategies and possible constraints in trait diversification, which may have important implications for the adaptability of species under sustained environmental change.”

Line 63-68: this sentence is too long to understand and please simplify it.

We have split the sentence into two sentences.

Line 125-130: it is known that there are various frog reproductive modes (Crump et al. 2015) such as trade-offs between clutch size and egg size, different number of breeding during one year, etc. These different reproductive forms may also influence the brain size evolution with food availability and seasonal variations. Please clarify it.

Yes, anurans do have varying reproductive modes, but to us, there is no a priori reason to assume that such variation would have a direct effect on brain evolution. Rather, in our opinion, different reproductive modes would have indirect effects by affecting the environment in which reproduction occurs. For example, larvae developing under different environmental conditions (substrate, larval density, egg provisioning etc.) might affect developmental trajectories that could influence how resources are available and allocated to different organs, including the brain. Alternatively, reproductive modes could influence the choice of environment for reproduction, thereby possibly affecting mating strategies and ultimately trait investments associated with these strategies. Given we were asked to shorten our paper, we believe that ‘environmental effects’ remains broad enough to encompass such variation, thereby not necessitating disentangling the different, and likely primarily indirect, ways that reproductive modes could be linked to brain evolution. However, if the reviewer would find it important to go into such detail in the paper, we will be happy to do so.

Line 186-187: it is necessary to mention here that the authors also conducted sensitivity analyses to apply 2{degree sign}C or 4{degree sign}C below their experimentally derived as thresholds to test the robustness of the results to data uncertainty.

We have added “(details on methodology and various sensitivity analyses for validation in Material and Methods)” to indicate the different types of sensitivity analyses, which included more than simply 2 or 4°C difference.

Line 188: please change "In phylogenetic regressions" to "after controlling for phylogenetic autocorrelation/pseudo-replication" or similar sentence here.

Our focus here was the phylogenetically informed GLS model rather than phylogenetic control itself. In the latter case, it would still not be clear what type of model was conducted with such phylogenetic control. To avoid any shorthand, we have reworded for more precision: “We employed phylogenetic generalized least-squares (PGLS) models, …”

Line 177-287: please provide the exact variance explained by different predictor variables in brumation duration, individual tissue investments, and brain evolution. I also suggest that the authors need consider conducting multi-model inference-based model averaging analysis to test the relative importance of different variables. In addition, the present analyses did not include the interaction terms among variables, which may be more important than the effect of each individual factor.

There may be some misunderstanding as these models represent separate analyses for each predictor as indicated by the associated λ values (never more than one value per model). We conducted separate models to determine which variables might even play a role in explaining variation in the corresponding response variables. Based on relevant predictors, we then conducted path analyses rather than general multi-predictor analyses. The relative effect sizes are represented by the correlation coefficients (r values) in the tables.

Reviewer #2 (Recommendations For The Authors):

Why exactly are the pairwise comparisons positively correlated (fig. S5) and then negatively correlated (fig. 3). What is actually driving this difference? For the phylogenetic path analyses 26 candidate models are chosen without explanation. What theory or hypotheses are these based on?

We assume the reviewer is referring to the brain-body fat association. The two ‘pairwise’ analyses they mention were not the same. The correlation in Fig. S5 was a standard (albeit phylogenetically informed) partial correlation between the two focal tissues, controlling for SVL. By contrast, as described when introducing the analyses, negative associations were derived when additionally controlling for testes and hindlimb muscles, all of which deviated from isometry against body size. Here, the total mass of the four main tissues was divided by their proportional contribution to that mass in each species, then standardized for comparison across species. Since the total mass of these four tissues scaled directly with body size, larger-bodied species did not invest a proportion of their body to these tissues than smaller-bodied species, thus essentially rendering body size irrelevant for this analysis. However, the relative representation of the four traits changed between species such that more resources devoted to body fat was associated with a smaller brain, hence a negative relationship. Similarly, the multivariate analysis as well as the PCA also suggested similar trends when all four tissues were considered rather than purely pairwise comparisons.

Regarding the second comment: We indeed used 28 pre-defined predictions for our larger path analysis.

The authors haven't really provided much additional context either, and the discussion is almost entirely a rehash of the results section. I can't see the analysis code but this may be of use to people performing similar analyses.

It is true that the traits and core message of the Discussion relate directly to our results, but we believe that our Discussion provides the essential biological context to our findings and to how they are connected. We tried not to go on tangents or too much speculation as the many results provided enough material to discuss, with several different ways that we expanded the prior state-of-the-art in the field. However, we have now expanded the concluding paragraph to place our findings in the context of climate change, given that this could affect anurans and the different traits examined in many ways that are directly related to the current study. Yet, we decided to keep this short because such extrapolation of our findings

We indeed held off making the code available to the public in case dramatic changes to the paper were requested by the reviewers. However, it will be published.

Additional recommendations from the Reviewing Editor:

• One of the reviewers and I found the text a little difficult to follow. I suggest simplifying the paper by being more concise. For example, the introduction could be shortened into a 3-4 paragraphs of relevant text without overwhelming the reader. One of the reviewers wanted a better explanation of statistical models and I agree. The discussion could benefit from some structure - consider adding subheadings that would guide the reader as to the topic. Finally, the figures are difficult to see and should be made larger. For example, the graphs in Figure 1c could be on a panel below A and B so that readers can interpret the graph. In Figure 3 - the legend is far too small - please put above or below the graphs. In summary - I hope you consider a major re-write that would strengthen the accessibility of your paper to a broad audience.

We have substantially shortened the paper despite adding further details on models and a broader context to the Discussion. We also condensed the Introduction to about two thirds of the original word count. However, we did not think that shortening it even further or splitting it into 3-4 paragraphs would improve readability. We still considered it important to introduce with sufficient context all major hypotheses that were tested against one another, provide at least some information on what was or was not known about the evolution of the focal traits and their links to one another or the environmental variables. We also found it important to touch on the differences between our study organisms and those typically studied in the context of hibernation or brain evolution, as this could affect the predictions. Given the number of hypotheses and traits, cutting the number of paragraphs would have meant merging some of them into very long ones, which we did not consider helpful.

We further added short subheadings to the Discussion and adjusted the figures as requested.

Author Response

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

We very much appreciate the constructive comments provided by the reviewers. We have incorporated many of their suggestions and believe the manuscript is much improved.

In brief, we updated the text as suggested and have included three additional panels in supplementary fig. S2E-G. This additional data provides further support that the ectopically persisting neuroblasts are actively dividing and that cell cycle defects alone do not account for temporal patterning phenotypes.

Reviewer #1 (Public Review):

In this manuscript, the authors are building on their previous work showing Delta-Notch regulates the entrance and exit from embryo-larval quiescence of neural stem cells of the central brain (called CB neuroblasts (NB) (PMID: 35112131)). Here they show that continuous depletion of Notch in NBs from early embryogenesis leads to cycling NBs in the adult. This - cycling NBs in the adult - is not seen in controls. The assumption here is that these Notch-RNAi NBs in adults are those that did not undergo terminal differentiation in pupal development. The authors show that Notch is activated by its ligand Delta which is expressed on the GMC daughter cell and on cortex glia. They determine that the temporal requirement for Notch activity is 0-72 hours after larval hatching (ALH) (i.e., 1st instar through mid-3rd instar at 25C). In NBs/GMCs depleted for Notch, early temporal markers were still expressed at time points when they should be off and late markers were delayed in expression. These effects were observed in ~20-40% of NBs (Figures 5 and 6). Through mining existing data sets, they found that the early temporal factor Imp - an RNA binding protein - can bind Delta mRNA. They state that Delta transcripts decrease over time (without any reference to a Figure or to published work), leading to the hypothesis that Delta mRNA is repressed by the late temporal factors. Over-expressing late factors Syp or E93 earlier in development leads to downregulation of a Delta::GFP protein trap. These results lead to a model in which Notch regulates expression of early temporal factors and early temporal factors regulate Notch activity through translation of Delta mRNA.

There are several strengths of this study. The authors report rigorous measurements and statistical analyses throughout the study. Their conclusions are appropriate for the results. Data mining revealed an important mechanism - that Imp binds Delta mRNA - supporting the model that early temporal factors promote Delta expression, which in turn promotes Notch signaling.

There are also several weaknesses:

1) The activation of Notch in NBs by Delta in GMCs was already shown by this group in their Dev 2022 paper, reducing some of the impact of this study.

In our previous work, we reported that Delta-expressing GMCs transactivate Notch in neuroblasts during the embryonic to larval transition. In the current manuscript, we show that Delta is expressed in GMCs and cortex glia and both sources transactivate Notch in neuroblasts during later developmental stages. This is in agreement with work published by others and while not novel per se, is a necessary first step for understanding which neighboring cell types control Notch pathway activity. During the embryonic to larval transition, glia do not contribute likely because they have not yet grown to ensheath CB NBs and their recently born progeny.

2) The authors do not explain their current results in context of their prior paper (2022 Dev) until the Discussion, but this would be useful to read in the Introduction. Similarly, it would be good to mention that in the 2022 paper, they find a significant number of wor>Notch RNAi NBs at 2 AHL that are cycling. Are the adult Notch RNAi in this study descended from those NBs at 2 hours ALH in the 2022 study? In other words, how does the early requirement for Notch between 0-72 hours ALH reported in the current study relate to the Notch-depleted NBs identified in the 2022 paper?

We have now included the following text in the intro: “We recently reported that Notch signaling regulates CB NB quiescence during the embryonic to larval transition (Sood et al., 2022). When Notch is knocked down, some CB NBs continue dividing during this transition. We also reported that Notch activity becomes attenuated in quiescent CB NBs because CB NBs are no longer dividing and producing Delta-expressing GMC daughters for Notch pathway transactivation. Moreover, low Notch is necessary for CB NBs to reactivate from quiescence in response to dietary nutrients (Sood et al., 2022).

Here we report that Notch signaling also regulates neurogenesis termination during pupal stages. When Notch is knocked down, CB NBs maintain early temporal factor expression longer resulting in a delay of late temporal factor expression with prolonged neurogenesis into late pupal stages and early adulthood. This defect in temporal patterning (switching from early to late) occurs after CB NB exit from quiescence suggesting that Notch is required at multiple times throughout development in controlling CB NB proliferation decisions.”

We do not know whether the neuroblasts that fail to enter quiescence are the same that fail to terminate divisions during pupal stages, however there are many more that fail to terminate divisions during pupal stages.

3) Most of the experiments rely upon continuous depletion of Notch from embryonic stage 8 until adulthood using the wor-GAL4 driver. There is no lineage tracing of this driver and there is no citation about the published expression pattern of this driver. The inclusion of these details is important for a broad audience journal.

The reference for the driver is included in supplementary data, under the heading “Experimental model:Drosophila melanogaster”. This GAL4 driver is widely used and one of the most accepted in the field.

4) Most of the experiments utilize a single RNAi transgene for Notch, Delta, Imp, Syp, E93. There are no experiments demonstrating the efficacy of the RNAi lines and no references to prior use and/or efficacy of these lines.

All RNAi lines used in these studies have been published previously, by our group as well as others and sources for the lines are listed in supplementary data, under the heading “Experimental model:Drosophila melanogaster”. Efficiency of these lines have been verified using antibody labeling (data not shown) and by assaying activity of Notch activity reporters (shown in Fig. 2).

An appraisal: The authors use temperature shifts with Gal80TS to show that Notch is required between 0-72 hours ALH. They show with the use of known markers of the temporal factors and Delta protein trap, that Imp promotes Delta protein expression and the later temporal factors reduce Delta, although the molecular mechanisms are not clearly delineated. Overall, these data support their model that the reduction of Delta expression during larval development leads to a loss of Notch activity.

As noted in the Discussion, this study raises many questions about what Notch does in larval CB NBs. For example, does it inhibit Castor or Imp? Is Notch required in certain neural lineages and not others. These studies will be of interest in the community of developmental neurobiologists.

Reviewer #2 (Public Review):

Embryonic stem cells extensively proliferate to generate the necessary number of cells that are required for organogenesis, and their proliferation must be timely terminated to allow for proper patterning. Thus, timely termination of stem cell proliferation is critical for proper development. Numerous studies have suggested that cell-extrinsic changes in the surrounding niche environment drive the termination of stem cell proliferation. By contrast, cell-intrinsic mechanisms that terminate stem cell proliferation remain poorly understood. Fruit fly larval brain neuroblasts provide an excellent model for mechanistic investigation of intrinsic control of stem cell proliferation due to the wealth of information on molecular marks, gene functions and lineage hierarchy. Sood et al. conducted a genetic screen to identify genes that are required for the termination of neuroblast proliferation in metamorphosis and found that Notch and its ligand Delta contribute to their exit from cell cycle. They showed that knocking down Notch or delta function in larval neuroblasts allows them to persist into adulthood and remain proliferative when no neuroblasts can be detected in wild-type adult brains. By carrying out a well-designed temperature-shift experiment, the authors showed that Notch is required early during larval development to promote timely exit from cell cycle in metamorphosis. The authors went on to show that attenuating Notch signaling prolongs the expression of temporal identity genes castor and seven-up perturbing the switch from Imp to Syp/E93. Finally, they showed that knocking down Imp function or overexpressing E93 can restore the elimination of neuroblasts in Notch/delta mutant brains.

Overall, the experiments are well conceived and executed, and the data are clear. However, the data reported in this study represent incremental progress in improving our mechanistic understanding of the termination of neuroblast proliferation.

We respectfully disagree with this statement. Because Notch signaling is implicated in neurogenesis termination and Notch activity is regulated by GMCs and glia, it strongly suggests that NB proliferation and timing cues are controlled in a non-autonomous manner through direct interactions with NBs and their neighbors. This is in contrast to temporal patterning during embryogenesis which is largely believed to be controlled NB-autonomously. In addition, to our knowledge, no one has yet reported that CB NBs fail to terminate cell divisions on time when Notch activity is reduced during normal development. In fact, reported NB phenotypes associated with Notch loss of function have been surprisingly subtle until now.

Some of the data seem to represent more careful analyses of previously published observations described in the Zacharioudaki et al., Development 2016 paper while others seem to contradict to the results in this study.

The Zacharioudaki et al., Development 2016 paper is terrific. One key difference between our work and theirs, is that we look at Notch pathway knockdown and loss of function phenotypes, whereas in the Zacharioudaki 2016 paper, the authors report phenotypes associated with Notch constitutive activation. It has been known for some time that constitutively active Notch leads to tumorigenic phenotypes particularly in type II lineages. Zacharioudaki and colleagues further determined that some of the classically known temporal transcription factors were ectopically expressed in these stem cell tumors.Here we show that under normal developmental conditions, Notch pathway activity controls CB NB temporal patterning.

Gaultier et al., Sci. Adv. 2022 suggested that Grainyhead is required for the termination of neuroblast proliferation in a neuroblast tumor model, and grainyhead is a direct target of Notch signaling. Thus, Grainyhead should be a key downstream effector of Notch signaling in terminating castor and seven-up expression. Identical to Notch signaling, Grainyhead is also expressed through larval development. Grainyhead can function as a classical transcription factor as well as a pioneer factor raising the possibility that temporal regulation of neurogenic enhancer accessibility might be at play in allowing Notch signaling in early larval development to set up termination of castor and seven-up expression in metamorphosis. Diving deeper into how dynamic changes in chromatin in neurogenic enhancers affect the termination of neuroblast proliferation will significantly improve our understanding of termination of stem cell proliferation in diverse developing tissue.

Reviewer #3 (Public Review):

In this study, the authors investigate the effects of Notch pathway inactivation on the termination of Drosophila neuroblasts at the end of development. They find that termination is delayed, while temporal patterning progression is slowed down. Forcing temporal patterning progression in a Notch pathway mutant restores the correct timing of neuroblast elimination. Finally, they show that Imp, an early temporal patterning factor promotes Delta expression in neuroblast lineages. This indicates that feedback loops between temporal patterning and lineage-intrinsic Notch activity fine tunes timing of early to late temporal transitions and is important to schedule NB termination at the end of development.

The study adds another layer of regulation that finetunes temporal progression in Drosophila neural stem cells. This mechanism appears to be mainly lineage intrinsic - Delta being expressed from NBs and their progeny, but also partly niche-mediated - Delta being also expressed in glia but with a minor influence. Together with a recent study (PMID: 36040415), this work suggests that Notch signaling is a key player in promoting temporal progression in various temporal patterning system. As such it is of broad interest for the neuro-developmental community.

Strengths

The data are based on genetic experiments which are clearly described and mostly convincing. The study is interesting, adding another layer of regulation that finetunes temporal progression in Drosophila neural stem cells. This mechanism appears to be mainly lineage intrinsic - Delta being expressed from NBs and their progeny, but also partly niche-mediated - Delta being also expressed in glia but with a minor influence. A similar mechanism has been recently described, although in a different temporal patterning system (medulla neuroblasts of the optic lobe - PMID: 36040415). It is overall of broad interest for the neuro-developmental community.

Weaknesses

The mechanisms by which Notch signaling regulates temporal patterning progression are not investigated in details. For example, it is not clear whether Notch signaling directly regulates temporal patterning genes, or whether the phenotypes observed are indirect (for example through the regulation of the cell-cycle speed). The authors could have investigated whether temporal patterning genes are directly regulated by the Notch pathway via ChIP-seq of Su(H) or the identification of potential binding sites for Su(H) in enhancers.

This is already known for svp and cas and we have now included this information in the discussion.Thank you.

“Whether Notch pathway activity curtails both Cas and Svp or just Cas remains an open question, however it has been reported that both cas and svp are associated with at least one enhancer that is responsive to Notch activity (Zacharioudaki et al., 2016).”

A similar approach has been recently undertaken by the lab of Dr Xin Li, to show that Notch signaling regulates sequential expression of temporal patterning factors in optic lobes neuroblasts (PMID: 36040415), which exhibit a different temporal patterning system than central brain neuroblasts in the present study. As such, the mechanistic insights of the study are limited.

Reviewer #1 (Recommendations For The Authors):

1) There are missing controls

a) Fig. 1F and Fig. 6A - The authors should generate and show images of control clones (FRT19A) stained with the same markers as Notch clones.

Fig. 1F is at 48 hours APF. In control clones, there are no Dpn positive cells present, as stated in the text and therefore no confocal images are shown. Same for Fig. 6A, there are no Dpn positive cells in control clones in the brain at this time, therefore nothing to double label.

2) This result is incorrectly described in the Results

a) P. 5 "Ectopically persisting N RNAi CB NBs expressed the NB transcription factor Deadpan (Dpn), the S-phase indicator pcnaGFP, and were small on average, similar in size to control CB NBs at earlier pupal stages (Fig. 1B,C,E)." The Notch RNAi NBs were larger (not smaller) than controls in Fig. 1E at 30, 48, 72 h APF and in adults.

Thank you for this comment. We have changed the language in the main text as follows:

“Ectopically persisting N RNAi CB NBs (CB NBs at 48 hours APF and beyond) expressed the NB transcription factor Deadpan (Dpn), the S-phase indicator pcnaGFP, and were small on average compared to control CB NBs during earlier developmental stages (L3 control, average diameter 10-15μms) (Fig. 1B,C,E). However, at 30 hours APF when control CB NBs are still present, N RNAi CB NBs were larger on average (Fig. 1B,C,E).”

3) This sentence needs clarification/editing

a) P. 4: " Independent of neurogenesis timing and the mechanism by which CB NB stop divisions, temporal patterning plays a key role". A key role in what?

Thank you again. We have changed the text to the following:

“Independent of neurogenesis timing and the mechanism by which CB NB stop divisions, temporal patterning plays a key role in controlling numbers and types of neurons made within each of the NB lineages (Maurange et al., 2008; Tsuji et al., 2008; Bahrampour et al., 2017; Yang et al., 2017; Pahl et al., 2019).”

4) Some sentences need references or data to support them.

a) P. 9 Please provide a reference to support the statement that Delta is a known Notch target

We have included a reference.

b) P. 9 - please provide a reference or data to support the statement that Delta transcripts decrease over time in larval CB NBs.

This result is shown in Fig. 7B.

5) Fig. 7A - it is difficult to appreciate the purple highlighting.

We have changed the colors as suggested.

Reviewer #2 (Recommendations For The Authors):

1) In Fig. 4C, why does late knockdown of delta lead to ectopic persistence of NBs but late knockdown of Notch has no effect?

This could be due to many things including differences in efficiency of UAS-RNAi lines. The point is that Delta/Notch is required early, but not late. Although some DeltaRNAi CB NBs are still present, the number compared to 48 hours APF is greatly reduced.

2) It is surprising that Delta expression in NBs/GMCs appears to play a more important role in activating Notch signaling in neuroblasts than Delta expression in cortex glia. Please explain how Delta can cell autonomously activate Notch signaling.

We are not proposing that Delta activates Notch cell autonomously, but are proposing that Delta in GMCs transactivates Notch in NBs. After NBs divide Delta is partitioned to GMCs. Quiescent NBs have low to no Notch pathway activity, likely because they are not producing Delta expressing GMC daughters (Sood, 2022).

Please also reconcile the difference in gene expression induced by delta[RNAi] in this study and the delta-mutant allele used in the Zacharioudaki et al study.

We are unsure what the reviewer is asking here and therefore can not reconcile any differences in gene expression between the dlRNAi line and the mutant allele. What gene expression needs to be reconciled? Zacharioudaki is listed as first author on four manuscripts. Which paper is being referred to?

3) In Fig. 2J-L, why does knocking down delta in glia lead to loss of Scrib expression in neuroblasts and their surrounding progeny?

We are not sure if it does or not. We only use Scrib as a membrane marker to identify and locate cells and neuropil regions of interest.

4) The phrase "Notch is active early" is misleading when multiple labs have shown that Notch signaling is active in neuroblasts throughout larval development.

Good point! We have rewritten the statement: “Somewhat paradoxically, we find that early Notch activity is required to terminate CB NB divisions late.”

5) Neuroblasts that persist into adulthood are "smaller and Dpn-positive/PCNA-GFP-positive". Are they really neuroblasts? Can the authors verify the identity of these "persistent neuroblasts" with other molecular markers as well as functional assessment by inducing lineage clones?

We have no doubt that these cells are NBs. Because we examine brains over time, these cells can be tracked using the markers, Scrib, Dpn, and pcna. These cells also undergo asymmetric cell division (Refer to Fig. S2F) and express other markers characteristic of CB NBs (mir and insc-not shown). We have made clones and see the same phenotype (ectopic persistence) in both MARCM clones and in “flip-out” clones.

Reviewer #3 (Recommendations For The Authors):

I have a few issues that need to be addressed to reinforce some of the conclusions:

1) It is unclear whether NBs that persist in late pupal or adult stages have just failed to differentiate or whether they continue to divide, leading to supernumerary progeny (as shown for NBs that are stalled in temporal patterning like in svp mutant NBs (Maurange et al. 2008)). EdU or PH3 staining could be done in adults to clarify this point.

In this manuscript, we make use of pcna:GFP, a reporter for E2F activity as an indicator of cell proliferation. We certainly observe Dpn positive cells that only weakly express the reporter, suggesting that these cells are not actively dividing or dividing at a reduced rate. However, by far most of the ectopically persisting CB NBs strongly express the reporter and generate pcnaGFP expressing progeny, indicating that these cells are dividing. We have also stained tissues with PH3 and have included an image of a telophase dlRNAi expressing CB NB at 48 hours APF (Fig. S2F).

2) It is unclear whether Notch signaling directly or indirectly regulates temporal transitions. One possibility is that knockdown of Notch signaling decreases cell-cycle speed leading to delayed temporal transitions. The authors should test whether Notch KD affects cell cycle speed using EdU incorporation or PH3 staining. This could be done best using Notch mutant MARCM clones as wt NBs can be used as controls.

We have quantified the number of PH3 positive CB NBs during wandering L3 stages in control and dlRNAi animals. We find that dlRNAi CB NBs are indeed proliferating at reduced rates compared to controls. To test whether reduced cell cycle times are causative for termination delay, we expressed a constitutively active form of PI3-kinase in dlRNAi animals to drive cell growth and proliferation. We found that CB NBs still ectopically persist (Fig. S2E-G).

We have included the following in the text:

“Defects in timing of temporal transitions could be due to defects in cell cycle progression, although embryonic NBs still transition independent of cell division (Grosskortenhaus et al., 2005). We used PH3 to assay CB NB mitotic activity. In Delta knock down animals, the percentage of PH3 positive CB NBs was reduced compared to control (Fig. S2E). At 48 h APF however, Delta knock down CB NBs were still dividing based on PH3 expression (Fig. S2F). To determine whether CB NBs ectopically persist due to defects in cell cycle rate, we co-expressed dp110 to constitutively activate PI3-kinase in Delta knock down animals. A significant number of pcnaGFP expressing, Dpn positive CB NBs were still observed, suggesting that defects in cell cycle timing and growth rates alone cannot account for ectopic persistence of CB NBs into later developmental stages and adulthood (Fig. S2G).”

3) Cas is expressed in NBs either during quiescence and shortly after quiescence. It is possible that the maintenance of Cas in Figure 5D, E is due to NBs that have not re-entered the cell-cycle or have exited quiescence with a strong delay.

Knockdown of Notch pathway has no effect on CB NB reactivation from developmental quiescence. In fact, low levels of Notch are required for CB NBs to reactivate in response to dietary nutrients (Sood, 2022).

Indeed, the authors have previously shown that Notch signaling is important for NB cell cycle reentry during early larval stages (PMID: 35112131). Are Cas and Svp also maintained in late larval N-/MARCM clones (MARCM clonew are made after quiescence exit)?

We have not assayed Cas or Svp expression past 48 hours ALH.

4) The authors have revisited some previously published RNA-seq data showing that Delta is temporally regulated in NB lineages. This is not clearly shown by the authors that the same is true at the protein level.

Moreover, they find that mis-expression of late temporal factors or Imp knockdown in early larval brains appear to decrease Delta expression. Such semi-quantitative analysis of gene expression by immunostainings in different conditions can be a bit complicated and not very convincing because variations on intensity levels can be due to slight variations in antibody concentration, or different parameters of image acquisition.

We totally agree, but in this case the difference compared to controls was so readily apparent, that we felt it was not necessary to carry out experiments in clones. All images were acquired with the same confocal settings, experiments were repeated, and we consistently observed the same results. The data shown in Fig. 7D-G is representative.

I suggest that the authors use clonal analysis rather than pan-neuroblast manipulation in order to have internal controls. For example, blocking temporal progression in Syp-RNAi clones (MARCM or Flp-out) and/or svp MARCM clones should lead to maintenance of Imp expression in late larval clones and maintenance of high levels of Delta, which would be easily assessed compared to surrounding NBs.

Minor points:

Fig 5: the sequential expression of Cas and Svp expression in larval NBs was first described by Maurange et al. 2008. Please cite appropriately.

We have now added the requested citation to the following:

“Over time, the percentage of Cas expressing CB NBs declined, while Svp expressing CB NBs modestly increased (Fig. 5B). Less than 1% of CB NBs co-expressed Cas and Svp at any stage and expression of both factors was absent by 48 hours ALH (Fig. 5B,C). This is consistent with work published previously (Isshiki et al., 2001; Tsuji et al., 2008; Chai et al., 2013; Maurange et al., 2008; Ren et al., 2017; Syed et al., 2017).”

Fig 6A: Please indicate which immunostainings are shown in the overlay panels.

Good catch! We have modified the figure.

P9: "Delta co-immunoprecipitated with Imp.": Add "Delta mRNA co-immunoprecipitated with Imp in RIP-seq experiments" Otherwise, it suggests that you are talking about the protein.

Done

The scheme in Figure 7H is rather complicated to understand. In my opinion, it does not clearly convey the idea that Notch signaling favors the Imp-to-Syp transition.

We have made a new model figure.

Author Response

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

Reviewer #1 (Public Review):

Several concerns are raised from the current study.

1) Previous studies showed that iTregs generated in vitro from culturing naïve T cells with TGF-b are intrinsically unstable and prone to losing Foxp3 expression due to lack of DNA demethylation in the enhancer region of the Foxp3 locus (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). It is known that removing TGF-b from the culture media leads to rapid loss of Foxp3 expression. In the current study, TGF-b was not added to the media during iTreg restimulation, therefore, the primary cause for iTreg instability should be the lack of the positive signal provided by TGF-b. NFAT signal is secondary at best in this culturing condition.

In restimulation, void of TGFb is necessary to cause iTreg instability. Otherwise, the setup is similar to the iTreg-inducing environment (Author response image 1). On the other hand, the ultimate goal of this study is to provide a scenario that bears some resemblance of clinical treatment, where TGFb may not be available. The reviewer is correct in stating that TGFb is essential for iTreg stability, we are studying the role played by NFAT in iTreg instability in vitro, and possibly in potential clinical use of iTreg .

Author response image 1.

Restimulation with TGFb will persist iTreg inducing environment, resulting in less pronounced instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, and then rested or restimulated in the presence of TGF-β for 2 d. Percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3 after 2 d.

2) It is not clear whether the NFAT pathway is unique in accelerating the loss of Foxp3 expression upon iTreg restimulation. It is also possible that enhancing T cell activation in general could promote iTreg instability. The authors could explore blocking T cell activation by inhibiting other critical pathways, such as NF-kb and c-Jun/c-Fos, to see if a similar effect could be achieved compared to CsA treatment.

We thank the reviewer for this suggestion. We performed this experiment according to see extent of the role that NFAT plays, or whether other major pathways are involved. As Author response image 2 shows, solely inhibiting NFAT effectively rescued the instability of iTreg. The inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), or a c-Jun/c-Fos complex (T5224) had no discernable effect, or in one case, possibly further reduction in stability. These results may indicate that NFAT plays a crucial and special role in TCR activation, which leads to iTreg instability. Other pathways, as far as how this experiment is designed, do not appear to be significantly involved.

Author response image 2.

Comparing effects of NFAT, NF-kB and c-Jun/c-Fos inhibitors on iTreg instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of listed inhibitors. Percentages of Foxp3+ cells were analyzed by intracellular staining after 2d restimulation.

3) The authors linked chromatin accessibility and increased expression of T helper cell genes to the loss of Foxp3 expression and iTreg instability. However, it is not clear how the former can lead to the latter. It is also not clear whether NFAT binds directly to the Foxp3 locus in the restimulated iTregs and inhibits Foxp3 expression.

T helper gene activation is likely to cause instability in iTregs by secreting more inflammatory cytokines, as shown in Figure Q9, for example, IL-21 secretion. Further investigation is needed to understand how these genes contribute to Foxp3 gene instability exactly. With our limited insight, there may be two possibilities. 1. IL-21 directly affects Foxp3 through its impact on certain inflammation-related transcription factors (TFs). 2. There could be an indirect relationship where NFAT has a greater tendency to bind to those inflammatory TFs when iTreg instability appears, promoting the upregulation of these Th genes like in activated T cells, while being less likely to bind to SMAD and Foxp3, representing a competitive behavior. We at the moment cannot comprehend the intricacies that lead to the differential effects on T helper genes and Treg related genes.

With that said, we have previously attempted to explore the direct effect of NFAT on Foxp3 gene locus. Foxp3 transcription in iTregs primarily relies on histone modifications such as H3K4me3 (Tone et al., 2008; Lu et al., 2011) rather than DNA demethylation (Ohkura et al., 2012; Hilbrands et al., 2016). Previous studies have reported that NFAT and SMAD3 can together promote the histone acetylation of Foxp3 genes (Tone et al., 2008). In our previous set of experiments, we simultaneously obtained information of NFAT binding sites and H3K4me3. In Foxp3 locus, we observed a decreasing trend in NFAT binding to the CNS3 region of Foxp3 in restimulated iTregs compared to resting iTregs (Author response image 3). Additionally, the H3K4me3 modification in the CNS3 region of Foxp3 decreased upon iTreg restimulation, but inhibiting NFAT nuclear translocation with CsA could maintain this modification at its original level (Author response image 3).

Author response image 3.

The NFAT binding and histone modification on Foxp3 gene locus. Genome track visualization of NFAT binding profiles and H3K4me3 profiles in Foxp3 CNS3 locus in two batches of dataset.

Based on these preliminary explorations, it is concluded that NFAT can directly bind to the Foxp3 locus, and it appears that NFAT decreases upon restimulation, resulting in a decrease in H3K4me3, ultimately leading to the close association of NFAT and Foxp3 instability. However, due to limited sample replicates, these data need to be verified for more solid conclusions. We speculate that during the induction of iTregs, NFAT may recruit histone-modifying enzymes to open the Foxp3 CNS3 region, and this effect is synergistic with SMAD. When instability occurs upon restimulation, NFAT binding to Foxp3 weakens due to the absence of SMAD's assistance, subsequently reducing the recruitment of histone modifications enzyme and ultimately inhibiting Foxp3 transcription.

Reviewer #2 (Public Review):

(1) Some concerns about data processing and statistic analysis.

The authors did not provide sufficient information on statistical data analysis; e.g. lack of detailed descriptions about

-the precise numbers of technical/biological replicates of each experiment

-the method of how the authors analyze data of multiple comparisons... Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

These inappropriate data handlings are ruining the evidence level of the precious findings.

We thank the reviewer for pointing out this important aspect. In the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were used.

(2) Untransparent data production; e.g. the method of Motif enrichment analysis was not provided. Thus, we should wait for the author's correction to fully evaluate the significance and reliability of the present study.

Per this reviewer’s request, we have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015).

The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters.

The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

(3) Lack of evidence in human cells. I wonder whether human PBMC-derived iTreg cells are similarly regulated.

This is a rather complicated issue, human T cells express FoxP3 upon TCR stimulation (PNAS, 103(17): 6659–6664), whose function is likely to protect T cells from activation induced cell death, and does not offer Treg like properties. In contrast in mice, FoxP3 can be used as an indicator of Treg. Currently, this is not a definitive marker for Treg in human, our FoxP3 based readouts do not apply. Nevertheless, we have now investigated whether inhibiting calcium signaling or NFAT could enhance the stability of human iTreg. As shown in Author response image 4, we found that the proportion of Foxp3-expressing cells did not show significant changes across the different conditions, while the MFI analysis revealed that CsA-treated iTreg exhibited higher Foxp3 expression levels compared to both restimulated iTreg and rest iTreg. However, CM4620 had no significant effect on Foxp3 stability, consistent with the observation of its limited efficacy in suppressing human iTreg long term activation. In summary, our results suggest that inhibiting NFAT signaling through CsA treatment can help maintain higher levels of Foxp3 expression in human iTreg.

Author response image 4.

Effect of inhibiting NFAT and calcium on human iTreg stability. Human naïve CD4 cells from PBMC were subjected to a two-week induction process to generate human iTreg. Subsequently, human iTreg were restimulated for 2 days with dynabeads followed by 2 days of rest in the prescence of CsA and CM-4620. Four days later, percentages of Foxp3+ cells and Foxp3 mean fluorescence intensity (MFI) were analyzed by intracellular staining.

(4) NFAT regulation did not explain all of the differences between iTregs and nTregs, as the authors mentioned as a limitation. Also, it is still an open question whether NFAT can directly modulate the chromatin configuration on the effector-type gene loci, or whether NFAT exploits pre-existing open chromatin due to the incomplete conversion of Treg-type chromatin landscape in iTreg cells. The authors did not fully demonstrate that the distinct pattern of chromatin regional accessibility found in iTreg cells is the direct cause of an effector-type gene expression.

To our surprise, the inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), and the c-Jun/c-Fos complex (T5224) resulted in minimal alterations, as shown in Fig Q1. This seems to argue that NFAT may play a more special role in events leading iTreg instability.

We hypothesize that NFAT takes advantage of pre-existing open chromatin state due to the incomplete conversion of chromatin landscape in iTreg cells. Because iTreg cells, after induction, already exhibit inherent chromatin instability, with highly-open inflammatory genes. Furthermore, when iTreg cells were restimulated, the subsequent change in chromatin accessibility was relatively limited and not rescued by NFAT inhibitor treatment (Author response image 5). Therefore, in the case of iTreg cells, we propose that NFAT exploits the easy access of those inflammatory genes, leading to rapid destabilization of iTreg cells in the short term.

In contrast, tTreg cells possess a relatively stable chromatin structure in the beginning, it would be interesting to investigate whether NFAT or calcium signaling could disrupt chromatin accessibility during the activation or expansion of tTreg cells. It is possible that NFAT might cause the loss of the originally established demethylation map and open up inflammatory loci, thereby inducing a shift in gene transcriptional profiles, equally leading to instability.

Author response image 5.

Chromatin accessibility of Rest, Retimulated, CsA/ORAIinh treated restimulated iTreg. PCA visualization of chromatin accessibility profiles of different cell types. Color indicates cell type.

To establish a direct relationship between gene locus accessibility and its overexpression, a controlled experimental approach can be employed. One such method involves precise manipulation of the accessibility of a specific genomic locus using CRISPR-mediated epigenetic modifications at targeted loci. Subsequently, the impact of this manipulation on the expression level of the target gene can be precisely examined. By conducting these experiments, it will be possible to determine whether the augmented gene accessibility directly causes the observed gene overexpression.

Reviewer #1 (Recommendations For The Authors):

1) It might be helpful to add TGF-b to the iTreg restimulation culture to remove the influence of the lack of TGF-b from the equation, and measure the influence of SOCE/NFAT on iTreg instability.

Please refer to Author response image 1.

2) Alternatively, authors can also culture iTreg cells with TGF-b for 2 weeks when they undergo epigenetic changes and become more stabilized (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). At this point, the stabilized iTregs can be used to measure the influence of SOCE/NFAT on iTreg instability.

In the study conducted by Polansky, it was observed in Figure 1 that prolonged exposure to TGF-β fails to induce stable Foxp3 expression and demethylation of the Treg-specific demethylated region (TSDR). Based on this finding, we could consider exploring alternative approaches to obtain a more stabilized iTreg population. One such approach could be isolating Foxp3+helios-Nrp1- iTreg cells directly from the peripheral in vivo, which are also known as pTregs. Generally, pTreg cells generated in vivo tend to be more stable compared to iTreg cells induced in vitro, and they already exhibit partial demethylation of the Treg signature, as shown in Fig 6C (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). Investigating the role of NFAT and calcium signaling in pTreg cells would provide further insights into the additional roles of NFAT in Treg phenotypical transitions, particularly its role in chromatin accessibility.

3) In Figure 3, NFAT binding to the inflammatory genes in iTreg cells was even stronger than in activated T conventional cells. This is possibly due to Tconv cells being stimulated only once while iTregs were restimulated. A fair comparison should be conducted with restimulated activated conventional T cells.

Figure 3 demonstrates the accessibility of inflammatory gene loci, rather than NFAT binding. Comparing restimulated Tconvs with restimulated iTreg cells is indeed a valuable suggestion, as their activation state and polarization in iTreg directions could lead to distinct chromatin accessibility. Although one is activated long term regularly and the other is activated long term under iTreg polarization, it is highly likely that the chromatin state of both activated Tconvs and iTreg cells is highly open, especially in terms of the accessibility of inflammatory genes. This may provide us with a new perspective to understand iTreg cells, but will unlikely affect our central conclusion.

4) In the in vivo experiment in Figure 6, a control condition without OVA immunization should be included as a baseline.

We have performed this experiment in the absence of OVA, as depicted in Author response image 6. In the absence of OVA immunization, both WT-ORAI and DN-ORAI iTreg exhibited substantial stability, although DN-ORAI demonstrated a slightly less stable trend. Upon activation with 40ug and 100ug of OVA, DN-ORAI iTreg demonstrated enhanced stability than WT-ORAI iTreg, maintaining a higher proportion of Foxp3 expression.

Author response image 6.

Stability of DN-ORAI iTreg in vivo with or without OVA immunization. WT-ORAI/DN-ORAI-GFP+-transfected CD45.2+ Foxp3-RFP+ OT-II iTregs were transferred i.v. into CD45.1 mice. Recipients were left or immunized with OVA323-339 in Alum adjuvant. On day 5, mLN were harvested and analyzed for Foxp3 expression by intracellular staining.

Reviewer #2 (Recommendations For The Authors):

Major

Some concerns about the data processing and statistic analysis, as mentioned in the public review. In the figure legend, what does it mean e.g. n=3, N=3? Technical triplicate experiments? Three mice? Independently-performed three experiments? The authors should define it at least in the "Statistical analysis" in the method section otherwise the readers cannot determine the reason why they mainly use SEM for the data description.

Moreover, in some cases, the number of experiments was not sure; e.g., Fig.1B, Fig. 5.

How did the authors analyze data including multiple comparisons? Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

We thank the reviewer for pointing out this omission. Now, in the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. For Fig. 1B, N=2, and for Fig 5, we have acquired NFAT Cut&Tag data for 2 times, N=2. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were involved apart from the Student's t-test.

In Figure 1A, the difference in suppressiveness seemed subtle. Data collection of multiple doses of Tconv:Treg ratio will enhance the reliability of such kind of analysis.

We have now attempted the suppression assay with varying Treg:Tconv ratios and observed that the suppressive effect of iTreg was more obvious than that of tTreg when co-cultured at a 1:1 ratio with Tconv cells. However, as the cell number of tTreg and iTreg decreased, the inhibitory effects converged.

Author response image 7.

Compare multiple dose of Tconv:Treg ratio in suppression function CFSE-labelled OT-II T cells were stimulated with OVA-pulsed DC, then different number of Foxp3-GFP+ iTregs and tTregs were added to the culture to suppress the OT-II proliferation. After 4 days, CFSE dilution were analyzed. Left, Representative histograms of CFSE in divided Tconvs. Right, graph for the percentage of divided Tconvs.

In Figure 3F, to which group did the shaded peaks belong? In this context, the authors should focus on "Activation Region" peaks (open chromatin signature in both TcAct & iTreg defined in Fig. 4D) but I did not find the peak in the focusing DNA regions in TcAct (e.g. the shaded regions in IL-4 loci). The clear attribution of the peaks to the heatmap will enhance the visibility and understanding of readers.

We have selected some typical peaks that belong to Fig 3D. These genes encompass some T-cell activation-associated transcription factors, such as Irf4, Atf3, as well as multiple members of the Tnf family including Lta, Tnfsf4, Tnfsf8, and Tnfsf14. Additionally, genes related to inflammation such as Il12rb2, Il9, and Gzmc are included. These genes show elevated accessibility upon T-cell activation, partially open in activated nTreg cells, referred to as the "Activation Region." They collectively exhibit high accessibility in iTreg cells, which may contribute to their instability.

Author response image 8.

Chromatin accessibility of some “Activation Region”. Genomic track showing chromatin accessibility of Irf4, Atf3, Lta, Tnfsf8, Tnfsf4, Tnsfsf14, Il12rb2, Il9, Gzmc in activated Tconv and iTreg.

In Figure 4A/S4A, the information on cell death will help the understanding of readers because the sustained SOCE is associated with cell survival as shown in Fig. S2. The authors can discuss the relationships between cell death and Foxp3 retention, which potentially leads to a further interesting question; e.g. the selective/resistance to activation-induced cell death as the identity of Treg cells.

As shown in Author response image 9, activated iTreg cells indeed exhibit a certain degree of cell death compared to resting iTreg cells. The inhibition of NFAT by CsA enhances the survival rate of iTreg cells, but the inhibition of ORAI by CM-4620 leads to more severe cell death. The cell death induced by CsA and CM-4620 is not consistent, indicating that there may not be a direct proportional relationship between cell death and the expression of Foxp3 and Treg identity.

Author response image 9.

Relationship of cell death and Foxp3 stability in restimulated iTregs.<br /> Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of CsA or CM-4620. After 2d restimulation, live cell percentage were analyzed by staining of Live/Dead fixable Aqua, and percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3. Upper, live cell percentage of iTregs. Lower, percentages of Foxp3 in iTregs.

In Figure 5, the information for the data interpretation was insufficient.

We have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015). The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters. The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

The correlation between the open chromatin status of the gene loci described in Fig.5E and the expression at mRNA level? e.g.; Do iTreg-Act cells produce a higher level of IL-21 than nTreg-act? The analysis in Fig.5F-G should be performed in parallel with nTreg cells to emphasize the distinct NFAT-chromatin regulation in iTreg cells.

We have now compared the secretion levels of IL-21 in tTreg and iTreg upon activation and treated with CsA by ELISA. As shown in Author response image 10, tTreg did not secrete IL-21 regardless of activation status (undetectable), while iTreg did not secrete IL-21 at resting state but exhibited IL-21 secretion after 48 h of activation. Moreover, the secretion of IL-21 was inhibited by CsA and CM-4620 treatment. This observation aligns with our earlier findings where we observed nuclear binding of NFAT to gene loci of these cytokines, enhancing their expression and pushing iTreg unstable under inflammatory conditions. These findings further underscore the likelihood that the inhibition of calcium and NFAT signaling might contribute to the stabilization of iTreg by suppressing the secretion of inflammatory cytokines.

Author response image 10.

IL-21 secretion in tTreg and iTreg upon activation.<br /> iTregs and tTregs were sorted and restimulated with anti-CD3 and anti-CD28 antibodies, in the presence of CsA and CM-4620. Cell culture supernatant were harvested after 2 d restimulation and IL-21 secretion was analyzed by ELISA.

Performing a parallel comparison of NFAT activity between tTreg and iTreg cells was initially part of our experimental plan. However, it proved challenging in practice, as we encountered difficulties in efficiently infecting tTreg cells with NFAT-flag. Consequently, we could not obtain a sufficient number of tTreg cells for conducting Cut&Tag experiments.

Based on our observations, we speculate that there might be substantial differences in the accessibility of genes in tTreg cells, leading to considerable variations in the repertoire of genes available for NFAT to regulate. As a result, we expect significant differences in the nuclear localization and activity of NFAT between iTreg and tTreg cells.

In Figure 6C, what does the FCM plot between Foxp3-CFSE look like?

The authors can discuss the mechanism of ORAI-DN-mediated through such analysis; e.g. the possibility that selective proliferation defect by ORAI-DN in Foxp3- cells led to an increased percentage of Foxp3, not only just unstable transcription of Foxp3.

This is an in vitro experiment to assess the suppressive effect of iTreg on Tconv proliferation. Therefore, CFSE is used to stain Tconv cells, but not iTreg cells, so we did not detect proliferation feature of iTreg.

Minor

Confusing terminology of "tTreg" at line 47, etc. "natural Treg" contains both thymic-derived Treg and periphery-derived Treg cells. (A Abbas et al. Nat Immunol. 2013)

We have now changed the designation to tTreg at line 47. tTreg refers to thymus-derived regulatory T cells, while nTreg includes both tTreg and pTreg. However, it is important to note that the Treg cells used in our study were isolated from the spleen of 2-4-month-old Foxp3-GFP or Foxp3-RFP mice. The CD4+ T cells were first enriched using the CD4 Isolation kit, and the FACSAriaII was utilized to collect CD4+ Foxp3-GFP/RFP+ Treg cells. Subsequently, Helios and Nrp-1 staining revealed that the majority of these cells were nTreg, with only approximately 6% being pTreg. Overall, we consider the cells we used as tTreg.

In all FCM analyses, the authors should clarify how to detect Foxp3 expression; Foxp3-GFP/Foxp3-RFP/Intracellular staining like Figure S5A (but not specified in the other FCM plots)

All Foxp3 expressions in the article were assessed using intracellular staining, as described in the methods section, and we have added specific descriptions to each figure legend. The reason for employing intracellular staining is that we used Foxp3-IRES-GFP mice, where GFP and Foxp3 are not fused into a single protein, existing as separate proteins after expression. Therefore, during induction, the appearance of GFP protein might potentially represent the presence of Foxp3. However, in cases of Foxp3 instability, the degradation of GFP protein may not be entirely synchronized with that of Foxp3 protein, making GFP an unreliable indicator of Foxp3 expression levels. As a result, for the purification of pure iTreg cells, we used Foxp3-GFP/RFP fluorescence, while for observing instability, we employed intranuclear staining of Foxp3.

In Figure 6B, the captions were lacking in the two graphs on the right side

The two restimulation conditions, 0.125+0.25 and 0.25+0.5, have been added into Fig 6B right side.

In Figure S2, the annotation of the x-y axis was missing.

Added.

Lack of reference at line 292.

Reference 42-46 were added.

In the method section, the authors should note the further product information of antibodies and reagents to enhance reproducibility and transparency. Making a list that clarifies the suppliers, Ab clone, product IDs, etc. is encouraged. The authors did not specify the supplier of recombinant proteins and which type of TGF-beta (TGF-beta 1, 2, or 3?).

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided and incorporated into the methods section.

In the method section, the authors should clarify which Foxp3-reporter strain. There are many strains of Foxp3-reporter mice in the world. In line 373, is the "FoxP3-IRES-GFP transgenic mice" true? Knock-in strain or BAC-transgene?

This mouse is a gift from Hai Qi Lab in Tsinghua University. They acquired this mouse strain from Jackson Laboratory, and the strain name is B6.Cg-Foxp3tm2Tch/J, Strain #:006772. An IRES-EGFP-SV40 poly A sequence was inserted immediately downstream of the endogenous Foxp3 translational stop codon, but upstream of the endogenous polyA signal, generating a bicistronic locus encoding both Foxp3 and EGFP.

The age of mice used in the experiments should be specified, and confusing words such as "young" should not be used in any method descriptions; e.g. line 405.

The detailed mouse age has been added in the methods section. “To prepare Tconv, tTreg and iTreg for experiments, spleen was isolated from 2-4-month-old Foxp3-GFP mice for Tconv and tTreg sorting, and 6-week-old mice for iTreg induction.”

The method of how the original ATAC-seq/Cut & Tag data were generated was not described in the method section.

Added in method section.

The reference section was incomplete, and the style was not unified. e.g.; ref 7, 24, 25, 26 ... I gave up checking all.

The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were modified.

Changes in manuscript:

Author Name: “Huiyun Lv” to “Huiyun Lyu”.

Fig 1A was updated according to Reviwer 2’s suggestion.

Fig S3E and associated description was added according to Reviwer 2’s suggestion.

Fig S4C and associated description was added according to Reviwer 1’s suggestion.

Fig 5H and associated description was added according to Reviwer 2’s suggestion.

Fig 6D were updated according to Reviwer 1’s suggestion.

Fig 2D was corrected, the labels for gapdh and actin in the iTreg panel were inadvertently switched. The mistake has been rectified, and the original gel image will be provided.

Fig 2A and Fig 4A was updated.

The style of Fig 6B and Fig S2A was modified.

Method:

Mice: FoxP3-IRES-GFP with more description.

Flow Cytometry sorting and FACS: the detailed mouse age has been added. RNA-seq analysis, ATAC-sequencing, ATAC-seq analysis, Cut&Tag assay, Cut&Tag data analysis: more description was added.

Statistical analysis: “Numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n.” were added.

Reference: Ref 42-46 and 49-52 were added. The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were corrected.

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided.

Author Response

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

Reviewer #1 (Public Review):

Summary:

The precise mechanism of how tetraspanin proteins engage in the generation of discs is still an open question in the field of photoreceptor biology. This question is of significance as the lack of photoreceptor discs or defects in disc morphogenesis due to mutations in tetraspanin proteins is a known cause of vision loss in humans. The authors of this study combine TEM and mouse models to tease out the role of tetraspanin proteins, peripherin, and Rom1 in the genesis of the photoreceptor discs. They show that the absence of Rom1 leads to an increase in peripherin and changes in disc morphology. Further rise in peripherin alleviates some of the defects observed in Rom1 knockout animals leading to the conclusion that peripherin can substitute for the absence of Rom1.

Strengths:

A mouse model of Rom1 generated by the McInnes group in 2000 predicted a role for Rom1 in rim closure. They also showed enlarged discs in the absence of Rom1. This study confirmed this finding and showed the compensatory changes in peripherin, maintaining the total levels of tetraspanin proteins. Lack of Rom1 leads to excessive open disks demonstrated by darkly stained tannic acid-accessible areas in TEM. Interestingly, increased peripherin expression can rescue some morphological defects, including maintaining normal disc diameters and incisures. Overall, these observations lead authors to propose a model that ROM1 can be replaced by peripherin.

Thank you for your kind summary of our work.

Weaknesses:

The compensatory increase in peripherin and morphological rescue in the absence of ROM1 is expected, given the previous work from authors showing i) absence of peripherin showing increased ROM1 and ii) "Eliminating Rom1 also increased levels of Prph2/RRCT: mean Prph2/RRCT levels in P30 Prph2+/R retinas were 34% of WT, while levels in Prph2+/R/Rom1−/− retinas were 59% of WT" from Conley, 2019. The current study provides a comprehensive quantitative analysis. However, the mechanism behind the mechanism is unclear and warrants discussion.

We referenced the result from the 2019 paper by Conley and colleagues in revision. As noted by the reviewer, new information in the current study consists of the precise quantification of the compensatory increase by a technique more accurate than semi-quantitative Western blotting. The nature of these compensatory increases is currently unknown and beyond the scope of experiments described in the current study. While this is an intriguing area for future investigation, we prefer not to speculate on the underlying mechanisms to avoid any appearance of data overinterpretation.

Photoreceptor morphology appears better when peripherin is overexpressed. Is there a rescue of rod function (assessed by ERG or equivalent measures) in peripherin OE/Rom1-/- mice? Given the extensive work in this area and the implications the authors allude to at the end, it is important to investigate this aspect.

It is indeed an interesting and potentially translationally relevant direction to address whether PRPH2 overexpression can rescue the long-term degeneration and functional defects of the loss of ROM1. Unfortunately, our work in this direction remains severely hindered by the fact that the current line of ROM1 knockout mice are notoriously poor breeders, allowing us to get only a handful of animals for each year of breeding. Therefore, we decided to limit our current study to addressing the structural roles of ROM1 and PRPH2 in supporting disc formation.

Reviewer #1 (Recommendations For The Authors):

Line 210: "ROM1 is able to form disc rims in the absence of PRPH2" is not demonstrated. The data shows that the tetraspanin domains are interchangeable similar to Conley, 2019. Similar concern for lines 225-226.

We agree with the point regarding the interchangeable tetraspanin domains and clarified it in the text by referring to the tetraspanin body of PRPH2 where applicable. However, the 2019 paper by Conley and colleagues did not show any ultrastructural images of disc rims in a mouse without at least one copy of WT PRPH2 being expressed. The presence of normally looking disc rims in the complete absence of the tetraspanin body of PRPH2 is an original observation of the present study.

Line 234: it is unclear what is meant by .."they are normally processed in the biosynthetic membranes" How does lack of ER localization lead to this conclusion?

We clarified this point by replacing “normally processed” with “not trapped”.

Lines 306-308: it is difficult to follow the rationale. How will a shift in the trafficking pathway affect disulfide bonds since these are formed in ER?

The reviewer makes a good point that at least the bulk of S-S bridge formation takes place during protein maturation in the ER and the ability of additional intramolecular S-S bond formation in the Golgi is questionable. We, therefore, removed this speculation from Discussion.

Given the poor development of OS, the authors could provide an estimate of how many OS-like structures were observed, with and without rims, in RRCT animals.

The gross development of outer segment structures in RRCT homozygous mice was part of the 2019 paper by Conley and colleagues. We prefer to limit repeating experiments from the previous study, but instead wanted to focus specifically on disc rim formation, which was not analyzed in RRCT homozygous mice in the previous study.

The term "function" is loosely defined throughout this manuscript. Specifically, the excess peripherin can resolve some of the morphological defects observed in Rom1 -/-, and these functional changes in morphology are the focus of this work.

We removed the word “function” in three occasions where there may be an ambiguity in its meaning, as noted by the reviewer.

Lines 115/116: Reference is missing for the statement that photoreceptor cell degeneration begins at P30.

These lines reference Figures 1A,B, which include quantification of the number of photoreceptor nuclei. These results show that ROM1 knockout retinas exhibit a modest but statistically significant degeneration at P30. The text is modified to eliminate any ambiguity.

Lines 143-144 are speculation and could be moved to the discussion section. "Prolonged delivery of disc membrane delivery to each disc" Any reference or experiments to support this statement?

We respectfully disagree with moving this short speculative sentence to Discussion. We believe that it helps the reader to follow the flow of the data, while being clearly presented as a potential explanation rather than a conclusion.

Line 245-246: Results explained in the following paragraph (247-254) do not answer the question "whether disc rim formation in PRPH2 2C150S/C150S knockin mice was driven by disulfide-linked ROM1 molecules", which is a valid and intriguing question. However, the results explained in 247-254 answer the question "if C150S PRPH2 can form discs in the absence of ROM1".

We changed the text to replace “To address this question” with “To explore whether disc rims can be formed in the absence of any disulfide-linked tetraspanin molecules”, which precisely reflects what was addressed.

Reviewer #2 (Public Review):

In this study, Lewis et al seek to further define the role of ROM1. ROM1 is a tetraspanin protein that oligomerizes with another tetraspanin, PRPH2, to shape the rims of the membrane discs that comprise the light-sensitive outer segment of vertebrate photoreceptors. ROM1 knockout mice and several PRPH2 mutant mice are reexamined. The conclusion reached is that ROM1 is redundant to PRPH2 in regulating the size of newly forming discs, although excess PRPH2 is required to compensate for the loss of ROM1.

This replicates earlier findings while adding rigor using a mass spectrometry-based approach to quantitate the ratio of ROM1 and PRPH2 to rhodopsin (the protein packed in the body of the disc membranes) and careful analysis of tannic acid labeled newly forming discs using transmission electron microscopy.

In ROM1 knockout mice PRPH2 expression was found to be increased so that the level of PRPH2 in those mice matches the combined amount of PRPH2 and ROM1 in wildtype mice. Despite this, there are defects in disc formation that are resolved when the ROM1 knockout is crossed to a PRPH2 overexpressing line. A weakness of the study is that the molar ratios between ROM1, PRPH2 and rhodopsin were not measured in the PRPH2 overexpressing mice. This would have allowed the authors to be more precise in their conclusion that a 'sufficient' excess of PRPH2 can compensate for defects in ROM1.

Thank you for these kind comments about our work. Regarding the stated weakness that we did not measure the molar ratios between PRPH2, ROM1 and rhodopsin in the ROM1 knockout line with PRPH2 overexpression: this is one experiment that we really hoped to do but were limited by the poor breeding of the ROM1 knockout line described above. With the current breeding rate, we estimate that we would need to wait for another year to get enough material to do this experiment, which we cannot do in the context of this manuscript revision. We hope, however, that eventually this may be a part of one of our future papers.

Reviewer #2 (Recommendations For The Authors):

The p-value for statistical significance is not listed, readers will assume the most commonly used 0.05 value was used but this should still be defined, especially since only asterisks summarizing the p-value range are provided in place of the actual p-values.

The definitions of various numbers of asterisks of significance (including p<0.05 as a minimal measure of significance) are provided in the Methods section, whereas the exact p-values are stated in figure captions.

There are 3 phrasing issues that are potentially misleading.

1) While PRHP2 and ROM1 are the most abundant tetraspanins in photoreceptors they are not the only ones. It would be more precise if for example the Table 1 title was changed to 'molar ratio of outer segment tetraspanins and rhodopsin'.

We have changed the title of Table 1 to “Quantification of molar ratios between PRPH2, ROM1 and rhodopsin in WT and Rom1-/- outer segments” to be more accurate.

2) The protein expressed in RRCT mice is described as the 'tetraspanin core' while the cartoon (and original paper) shows the protein as simply being ROM1 with a different cytoplasmic C-terminus (from PRHP2). Tetraspanin core in other places is used to mean just the transmembrane bundle or that bundle with the EC loops.

We agree that the term “tetraspanin core” may be confusing. We modified the text to not use this term and, when needed, refer to this main part of the tetraspanin molecule as a “body”.

3) Line 203-205, the 'somewhat restored' qualifier should be removed. If the authors think there is an effect that is different from chance, they should use a different alpha and justify that choice.

We removed this line, as suggested.

Reviewer #3 (Public Review):

In this manuscript, Lewis et al. investigate the role of tetraspanins in the formation of discs - the key structure of vertebrate photoreceptors essential for light reception. Two tetraspanin proteins play a role in this process: PRPH2 and ROM1. The critical contribution of PRPH2 has been well established and loss of its function is not tolerated and results in gross anatomical pathology and degeneration in both mice and humans. However, the role of ROM1 is much less understood and has been considered somewhat redundant. This paper provides a definitive answer about the long-standing uncertainty regarding the contribution of ROM1 firmly establishing its role in outer segment morphogenesis. First, using an ingenious quantitative proteomic technique the authors show PRPH2 compensatory increase in ROM1 knockout explaining the redundancy of its function. Second, they uncover that despite this compensation, ROM1 is still needed, and its loss delays disc enclosure and results in the failure to form incisures. Third, the authors used a transgenic mouse model and show that deficits seen in ROM1 KO could be completely compensated by the overexpression of PRPH2. Finally, they analyzed yet another mouse model based on double manipulation with both ROM1 loss and expression of PRPH2 mutant unable to form dimerizing disulfide bonds further arguing that PRPH2-ROM1 interactions are not required for disc enclosure. To top it off the authors complement their in vivo studies by a series of biochemical assays done upon reconstitution of tetraspanins in transfected cultured cells as well as fractionations of native retinas. This report is timely, addresses significant questions in cell biology of photoreceptors, and pushes the field forward in a classical area of photoreceptor biology and mechanics of membrane structure as well. The manuscript is executed at the top level of technical standard, exceptionally well written, and does not leave much more to desire. It also pushes standards of the field- one such domain is the quantitative approach to analysis of the EM images which is notoriously open to alternative interpretations - yet this study does an exceptional job unbiasing this approach.

According to my expertise in photoreceptor biology, there is nothing wrong with this manuscript either technically or conceptually and I have no concerns to express.

Thank you for these incredibly kind comments.

Reviewer #3 (Recommendations For The Authors):

I have no recommendations to make.

Author Response

We would like to thank you and the reviewers for evaluating this manuscript and providing constructive recommendations. Please see our provisional response to the major comments made by the reviewers.

Reviewer #1 (Public Review):

"…the authors never show that HFS of cortical inputs has no effect in the absence of thalamic stimulation. It appears that there is a citation showing this, but I think it would be important to show this in this study as well"

We understand that the reviewer would like us to induce an HFS protocol on cortical input and then test if there is any change in synaptic strength in thalamic input. We agree this is an important experiment which we will do.

Reviewer #2 (Public Review):

“…The experimental schemes in Figs. 1 and 3 (and Fig. 4e and extended data 4a,b) show that one group of animals was subjected to retrieval in the test context at 24 h, then received HFS, which was then followed by a second retrieval session. With this design, it remains unclear what the HFS impacts when it is delivered between these two 24 h memory retrieval sessions."

We understand that the reviewer has raised the concern that the increase in freezing we observed after the HFS protocol (ex. Fig. 1b, the bar labeled as Wth+24hHFSth) could be caused or modulated by the recall prior to the HFS (Fig. 1a, top branch).

If our interpretation of the concern is correct, we think this is unlikely to be the case. The first test, and the following HFS protocol, and the second test, (Fig. 1a, top branch) were all performed in the same chamber. For both the first and the second tests, animals received two 30-second recall trials, separated by 2 minutes (the data presented as the average of the two trials). We did not see a difference in freezing between the first and the second recall trials within each session (data not shown). It was only after the HFS protocol that we observed an increase in freezing.

This shows that in our paradigm the first recall does not impact the next recall in terms of the animals’ freezing levels. It must be noted that in cases where we did not do any testing prior to the HFS protocol, we still observed an increase in freezing after the HFS protocol (ex. Fig. 1a, middle branch and the corresponding data in Fig. 1b, the bar labeled as Wth+HFSth). Also, relevant is the data shown in Fig. 3c. Here, although animals were tested twice (Fig3. a, top branch), there was no increase in freezing in the second test (Fig. 3c, middle panel, Wth+24HFSCtx). That is, in the absence of an effective LTP, there is no significant difference between the two tests.

To further confirm this, in a new group of mice, 24 hours after weak conditioning, we will induce the HFS protocol, followed by testing (that is, no testing prior to the HFS protocol).

“The final experiment (Fig. 5a-c, extended data 5c) combines behavioral assessments with in vivo LFP recordings before and 24 h after hetero-HFS. While this experiment is demanding, it seems a bit underpowered”

We agree with the reviewer that the number of mice used in this experiment is on the lower side. However, this is not unusual for such an experimental configuration. As the reviewer mentioned, this is a demanding experiment for multiple reasons. For example, to confidently demonstrate that our HFS protocol, in addition to long-lasting behavioral changes, produces long-lasting synaptic changes, we must see a significant increase in evoked LFP after the manipulation which is predicted to last at least 24 hours. That is, the change in evoked LFP is not caused by non-related fluctuations, such as movement of the recording probe. For this reason, 3-4 days prior to conditioning, each day we measured evoked LFP. Only those mice that had a stable evoked LFP during this time were used for further conditioning. We will provide exclusion criteria for this experiment in the revised manuscript.

“ It would be critical to know if LFPs change over 24 h in animals in which memory is not altered by HFS,..”

We will perform an experiment where mice undergo a weak conditioning protocol and will record the evoked LFP 1-2 hours following the conditioning protocol, as well as the next day.

“…the slice experiments (Fig. 5d-f) are not well aligned with the in vivo experiments (juvenile animals, electrical vs. opto stimulation, different HFS protocols, timescale of hours).”

Our aim in this part was to demonstrate that the pathways we chose for our study can undergo heteroLTP. For this purpose, we used an already established protocol, which uses electrical stimulation (Fonseca, 2013). For clarification, I have tried to induce optical LTP with a high-frequency stimulation protocol in slices, but I did not succeed. I am not aware of a work that successfully induced optical LTP with a high-frequency protocol.

Author Response

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

We thank Reviewer 1 for their time reviewing our revised manuscript and appreciate their thoughtful suggestions for further clarity. In regard to the public review statement, "However, parts of the methods (e.g. assessment of blanks and data filtering) and results (e.g. visualization of plant community data) could still be polished, and the figures should be improved to increase the clarity of the manuscript", we have made small modifications in the text and figures during production of the Version of Record to address these important suggestions.

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

Reviewer #1 (Public Review):

This manuscript compiles the colonization of shrubs during the Late Pleistocene in Northern America and Europe by comparing plant sedimentary ancient DNA (sedaDNA) records from different published lake sediment cores and also adds two new datasets from Island. The major findings of this work aim to illuminate the colonization patterns of woody shrubs (Salicaceae and Betulaceae) in these sediment archives to understand this process in the past and evaluate its importance under future deglaciation and warming of the Arctic.

We greatly appreciate the time and detailed consideration of our manuscript by Reviewer 1. Our responses to individual comments are highlighted in blue, with the original comments provided by the reviewer in black.

The strength of evidence is solid as methods (sedimentary DNA) and data analyses broadly support the claims because the authors use an established metabarcoding approach with PCR replicates (supporting the replicability of PCR and thereby proving the occurrence of Salicaeae and Betulaceae in the samples) and quantitative estimation of plant DNA with qPCR (which defines the number of cycles used for each PCR amplification to prevent overamplification). However, the extraction methods need more explanation and the bioinformatic pipeline is not well-known and needs also some further description in the main text (not only referring to other publications).

Thank you for bringing this to our attention. We have now provided greater detail on our extraction methods and bioinformatic pipeline.

The authors compare their own data with previously published data to indicate the different timing of shrubification in the selected sites and show that Salicaceae occurs always like a pioneer shrub after deglaciation, followed by Betaluaceae with a various time lag. The successive colonization of Salicaceae followed by Betulaceae is explained by its differences in environmental tolerance, the time lag of colonization in the compared records is e.g. explained by varying distance to source areas.

However, there are some weaknesses in the strength of evidence because full sedaDNA plant DNA assessment, quality of the sedaDNA data (relative abundance and richness of sedaDNA plant composition) and results from Blank controls (for sedaDNA) are not fully provided. I think it is important to show how the plant metabarcoding in general worked out, because it is known that e.g. poor richness can be indicative of less preserved DNA and a full plant assessment (shown in the supplement) would be more comprehensive and would likely attract a larger readership.

Thank you for bringing these important points to our attention. The DNA dataset including the full taxa assemblage will be included with the manuscript upon publication and apologize for not including it during the review process. This dataset will also include information on positive and negative blanks used for quality control. Following suggestions from Reviewer 2, we have now also calculated some recently proposed DNA quality metrics (Rijal et al., 2021), which collectively support our earlier conclusions that our record is of sufficient quality to draw the current conclusions. We hope that the inclusion of the complete DNA dataset will indeed draw a larger readership.

Further, it would allow us to see the relative abundance in changes of plants and would make it easier to understand if the families Salicaeae and Betulaceae are a major component of the community signal. Further, the possibility to reach higher taxonomic resolution with sedaDNA compared to pollen or to facilitate a continuous record (which is different from macrofossils) is not discussed in the manuscript but should be added. Also, the taxonomic resolution within these families in the discussed datasets would be of interest, also on the sequence type level if tax. assignments are similar.

Thank you for these suggestions. We have focused on these two families as it is known from numerous pollen records and floras that they are the major component of the vascular plant communities in the regions investigated. Betula (birch) and Salix (willow) are indeed the most dominant woodland shrubs of the tundra biome, which covers expansive areas of the Arctic. For example, in Iceland natural woodlands, which cover 1.5% of the total land area, are composed of 80% birch shrubs (Snorrason et al. 2016, Náttúrufræðingurinn 86). Salix mixes in with Betula, especially around wet sites. Species from both genera are common and wide-spread throughout Iceland, but dwarf and cold tolerant species thrive best on the highland or at glacial sites, while shrub-like species are more common on the lowland, coastal area and in sheltered valleys. Flora of Iceland (http://www.floraislands.is/PDF-skjol/Checklist-vascular.pdf) lists Betula as the only genus of Betulaceae native to Iceland (page 79/80) and Salix as the major genus of Salicaceae (page 82-85), although Populus tremula (Salicaceae) exists in the wild but is rare (perhaps just a countable number of trees/shrubs in the whole country). The point is that, for Iceland, Betulaceae is Betula and Salicaceae is Salix, meaning that our sedaDNA method has the taxonomic resolution at the genus level. And with the help of pollen analysis of the site near Stóra Viðarvatn (the novel sedaDNA work of the present paper), i.e., Ytri-Áland site (Karlsdóttir et al. 2014), it is possible to interpret our results even to the species level, which we have only mention in the discussion. It has been suggested that matching sedaDNA results with botanical knowledge about the study site and the vegetation history (local reference database) is one way to increase taxonomic resolution of the sedaDNA approach (e.g. Elliott et al. 2023, Quaternary 6,7). In the same way we find our sedaDNA analysis having sufficient resolution to answer the questions asked in the present study. For the future, although we do not include it in the discussion this time, it should be possible to increase the taxonomic resolution of plant metabarcoding by priming multiple genes simultaneously like that is described as a proof of concept by Foster et al. (2021, Front Ecol Evol 9: 735744). In the revised version of the manuscript, we have now expanded on the power of sedaDNA in terms of increased taxonomic resolution and application in continuous lake sediment records in the introduction of the manuscript. Following Reviewer 2’s suggestion, we have now included the sequences used for taxonomic assignment in the supplement information.

Another important aspect is how the abundance/occurrence of Salicaceae is discussed. Many studies on sedaDNA confirm an overrepresentation of this family due to better preservation in the sediment, far-distance transport along rivers, or preferences of primers during amplification etc. As this family is the major objective of this study, such discussion should be added to the manuscript and data should be presented accordingly.

Thank you for raising this point. The reviewer is indeed correct that Salicaceae is typically overrepresented in read abundance compared to other vascular plant taxa in sedaDNA studies. However, as we mention in the Results and Interpretation section for Stóra Viðarvatn “As PCR amplification results in sequence read abundances that may not reflect original relative abundances in a sample (Nichols et al., 2018), we focus our discussion on taxa presence/absence,” we do not place weight on the indeed greater relative abundance of Salicaceae in our own dataset. As such, this different relative abundance of plant taxa reads should not influence the conclusions drawn in the manuscript.

I also miss more clarity about how the authors defined the source areas (refugia) of the shrubs. If these source areas are described in other literature I suggest to show them in a map or so. Further, it should be also discussed and explained more in detail which specific environmental preferences these families have, this is too short in the introduction and too unspecific. Also, it would be beneficial to show relative abundances rather than just highlighted areas in the Figures and it would allow us to see if Salicaeae will be replaced by Betulaceae after colonizing or if both families persist together, which might be important to understand future development of shrubs in these areas.

Thank you for allowing us to clarify. As the regions studied with the lake sediment records shown in this manuscript were all covered by extensive ice sheets during the Last Glacial Maximum (LGM, Fig. 1), plant refugia and source areas must have been located somewhere south of the ice sheet margins. Thus, we calculate our distance to source as the minimum distance from a lake site to land beyond the extent of the ice sheet during the LGM. This has now been clarified in the text and highlighted in Fig. 1. We have also added in the discussion molecular results from Thórsson et al. (2010, J Biogeogr 37) on possible source origins of Betula in Iceland. Details on taxa environmental preferences have now been expanded upon in the Discussion section where we explore the various trait-based factors that may influence the relative differences in colonization timing between Salicaceae and Betulaceae. We have now also edited Figs. 3 and 4 to include PCR replicates instead of highlighted bars to better compare the DNA and pollen datasets from Iceland.

The author started a discussion about shrubification in the future, but a more defined evaluation and discussion of how to use such paleo datasets to predict future shrubification and its consequences for the Arctic would give more significance to the work.

Thank you for this suggestion and allowing us to expand on potential future changes. We have now edited this final section of the paper to provide a little more detail on how we envision these records being used to predict future shrubification and climate change.

Reviewer #1 (Recommendations For The Authors):

I list some more specific details here.

You speak about "read counts", I guess you used relative abundance of read counts, you should state it like this.

Thank you for allowing us to clarify. The data that we refer do in terms of read counts is from the previously published studies in the circum North Atlantic. The data provided from these studies is raw read counts, and not relative abundance.

Line 100: What do you mean here: "temperature changes in prior warm periods"?

Thank you for allowing us to clarify. We have rephrased to sentence to “higher temperature in prior warm periods”, which we hope is clearer for the reader.

Line 134: How is DNA diluted by minerogenic sediment? Did the sedimentation rate increase? Typically minerogenic input should be beneficial for DNA preservation.

Thank you for allowing us to clarify. These samples were primarily comprised of tephra glass with minimal organic content. While we agree that minerogenic sediment is generally beneficial for DNA preservation, the predominance of inorganics (tephra) that fell from the sky, rather than being washed into the lake from the landscape, would not carry organic sediment with it. We have rephrased the sentence to make this clearer.

I would suggest adding more citations to the text (for example statements in lines 106, 110, 368)

Thank you for the suggestion. The manuscript has been edited accordingly.

Better divide your discussion part: discussion about dispersal mechanisms occur in both sections. Maybe you could divide it into environmental factors for colonization and traitbased factors (only an idea).

Thank you for the suggestion. We have now edited the second dispersal section to “Environmental dispersal mechanisms” to be clearer about our focus on factors such as wind, sea ice, and birds that may transport the seeds across the North Atlantic. The previous section retains the trait-based factors that may influence relative timing in colonization between Salicaceae and Betulaceae.

Which type of sequencing did you use, paired-end 76bp is unknown to me.

Methods have now been edited to clarify this, along with details related to extraction methods as requested in the Public Review.

Reviewer #2 (Public Review):

Harding et al have analysed 75 sedaDNA samples from Store Vidarvatn in Iceland. They have also revised the age-depth model of earlier pollen, macrofossil, and sedaDNA studies from Torfdalsvatn (Iceland), and they review sedaDNA studies for first detection of Betulaceae and Salicaceae in Iceland and surrounding areas. Their Store Vidarvatn data are potentially very interesting, with 53 taxa detected in 73 of the samples, but only results on two taxa are presented. Their revised age-depth model cast new light on earlier studies from Torfdalsvatn, which allows a more precise comparison to the other studies. The main result from both sedaDNA and the review is that Salicaceae arrives before Betulaceae in Iceland and the surrounding area. This is a well-known fact from pollen, macrofossil, and sedaDNA studies (Fredskild 1991 Nordic J Bot, Birks & Birks QSR 2014, Alsos et al. 2009, 2016, 2022) and as expected as the northernmost Salix reach the Polar Desert zone (zone A, 1-3oC July temperature) whereas the northernmost Betula rarely goes beyond the Southern Tundra (zone D, 8-9 oC July temperature, Walker et al. 2005 J. Veg. Sci., Elven et al. 2011 http://panarcticflora.org/ ).

We greatly appreciate the time and detailed consideration of our manuscript by Reviewer 2. Our responses to individual comments are highlighted in blue, with the original comments provided by the reviewer in black.

While we agree that previous studies have indeed indicated a relative delay in Betula colonization relative to Salix, most of these have relied on pollen and macrofossil evidence, which are complicated to use as proxies for the first appearance of a given taxa (see our Introduction in the main manuscript). A few studies have shown this also with sedaDNA (e.g., Alsos et al., 2022), which is a more robust proxy for a plant taxa’s presence, but these have been limited geographically (e.g., northern Fennoscandia). In our study, we show that this pattern is reflected in 10 different lakes across the North Atlantic, emphasizing the broad nature of Betula’s delayed colonization relative to other woody shrubs, such as Salix.

My major concern is their conclusion that lag in shrubification may be expected based on the observations that there is a time gap between deglaciation and the arrival of Salicaceae and between the arrival of Salicaceae and Betulaceae. A "lag" in biological terms is defined as the time from when a site becomes environmentally suitable for a species until the species establish at the site (Alexander et al. 2018 Glob. Change Biol.). The climate requirement for Salicaceae highly depends on species. In the three northernmost zones (A-C), it appears as a dwarf shrub, and it only appears as a shrub in the Southern Tundra (D) and Shrub Tundra (E) zone, and further south it is commonly trees. Thus, Salicaceae cannot be used to distinguish between the shrub tundra and more northern other zones, and therefore cannot be used as an indicator for arctic shrubification. Betulaceae, on the other hand, rarely reach zone C, and are common in zone D and further south. Thus, if we assume that the first Betulaceae to arrive in Iceland is Betula nana, this is a good indicator of the expansion of shrub tundra. Thus, if they could estimate when the climate became suitable for B. nana, they would have a good indicator of colonisation lags, which can provide some valuable information about time lags in shrub expansion (especially to islands). They could use either independent proxy or information from the other species recorded in sedaDNA to reconstruct minimum July temperature (see e.g. Parducci et al. 2012a+b Science, Alsos et al. 2020 QSR).

We appreciate the reviewer’s insight into the implications of our use of the word “lag”. Indeed, as we do not have site-specific climate timeseries for each lake record, we have adjusted our wording to “delay”, which we believe is more general and descriptive of our observations. We recognize the importance of independent paleotemperature records for each lake, but these are not yet available for all records, so we prefer to keep our study focused on the delay instead. In addition, we prefer not to derive temperature records from the vegetation sedaDNA records, as these are not independent and will incorporate changes driven by additional factors, such as soil and light (e.g., Alsos et al., 2022). We have added some text to the final section on Future Outlook that elaborates on the need for complimentary records of past climate to pair with paleoecological records of colonization. We hope that this motivates the community to pursue these lines of research that we agree are needed.

The study gives a nice summary of current knowledge and the new sedaDNA data generated are valuable for anyone interested in the post-glacial colonisation of Iceland. Unfortunately, neither raw nor final data are given. Providing the raw data would allow re-analysing with a more extensive reference library, and providing final data used in their publication will for sure interest many botanists and palaeoecologist, especially as 73 samples provide high time resolution compared to most other sedaDNA studies.

Finally, the raw and final data, including blank controls, used in our study for Stóra Viðarvatn will ultimately be provided with the manuscript’s publication. We apologize for not including it with the original submission.

Reviewer #2 (Recommendations For The Authors):

Line 112-113: Difference in northward expansion rate is not the same as lag. Thus, your conclusion "As a result, the biospheres role in future high latitude temperature amplification may be delayed." does not derive directly from the data you present.

Thank you for allowing us to clarify our wording. We have rephrased the sentence to align with our results more closely as stated in the Abstract of the manuscript.

.Line 133: From Figure S3, it looks like three or possibly four samples failed.

Thank you for pointing this out. First, we realized that the DNA reads originally included in Figure S3 were from after filtering. We have now updated the figure to include the total raw reads, which is a better indicator of DNA reliability (Rijal et al., 2021). Based on the total raw reads, only two samples failed with total reads of 2 and 5.

Line 141: You say you focus on presence/absence, but you do show quantitative results for Salix and Betula (0-5 PCR repeats) in Figure 2.

Thank you for allowing us to clarify. Fig 2 shows the number of replicates that meet our criteria for taxa presence, where a higher number of replicates corresponds to a higher likelihood of presence.

Line 142: Where are the other 51 taxa shown?

We are providing the full DNA record in the supplement, which will be published alongside the main manuscript. We have also now included a plot of species richness against sample depth in Fig. S2.

Line 178-179: Note that the revised date of first detection is close to what has been previously published (Salix ~10300 vs. 10227, Betula ~9500 vs 9680), so it does not make any changes to previous interpretation.

Yes, this is true. However, we still believe it is important to always consider improvements in age models to best correlate the timing of events between different paleo records.

Line 191-194 and Figure S2: I leave the evaluation of revised age-depth model to the geologist.

As this aspect was not commented on, we assume that both reviewers are satisfied.

Line 197: "Delay" is a more correct word than "lag".

Thank you, edited.

Line 210: Where do 1700 and 2500 come from? If your revised age of ice retreat is 11 800, and your revised date of Salix and Betula arrival are ~10 300 and ~9500, I make this 1500 and 2300.

Yes, this is correct. Thank you for pointing out this error.

Line 215-217: To be more certain about any bias caused by low DNA quality, I suggest you explore your data using the tools presented in Rijal et al. 2021 Science Advances. As you do not provide your data, I cannot evaluate the quality of them.

Thank you for the suggestion. We have now calculated the various DNA quality indices developed by Rijal et al. (2021). This has been added to the methods and results section for the Stóra Viðarvatn record, as well as in Fig. S3. The MTQ and MAQ scores are known to correlate with species richness when richness is low (n<30, Rijal et al., 2021), which is likely an artifact of the requirement that the 10 best represented barcode sequences are required to calculate these scores. As this correlation is observed in our dataset and given that our species richness is low (n<30, Fig. S2), the low MTQ and MAQ score are not likely indicative of low-quality DNA. We therefore judge the quality of our DNA on total raw reads and CT values, which remain relatively constant through time (Fig. S2).

Line 226: Do you mean TDV?

We intended to omit unnecessary abbreviations throughout the manuscript, such as lake names, in our original manuscript. We have now changed TORF, which we use as the lake’s abbreviation, to the full lake name, Torfdalsvatn.

Line 282-283: Given that the basal sediments of Nordivatnet are marine (Brown et al. 2022 PNAS Nexus), even a low detection may be a strong indication of local presence.

Thank you for this point. However, to standardize the records and compare across a wide range of geographical and depositional settings, we prefer to apply the same criteria for the taxa’s presence to each lake as outlined in our Methods.

Line 289: See the definition of "lag"

Changed to “delayed” per your earlier suggestion. Thank you.

Line 298-303: I agree that the late appearance of Betula at Langfjordvatnet (10 000 cal BP) is anomalously long and a bit unexpected given that it is found at five other lakes in the region 13000-10200 cal BP (Alsos et al. 2022). However, a likely explanation is the lack of area with stable soil - B. nana requires a greater degree of soil development compared to other heath shrubs (Whittaker 1993) and Langfjordvatnet is surrounded by steep scree slopes (Otterå 2012 master thesis Univ. Bergen). At Jøkelvatnet, Salix appears in the four available samples from 10453 to 9811 whereas Betula arrives 9663. Here, the arrival of Betula is just at the drop of local glacier activity and at the temperature rise, suggesting that it arrives immediately after the climate becomes suitable (Elliott et al. 2023 Quaternary). Thus, based on N Fennoscandia where we have more data available, it does not show lags and does not support delayed shrubification (which contrasts with what we have shown for many other species including common dwarf shrubs, see Alsos et al. 2022). Would be very interesting to have similar data from Iceland, which has a large dispersal barrier.

Thank you for these further considerations. We have incorporated those related to Langfjordvannet into the manuscript accordingly. We also appreciate the point regarding Jøkelvatnet. However, as stated in our Methods section for “Published sedaDNA datasets”, we do not include Jøkelvatnet in our comparison due to the impact of glacier activity as the reviewer notes: “Finally, both Jøkelvatnet and Kuutsjärvi were impacted by glacial meltwater during the Early Holocene when woody taxa are first identified (Wittmeier et al., 2015; Bogren, 2019), and thus the inferred timing of plant colonization is probably confounded in this unstable landscape by periodic pulses of terrestrial detritus.” Due to the glacier’s presence in the lake catchment, it is not possible to discern whether delay in Betulaceae would have occurred if the glacier were not present. Therefore, we prefer to keep this record excluded from our comparisons.

Line 316-319 and 344: Based on contemporary genetic patterns, Alsos et al. analyse the relative importance of adaptation to dispersal compared to other factors.

Thank for you bringing up this important point. We have now expanded our discussion to include these analyses from Alsos et al. (2022).

Line 342+350: Original publication is Alsos et al. 2007 Science

Thank you, edited.

Line 343: Alsos et al. 2009 Salix study is the wrong citation here. Eidesen et al. 2015 Mol. Ecol. shows phylogeography of Greenland population but not Baffin - I am not aware of any contemporary genetic studies of Betula from Baffin.

Thank you for pointing this out. We will also include the Eidesen et al. (2015) citation for reference to Greenland. However, there is one data point included for southern Baffin Island in Alsos et al. (2009), so we will retain this citation here as well.

Line 351-353: See comment about Betula from Baffin above. Also, I am not sure I follow here - what do you mean by "these populations" - the Svalbard ones or Iceland? Eidesen et al. 2015 is the wrong citation for Salix - use Alsos et al. 2009. Alsos et al. 2009 suggest Iceland (and E Grenland) was colonized from north Scandinavia, although this was uncertain as no data were available from Faroe/Shetland. Svalbard was colonized from N Fennoscandia (Alsos et al. 2007).

Regarding Baffin Island sources, we refer the reviewer to our response to their previous comment. We have clarified the wording of our sentence from “these populations” to “the modern populations from these locations [Baffin Island, Greenland, and Svalbard]”. We have removed reference to Eidesen et al. (2015), as this is for Betula rather than Salix. Finally, we have added a citation for Alsos et al. (2007) here for Svalbard.

Line 354-355: AFLP suggest that Baffin and W Greenland were colonised from a refugia south of the Wisconsin Ice Sheet, see Alsos et al. 2009.

Yes, we are aware, thank you. Our reference to “mid-latitude North America” in the sentence acknowledges this refugia, but we have now added “south of the Laurentide Ice Sheet” for further clarification.

Line 363-381: See comment above; your Store Vidarvatn data do currently not demonstrate a lag between environmental suitability and climate, but using the rest of the DNA record, potentially it could. Would also be good to reflect on the distance to the source area for shrubs Late Glacial/Early Holocene compared to now.

Thank you for these suggestions. We have edited this section of the manuscript to elaborate on the need for independent climate reconstructions as well as the fact that distances to plant refugia are shorter now than during the last postglacial period.

Line 396-416: I am not an expert on tephra so I will not comment on this part.

As this aspect was not commented on, we assume that both reviewers are satisfied.

Line 459-457: Please provide results of how much data is lost at each step of filtering.

We added the read loss following each filtering step as a table in the supplemental information (Table S4).

Throughout the manuscript, you go only to species level although DNA in most cases is able to distinguish to genus level within Salicaceae and Betulaceae - which sequences did you identify?

Sequences are now provided in the supplemental for Salicaceae and Betulaceae. Based on our bioinformatic pipeline, reference library and requirement for 100% match between sequence and taxonomy, we were only able to distinguish between species level.

Figure 2: The detection of Betulaceae is very sporadic in Stóra Vidarvatn with occurrence in only seven samples and hardly ever in all 5 repeats, suggesting that if you apply a statistical model to estimate first arrival (see Alsos et al. 2022), you will have a large confidence interval. Thus, these uncertainties should be considered when estimating the delayed arrival of Betula compared to Salix. The data from Torfdalsvatn (which I assume are from Alsos et al. 2021 although not specified in the figure legend), shows detection in all samples from the first appearance and mostly in 8 of 8 repeats (shown in the original publication - you could to the same here), thus providing a more accurate estimate for the time gap between arrival of Salix and Betula.

Thank you for bringing up this important point. The detection of Betulaceae is indeed sporadic, but we believe it reflects the genuine nature of its presence/absence during the Holocene in Northeast Iceland. This is supported by Betula pollen from a nearby peat record that shows a similar history (Fig. 4, Karlsdóttir et al., 2014), which we have now elaborated on in the Results and Interpretation section. As for the timing of Betulaceae colonization at this site, the first appearance in the DNA record should be a close minimum estimate as shown with modern DNA and plant survey comparisons (e.g., Sjögren et al., 2017; Alsos et al., 2018). The true first appearance could be biased by small amounts of plants being present in the early stages of colonization and not registering the sedimentary record until enough dead plant material is transported to the depocenter of the lake. However, this is likely less than age model uncertainties and therefore not likely relevant on geologic timescales as in this study. In this sense, our age models and those published for the other records indicate this is generally on the order of several hundred years. In addition, we have now added the Alsos et al. (2021) reference for Torfdalsvatn. Unfortunately, this Torfdalsvatn study does not provide number of PCR repeats so we will keep the figure as is as it best represents the available data.

Figure 5: I suggest adding lake names to the figure. Is there a dot missing for lake 5 for Salicaceae?

Thank you for the suggestion, we have added lake names to the figure. There is a dot marked for Salicaceae for lake 5, however, not for Betulaceae as this taxon was not identified. We refer the reviewer to the Discussion Section “Postglacial sedaDNA records from the circum North Atlantic” and the lake’s original publication (Volstad et al., 2020).

Figure 6: I find it more relevant to plot colonization time versus distance to LGM sheetice margin - lake number is just an arbitrary number.

We appreciate the suggestion and have modified the figure accordingly.

Author Response

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

Reviewer #1 (Public Review):

In the present manuscript, Abele et al use Salmonella strains modified to robustly induce one of two different types of regulated cell death, pyroptosis or apoptosis in all growth phases and cell types to assess the role of pyroptosis versus apoptosis in systemic versus intestinal epithelial pathogen clearance. They demonstrate that in systemic spread, which requires growth in macrophages, pyroptosis is required to eliminate Salmonella, while in intestinal epithelial cells (IEC), extrusion of the infected cell into the intestinal lumen induced by apoptosis or pyroptosis is sufficient for early pathogen restriction. The methods used in these studies are thorough and well-controlled and lead to robust results, that mostly support the conclusions. The impact on the field is considered minor as the observations are somewhat redundant with previous observations and not generalizable due to cited evidence of different outcomes in other models of infection and a relatively artificial study system that does not permit the assessment of later time points in infection due to rapid clearance. This excludes the study of later effects of differences between pyroptosis and apoptosis in IEC such as i.e. IL-18 and eicosanoid release, which are only observed in the former and can have effects later in infection.” We thank the reviewer for their time and effort in assessing our manuscript.

We agree with the reviewer’s overall assessment. One minor clarification is that the engineering used does not express the proteins in “all growth phases”, but rather only when the SPI2 T3SS is expressed; we used the sseJ promoter, which is a SPI2 effector.

Reviewer #2 (Public Review):

In this study, Abele et al. present evidence to suggest that two different forms of regulated cell death, pyroptosis and apoptosis, are not equivalent in their ability to clear infection with recombinant Salmonella strains engineered to express the pro-pyroptotic NLRC4 agonist, FliC ("FliC-ON"), or the pro-apoptotic protein, BID ("BID-ON"). In general, individual experiments are well-controlled, and most conclusions are justified. However, the cohesion between different types of experiments could be strengthened and the overall impact and significance of the study could be articulated better. ”

We thank the reviewer for their time and effort in assessing our manuscript. We agree with the reviewer’s overall assessment.

Reviewer #1 (Recommendations For The Authors):

Abstract: While new terms are sometimes useful for the visualization of concepts and I appreciate the "bucket list" analogy, it is not yet an accepted term in cell death research, and using it twice in the abstract seems out of order. ”

We opted to keep the term, but reduce its use to once in the abstract with a specific comment on the recent coining of the term: “We recently suggested that such diverse tasks can be considered as different cellular “bucket lists” to be accomplished before a cell dies.” We recently coined this term in a review in Trends in Cell Biology, where three reviewers had quite positive comments about the concept. Time will tell whether this is a useful term for the cell death field or not.

“In figure 2C-F Caspase 1 and Gsdmd deficient animals have higher levels of vector control strain than WT or Nlrc4. Could this be due to the redundancy with Nlrp3 in systemic infection described by Broz et al? Please mention in the description of the results.”

The reviewer correctly points out a trend in the data. However, our experiments are not powered to show that this difference is statistically significant. Nevertheless, we now make note of the trend, and cite prior papers that have observed NLRC4 and NLRP3 redundancy against non-engineered S. Typhimurium strains.

“The observation that apoptosis does not affect Salmonella systemically would be strengthened if the experiments using the BIDon strain could be taken out to a later time point, i.e. 72 or 96 h.”

Indeed, we wanted to extend our studies to these timepoints. However, although expression of the SspH1 translocation signal is benign for 48 h, by 72 h this causes mild attenuation (regardless of whether the BID-BH3 domain is attached as cargo). We think that the degree of difficulty for SPI2 effectors to reprogram the vacuole increases over time, and that only beyond 48 h does SPI2 need to function at peak efficiency. This observation will be reported in a second manuscript that is written and will be submitted within this month. We are happy to supply this manuscript to reviewers if they would like to see the results. We also added text to the discussion to alert the reader to the caveats of engineering S. Typhimurium at later timepoints.

“Discussion: The authors claim that pyroptotic and apoptotic signaling in IEC have the same outcome and IEC only has extrusion as a task. However, upon pyroptosis, IEC also releases IL-18 and eicosanoids, which is not the case during apoptosis. While the initial extrusion makes all the difference in early infection, Mueller et al 2016 showed that lack of IL-18 has an effect on salmonella dissemination at a 72h time point. The FlicON model can not test later time points as the bacteria will be cleared by then, but this caveat should be discussed.”

We revised the text in the discussion to make it clear that extrusion is not the only bucket list item for IECs, and that IL-18 and eicosanoids are included in the bucket list for IECs after caspase-1 activation, and add the citation to Muller et al.

Reviewer #2 (Recommendations For The Authors):

1) The manuscript is written in a rather colloquial style. Additional editing is recommended. ”

We edited the abstract to limit the use of the bucket list term and to make more clear that this is a new term that our lab has proposed in a recent review in Trends in Cell Biology. The managing editor for the current manuscript at eLife commented that the prose was lively and thoughtful. We would be happy to make edits if the reviewer has more specific suggestions.

2) It is not obvious from the Results section that all mouse infections were, in fact, mixed infections. This should be stated more clearly. Additionally, there is a minor concern regarding in vivo plasmid loss over time.

We added text to the results to make this clearer at the beginning of each in vivo figure in the paper. Our experiments are intentionally blind to any Salmonella that have lost the plasmid. These bacteria essentially convert to a wild type phenotype, and thus are no longer representative of FliCON or BIDON bacteria. We also verify the long established equal competition between pWSK29 (amp) and pWSK129 (kan) in Supplemental Figure 2A-B. Prior experiments from the laboratory of Sam Miller and others in the 1990s showed that plasmid loss occurs at a rate of less than 1%.

3) Results shown in Figure 4 are difficult to interpret. Essentially, the experiment is aimed at comparing the two engineered Salmonella strains (FliC-ON and BID-ON). However, these strains are very different from one another, which may have a confounding effect on the interpretation of the data.”

The reviewer has interpreted the experiment correctly. We wanted to make clear to the reader that the two strains induce apoptosis under different kinetics. Indeed, it would be very surprising if two different engineering methods created strains that caused apoptosis with identical kinetics. We make two text edits to the results to make this clearer, concluding with “Overall, both ways of achieving apoptosis are successful in vitro, but with slightly different kinetics.”.

4) What new insights into mechanisms of bacterial pathogenesis and host response are gained by using recombinant Salmonella (over)expressing a pro-apoptotic protein is not clearly stated.”

We modify the introduction to make this more clear, stating: “Here, we investigate whether apoptotic pathways could be useful in clearing intracellular infection. Because S. Typhimurium likely evades apoptotic pathways, we again use engineering in order to create strains that will induce apoptosis. This allows us to study apoptosis in a controlled manner in vivo.”

5) The Discussion section, while provocative, seems speculative and should be revised. Concepts of "backup apoptosis" and crosstalk between pyroptosis and apoptosis are intriguing, but it seems implausible to this reviewer that a cell might "know" that it will die, might "choose" how to die, and might aim to complete a "bucket list" before it loses all functional capacity. The usage of these types of terms does not help bolster the authors' central conclusions. ”

We agree that cells do not “choose” pathways for regulated cell death. We had over-anthropomorphized the concepts surrounding these interconnected cell death pathways that are created by evolution. We edited the introduction and discussion to remove the “choose” term. However, we kept the second phrase using “know” in the discussion with an added clarifier: “Once a cell initiates cell death signaling, it “knows” that it will die (or rather evolution has created signaling cascades that are predicated upon the initiation of RCD).”. Sometimes anthropomorphizing scientific concepts can be a useful tool to facilitate understanding of complex scientific concepts. For example, the “Red Queen hypothesis” clearly anthropomorphizes the concept of continuous evolution to maintain an evolutionary equilibrium. We have found that scientists in the cell death field often think that modes of cell death are or should be interchangeable. We hope that the idea of the “bucket list” will help to crystalize the idea that distinct processes leading up to different types of regulated cell death can have very different consequences during infection.

Additional Comments from the Reviewing Editor:

1) The authors show that FliC-ON is not cleared from the spleen of Casp1 KO or Gsdmd KO mice. The conclusion is that the backup apoptosis pathways that should be present in these mice are insufficient to clear the bacteria from the spleen. However, although it is shown that bone marrow macrophages undergo apoptosis in vitro, I believe it is not shown that the apoptotic pathways are actually activated in the spleen. This seems like an important caveat. Could it be shown (or has it previously been shown) that the cells infected in the spleens of Casp1 KO or Gsdmd KO are activating apoptosis? If not, it seems possible that the reason the bacteria are not cleared is due to a lack of apoptosis activation rather than an ineffectiveness of apoptosis, and the authors could consider explicitly acknowledging this.”

We agree, and added to the discussion “A final possibility is that our engineered strains are not successfully triggering apoptosis within splenic macrophages. This could be due to intrinsic differences between BMMs and splenic macrophages or could be due to bacterial virulence factors that fail to suppress apoptosis only in vitro. It is quite difficult to experimentally prove that apoptosis occurs in vivo due to rapid efferocytosis of the apoptotic cells.”

2) Both reviewers were somewhat unhappy about some of the new terminology/metaphors that are introduced in the manuscript. I understand the reviewers' concerns but also feel that the writing is lively and thoughtful. It is up to the authors to decide whether to retain their new terminology, but the response of two expert reviewers might give the authors some pause. At a minimum, to address the concern about an unfamiliar term being used in the abstract, perhaps explicitly state that you are introducing "bucket list" as a new concept to help explain the results. The introduction of this concept may indeed be one of the novel contributions of the manuscript.”

We opted to keep the term, but reduce its use to once in the abstract with a specific comment on the recent coining of the term: “We recently suggested that such diverse tasks can be considered as different cellular “bucket lists” to be accomplished before a cell dies.” We recently coined this term in a review in Trends in Cell Biology, where three reviewers had quite positive comments about the concept. Time will tell whether this is a useful term for the cell death field or not.

3) Perhaps this is implied in the discussion already, but it might make sense to state the obvious difference between IECs and splenic macrophages which is that the death of the former results in the removal of the cell and its contents (i.e., Salmonella) from the tissue, whereas the death of the latter does not. This seems like the simplest explanation for why apoptosis restricts bacterial replication in IECs but not macrophages, and I am not sure if introducing the concept of a "bucket list" improves the explanation or not.”

We agree that this narrative nicely distills the differences between these cell types. We edited the final paragraph of the discussion to include this narrative.

4) Lastly, some minor comments

-- p.2 "hyperactivate" instead of "hyperactive"?”

Corrected.

-- the authors may also want to mention Shigella, as it might provide another example that apoptotic C8dependent backup protects IECs”

Yes, indeed, this is a good comparison to make. We added this to the discussion.

-- p.8, in case readers are unfamiliar with the concept of a PIT, the authors should perhaps cite their own work when they first mention this concept (at the top of the page)”

Indeed, citation added.

Author Response

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

We are very grateful to the reviewers for their thoughtful comments on the manuscript and to the editors for their assessment.

We thank the reviewers for their positive feedback and appreciate that they consider our method a valid addition to previously established systems for generating recombinant RNA viruses.

To strengthen this point, we have now included additional validation by the rescue of recombinant Chikungunya and Dengue virus from viral RNA directly, using the CLEVER protocol. This strengthens the potential of this method as a reverse genetics platform for positive-stranded viruses in general.

The supportive data has been amended in the Results section, taken into account in Materials and Methods, and the corresponding supplementary figure (Figure S4) has been added.

One key point raised by one of the reviewers, a comparison with different systems, could not be addressed in this manuscript as our lab does not at all perform BAC cloning. We currently do not have the necessary expertise to conduct an unbiased side-by-side comparison.

All other comments were addressed in detail, either by including additional data or through specific clarification in the revised text. We are grateful for the careful review and constructive criticisms raised by the reviewers and feel that the corrections and additions have significantly improved the manuscript.

We have revised the latest version posted May 30, 2023 on bioRxiv (https://doi.org/10.1101/2023.05.11.540343).

Reviewer #1:

Public Review:

In this manuscript, Kipfer et al describe a method for a fast and accurate SARS-CoV2 rescue and mutagenesis. This work is based on a published method termed ISA (infectious subgenomic amplicons), in which partially overlapping DNA fragments covering the entire viral genome and additional 5' and 3' sequences are transfected into mammalian cell lines. These DNA fragments recombine in the cells, express the full length viral genomic RNA and launch replication and rescue of infectious virus.

CLEVER, the method described here significantly improves on the ISA method to generate infectious SARS-CoV2, making it widely useful to the virology community.

Specifically, the strengths of this method are:

1) The successful use of various cell lines and transfection methods.

2) Generation of a four-fragment system, which significantly improves the method efficiency due to lower number of required recombination events.

3) Flexibility in choice of overlapping sequences, making this system more versatile.

4) The authors demonstrated how this system can be used to introduce point mutations as well as insertion of a tag and deletion of a viral gene.

5) Fast-tracking generation of infectious virus directly from RNA of clinical isolates by RT-PCR, without the need for cloning the fragments or using synthetic sequences.

One weakness of the latter point, which is also pointed out by the authors, is that the direct rescue of clinical isolates was not tested for sequence fidelity.

The manuscript clearly presents the findings, and the proof-of-concept experiments are well designed.

Overall, this is a very useful method for SARS-CoV2 research. Importantly, it can be applicable to many other viruses, speeding up the response to newly emerging viruses than threaten the public health.

We thank the reviewer for this positive feedback and the summary of the main points. Nevertheless, we would like to comment on point 5): “the direct rescue of clinical isolates was not tested for sequence fidelity”

This impression by the reviewer suggests that the data was not sufficient on this point. However, the sequence fidelity after direct rescue from RNA was indeed tested in this study, even on a clonal level (please see: Table S2, or raw NGS data SRX20303605 - SRX20303607). For higher clarity, we added the following sentence to the manuscript:<br /> “Indeed, a slight increase of unintentional mutations was observed when sequencing clonal virus populations rescued from RNA directly”.

Recommendations for the authors:

Minor Points:

1) On page 8, the authors write: "levels correlated very well with the viral phenotype". This sentence is not clear. Please clarify what you mean by "viral phenotype". Do you mean CPE on Vero cells?

We corrected the sentence to: “(…) staining intensity and patterns correlated very well with the wild-type phenotype.”

2) Page 9 "sequences were analyzed with a cut-off of 10%. Cutoff of what? please clarify.

The sentence was rephrased to: “(…)mutations with a relative abundance of >10% in the entire virus population were analyzed”

3) Page 15: The authors refer to the time required for completion of each step of the process. It would be helpful and informative for the readers to include a panel in figure 4, visualizing the timelines.

We included a timeline in Figure 4, Panel A.

4) Materials and methods, first paragraph: Please specify which human samples were collected. Do the authors refer to clinical virus isolates?

We added the following information to the Materials and Methods section:<br /> “Human serum samples for neutralization assays were collected from SARS-CoV-2 vaccinated anonymous donors (…)”

Clinical virus isolates (Material and Methods; Virus) were used for control experiments, neutralization assays, or as templates for RT-PCR.

5) Supplementary figure 4A: The color scheme makes it hard to differentiate between the BA.1 and BA.5 fragments. Please choose colors that are not as similar to each other.

Colors were adapted for better distinction.

Reviewer #2:

Public Review:

The authors of the manuscript have developed and used cloning-free method. It is not entirely novel (rather it is based on previously described ISA method) but it is clearly efficient and useful complementation to the already existing methods. One of strong points of the approach use by authors is that it is very versatile, i.e. can be used in combination with already existing methods and tools. I find it important as many laboratories have already established their favorite methods to manipulate SARS-CoV-2 genome and are probably unwilling to change their approach entirely. Though authors highlight the benefits of their method these are probably not absolute - other methods may be as efficient or as fast. Still, I find myself thinking that for certain purposes I would like to complement my current approach with elements from authors CLEVER method.

The work does not contain much novel biological data - which is expected for a paper dedicated to development of new method (or for improving the existing one). It may be kind of shortcoming as it is commonly expected that authors who have developed new methods apply it for discovery of something novel. The work stops on step of rescue the viruses and confirming their biological properties. This part is done very well and represents a strength of the study. The properties of rescued viruses were also studied using NSG methods that revealed high accuracy of the used method, which is very important as the method relies on use of PCR that is known to generate random mistakes and therefore not always method of choice.

What I found missing is a real head-to-head comparison of the developed system with an existing alternatives, preferably some PCR-free standard methods such as use of BAC clones. There are a lot of comparisons but they are not direct, just data from different studies has been compared. Authors could also be more opened to discuss limitations of the method. One of these seems to be rather low rescue efficiency - 1 rescue event per 11,000 transfected cells. This is much lower compared to infectious plasmid (about 1 event per 100 cells or so) and infectious RNAs (often 1 event per 10 cells, for smaller genomes most of transfected cells become infected). This makes the CLEVER method poorly suitable for generation of large infectious virus libraries and excludes its usage for studies of mutant viruses that harbor strongly attenuating mutations. Many of such mutations may reduce virus genome infectivity by 3-4 orders of magnitude; with current efficiencies the use of CLEVER approach may result in false conclusions (mutant viruses will be classified as non-viable while in reality they are just strongly attenuated).

We thank reviewer 2 for the careful review of our work and the valuable feedback. We agree that a direct comparison with other (PCR-free) methods such as BAC cloning, could be useful for demonstrating the unique benefits of the CLEVER method. However, as our laboratory does not use any BAC or YAC cloning methods, we could not ensure an unbiased side-byside comparison using different techniques.

We would like to highlight the avoidance of any yeast/bacterial cloning steps that render the CLEVER protocol significantly faster and easier to handle. A visualization of the key steps that could be skipped using CLEVER in comparison to common reverse genetics methods is given in Figure 6.

Further, we firmly believe that the benefits of the CLEVER method become especially apparent for large viral genomes such as the one of SARS-CoV-2, where assembly, genome amplification and sequence verification of plasmid DNA are highly inefficient and more timeconsuming than for small viruses like DENV, CHIKV or HIV.

We agree with the reviewer that the overall transfection and recombination efficiencies observed with CLEVER seemed rather low. Although data on transfection/rescue efficiency is known for many techniques and viruses, we did not find any published data on the reconstitution of SARS-CoV-2 or viruses with similar genome sizes. Therefore, a useful comparator for our observations in relation to other techniques is currently simply missing. We therefore emphasize that the efficiencies of CLEVER were achieved with one of the largest plus-stranded RNA virus genomes, and our data can’t be directly compared to transfection efficiencies of short infectious RNAs.

On the contrary, it was rather interesting to observe the very high rescue efficiency of infectious virus progeny. During the two years of establishing and validating the CLEVER protocol, we reached success rates for the genome reconstitution after transfection of >95 %. This was even obtained with highly attenuated mutants including rCoV2∆ORF3678 (joint deletion of ORF3a, ORF6, ORF7a, and ORF8) (Liu et al., 2022)(see Author response image 1). We amended this data in response to the reviewers’ comment and as an example of the successful rescue of an attenuated virus from five overlapping genome fragments (fragments A, B, C, D1, and D2∆ORF3678).

The latter data were not added to the main manuscript since in this case the deletions were introduced using a different method: from the plasmid-based DNA fragment D2∆ORF3678 and not directly from PCR-based mutagenesis.

Further, CLEVER was used for related substantial manipulations, including the complete deletion of the Envelope gene (E) which led to the creation of a single-cycle virus that may serve as a live, replication-incompetent vaccine candidate (Lett et al., 2023).

Author response image 1.

rCoV2∆ORF3678. Detection of intracellular SARS-CoV-2 nucleocapsid protein (N, green) and nuclei (Hoechst, blue) in Vero E6TMPRSS2 cells infected with rCoV2∆ORF3678 by immunocytochemistry. Scalebar is 200 µm in overview and 50 µm in ROI images.

Recommendations for the authors:

The work is nicely presented and the method authors has developed is clearly valuable. As indicated in Public review section the work would benefit from direct comparison of CLEVER with that of infectious plasmid (or RNA) based methods; direct comparison of data would be more convincing that indirect one. Authors should also discuss possible limitations of the method - this is helpful for a reader.

We were not able to perform a direct comparison of CLEVER with other methods (see our statement above).

We added the following section to the discussion: “Along with the advantages of the CLEVER protocol, limitations must be considered: Interestingly, virus was never rescued after transfecting Vero E6 cells, as has been observed previously (Mélade et al., 2022). Whether this is due to low transfection efficiency or the cell’s inability to recombine remains to be elucidated. Other cell lines not tested within this study will have to be tested for efficient recombination and virus production first. Further, the high sequence integrity of rescued virus is highly dependent on the fidelity of the DNA polymerase used for amplification. The use of other enzymes might negatively influence the sequence integrity of recombinant virus, as it has been observed for the direct rescue from viral RNA using a commercially available onestep RT-PCR kit. Another limitation when performing direct mutagenesis is the synthesis of long oligos to create an overlapping region. Repetitive sequences, for example, can impair synthesis, and self-annealing and hairpin formation increase with prolonged oligos.”

Some technical corrections of the text would be beneficial. In all past of the text the use of terms applicable only for DNA or RNA is mixed and creates some confusion. For example, authors state that "the human cytomegalovirus promoter (CMV) was cloned upstream of 5' UTR and poly(A) tail, the hepatitis delta ribozyme (HDVr) and the simian virus 40 polyadenylation signal downstream of the 3' UTR". Strictly speaking it is impossible as such a construct would contain dsDNA sequence (CMV promoter) followed by ssRNA (5'UTR, polyA tail and HDV ribozyme) and then again dsDNA (SV40 terminator). So, better to be correct and add "sequences corresponding to", "dsDNA copies of" to the description of RNA elements

We thank the reviewer for the advice but would like to state that in scientific language it is common to assume that nucleic acid cloning is based on DNA.

We have corrected the description in the Methods section: “The human cytomegalovirus promoter (CMV) was cloned upstream of the DNA sequence of the viral 5’UTR; herein, the first five nucleotides (ATATT) correspond to the 5’UTR of SARS-CoV. Sequences corresponding to the poly(A) tail (n=35), the hepatitis delta virus ribozyme (HDVr), and the simian virus 40 polyadenylation signal (SV40pA) were cloned immediately downstream of the DNA sequence of the viral 3’UTR.”

For ease of reading and for consistent terminology, we kept the original spelling in the rest of the manuscript.

In description of neutralization assay authors have used temperature 34 C for incubation of virus with antibodies as well as for subsequent incubation of infected cells. Why this temperature was used?

The following sentence was added (Materials and Methods; Cells): “A lower incubation temperature was chosen based on previous studies (V’kovski et al., 2021).”

References

Lett MJ, Otte F, Hauser D, Schön J, Kipfer ET, Hoffmann D, Halwe NJ, Ulrich L, Zhang Y, Cmiljanovic V, Wylezich C, Urda L, Lang C, Beer M, Mittelholzer C, Klimkait T. 2023. Single-cycle SARS-CoV-2 vaccine elicits high protection and sterilizing immunity in hamsters. doi:10.1101/2023.05.17.541127

Liu Y, Zhang X, Liu J, Xia H, Zou J, Muruato AE, Periasamy S, Kurhade C, Plante JA, Bopp NE, Kalveram B, Bukreyev A, Ren P, Wang T, Menachery VD, Plante KS, Xie X, Weaver SC, Shi P-Y. 2022. A live-attenuated SARS-CoV-2 vaccine candidate with accessory protein deletions. Nat Commun 13:4337. doi:10.1038/s41467-022-31930-z

V’kovski P, Gultom M, Kelly JN, Steiner S, Russeil J, Mangeat B, Cora E, Pezoldt J, Holwerda M, Kratzel A, Laloli L, Wider M, Portmann J, Tran T, Ebert N, Stalder H, Hartmann R, Gardeux V, Alpern D, Deplancke B, Thiel V, Dijkman R. 2021. Disparate temperaturedependent virus–host dynamics for SARS-CoV-2 and SARS-CoV in the human respiratory epithelium. PLoS Biol 19:e3001158. doi:10.1371/journal.pbio.3001158

Author Response

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

Note to reviewer and editor:

In the previous version of the manuscript, we referred to ‘prevalent’ disease at baseline (e.g., prevalent cardiovascular disease). We have since changed this throughout the manuscript to ‘past or prevalent’ disease. This is a more accurate description as we ascertained diseases which occurred prior to baseline but may have been resolved by the time of the accelerometry study.

Responses to reviewer 1:

• I assume that not every participant provided data on all 7 nights. Did the authors exclude those who had fewer number of nights with accelerometer data (e.g., only 2-3 days), as the SRI based on fewer nights may not reliably reflect sleep regularity compared with SRI based all 7 consecutive nights?

It is correct that not every participant provided complete accelerometry data. Most participants (88%) provided complete data. We only included participants who provided at least 2 valid measurements of the SRI (requiring valid data for at least 2 pairs of contiguous 24-hour periods). This is described in the appendix, but we have additionally now added this detail to the main text:

“Most participants (88%) provided complete accelerometry data. Participants with fewer than two valid SRI measurements (i.e., less than 2 contiguous 24-hour wear periods; <1%) were excluded.”

We would also like to note that our statistical analysis accounted, to some extent, for the lower reliability of SRI estimates in those with fewer nights of data. In those with sparse data, their estimated average SRI value would be pulled towards the overall sample average relatively more than for those with complete data. This is a consequence of the ‘partial pooling’ of the linear mixed effects model.

• The primary analysis and results were based on restricted cubic spline models that allow assessment of nonlinearity. This is different from the usual strategy that starts with the simpler linear relationship and further explores potential nonlinear relationships. Did the authors have a strong rationale for a nonlinear dose-response relationship between sleep regularity and mortality, so that the assessment of linear relationships was skipped?

We chose to model the SRI with a restricted cubic spline for two reasons. Firstly, we did expect non-linearity to be present a-priori. Partly this was because other sleep exposures (especially sleep time) have known non-linear relationships with health outcomes. We also thought that it is was plausible that a ‘plateau’ might be present, which we wanted to capture. Secondly, we decided that our primary model should be sufficiently flexible from the outset in order that we did not need to make data-driven adjustments to our model specification (e.g., adding non-linear terms depending on the results of hypothesis tests). This approach we believe to be generally safer as making data-driven changes can undermine the validity of standard errors and p-values.1

• Was the proportional hazards assumption violated in the Cox modeling? Were discrete-time hazard models used to address the violation of the modeling assumption? Please clarify.

Yes, the proportional hazards assumption was violated for all models except for the cardiovascular disease death model. This was the rationale for the use of the discrete time hazards model. They allowed for the inclusion of a flexible time by SRI interaction, allowing the hazard ratio to vary over the follow-up period. We have made this clearer in our revision. The following text has been added to the statistical methods:

“In addition to Cox models, discrete-time hazards models, including an interaction between SRI and time (aggregated into 3-month intervals and modeled with a restricted cubic spline with knots at the 5th, 35th, 65th, and 95th percentiles), were fitted to relax the assumption of proportionality and allow hazard ratios (HRs) to vary over time. The SRI by time interaction in this model provided a test of proportionality (a small p value would indicate strong evidence against the proportional hazards assumption).”

• Please provide correlations between different sleep regularity measures. Although different measures lead to the same conclusion, it is interesting that SRI appeared to provide stronger signals with mortality than the other two SD measures. In addition to what was discussed by the authors, another possibility is that SRI also captures the regularity of napping during the day which is common in older populations.

Thank you for this helpful suggestion. We have added a correlation matrix for the different sleep regularity measures (Table S1). We have additionally added the following text to the Results:

“The SRI was modestly negatively correlated with the sleep duration SD (-0.32) and sleep onset time SD ( 0.42; see correlation matrix in Table S1).”

Regarding napping during the day, the algorithm we used to make determinations of sleep and wake unfortunately is not able to identify napping. This is because, in the absence of a sleep diary, it is very difficult to distinguish napping from inactivity in accelerometry data. The algorithm that we used requires the estimation of a ‘sleep period time window’, defining the period from the beginning to the end of the main sleep bout, in which sleep can be identified. Any sleep outside of this window is treated as inactivity. While some methods have been developed to estimate napping time from accelerometry without a sleep diary, we are not aware of any that are validated for adults using wrist worn accelerometers.

This is something that was not sufficiently clear from the current manuscript. We have had added the following text to ensure this is clear in the revised version.

Methods:

“To distinguish sleep from sustained periods of inactivity without reference to a sleep diary (not available in the UKB), GGIR uses an algorithm to determine a daily ‘sleep period time window’ for each participant.11 This defines the time window between the onset and end of the main daily sleep period, during which periods of sustained inactivity are interpreted as sleep. The algorithm does not, by default, detect bouts of sleep outside of this window and hence is not able to identify naps.”

Discussion:

“In addition, sleep diaries in the UKB were not available. Consequently, the algorithm we used to determine sleep and wake relied on the identification of a main ‘sleep period time window’ and did not identify napping..”

• Table 1 - I would suggest adding additional columns showing the variable distributions across quantiles of the SRI, which can help understand the confounding structure and the covariate associations with SRI.

We agree that this is a good idea and we have adjusted Table 1 accordingly.

• Figure 1 and related supplemental Figures: it would be good to label in the figure the specific HR estimate and 95% CI mentioned in the manuscript.

Thank you for this suggestion. We agree that this would be helpful. After some consideration, we have decided to leave the figures as they are for one primary reason. This is that we want to avoid over-emphasising the 5th and 95th quantiles. As discussed above, we chose to present HRs for these quantiles as they would provide a complement to the Figures which would assist in communication (for some readers, the key results might be easier to glean from these numeric summaries than from the Figures). However, we don’t wish to overemphasise these quantiles when the full ‘dose-response’ function we believe to be of the greatest interest.

• Additional stratified analyses by main sociodemographic factors (age, sex, SES, etc) and sleep variables (sleep duration and sleep quality) would be informative to understand the population heterogeneity in the association between sleep regularity and mortality

Thank you for this suggestion. We have assessed effect modification across a range of key background variables (age, sex, household income, sleep duration, moderate to vigorous physical activity, prevalent CVD, and prevalent cancer). This has been added to the results. Where meaningful evidence of effect modification was noted, we have presented results within strata of the effect modifier.

• Some brief discussion on socioeconomic aspects of sleep is needed (the authors focused on the biological mechanisms underlying the observed association), as emerging evidence suggests that sleep health is not only a biological but also a social construct. For example, a recent study in the US found that sleep regularity is the most important contributor to racial/ethnic disparities in sleep health (see PMID: 34498675).

We agree that sleep health is both a biological and social construct. We have added the following text to the discussion to address this comment:

Discussion:

“Furthermore, identifying the determinants of poor sleep regularity may be of import, not only considering biological factors, but broader social determinants that impact circadian rhythmicity (e.g., racial/ethnic disparities32, neighbourhood factors33) and consequently overall health.”

References

1. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. vol 608. Springer; 2001.

Author Response

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

Reviewer 1:

Public review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) aJer 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:

• The RCT does not follow CONSORT statement and reporting guidelines

We thank you for this suggestion and have now amended the order and content of the manuscript to follow the CONSORT statement as closely as possible.

• The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. AJer 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.

We thank the reviewer for bringing up this central topic. The answer to this question has both a historical and practical component. This trial was initiated in June of 2020 and was completed in June of 2021. At that time there were no known treatments for the severe immune pathology of COVID19 pneumonia. In June 2020, dexamethasone data came out and we incorporated dexamethasone into the study design. It took much longer for all other anti-inflammatories to be tested. Hence, our decision to trial an approved endonuclease was based purely on basic science work on the pathogenic role of cell-free chromatin and NETs in murine sepsis and flu models and the ability of DNase I to clear them and reduce pathology in these animal models. In addition, evidence for the presence of cell-free chromatin components in COVID-19 patient plasma had already been communicated in a pre-print. Finally, several studies had reported the anti-inflammatory effects of dornase treatment in CF patients. Hence there was a strong case for a cheap, safe, pulmonary noninvasive treatment that could be self-administered outside the clinical se]ng.

The Identification of novel/repurposed treatments effective for COVID-19 were hampered by patient recruitment to competing studies during a pandemic. This resulted in small studies with inconclusive or contrary findings. In general, effective treatments were only picked up in very large RCTs. For example, demonstrating dexamethasone as effective in COVID-19 required recruitment of 6,425 patients into the RECOVERY study. Multiple trials with anti-IL-6 gave conflicting evidence until RECOVERY recruited 4116 adults with COVID-19 (n=2022, tocilizumab and 2094, control) similar for Baracitinib (4,148 randomised to treatment and 4,008 to standard care). Anakinra is approved for patients with elevated suPAR, based on data from one randomized clinical trial of 594 patients, of whom 405 had active treatment (PMID: 34625750). However, a systematic review analysing over 1,627 patients (anakinra 888, control 739) with COVID-19 showed no benefit (PMID: 36841793). Regarding the choice of the primary endpoint, there is a wealth of clinical evidence to support the relevance of CRP as a prognostic marker for COVID-19 pneumonia patients and it is a standard diagnostic and prognostic clinical parameter in infectious disease wards. This choice in March 2020 was supported by evidence of the prognostic value of IL-6; CRP is a surrogate of IL-6. We also provide our own data from a large study of severe COVID-19 pneumonia in figure 1, showing how well CRP correlates with survival.

In summary, our data suggest that Dornase yields an anti-inflammatory effect that is comparable or potentially superior to cytokine-blocking monotherapies at a fraction of the cost and potentially without the additional adverse effects such as the increase for co-infections.

We now provide additional justification on these points in the introduction on pg.4 as follows:

“The trial was ini.ated in June 2020 and was completed in September of 2021. At the start of the trial only dexamethasone had been proven to benefit hospitalized COVID-19 pneumonia pa.ents and was thus included in both arms of the trial. To increase the chance of reaching significance under challenging constraints in pa.ent access, we opted to increase our sample size by using a combina.on of randomized individuals and available CRP data from matched contemporary controls (CC) hospitalized at UCL but not recruited to a trial. These approaches demonstrated that when combined with dexamethasone, nebulized DNase treatment was an effec.ve an.-inflammatory treatment in randomized individuals with or without the implementa.on of CC data.”

We also added the following explanation in the discussion on pg. 16:

“Our study design offered a solution to the early screening of compounds for inclusion in larger platform trials. The study took advantage of frequent repeated measures of quantifiable CRP in each patient, to allow a smaller sample size to determine efficacy/futility than if powered on clinical outcomes. We applied a CRP-based approach that was similar to the CATALYST and ATTRACT studies. CATALYST showed in much smaller groups (usual care, 54, namilumab, 57 and infliximab, 35) that namilumab that is an antibody that blocks the cytokine GM-CSF reduced CRP even in participants treated with dexamethasone whereas infliximab that targets TNF-α had no significant effect on CRP. This led to a suggestion that namilumab should be considered as an agent to be prioritised for further investigation in the RECOVERY trial. A direct comparison of our results with CATALYST is difficult due to the different nature of the modelling employed in the two studies. However, in general Dornase alfa exhibited comparable significance in the reduction in CRP compared to standard of care as described for namilumab at a fraction of the cost. Furthermore, endonuclease therapies may prove superior to cytokine blocking monotherapies, as they are unlikely to increase the risk for microbial co-infections that have been reported for antibody therapies that neutralize cytokines that are critical for immune defence such as IL-1β, IL-6 or GM-CSF. “

• Please provide in Methods the timeframe for the investigation of the primary endpoint

This information is provided in the analysis on pg. 8:

“The primary outcome was the least square (LS) mean CRP up to 7 days or at hospital discharge whichever was sooner.”

• Why day 35 was chosen for the read-out of the endpointt?

We now state on pg. 8 that “Day 35 was chosen as being likely to include most early mortality due to COVID-19 being 4 weeks after completion of a week of treatment. ( i.e. d7 of treatment +28 (4 x 7 days))”

• The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.

We initially aimed at a fully randomized trial. However, the swiJ implementation of trial prioritization strategies towards large and pre-established trial plamorms in the UK made the recruitment COVID19 patients to small studies extremely challenging. Thus, we struggled to gain access to patients. Our power calculations suggested that a mixed trial with randomized and contemporary controls was the best way forward under these restrictions in patient access that could provide sufficient power.

That being said, we also provide the primary endpoint (CRP) results in Fig. 3B as well as the results for the length of hospitalization (Fig. S3D) for the randomized subjects only.

• Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.

We apologize if this point was confusing. We performed the analysis on the ITT as defined in our SAP: “The primary analysis population will be all evaluable patients randomised to BAC + dornase alfa or BAC only who have at least one post-baseline CRP measurement, as well as matched historical comparators.”

We understand that the reason this might be mistaken as an mITT is because the N in the ITT (39) doesn’t match the number randomised and because we had stated on pg. 8 that “ Efficacy assessments of primary and secondary outcomes in the modified inten.on-to-treat popula.on were performed.”

However, we did randomise 41 participants, but:

One participant in the DA arm never received treatment. The individual withdrew consent and was replaced. We also have no CRP data for this participant in the database, so they were unevaluable, and we couldn’t include them in the baseline table even if we wanted to. In addition, 1 participant in BAC only had a baseline CRP measurement available. Hence not evaluable as we only have a baseline CRP measurement for this participant.

We have corrected the confusing statement on pg. 8 and added an additional explanation.

“Efficacy assessments of primary and secondary outcomes in the inten.on-to-treat (ITT) popula.on were performed on all randomised par.cipants who had received at least one dose of dornase alfa if randomized to treatment. For full details see Sta.s.cal Analysis Plan. The ITT was adjusted to mi.gate the following protocol viola.ons where one par.cipant in the BAC arm and one in the DA arm withdrew before they received treatment and provided only a baseline CRP measurement available. The par.cipant in the DA arm was replaced with an addi.onal recruited pa.ent. Exploratory endpoints were only available in randomised par.cipants and not in the CC. In this case, a post hoc within group analysis was conducted to compare baseline and post-baseline measurements.”

• It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.

Our protocol pre-specified that the primary analysis population should have at least one postbaseline CRP measurement (pg. 13 of protocol). The patient that was excluded was one that initially joined the trial but withdrew consent after the first treatment but before the first post-treatment blood sample could be drawn. Hence, the pre-treatment CRP of this patient alone provided no useful information.

• In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.

Table 1 includes all 39 patients plus 60 CCs.<br /> Table 2 shows additional treatments given for COVID-19 as part of BAC.

• In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.

One of the main criticisms we have encountered in this study has been the choice of the primary endpoint. The best way respond to these questions was to provide data to support the prognostic relevance of CRP in COVID-19 pneumonia from a separate independent study where no other treatments such as dexamethasone, anakinra or anti-IL6 therapies were administered. We think this is very useful analysis and provides essential context for the trial and the choice of the primary endpoint, indicating that CRP has good enough resolution to predict clinical outcomes.

• Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.

The contemporary controls were selected to best match the characteristics of the randomized patients including that the first CRP measurement upon admission surpassed the trial threshold, so we do not see how this selection process introduces biases, as it was blinded with regards to the course of the CRP measurements. Given that this was a small trial, matching for baseline characteristics is necessary to minimize confounding effects.

• The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.

We now provide the mortality numbers in the following paragraph on pg. 13.

“Over 35 days follow up, 1 person in the BAC + dornase-alfa group died, compared to 8 in the BAC group. The hazard ra.o observed in the Cox propor.onal hazards model (95% CI) was 0.47 (0.06, 3.86), which es.mates that throughout 35 days follow-up, there was a 53% reduced chance of death at any given .mepoint in the BAC + dornase-alfa group compared to the BAC group, though the confidence intervals are wide due to a small number of events. The p-value from a log-rank test was 0.460, which does not reach sta.s.cal significance at an alpha of 0.05.”

• It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

As we explained above, the Berlin cohort CRP data serve to substantiate the relevance of CRP as a primary endpoint in a cohort that experienced sufficient mortality as this cohort did not receive any approved anti-inflammatory therapy. Mortality in our COVASE trial was minimal, since all patients were on dexamethasone and did not reach the highest severity grade, since we opted to treat patients before they deteriorated further. The overall mortality was 8% across all arms of our study, which does not provide enough events for mortality measurements. In contrast the Berlin cohort did not receive dexamethasone and all patients had reached a WHO severity grade 7 category with mortality at 30%.

My other concerns are:

• This report is about an RCT and the authors should follow the CONSORT reporting guidelines. Please amend the manuscript and Figure 1b accordingly and provide a CONSORT checklist.

We now provide a CONSORT checklist and have amended the CONSORT diagram accordingly.

• Please provide in brief the exclusion criteria in the main manuscript

We have now included the exclusion criteria in the manuscript on pg. 6.

“1.1.1 Exclusion criteria

1. Females who are pregnant, planning pregnancy or breasmeeding

2. Concurrent and/or recent involvement in other research or use of another experimental inves.ga.onal medicinal product that is likely to interfere with the study medica.on within (specify .me period e.g. last 3 months) of study enrolment 3. Serious condi.on mee.ng one of the following:

a. Respiratory distress with respiratory rate >=40 breaths/min

b. oxygen satura.on<=93% on high-flow oxygen

1. Require mechanical invasive or non-invasive ven.la.on at screening

2. Concurrent severe respiratory disease such as asthma, COPD and/or ILD

3. Any major disorder that in the opinion of the Inves.gator would interfere with the evalua.on of the results or cons.tute a health risk for the trial par.cipant

4. Terminal disease and life expectancy <12 months without COVID-19

5. Known allergies to dornase alfa and excipients

6. Par.cipants who are unable to inhale or exhale orally throughout the en.re nebulisa.on period So briefly Pa.ents were excluded if they were:

7. pregnant, planning pregnancy or breasmeeding 2. Serious condi.on mee.ng one of the following:

a. Respiratory distress with respiratory rate >=40 breaths/min

b. oxygen satura.on<=93% on high-flow oxygen

1. Require ven.la.on at screening

2. Concurrent severe respiratory disease such as asthma, COPD and/or ILD

3. Terminal disease and life expectancy <12 months without COVID-19

4. Known allergies to dornase alfa and excipients

5. Par.cipants who are unable to inhale or exhale orally throughout the en.re nebulisa.on period”

• "The final trial visit occurred at day 35." "Analysis included mortality at day 35". I am not sure I understand why. In clinicaltrials.gov all endpoints are meant to be studies at day 7 except for mortality rate day 28. Why day 35 was chosen? Please be consistent.

Thank you for identifying this inconsistency. We have amended the record on clinicaltrials.gov to read ‘’the time to event data was censored at 28 days post last dose (up to d35) for the randomised participants and at the date of the last electronic record for the CC.”

• Please provide in Methods the timeframe for the investigation of the primary endpoint

• The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.

• Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.

• It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.

• Figure 1b as in CONSORT statement, please provide reasons why screened patients were not enrolled.

• In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatment as BAT received those patients except for dexamethasone.

• In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.

• In Figure 2 the authors draw results about ITT although in methods describe that they performed an mITT analysis. Please be consistent.

Please see answers provided to these queries above.

Reviewer #2 (Recommendations For The Authors):

1) Suppl Figure 2B would be more informative if presented as a Table with N of patients with per day sampling

We now provide the primary end point daily sampling table in Table 3.

2) The numbers at risk should figure under the KM curves

The numbers at risk for figures 1E, 2C, 2D have been added as graphs either in the main figures or in the supplement.

3) HD in Supplementary figure 3 should be explained

We apologize for this omission. We now provide a description for the healthy donor samples that we used in the cell-free DNA measurements in figure S3B on pg. 14:

“Compared to the plasma of anonymized healthy donors volunteers at the Francis Crick ins.tute (HD), plasma cf-DNA levels were elevated in both BAC and DA-treated COVASE par.cipants.

4) Presentation is inappropriate for Table S4

We thank the reviewer for pointing this issue. We have now formaxed Table S4 to be consistent with all other tables.

Author Response

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

Reviewer #1 (Public Review):

The manuscript by Hage et al. presents interesting results from a foraging behavior in Marmosets that explores the interactions of saccade and lick vigor with pupil dilation and performance as well as a marginal value theory and foraging theory-inspired value-based decision-making model thereof. The results are generally robust and carefully presented and analyses, particularly of vigor, are carefully executed.

The authors constructed a model that makes two predictions: "In summary, this simple theory made two sets of predictions: in response to an increased cost of harvest, one should work longer, but move with reduced vigor. In response to an increased reward value, as in hunger, one should also work longer, but now move with increased vigor." Their behavioral data meets these predictions. It is not clear if the model was designed and tweaked in order to make those predictions and match the data, or derived from principles. Furthermore, it is not clear what other models would make similar predictions. It would help to assess what is predicted by other simple models, as well as different functional forms for the effort costs in their model.

We chose this formulation of utility (Eq. 1) because it is a normative approach that ecologists have used to understand the decisions that animals make regarding how far to travel for food, what mode of travel to use, and how long to stay before moving on to another reward opportunity (Richardson and Verbeek 1986; Stephens and Krebs 1986; Bautista et al. 2001). In a typical formulation of the theory, the numerator represents the reward gained (in units of energy), minus the effort expended (also in units of energy). The denominator represents the amount of time spent during that behavior. We represented this idea in Eq. (1) with saccades that produced reward accumulation, and licks that produced reward consumption. Thus, the utility that we are trying to maximize is the rate of energy gained.

The specific functions that we used to represent the energy acquired through reward acquisition, and the energy expended through effort expenditure, came a priori either from experiment design, or from the measurements we have made in other experiments. We modeled reward accumulation as a linear rise in energy stored because successful saccades produced a linear increase in the food cache. We modeled consumption of the food as a hyperbolic function of the number of licks to represent the fact that as the licking bout began, each successful lick depleted the food, and thus the first few licks produced a greater amount of food consumption than the last few licks. We modeled the effort cost of licking to grow linearly with the number of licks.

A critical assumption that we made is that energy spent performing the saccade trials (which grew faster than linearly as a function of the number of trials attempted), grew faster than the time spent attempting those same trials (which grew linearly with the number of trials). This assumption is based on the heuristic that the average rate of energy lost following a large number of attempted trials is greater than the average rate of energy lost following a small number of attempted trials.

Sensitivity to parameter values: The model’s simplicity provides closed-form solutions across all parameter values, allowing one to make predictions without having to fit the model to the measured data. For example, for all parameter values that produce a real solution (as opposed to imaginary), the optimal number of saccade trials increases with the square root of the cost of licking. Thus, the basic prediction of the model is that in order to maximize the capture rate, an increase in the effort that it takes to harvest the reward should produce a greater willingness to work longer, caching more food. The closed-form solutions are presented in the Mathematica supplementary document.

Other models of utility: In composing our utility (Eq. 1), we chose to combine reward and effort additively. This is in contrast to other approaches in which effort discounts reward multiplicatively (47–49). Here, let us show that multiplicative interactions may have the limitation that they are incompatible with the observation that reward invigorates movements. To compare additive and multiplicative approaches, let us consider an arbitrary function 𝑈(𝑇) that specifies how effort varies with movement duration. Typically, this is a U-shaped function that describes energy expenditure as a function of movement duration, as in Shadmehr et al. (2016). In the case of multiplicative interaction between reward and effort, we can consider the following representation of utility:

In the above formulation, reward 𝛼 is discounted hyperbolically with time, and an increase in reward increases the utility of the action. The optimum movement vigor has the duration 𝑇∗ that maximizes this utility. Notably, because increasing reward merely scales this utility, it has no effect on vigor. Thus, a utility in which reward is multiplied by a function of effort generally fails to predict dependence of movement vigor on reward.

Line 37 page 6; the link of pupil to NE/LC is tenuous. Other modulators systems and circuits may be equally important and should be mentioned (e.g. Reimer, Jacob, Matthew J. McGinley, Yang Liu, Charles Rodenkirch, Qi Wang, David A. McCormick, and Andreas S. Tolias. "Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex." Nature communications 7, no. 1 (2016): 13289.)

Reimer et al. (2016) used two-photon microscopy to measure activity of ACh and NE projections in layer 1 of mouse visual cortex while tracking pupil diameter fluctuations. During stillness, elevated pupil diameter was followed by cholinergic and noradrenergic axonal activity. Notably, NE activity levels were larger and with shorter latency than ACh. In primates, Joshi et al., (2016) recorded from LC during a fixation task. Using spike-triggered averaging, they found that following a spike in an LC neuron, there was pupil dilation at 200-300 ms latency. Moreover, microstimulation in LC produced pupil dilation at 500ms latency. More recently, Breton-Provencher and Sur (2019) provided causal evidence that LC activity drives pupil size. They optogenetically activated (1s) or silenced (5 sec) locus coeruleus noradrenergic neurons and found strong increase in pupil size or modest decrease: increase had a slow time scale of 1 second or more, similar slow timescale for decrease. The LC-NA neurons are surrounded by GABA-ergic neurons. Stimulation of the GABA-ergic neurons produced mild, slow constriction. They identified GABA-ergic and NA neurons by photo-tagging and then tried to identify them via spike shape and found that “spike shape of some GABA neurons were not well separated from NA neurons, demonstrating the difficulty of cell-type identification based on spike shape alone.” They noted that a subset of GABAergic neurons received coincident inputs with the NA neurons. When the GABA neurons were excited, the gain of the pupil response to an auditory tone was diminished, producing an increase as a function of tone intensity that had a lower gain. Thus, LC-NA neurons causally drive pupil size, and the GABA neurons that surround them control the gain of the response of LC-NA neurons to arousal stimuli.

Line 35 page 6-page 7 line 10 emphasizes a cognitive interpretation of the pupil dilations that is emphasized, in relation to effort costs. But there are also more concomitant vigorous movements. Could all of their pupil results be explained by motor correlates? This should be tested and ruled out before making cognitive interpretations.

Pupil dilation is a proxy for activity in the brainstem neuromodulatory system (Vazey et al., 2018) and is a measure of arousal (Mathot, 2018). Control of pupil size is dependent on spiking of norepinephrine neurons in locus coeruleus (LC-NE): an increase in the activity of these neurons produces pupil dilation (Joshi et al., 2016; Breton-Provencher and Sur, 2019). Some of these neurons show a transient change in their activity when acquisition of reward requires expenditure of physical effort (Bornert and Bouret, 2021). However, the link between effort costs and pupil size appears to go beyond motor control, as a recent paper found that pupil size increases during effortful speech perception (Contadini-Wright et al., 2023). Thus, although in our work increases in pupil size were always associated with increased movement vigor, the results from other studies suggest that economic variables such as cognitive effort in tasks in which there is no concomitant movement also drive an increase in pupil size.

Page 7, line 37-42: How would the model need to be modified in order to account for this discrepancy with the data? Ideally, this would be tested.

We comment on potential modifications that can be made to the model that may account for the discrepancy referred to by the reviewer in the discussion section: “Notably, some of the predictions of the theory did not agree with the experimental data. An increased effort cost did not accompany a reduction in the duration of harvest, and hunger did not increase saccade vigor robustly. Indeed, earlier experiments have shown that if the effort cost of harvest increases, animals who expend the effort will then linger longer to harvest more of the reward that they have earned (2). This mismatch between observed behavior and theory highlights some of the limitations of our formulation. For example, our capture rate reflected a single work-harvest period, rather than a long sequence. Moreover, the capture rate did not consider the fact that the food tube had finite capacity, beyond which the food would fall and be wasted. This constraint would discourage a policy of working more but harvesting less. Finally, if we assume that a reduced body weight is a proxy for increased subjective value of reward, it is notable that we observed a robust effect on vigor of licks, but not saccades. A more realistic capture rate formulation awaits simulations, possibly one that describes capture rate not as the ratio of two sums (sum of gains and losses with respect to sum of time), but rather the expected value of the ratio of each gain and loss with respect to time (Bateson et al., 1995 & 1996).”

Page 9, line 2-11: In this section, it would help to also consider 'baseline' pupil size (in between trials). This would give a signal that is not 'contaminated' by movements, and may reflect control state. Relatedly, changes in control state may impact and confound the movement-related dilation magnitudes due to e.g. floor and ceiling effects on pupil size, which has a strong tendency for reversion to the mean.

The experiment design included little or no between-trial periods because during the trials the subjects worked (performed saccades to accumulate reward), while after completing a few trials they stopped working and started harvesting through licking. Because primates make saccades during their entire wake state, it is probably not possible to find a significant period in which the subjects do not make any movements. We selected a window of 500 ms around each lick in the harvest period, and each saccade during the work period, and computed the average pupil size per movement, which includes data from both before and after movements. We then computed a within-session z-score by normalizing these measures by the average pupil size acquired for that day.

The hunger-related and reward-size related analyses are both heavily confounded since they were not manipulated directly and could co-vary with many latent factors. For example, why might a given Marmoset be lower weight on a given day? Could it affect sleep, stress, activity, or other factors during the preceding 24 hours? If so, could these other variables be driving the results that are interpreted as 'hunger?' Relatedly, since the reward size is determined by the animals behavior on each trial (how much they worked), factors (internal brain state, external noises, etc.) that alter how much they worked will influence the subsequent reward size. Therefore interpretations about reward expectancy are confounded. Both of these issues should be discussed and manipulations of them (different feeding schedules and reward size-work functions proposed, respectively).

Weight of the subjects was measured prior to the start of the experiment on each day. The natural fluctuations are typically the result of factors such as time of the experiment and corresponding weight measurement (AM vs PM) relative to the time of feeding on the previous day, day of the week of the experiment (following a weekend vs. during the week), and volume of food given during the previous day. Animals were maintained at 90% of their baseline weight during food restriction, and fluctuations typically occurred within that range (Sedaghat-Nejad et al., 2019). We used weight as a proxy for hunger, and thus value of reward, and the resulting analyses yielded results consistent with predictions made by our model, as seen in Fig. 5. Critically, other factors that may co-vary with lower weights, like those mentioned by the reviewer (sleep conditions, stress levels, and activity levels) often lead to very poor task performance by the subjects. In sharp contrast, the model predicted increased work period, and increased movement vigor for high reward value, both of which we observed when the subject’s weight was low. Thus, a low relative weight did not seem to impair performance, but rather act as a motivating factor. Subjects were closely monitored for well characterized stress-related behaviors and impaired attentive states by experimenters, veterinarian staff, and caretaker staff, and, in the event of abnormalities, were removed from food restriction and experimentation until behavior stabilized.

Effect of reward size: As you noted, we did not manipulate reward size directly. Rather, because our emphasis was on quantifying the effect of effort, the subjects received the same increment of reward per each completed trial, but on some sessions this reward was easy to harvest, while in other sessions the reward required greater effort to harvest. Because the reward amount accumulated during the work period, some harvests encountered a small amount of reward, while other harvests encountered a large amount of reward. Indeed, the amount of reward available for harvest depended linearly on the number of successful saccade trials completed during the work period. We found that the vigor of licks grew with the reward magnitude.

A major issue is a lack of alternative models. The authors seem to have constructed a particular model designed to capture the behavioral patterns they observed in the data. The model fails in some instances, as they point out. Even more importantly, there are no results or discussion about how other plausible models could or couldn't fit the data. The lack of model comparisons makes it difficult to interpret the conclusions or put the results in a broader context.

To model behavior, we chose a formulation of utility that represented a normative approach that ecologists have used to understand the decisions that animals make regarding how far to travel for food, what mode of travel to use, and how long to stay before moving on to another patch. In the model, the objective of decisions and actions is to maximize the sum of reward acquired, minus the efforts expended, divided by time. This is termed the capture rate. However, there are other models to consider, and thus we added a new section titled Model formulation and Other models of utility.

Reviewer #2 (Public Review):

The model proposed in the paper takes a very specific functional form that is neither motivated by the previous literature nor particularly useful for indexing the behavioral tendencies of individual monkeys (or of the same monkey in different contexts). For example, while it is clear that the saccade effort cost will need to outgrow the increase in the utility of the accumulated food for the monkey to start feeding, it is unclear why this needs to be modeled with a fixed quadratic exponent on the number of saccades? Similarly, why do licks deplete the food stash with the specific rate hard-coded in the model?

We added a section titled Model formulation and Other models of utility to better explain the rationale behind the model.

We chose this formulation of utility (Eq. 1) because it is a normative approach that ecologists have used to understand the decisions that animals make regarding how far to travel for food, what mode of travel to use, and how long to stay before moving on to another reward opportunity (Richardson and Verbeek, 1986; Stephens and Krebs, 1986; Bautista et al., 2001). In a typical formulation of the theory, the numerator represents the reward gained (in units of energy), minus the effort expended (also in units of energy), while the denominator represents the amount of time spent during that behavior. We represented this idea in Eq. (1) with saccades that produced reward accumulation, and licks that produced reward consumption. Thus, the utility that we aim to maximize is the rate of energy gained.

The specific functions that we used to represent the energy gained through reward acquisition, and the energy expended through effort expenditure, came either from experiment design, or from the measurements we have made in other experiments. We modeled reward accumulation as a linear rise in energy stored because successful saccades produced a linear increase in the food cache. We modeled consumption of the food as a hyperbolic function of the number of licks to represent the fact that as the licking bout began, each successful lick depleted the food, and thus the first few licks produced a greater amount of food consumption than the last few licks. We modeled the effort cost of licking to grow linearly with the number of licks.

A critical assumption that we made is that energy expended performing the saccade trials (which grew faster than linearly as a function of the number of trials attempted), grew faster than the time spent attempting those same trials (which grew linearly with the number of trials). This assumption is based on the heuristic that the average rate of energy lost following a large number of attempted trials is greater than the average rate of energy lost following a small number of attempted trials. A quadratic function is one example of such a function, which has the advantage of providing closed form solutions for the optimal policy.

The model’s simplicity provided closed-form solutions across all parameter values, allowing us to make predictions without having to fit the model to the measured data. Critically, for all parameter values that produce a real solution (as opposed to imaginary), the optimal number of saccade trials increases with the square root of the cost of licking. Thus, the basic prediction of the model is that to maximize the capture rate, regardless of parameter values, an increase in the effort required for harvest should be met with a greater willingness to work. The closed-form solutions are presented in the supplementary document (simulations.nb).

Finally, the proportion of successful saccades and lick events is assumed to be fixed, even though it very likely to be directly influenced by movement speed (speed- accuracy trade-off), which is also contained in the model. It would strongly increase the plausibility and potential impact of the model if the authors could clearly state where these hard-coded model terms come from. Ideally, they would formulate the model in more general terms and also consider other functional forms, as briefly suggested in the discussion. This latter point would be particularly important since not all model predictions were actually borne out in the data.

Thank you for this excellent suggestion. Regarding saccades, contrary to the speed accuracy trade-off hypothesis, we found that faster saccades were also more accurate (Fig. 3C). Thus, increased pupil size was not only associated with more vigorous saccades, but also more accurate saccades. Importantly, these vigor-related changes in accuracy were too small to affect the probability of reward: the reward area for the saccades was much larger (1.5 deg) than the endpoint accuracy changes that was produced due to changes in the food tube distance. For example, on average saccade vigor changed from 0.95 to 1.05 when the food tube distance changed from 12 mm to 8 mm. These changes in vigor would produce a fraction of degree reduction in endpoint error (Fig. 3C).

Regarding licks, we added new data to the manuscript to assess the relationship between vigor of the licks and endpoint accuracy. We saw no consistent relationship, across subjects or effort conditions, between protraction speed and the outcome of a lick, that is, if the lick was successful in making it inside the tube. On average, in subject R we observed an improvement in lick accuracy with increased vigor, and in subject M we saw no change (Fig. 4F). Thus, we used the average success rate of licks, which was roughly 30% for both subjects.

The authors derive qualitative predictions, by simulating their model with apparently arbitrary parameters. They then test these qualitative predictions with conventional statistics (e.g., t-tests of whether monkeys lick more for high vs low effort trials). The reader wonders why the authors chose this route, instead of formulating their model with flexible parameters and then fitting these to data. This would allow them (and future researchers) to test their model not just qualitatively but also quantitatively, and to compare the plausibility of different functional forms. The authors certainly have enough data and power to do this, given the vast number of sessions the monkey completed.

The model’s simplicity provides closed-form solutions across all parameter values, allowing one to make predictions without having to fit the model to the measured data. For example, for all parameter values that produce a real solution (as opposed to imaginary), the optimal number of saccade trials increases with the square root of the cost of licking. Thus, the basic prediction of the model is that to maximize the capture rate, an increase in the effort that it takes to harvest the reward should produce a greater willingness to work longer, caching more food. The closed-form solutions are presented in the Mathematica supplementary document.

The effort manipulation chosen by the authors (distance of food tube) goes hand in hand with a greater need for precision since the monkey's tongue needs to hit an opening of similar size, but now located at a greater distance. This raises the question of whether the monkeys moved slower to enhance its chance of collecting the food (in line with a speed-accuracy trade off). The manuscript would benefit from an explicit test of this possibility, for example by reporting whether for each of the two conditions, the speed of tongue movements on a trial-by-trial basis predicts the probability of food collection? At the very least, the manuscript should explicitly discuss this issue and how it affects the certainty with which effects of tube distance can be linked to anticipated effort cost alone.

Thank you for the excellent point. We looked for but found no consistent relationship, across subjects or effort conditions, between protraction speed of the tongue and the success probability of a lick (probability of insertion into the tube). Regardless, we agree with you that it is an excellent alternate hypothesis that reductions in lick vigor that accompanied increased distance of the tube may be due to a desire to maintain accuracy, and not a reflection of increased effort cost of reward. To incorporate this idea into the model, we would need a measure of speed-accuracy for the licks, something that we do not have but hope to develop in the future.

However, perhaps the most interesting aspect of our results is that when we increased tube distance, making reward more effortful, there was not only a reduction in lick vigor, but also a reduction in saccade vigor. That is, the decisions and actions during the work period responded to the increased effort cost of reward during the harvest period. These changes accompanied dilation of the pupil, both in the work period and in the harvest period. We now include a paragraph regarding this in the Discussion.

The manuscript measures pupil dilation in a time period ranging from -250ms before to 250 ms after saccade onset. However, the pupil changes strongly during saccade execution relative to the preceding baseline, leaving doubts as to whether the aggregated measure blurs several interesting and potentially different effects. It would be more conclusive if the manuscript could report the analyses of pupil size separately for a period prior to saccade onset and during/after the saccade.

Our goal was to test for general correlations between the state of the pupil and both movement vigor and decisions. We chose a window of 500 ms around saccade onset, as referred to by the reviewer, as it allowed us a large enough time window to measure pupil size outside of the movement itself (~30 ms duration), to accurately capture the state of the animal around initiation and end of a saccade. Critically, pupil tracking during a saccade itself, when using infrared eye tracking techniques, can be prone to slight measurement error in certain cases due to tracking jitter. Thus, averaging across this window, following processing of the signal, results in a more accurate measure of pupil size.

Author Response

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

We appreciate the critical review of our manuscript. We believe that we have addressed the questions and concerns raised by the reviewers to the best of our ability. As part of the revision, we conducted two new experiments to enhance the rigor of the conclusions and to provide more insights into the mechanism of STEAP proteins, and we reorganized the Results section, as suggested by the reviewers, following to a clearer logical thread. The new data are briefly summarized below.

1) Reduction of L230G STEAP1 by reduced FAD. We made Leu230Gly STEAP1 mutant and measured the rate of heme reduction by reduced FAD. We found that the rate of heme reduction in L230G STEAP1 is slower than that in the wild type STEAP1. Since Leu230 is solvent accessible only from the intracellular side, this result supports the conclusion that reduced FAD binds to STEAP1 on the intracellular side and reduces the heme. This result also indicates that leucine, which is found at the equivalent position in STEAP1, 2 and 3, and Phe359 in STEAP4, has a significant role in mediating electron transfer from FAD to the bound heme.

2) Reduction of STEAP2 by reduced FAD. We showed that STEAP2 can be reduced when supplied with reduced FAD, and that the rate of heme reduction is significantly slower than that of reduction of STEAP1 by reduced FAD. This result is consistent with presence of the oxidoreductase domain (OxRD)† in STEAP2, which hampers direct entrance of the isoalloxazine ring of FAD to its binding pocket in the transmembrane domain (TMD). On the other hand, the rate of heme reduction by reduced FAD is much faster than that of heme reduction in the presence of NADPH and FAD, indicating that reduction of FAD by NADPH is rate-limiting in the electron transfer chain in STEAP2.

†: To be consistent with literature, we adopted the nomenclature “oxidoreductase domain (OxRD)” for the N-terminal soluble domain in STEAP proteins. We used the term “reductase domain (RED)” in the previous version of our manuscript.

Reviewer #1 (Public Review):

This important study reveals the structure of human STEAP2 for the first time and suggests the electron transport pathway, but some questions remain regarding the interpretation of the in vitro electron transport experiments, the lack of available redox couples, and potential alternative hypotheses that would if addressed, strengthen the claims in the manuscript.

Strengths

One of the clear strengths of the manuscript that stands out is the determination of the structure of human STEAP2. The structures of some other homologs are known, but STEAP2's structure was not, and STEAP2 seems to have an unusually low activity towards certain metal chelates. The approach of producing the human STEAP2 in insect cells with the supplementation of cofactor biogenesis components nicely results in cofactor-replete protein. The structure of STEAP2 reveals a domain-swapped trimer, with the NADPH-binding domain of the neighboring protomer within electron-transport distance of the FAD-heme axis. The FAD has an interesting and somewhat unusual extended conformation and abuts a Leu residue that may regulate electron transport. Another strength of the manuscript is the demonstration that STEAP1, which does not have the internal NADPH binding domain, can interact modestly and shuttle electrons to the heme in STEAP1 through FAD. These experiments nicely expand information on the function of STEAP1 and provide a structural basis for electron transport in STEAP2.

Weaknesses

A major weakness in the manuscript lies with the kinetics data and how the data, as presented, are unclear to the reader regarding their impact and their connection to the purported electron transport scheme. While multiple sets of data are reported, the analysis in all cases is simply the reduction of a hexacoordinate heme and its related spectra and kinetic parameters. In most cases, it's unclear to the reader which part of the electron pathway is being tested in which experiment. Simple diagrams would be helpful in each case. However, it's also unclear if all of the potential order of addition experiments were actually performed; i.e., flavin but no NADPH; NADPH but no flavin; flavin before NADPH; flavin after NADPH, etc. As there are multiple permutations that should be tested to demonstrate the electron transport pathway, presenting the data in a way that makes it clear to the reader is challenging. Particularly missing are the determined redox potentials of the hemes in both STEAP1 and STEAP2. Could differences in these heme redox potentials be drivers of the difference in metal reduction rates?

We re-structured the manuscript to follow a clearer logical thread. We provided explanations for which electron transfer steps are being examined in each experiment.

We cannot reliably determine EM due to insufficient amount of purified proteins. We are inclined to think that the bound heme on STEAP1 and STEAP2 have similar EM, based on their similar coordination geometry and nearly identical UV-Vis and MCD spectra. Thus, different rates of Fe3+-NTA reduction by STEAP1 and STEAP2 are likely due to differences in substrate binding site rather than different EM.

Also, the text indicates that STEAP2 does not show a reduction rate dependence on the [Fe3+NTA], but Figure 1A shows a difference in rates dependent on [Fe3+-NTA], and the shape of the reduction curve also changes based on [Fe3+-NTA]. This discrepancy should be rectified.

We fixed this error. The reduction of Fe3+-NTA by ferrous STEAP2 shows multiple phases and the reaction rates within the initial 2 seconds are weakly dependent on [Fe3+-NTA].

A second major weakness is the lack of any verification of the relevance of the STEAP2 oligomerization to its in vivo function. Is the same domain-swapped trimer known to exist in vivo? If the protein were prepared in other detergents, is the oligomerization preserved? It is alluded to in the text that another STEAP protein is also a trimer. Was this oligomerization verified in vivo?

The domain-swapped assembly is an interesting phenomenon, and it seems to provide a solution for bringing the FAD closer to heme. The same domain swapped trimeric assembly is also observed in the structure of STEAP4, which was purified in a different detergent (Nat Commun (2018), 9, page 4337). It is likely that this feature is shared by STEAP2, 3, and 4, and preserved during the purification process.

Could this oligomerization be disrupted to impede or abrogate electron transport to underscore the oligomerization relevance? This point is germane, as it would further suggest that the domain-swapped trimer observed in the STEAP2 cryo-EM structure is physiologically relevant, especially given how far the distance between the NADPH and the FAD would otherwise be to support electron transport.

We agree with the reviewer’s reasoning that the oligomeric assembly is required for proper function of STEAPs and thus could potentially be utilized for functional regulation. However, we are not aware of any physiologically relevant stimuli or treatment that would allow regulation of STEAP functions by inducing or forming different oligomeric states. Our experience with STEAP proteins is that the trimeric assembly is stable and well-preserved during the purification process and can only be disrupted under denaturing conditions such as SDS-PAGE.

Beyond these two areas in which the manuscript could be improved there are a couple of minor considerations. First, the modest resolution of the STEAP2 structure prevents assigning the states of NADP+/NADPH and FAD/FADH2 with confidence. An orthogonal measure would be useful for modeling the accurate states in the structure.

We agree. We clarified the ambiguity and stated in the main text that the cryo-EM structure of STEAP2 was determined in the presence of NADP+ and FAD.

Finally, the BLI b5R/STEAP1 binding/unbinding fits seem somewhat poor, especially at high concentrations of b5R in the dissociation regime, which likely influences the derived value of Kd. A different fitting equilibrium might yield better agreement between the experimental and theoretical results. Moreover, whether this binding strength is influenced by the reduction state of the NADPH would be helpful in understanding and contextualizing the weak binding affinity.

We think that non-specific binding likely causes deviations from the simple binding model at higher b5R concentrations. We made a comment on this in the main text. We agree with the reviewer that the interactions between b5R and STEAP1 could be redox dependent, for example, a reduced FAD on b5R may enhance the affinity. We could implement this by performing the binding experiments in an anaerobic chamber, but this is beyond the scope of the current study.

Reviewer #2 (Public Review):

The manuscript provides new insight into a family of human enzymes. It demonstrates that STEAP2 can reduce iron and STEAP1 can be promiscuous regarding the source of electron donors that it can use. The quality of the kinetics experiment and the structural analysis is excellent. I am less enthusiastic about the interpretation of data and the take-home message that the manuscript intends to deliver. Above all, the work combines data on STEAP2 and STEAP1 and it remains unclear which questions are ultimately addressed. A second critical point is about the interpretation of the experiment demonstrating that STEAP1 can be reduced by cytochrome b5 reductase. The abstract states that "We show that STEAP1 can form an electron transfer chain with cytochrome b5 reductase" whereas the main text discusses the data by suggesting that "we speculate that FAD on b5R may partially dissociate to straddle between the two proteins". The two statements seem to be partly contradictory. Cytochrome b5 reductases do not easily release FAD but rather directly donate electrons to heme-protein acceptors (PMID: 36441026). According to the methods section, no FAD was added to the reaction mix used for the cytochrome b5 reductase experiment. Overall, the data seem to indicate that the reductase might reduce the heme of STEAP1 directly. Would it be possible to check whether FAD addition affects the kinetics of the process?

We agree with the reviewer on this point. We do not have evidence indicating that FAD fully or partially dissociates from b5R to interact with STEAP1. We removed the statement in the revision.

We have not tried to add free reduced FAD to the mixture of STEAP1/b5R/NADH, because we feel that this would increase the complexity of the system and complicate data interpretation. We are working on determining the structure of b5R in complex with STEAP1 to visualize the electron transfer pathway, and we hope that such a structure would provide a framework for understanding electron transfer between the two proteins.

And to perform a control experiment to check that NAD(P)H does not directly reduce the heme of STEAP1 (though unlikely)?

We did the control experiment and included data in Fig. S3A. No reduction of heme by NADH alone.

A final point concerns the "slow Fe3+-NTA reduction by STEAP2". The reaction is not that slow as the initial phase is 2 per second. The reaction does not show dependence on the substrate concentration suggesting "poor substrate binding". I am not convinced by this interpretation. Poor substrate binding would give rise to substrate dependency as saturation would not be achieved. A possible interpretation could be that substrate binding is instead tight and the enzyme is saturated by the substrate. Can it be that the reaction is limited by non-productive substrate-binding and/or by interconversions between active and non-active conformations? We re-analyzed the data and revised the Results and Discussion.

We agree with the reviewer on this point. We re-analyzed the data and found that the reaction rates within the first 2 seconds are weakly dependent on [Fe3+-NTA] while the rates beyond 2 seconds do not show dependence on [Fe3+-NTA]. More studies are required to unravel the mechanism that leads to the complicated kinetic data.

Reviewer #3 (Public Review):

The six-transmembrane epithelial antigen of the prostate (STEAP) family comprises four members in metazoans. STEAP1 was identified as integral membrane protein highly upregulated on the plasma membrane of prostate cancer cells (PMID: 10588738), and it later became evident that other STEAP proteins are also over expressed in cancers, making STEAPs potential therapeutic targets (PMID: 22804687). Functionally, STEAP2-4 are ferric and cupric reductases that are important for maintaining cellular metal uptake (PMIDs: 16227996, 16609065). The cellular function of STEAP1 remains unknown, as it cannot function as an independent metalloreductase. In the last years, structural and functional data have revealed that STEAPs form trimeric assemblies and that they transport electrons from intracellular NADPH, through membrane bound FAD and heme cofactors, to extracellular metal ions (PMIDs: 23733181, 26205815, 30337524). In addition, numerous studies (including a previous study from the senior authors) have provided strong implications for a potential metalloreductase function of STEAP1 (PMIDs: 27792302, 32409586).

This new study by Chen et al. aims to further characterize the previously established electron transport chain in STEAPs in high molecular detail through a variety of assays. This is a wellperformed, highly specialized study that provides some useful extra insights into the established mechanism of electron transport in STEAP proteins. The authors first perform a detailed spectroscopic analysis of Fe3+-NTA reduction by STEAP2 and STEAP1, confirming that both purified proteins are capable of reducing metal ions. A cryo-EM structure of STEAP2 is also presented. It is then established that STEAP1 can receive electrons from cytochrome b5 reductase, and the authors provide experimental evidence that the flavin in STEAP proteins becomes diffusible.

The specific aims of the study are clear, but it is not always obvious why certain experiments are performed only on STEAP2, on STEAP1, or on both isoforms. A better justification of the performed experiments through connecting paragraphs and proper referencing of the literature would improve readability of the manuscript. Experimentally, the conclusions are appropriate and mostly consistent with the experimental data, although one important finding can benefit from further clarification. Namely, the observation that STEAP1 can form an electron transfer chain with cytochrome b5 reductase in vitro is an exciting finding, but its physiological relevance remains to be validated. The metalloreductase activity of STEAP1 in vitro has been described previously by the authors and by others (PMIDs: 27792302, 32409586). However, when over expressed in HEK cells, STEAP1 by itself does not display metal ion reductase activity (PMID: 16609065), and it was also found that STEAP1 over expression does not impact iron uptake and reduction in Ewing's sarcoma (cancer) cells (PMID: 22080479). Therefore, the physiological relevance of metal ion reduction by STEAP1 remains controversial. The current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase in vitro with purified proteins. However, the conformation of this metalloreductase activity of the STEAP1-cytochrome b5 complex will be required in a cell line to prove that the two proteins indeed form a physiological relevant complex and that the results are not just an in vitro artefact from using high concentrations of purified proteins.

The work will be interesting for scientists working within the STEAP field. However, some of the presented data are redundant with previous findings, moderating the study's impact. For instance, the new structural insights into STEAP2 are limited because the structure is virtually identical to the published structures of STEAP4 and STEAP1 (PMIDs: 30337524, 32409586), which is not surprising because of the high sequence similarity between the STEAP isoforms. Moreover, the authors provide experimental evidence to prove the previous hypothesis (PMID: 30337524) that the flavin in STEAP proteins becomes diffusible, but the molecular arrangement of a STEAP protein, in which the flavin interacts with NADPH, remains unknown. Based on the manuscript title, I would also expect the in-depth characterization of STEAP1/STEAP2 hetero trimers (first identified by the authors), but this is only briefly mentioned. When taken together, this study by Chen et al. strengthens and supports previously published biochemical and structural data on STEAP proteins, without revealing many prominent conceptual advances.

We thank the reviewer for information and the broader context. We have revised the manuscript to have a clearer logical thread.

Reviewer #1 (Recommendations For The Authors):

Please see the "Public Review" for recommendations.

Reviewer #2 (Recommendations For The Authors):

Specific suggestions

-The introduction should more clearly state which questions are being addressed and why STEAP1 and STEAP2 are investigated.

We have revised the Introduction to make that clearer.

-The manuscript should discuss more extensively and provide possible explanations for the substrate-independent kinetics of iron-reduction by STEAP2.

We re-analyzed the data and found the rate constants of the reactions before 2 s are weakly [Fe3+NTA]-dependent. We ascribe the weak [Fe3+-NTA]-dependence to the partial rate-limiting by substrate binding. However, we do not have a good interpretation for the reaction kinetics after 2 s which does not show [Fe3+-NTA]-dependence.

-"The rate of STEAP1(Fe(II)) oxidation by Fe3+-NTA is similar to those by Fe3+-EDTA or Fe3+-citrate, but the rates are significantly faster than STEAP2(Fe(II)) re-oxidation by Fe3+NTA (Fig. 1B)." The rates for STEAP1 should be given to substantiate this statement.

We added Table S1 in the supplementary materials that includes the 2nd order association (kon) and the 1st order dissociation rate constants (koff) of iron substrates in STEAP1 and STEAP2. Data on Fe3+-EDTA or Fe3+-citrate by STEAP1 are from our previous study (Biochemistry, 2016). We also calculated the KDs of different iron substrates for STEAP1 and STEAP2.

• "Our results indicate that STEAP2 can supply reduce FAD to initiate electron transfer in STEAP1." As discussed above, this statement should be discussed and analyzed

We mixed 0.9 μM STEAP1, 1.1 μM STEAP2, and 2.2 μM FAD and added 60 μM NADPH to the system and found that the heme on both STEAP1 and STEAP2 are reduced. Since adding NADPH to STEAP1 plus FAD alone does not reduce the heme (Fig. S3B), we reasoned that reduction of the heme on STEAP1 is achieved by the reduced FAD produced on STEAP2. The reduced FAD likely dissociates from STEAP2 and then bind to STEAP1.

-Experiments on "STEAP1 reduction by STEAP2" The experiments show that "STEAP2 can supply reduce FAD to initiate electron transfer in STEAP1." Could these results be explained by heterotrimer formation in agreement with the previous data published by the authors?

In our experience, STEAP1 and STEAP2 homotrimers are stable and do not form heterotrimers when mixed. STEAP1/2 heterotrimers form only when the two proteins are co-expressed in cells (Biochemistry (2016) 55, 6673-6684).

Reviewer #3 (Recommendations For The Authors):

Major points:

1) As a very general point: the order in which the results are presented could be greatly improved to increase the readability for non-experts. To elaborate: The manuscript starts with the spectroscopic characterization of STEAP2, then suddenly the reductase activities of STEAP1 and STEAP2 are compared; subsequently, experiments are described involving STEAP1 and cytochrome b5 reductase; then the cryo-EM structure of STEAP2 is presented etc. As a non-expert reader, this presentation of the results is confusing, especially because the paragraphs are not always connected well, and there is a lot of switching between STEAP1 and STEAP2 data. A more logical order would be to first present the STEAP2 spectroscopy and structural data, then write a connecting paragraph on why it is important to also study the electron transfer chain in STEAP1, followed by the comparison of the STEAP isoforms and the data on STEAP1 alone. The authors should include sentences on why they performed each experiment. For example, why did they determine the structure of STEAP2. What were they after that they could not retrieve from the homologous STEAP4 and STEAP1 structures? Justification of the performed experiments will make it easier for the reader, and will establish a better connection between the paragraphs.

We reorganized the data presentation in Results per the reviewer’s suggestions.

2) The physiological relevance of metal ion reduction by STEAP1 remains controversial. Because the current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase, could the authors perform an easy experiment where they over express both STEAP1 and cytochrome b5 reductase in a cell line? If such an experiment would reveal STEAP1-dependent metal-ion reduction, it would greatly improve this part of the manuscript. If no activity is observed, the established electron transfer chain could just represent an in vitro artifact from using high concentrations of purified proteins.

This is an excellent point. We are not set up to perform the proposed experiment but will do so in the future.

3) The manuscript states that metal ion reduction of purified STEAP2 is slow, and the authors explain this by the absence of density for the extracellular region between helices 3 and 4 that are present in the structures of STEAP4 and STEAP1, resulting in a less-well defined substratebinding site. Can the authors exclude that the less-well defined substrate-binding site is a result of the detergent extraction of STEAP2? Would it be possible to measure the reductase activity of STEAP2 in purified membranes?

Detergent mostly interacts with the transmembrane domains and since the TMD region of STEAP2 aligns well with those of STEAP1 and STEAP4, we do not think that the disordered substrate binding region in STEAP2 is a consequence of detergent solubilization. It is difficult to conduct pre-steady state kinetic experiments using STEAP2 in membrane fractions.

4) The manuscript would greatly benefit from citing the literature more comprehensively to acknowledge insightful findings from authors in the field; for example, the important work by the Lawrence lab from 2015 (PMID: 26205815), which biochemically proved that STEAPs bind a single heme and that FAD bridges the TMD and RED, is not cited. The authors also mention that STEAP proteins belong to the same family as NOX proteins and cite some NOX structure papers. However, they fail to cite the first NOX structure paper (PMID: 28607049), as well the manuscript that structurally compares NOXs and STEAPs (PMID: 32815713). Similarly, the authors use SerialEM for their cryo-EM data collection but cite an old paper instead of the more recent (and relevant) SerialEM publication (PMID: 31086343).

We agree and added the references.

5) Generally, the data presented in the manuscript appear of good technical quality. However, a 'Table 1' with all relevant cryo-EM data collection and refinement statistics is completely missing as far as I can see. The authors should definitely add this to allow for the judgement of structural data quality. Without it, the manuscript is not suitable for publication.

We added Table S2 that includes relevant cryo-EM statistics.

Minor points:

6) The authors write in the abstract: 'STEAP2 - 4, but not STEAP1, have an intracellular domain that binds to NADPH and FAD'. This is not correct, because it has clearly been established that FAD also majorly binds to the transmembrane domain (this is even shown by the authors in the current manuscript as well).

Agree, we corrected that in the revision.

7) Sentence from the abstract and introduction state: 'It is also unclear whether STEAP1 has metal ion reductase activity' and 'it is unclear whether STEAP1 can form a competent electron transfer chain from NADPH'. The authors should definitely add "physiologically relevant" to these sentences. Namely, the senior authors themselves concluded in their 2016 Biochemistry paper (PMID: 27792302) that STEAP1 is capable of reducing metal ion complexes. Further indications that the transmembrane domain of STEAP1 displays metalloreductase activity was published by the Gros lab (PMID: 32409586), and it was also shown that fusing the RED of STEAP4 to the TMD of STEAP1 yields a functional protein in cells that reduces metal ions.

Good point and we revised the text and included the references.

8) Why is scheme 1 not just a summarizing figure?

We could change Scheme 1 to a Figure if required by the copy editor.

9) What is the purpose of Fig. 6? A larger depiction of Fig. 5e would likely be more relevant and should be considered as a replacement. Alternatively, the structure of STEAP1 (pdb 6y9b) could be shown in combination with Fig. 7, as the mutation is performed in STEAP1.

We agree and made changes to the structural figures to enhance clarity.

10) The manuscript now contains many, single panel figures. Certain main figures could easily be combined, for example, Fig. 1 and 2 and/or Fig. 3 and 4.

We agree and made changes to reduce single panel figures.

11) In Fig. 2, 3 and 4, the spectra show changes in peak heights as a result of the ferric to ferrous heme transition. However, a time component is missing in the legend. How long do these transitions take?

We added the reaction times to the figure legends.

12) The last part of the discussion states: 'The assembly of an intracellular RED with a membrane-embedded TMD observed in NOX, DUOX, and STEAPs naturally led to the notion that NADPH, FAD, and heme form an uninterrupted rigid electron-transfer chain that shuttles electron from the intracellular cellular NADPH to the extracellular substrates. While this may be true for NOX and DUOX, in which rapid supply of electrons to their extracellular substrates are essential to their biological functions, it may not apply similarly to STEAPs since it has only one heme in the TMD, and their electron transfer relies on shuttling of FAD.' The authors should mention here that the activity of NOX and DUOX is tightly regulated by accessory proteins, Ca2+ etc. Similarly, do the authors expect that the large distance between NADPH and FAD in the structures could represent a way to regulate/dampen the metal ion reduction rates of STEAPs in vivo?

We agree. We mentioned the regulation of NOX and DUOX in Discussion. We remain puzzled by the large distance between NADPH and FAD in STEAPs and are in pursuit of a structure in which the two cofactors are “in touch” for electron transfer.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

The conclusions of this paper are mostly well supported by data, but some aspects need to be corrected.

1) Line 99. The title is not suitable for summarizing this part of the results. In this paragraph, the results mainly describe SRSF1 expression pattern and binding of spermatogonia-associated gene's transcripts in testes. There is no functional assay to conclude SRSF1 has an essential role in mouse testes. The data only indicate that SRSF1 may have a vital role in posttranscriptional regulation in the testes.

Thank you for the professional suggestions. Following this advice, we have corrected the text in this revised version (Page 4, Line 98 and 112).

2) Line 141. In the mating scheme, Vasa-Cre Srsf1Fl/del mice should be obtained instead of Vasa-Cre Srsf1Fl/Fl mice.

Thank you for the professional suggestions. Following this advice, we have corrected the text in this revised version (Page 4, Line 118).

3) Fig 2 C, "PZLF" should be corrected to "PLZF".

Thank you very much for the helpful comments. We have corrected this in Figure 2C.

4) Fig 5 B, "VASA" and "Merge" should be interchanged.

Thank you very much for the helpful comments. We have interchanged "VASA" and "Merge" in Figure 5B.

5) Fig 5 D, "Ctrl" should be added in the up panel.

Thank you very much for the helpful suggestions. We have added "Ctrl" in Figure 5C.

6) The legend for Figure 6 D should be revised.

Thank you very much for the helpful suggestions. We have revised the legend for Figure 7D

7) The legend for Figure 7 G should be revised.

Thank you very much for the helpful suggestions. We have revised the legend for Figure 8D

8) Immunoprecipitation mass spectrometry (IP-MS) data showed that t SRSF1 interacts with other RNA splicing-related proteins (e.g., SRSF10, SART1, RBM15, SRRM2, SF3B6, and SF3A2). The authors should verify the interactions in testis or cells.

We thank the reviewer for the professional comments and suggestions. Following this advice, we performed co-transfection and co-IP to verify the protein-protein interactions in 293T cells, the results showed that the RRM1 domain of SRSF1 interacted with SART1, RBM15 and SRSF10 in 293T cells. In addition, the fluorescence results showed complete co-localization of mCherry-SRSF1 with eGFP-SART1, eGFP-RBM15 and eGFP-SRSF10 in 293T cells. Therefore, we have incorporated the data into the Figure 9G-J. Meanwhile, these have been incorporated into the text, given descriptions, and highlighted (Page 17, Lines 338-347).

9) To avoid overstatement, the authors should pay attention to the use of adjectives and adverbs in the article, especially when drawing conclusions about the role of Tail1.

We thank the reviewer for the professional comments and suggestions. To avoid overstatement, we have revised the entire text (Page 4, Lines 98, and 112; Page 16, Lines 308; Page 17, Lines 346-347; Page 20, Lines 413-414; Page 21, Lines 432-433).

Reviewer #2 (Recommendations For The Authors):

Major

1) I find the use of "SSC homing" misleading/confusing because this "homing" or relocation of postnatal gonocytes/nascent spermatogonia to the basement membrane precedes the maturation of the nascent spermatogonia into SSCs. In addition, "SSC homing" is commonly used in the SSC transplantation field to describe a transplanted SSC's ability to find and colonize its niche within the seminiferous tubules. I appreciate that "postnatal gonocytes/nascent spermatogonia homing" is not easily grasped by a broader audience. Perhaps "homing of precursor SSCs" is more appropriate.

Thank you very much for the helpful comments and suggestions. Following this advice, we have corrected the text in this revised version (Line 1-2, 39, 44, 49, 54-55, 68, 70, 72-73, 77, 84, 93-95, 191, 201, 240, 384-387, 397, 417-422, and 433)

2) If I am misunderstanding the description of the Srsf1 cKO phenotype, and the authors truly believe SSCs have formed in the Srsf1 cKO testis, I strongly recommend immunostaining to show that the cKO germ cells robustly express SSC markers, not just markers of undifferentiated spermatogonia.

We thank the reviewer for the professional suggestions. We fully agree with the reviewer. Immunohistochemical staining for FOXO1 and statistical results indicated a reduced number of prospermatogonia (Figure 6C-E). So, we have corrected the text in this revised version (Line 1-2, 39, 44, 49, 54-55, 68, 70, 72-73, 77, 84, 93-95, 191, 201, 240, 384-387, 397, 417-422, and 433).

3) If the authors have the available resources, the significance of this report would be enhanced by additional characterization of the cKO phenotype at the transition from gonocyte to nascent spermatogonia. Do any cKO germ cells exhibit defects in maturing from gonocytes to nascent spermatogonia at the molecular level? I.e., by P5-7, do all cKO germ cells express PLZF and localize FOXO1 to cytoplasm, as expected of nascent spermatogonia? If the cKO germ cells are actually a heterogenous population of gonocytes and nascent spermatogonia, what is the distribution of each subpopulation in the lumen vs basement membrane?

Thank you for the professional suggestions. Following this advice, immunohistochemical staining for FOXO1 was performed on 5 dpp mouse testis sections (Figure 6C). Further, germ cell statistics of FOXO1 expression in the nucleus showed a reduced number of prospermatogonia in cKO mice (Figure 6D). And germ cells in which FOXO1 is expressed in the nucleus similarly undergo abnormal homing (Figure 6E). Thus, all the above data indicated that SRSF1 has an essential role in the homing of precursor SSCs. we have incorporated the data into the Figure 6C-E. Meanwhile, these have been incorporated into the text, given descriptions, and highlighted (Page 9, Lines 191-201; Page 20, Lines 389-391).

Minor

1) Could the authors clarify why Tial1 exon exclusion in the cKO results in reduced protein expression? Is it creating a transcript isoform that undergoes nonsense-mediated decay?

Thank you for the professional suggestions. Following this advice, we analyzed Tial1 transcripts again, and we found that Tial1 exon exclusion resulted in reduced expression of protein isoform X2 (Figure 8J). Since this region is not in the CDS, no clear evidence of nonsense-mediated decay was found in the analysis.

2) Could the authors confirm that the TIAL1 antibody is not detecting the portion of the protein encoded by the alternatively spliced exon?

Thank you for the helpful comments. The TIAL1 monoclonal antibody is produced by Proteintech Group under the product number 66907-1-Ig. Immunogen is TIAL1 fusion protein Ag11981. The sequence is as follows. MDARVVKDMATGKSKGYGFVSFYNKLDAENAIVHMGGQWLGGRQIRTNWATRKPPAPKSTQENNTKQLRFEDVVNQSSPKNCTVYCGGIASGLTDQLMRQTFSPFGQIMEIRVFPEKGYSFVRFSTHESAAHAIVSVNGTTIEGHVVKCYWGKESPDMTKNFQQVDYSQWGQWSQVYGNPQQYGQYMANGWQVPPYGVYGQPWNQQGFGVDQSPSAAWMGGFGAQPPQGQAPPPVIPPPNQAGYGMASYQTQ The homology was 99% in mice and all TIAL1 isoforms were detected. So, TIAL1 antibody is detecting the portion of the protein encoded by the alternatively spliced exon.

3) Lines 143: should "cKO" actually be "control"?

Thank you for the helpful suggestions. There is a real problem in the text description. we have corrected the text in this revised version (Page 6, Line 138-139).

4) Lines 272-3 "visual analysis using IGV showed the peak of Tial1/Tiar was stabilized in 5 dpp cKO mouse testes (Figure 7H)": "peak stabilization" is not evident to me from the figure nor do I see Tial1 listed as differentially expressed in the supplemental. I would refrain from using IGV visualization as the basis for the differential abundance of a transcript.

Thank you very much for the helpful comments and suggestions. Tial1/Tiar is one of 39 stabilizing genes that are bound by SRSF1 and undergo abnormal AS. Following this advice, we have substituted Tial1/Tiar's FPKM for his peaks (Figure 8H). Meanwhile, we have corrected the text in this revised version (Page 15, Line 296-300; Page 16, Line 303-304).

5) Lines 468-473: please clarify the background list used for GO enrichment analyses. By default, the genes expressed in the testis are enriched for spermatogenesis-related genes. To control for this and test whether a gene list is enriched for spermatogenesis-related genes beyond what is already seen in the testis, I recommend using a list of all expressed genes (for example, defined by TPM>=1) as the background list.

We thank the reviewer for the professional comments and suggestions. Following this advice, all expressed genes (TPM sum of all samples >=1) are listed background for GO enrichment analyses. The results of GO enrichment analysis of the AS gene turned out to be the same. The results of GO enrichment analysis of the SRSF1 peak-containing genes, differential genes, and IP proteins-associated genes have corrected in the figure (Figure 2A, 7E, and 9E)

6) Figure 2B: Could the authors mark where the statistically significant peaks appear on the tracks? There are many small peaks and it's unclear if they are significant or not.

Thank you for the helpful suggestions. Following this advice, we have marked the areas of higher peaks in the figure (Figure 2B). We generally believe that any region above the peaks of IgG is likely to be a binding region, and of course, the higher the peak value, the more pre-mRNA is bound by SRSF1 in that region.

7) Figure 7A: I assume the SRSF1 CLIP-seq genes are all the genes from the adult testis experiments. I would suggest limiting the CLIP-seq gene set to only those expressed in the P5 RNA-seq data, as if the target is not expressed at P5, there's no way it will be differentially expressed or differentially spliced in at P5.

Thank you very much for the helpful comments and suggestions. Following this advice, we found that 3543 of the 4824 genes bound by SRSF1 were expressed in testes at 5 dpp. we have corrected in the figure (Figure 8A). these have been incorporated into the text, given descriptions, and highlighted (Page 14, Lines 274-277).

8) Figure 7F: Could the authors clarify where the alternatively spliced exon is relative to the total transcript, shown in 7H?

Thank you for the helpful suggestions. Following this advice, we have labeled the number of exons where variable splicing occurs. (Figure 8F).

9) Please include where the sequencing and mass spec data will be publicly available.

Thank you very much for the helpful comments and suggestions. Following this advice, these have been incorporated into the text, given descriptions, and highlighted (Page 25, Lines 560-565).

Reviewer #3 (Recommendations For The Authors):

Suggestions for improving the data and analysis

1) The claim that TIAL1 mediates SRSF1 effects is not well supported; this claim should be adjusted or additional supporting data should be provided. To support a claim that alternative splicing of Tial1 mediates the effects of SRSF1, at least two additional pieces of data are needed: first, a demonstration that the two alternative protein isoforms have different molecular functions, either in vitro or in vivo; and second, a better quantitation of the levels and ratios of expression of the two different isoforms in vivo.

Thank you for the helpful comments and suggestions. Following this advice, we quantified the expression levels and ratios of two different isoforms in vivo, and we found that Tial1 exon exclusion resulted in reduced expression of protein isoform X2 (Figure 8J). However, it is not possible to prove that the two alternative protein isoforms have different molecular functions. So, this claim has been adjusted in the text. these have been incorporated into the text, given descriptions, and highlighted (Lines 1-2, 43-45, 95, 306, 323-325, 408, 413-414).

2) Likewise, the claim that "SRSF1 is required for "homing and self-renewal" of SSCs should be adjusted or better supported. As of now, the data supports a claim that SRSF1 is required for the establishment of the SSC population in the testis after birth. This could be due to defects in homing, self-renewal, or survival. To support claims about homing and self-renewal, these phenotypes should be tested more directly, for example by quantitating numbers of spermatogonia at the basal membrane in juvenile testes (homing) and expression of SSC markers in addition to the pan-germ cell marker VASA across early postnatal time points.

Thank you very much for the helpful comments and suggestions. Immunohistochemical staining for FOXO1 was performed on 5 dpp mouse testis sections (Figure 6C). Further, germ cell statistics of FOXO1 expression in the nucleus showed a reduced number of prospermatogonia in cKO mice (Figure 6D). And germ cells in which FOXO1 is expressed in the nucleus similarly undergo abnormal homing (Figure 6E). Thus, all the above data indicated that SRSF1 has an essential role in the homing of precursor SSCs. we have incorporated the data into the Figure 6C-E. These have been incorporated into the text, given descriptions, and highlighted (Page 9, Lines 191-201; Page 20, Lines 387-389). Meanwhile, "homing and self-renewal" of SSCs have corrected the text in this revised version (Line 1-2, 39, 44, 49, 54-55, 68, 70, 72-73, 77, 84, 93-95, 191, 201, 240, 384-387, 397, 417-422, and 433).

3) Additional, more detailed analyses of CLIP-seq and RNA-seq data at least showing that the libraries are of good quality should be provided.

Thank you very much for suggestions. Following this advice, detailed analyses of RNA-seq data have been incorporated the data into the figures (Figure S2). But detailed analyses of CLIP-seq have already been used in another paper (Sun et al., 2023), and we have not provided it in order to avoid multiple uses of one figure. Meanwhile, we made a citation in the article (Page 4, Lines 105; Page 25, Lines 564-565).

4) Gene Ontology analyses should be redone with a more appropriate background gene set.

Thank you for the helpful suggestions. All expressed genes (TPM sum of all samples >=1) are listed background for GO enrichment analyses. The results of GO enrichment analysis of the AS gene turned out to be the same. The results of GO enrichment analysis of the SRSF1 peak-containing genes, differential genes, and IP proteins-associated genes have been corrected in the figure (Figure 2A, 7E, and 9E)

Minor points about the text and figures

5) The species (mouse) should be stated earlier in the Introduction.

Thank you for the professional suggestions. Following this advice, the mouse has been stated earlier in the Introduction (Page 3, Line 65).

6) In Fig. 1C (Western blot), the results would be more convincing if quantitation of band intensities normalized to the loading control was added.

Thank you very much for comments and suggestions. Following this advice, ACTB served as a loading control. The value in 16.5 dpc testes were set as 1.0, and the relative values of testes in other developmental periods are indicated. Therefore, we have incorporated the data into the figures (Figure 1C).

7) In Fig 5D, TUNEL signal in the single-channel image is difficult to see; please adjust the contrast.

Thank you for the professional suggestions. Following this advice, the images of the channels have been replaced by enlarged images for better visibility (Figure 5C).

Major comments

1) In Fig 1D, it appears that SRSF1 is expressed most strongly in spermatogonia by immunofluorescence, but this is inconsistent with the sharp rise in expression detected by RT-qPCR at 20 days post partum (dpp) (Fig. 1B), which is when round spermatids are first added; this discrepancy should be explained or addressed.

We appreciate the important comments from the reviewer. In another of our studies, we showed that SRSF1 expression is higher in pachytene spermatocytes and round spermatids (Sun et al., 2023). So, it is normal for the sharp rise in expression detected by RT-qPCR at 20 days post partum (dpp).

Author response image 1.

Dynamic localization of SRSF1 in male mouse germ cells. (Sun et al., 2023)

2) It is important to provide a more comprehensive basic description of the CLIP-seq datasets beyond what is shown in the tracks shown in Fig. 2B. This would allow a better assessment of the data quality and would also provide information about the transcriptome-wide patterns of SRSF1 binding. No information or quality metrics are provided about the libraries, and it is not stated how replicates are handled to maximize the robustness of the analysis. The distribution of peaks across exons, introns, and other genomic elements should also be shown.

Thank you very much for the helpful comments and suggestions. In fact, detailed analyses of CLIP-seq have already been presented in another paper (Sun et al., 2023), and we have not provided it in order to avoid multiple uses of one figure. Meanwhile, we made a citation in the article (Page 4, Lines 105; Page 25, Lines 564-565). In addition, the distribution of peaks in exons, introns, and other genomic elements is shown in Figure 2B.

3) The claim that SRSF1 is required for "homing and self-renewal" of SSCs is made in multiple places in the manuscript. However, neither homing nor self-renewal is ever directly tested. A single image is shown in Fig. 5E of a spermatogonium at 5dpp that does not appropriately sit on the basal membrane, potentially indicating a homing defect, but this is not quantified or followed up. There is good evidence for depletion of spermatogonia starting at 7 dpp, but no further explanation of how homing and/or self-renewal fit into the phenotype.

Thank you very much for the helpful comments and suggestions. Following this advice, immunohistochemical staining for FOXO1 was performed on 5 dpp mouse testis sections (Figure 6C). Further, germ cell statistics of FOXO1 expression in the nucleus showed a reduced number of prospermatogonia in cKO mice (Figure 6D). And germ cells in which FOXO1 is expressed in the nucleus similarly undergo abnormal homing (Figure 6E). Thus, all the above data indicated that SRSF1 has an essential role in the homing of precursor SSCs. we have incorporated the data into the Figure 6C-E. These have been incorporated into the text, given descriptions, and highlighted (Page 9, Lines 191-201; Page 20, Lines 387-389). Meanwhile, "homing and self-renewal" of SSCs have corrected the text in this revised version (Line 1-2, 39, 44, 49, 54-55, 68, 70, 72-73, 77, 84, 93-95, 191, 201, 240, 384-387, 397, 417-422, and 433).

4) In Fig. 6A (lines 258-260) very few genes downregulated in the cKO are bound by SRSF1 and undergo abnormal splicing. The small handful that falls into this overlap could simply be noise. A much larger fraction of differentially spliced genes are CLIP-seq targets (~33%), which is potentially interesting, but this set of genes is not explored.

Thank you for the helpful comments. Following this advice, this was specifically indicated by the fact that 39 stabilizing genes were bound by SRSF1 and underwent abnormal AS. In our study, Tial1/Tiar is one of 39 stabilizing genes that are bound by SRSF1 and undergo abnormal AS. Therefore, we fully agree with the reviewers' comments. These have been added in this revised version (Page 14, Lines 279-280; Page 15, Lines 296-300).

5) The background gene set for Gene Ontology analyses is not specified. If these were done with the whole transcriptome as background, one would expect enrichment of spermatogenesis genes simply because they are expressed in testes. The more appropriate set of genes to use as background in these analyses is the total set of genes that are expressed in testis.

We thank the reviewer for the professional comments and suggestions. All expressed genes (TPM sum of all samples >=1) are listed background for GO enrichment analyses. The results of GO enrichment analysis of the AS gene turned out to be the same. The results of GO enrichment analysis of the SRSF1 peak-containing genes, differential genes, and IP proteins-associated genes have been corrected in the figure (Figure 2A, 7E, and 9E)

6) In general, the model is over-claimed: aside from interactions by IP-MS, little is demonstrated in this study about how SRSF1 affects alternative splicing in spermatogenesis, or how alternative splicing of TIAL1 specifically would result in the phenotype shown. It is not clear why Tial1/Tiar is selected as a candidate mediator of SRSF1 function from among the nine genes that are downregulated in the cKO, are bound by SRSF1, and undergo abnormal splicing. Although TIAL1 levels are reduced in cKO testes by Western blot (Fig. 7J), this could be due just be due to a depletion of germ cells from whole testis. The reported splicing difference for Tial1 seems very subtle and the ratio of isoforms does not look different in the Western blot image.

Thank you very much for the helpful comments and suggestions. In our study, Tial1/Tiar is one of 39 stabilizing genes that are bound by SRSF1 and undergo abnormal AS. However, Western blotting showed that expression levels of TIAL1/TIAR isoform X2 were significantly suppressed (Figure 8J). So, the data indicate that SRSF1 is required for TIAL1/TIAR expression and splicing.

Sun, L., Chen, J., Ye, R., Lv, Z., Chen, X., Xie, X., Li, Y., Wang, C., Lv, P., Yan, L., et al. (2023). SRSF1 is crucial for male meiosis through alternative splicing during homologous pairing and synapsis in mice. Sci Bull 68, 1100-1104. 10.1016/j.scib.2023.04.030.

Author Response

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

Reviewer #1 (Public Review):

This manuscript represents an elegant bioinformatics approach to addressing causal pathways in vascular and liver tissue related to atherosclerosis/coronary artery disease, including those shared by humans and mice and those that are specific to only one of these species. The authors constructed co-expression networks using bulk transcriptome data from human (aorta, coronary) and mouse (aorta) vascular and liver tissue. They mapped human CAD GWAS data onto these modules, mapped GWAS SNPs to putatively causal genes, identified pathways and modules enriched in CAD GWAS hits, assessed those shared between vascular and liver tissues and between humans and mice, determined key driver genes in CAD-associated supersets, and used mouse single-cell transcriptome data to infer the roles of specific vascular and liver cell types. The overall approach used by the authors is rigorous and provides new insights into potentially causal pathways in vascular tissue and liver involved in atherosclerosis/CAD that are shared between humans and mice as well as those that are species-specific. This approach could be applied to a variety of other common complex conditions.

The conclusions are largely supported by the analyses. Some specific comments:

1) It appears that GWAS SNPs were mapped to genes solely through the use of eQTLs. Current methods involve a number of other complementary approaches to map GWAS SNPs to effector genes/transcripts and there is the thought that eQTLs may not necessarily be the best way to map causal genes.

We agree with the reviewer that multiple approaches can be used to map GWAS SNPs to genes, and eQTLs is only one way to do so. We focused on eQTLs mainly because we aim to address tissue-specificity of eQTLs and the relative higher abundance of eQTLs compared to other tissue-specific functional genomics data, such as pQTLs and epiQTLs. We now acknowledge this limitation in the discussion section in our revised manuscript and point to future studies utilizing other approaches to map GWAS signals to downstream effectors.

2) Given the critical causal role of circulating apoB lipoproteins in atherosclerosis in both mice and humans and the central role of the liver in regulating their levels, perhaps the authors could use the 'metabolism of lipids and lipoproteins' network in Fig 3B as a kind of 'positive control' to illustrate the overlap between mice and humans and the driver genes for this network.

We appreciate the reviewer’s excellent suggestion and now elaborate the findings in Fig 3B as a positive control in the results section.

3) Is it possible to infer the directionality of effect of key driver genes and pathways from these analyses? For example, ACADM was found to be a KD gene for a human-specific liver CAD superset pathway involving BCAA degradation. Are the authors able to determine or predict the effect of genetically increased expression of ACADM on BCAA metabolism and on CAD? Or the directionality of the effect of the hepatic KD gene OIT3 on hepatic and plasma lipids and atherosclerosis.

The Bayesian networks only have information on which genes likely regulate the others but not the up or down-regulation direction, and the network key driver analysis only considers the enrichment of GWAS candidate genes in the neighborhood of each key driver. Therefore, it is not possible to directly infer whether increasing or decreasing a key driver will lead to up or downregulation of the downstream pathways based on our current analysis. We could, however, examine correlations of key driver genes with downstream genes, or disease traits in relevant datasets. For instance, we checked the mouse atherosclerosis HMDP datasets for the correlations between select key drivers and clinical traits and found various key drivers shared and species-specific in aorta and liver significantly correlate with aortic lesion area and other traits of interest such as LDL levels, and inflammatory cytokines. We have added these new findings into the results section and supplemental tables.

4) While likely beyond the scope of this manuscript, the substantial amount of publicly available plasma proteomic and metabolomic data could be incorporated into this multiomic bioinformatic analysis. Many of the pathways involve secreted proteins or metabolites that would further inform the biology and the understanding of how these pathways relate to atherosclerosis.

We appreciate the reviewer’s valuable suggestion. Here we focused on liver and aorta gene regulatory networks to understand the tissue-specific mechanisms at the gene level. Indeed, plasma proteomic and metabolomic data could be further incorporated in future studies to understand the pathways captured in the circulation that can capture cross-tissue interactions mediated by secreted proteins and metabolites from different tissues. We have addressed this as a future direction in the discussion section.

The findings here will motivate the community of atherosclerosis investigators to pursue additional potential causal genes and pathways through computational and experimental approaches. It will also influence the approach around the use of the mouse model to test specific pathways and therapeutic approaches in atherosclerosis. In addition, the computational approach is robust and could (and likely will) be applied to a variety of other common complex conditions.

Reviewer #2 (Public Review):

Summary:

Mouse models are widely used to determine key molecular mechanisms of atherosclerosis, the underlying pathology that leads to coronary artery disease. The authors use various systems biology approaches, namely co-expression and Bayesian Network analysis, as well as key driver analysis, to identify co-regulated genes and pathways involved in human and mouse atherosclerosis in artery and liver tissues. They identify species-specific and tissue-specific pathways enriched for the genetic association signals obtained in genome-wide association studies of human and mouse cohorts.

Strengths:

The manuscript is well executed with appropriate analysis methods. It also provides a compelling series of results regarding mouse and human atherosclerosis.

Weaknesses:

The manuscript has several weaknesses that should be acknowledged in the discussion. First, there are numerous models of mouse atherosclerosis; however, the HMDP atherosclerosis study uses only one model of mouse atherosclerosis, namely hyperlipidemic mice, due to the transgenic expression of human apolipoprotein ELeiden (APOE-Leiden) and human cholesteryl ester transfer protein (CETP). Therefore, when drawing general conclusions about mouse pathways not being identified in humans, caution is warranted. Other models of mouse atherosclerosis may be able to capture different aspects of human atherosclerosis.

We appreciate the reviewer’s valuable insight! Indeed, the specific HMDP atherosclerosis model may miss important mouse pathways that could have overlapped with the human pathways. We have added this important point to the limitations section under the discussion to caution the interpretation of the human-specific pathways, as they could be present in mice but are missed by the specific HMDP atherosclerosis dataset used.

Second, the mouse aorta tissue is atherosclerotic, whereas the atherosclerosis status of the GTEX aorta tissues is not known. Therefore, it is possible that some of the human or mouse-specific gene modules/pathways may be due to the difference in the disease status of the tissues from which the gene expression is obtained.

We agree with the reviewer that GTEx vascular tissues have unclear atherosclerosis status. However, in addition to GTEx, we also included the human STARNET dataset which contains vascular tissues from human patients with CAD. Therefore, we believe the comparability of the human and mouse vascular tissue datasets is reasonable.

Third, it is unclear how the sex of the mice (all female in the HMDP atherosclerosis study and all male in the baseline HMDP study) and the sex of the human donors affected the results. Did the authors regress out the influence of sex on gene expression in the human data before performing the co-expression and preservation studies? If not, this should be acknowledged.

We acknowledge that the effect of sex in the mouse and human datasets were not regressed out in our analysis. We have added this under the limitations section.

Fourth, some of the results are unexpected, and these should be discussed. For example, the authors identify that the leukocyte transendothelial migration pathway and PDGF signaling pathway are human-specific in their vascular tissue analysis. These pathways have been extensively described in mouse studies. Why do the authors think they identified these pathways as human-specific? Similarly, the authors identified gluconeogenesis and branched-chain amino acid catabolism as human and mouseshared modules in the vascular tissue. Is there evidence of the involvement of these pathways in atherosclerosis in vascular cells?

We agree with the reviewer that these unexpected findings warrant further discussion. As pointed out by this reviewer, it is possible that the mouse HMDP atherosclerosis dataset cannot fully represent all mouse atherosclerosis biology and therefore missed the leukocyte migration and PDGF pathways that were identified in the human datasets. Regarding the surprising findings of pathways such as BCAA catabolism in vascular tissues, we acknowledge that future studies will need to further investigate such pathway predictions but also highlight that these pathway terms have many shared genes with more commonly known pathways such as the TCA cycle, which may indicate the involvement of energy metabolism in vascular tissues in CAD development. We have added these points to the discussion section under limitations and concluding remarks.

Overall, acknowledging these drawbacks and adding points to the discussion will strengthen the manuscript.

Reviewer #1 (Recommendations For The Authors):

1) Could the authors comment on why MEGENA produces so many more co-expression modules per tissue than WCGNA?

As described in the methods section, MEGENA uses a multi-scale clustering structure to generate network modules at different scales, with each scale representing a different compactness level of the modules. At lower compactness scales larger modules are generated; at higher compactness scales, smaller modules are generated. By using all modules obtained from different scales, the total number of modules is much larger than WGCNA which only generates a network at one scale.

2) Much of the results section involves repeating in the text lists of pathways, modules, and genes that are also listed in Figures 2 and 3. The text in this part of the results could be used more productively to focus on illustrative examples or potential new biology.

We have revised the results section to reduce repeating long lists of pathways, modules, and genes as suggested.

Reviewer #2 (Recommendations For The Authors):

In addition to the weaknesses I mentioned in the public review comments, there are a few minor issues that I outline below:

1) The authors should introduce atherosclerosis as the underlying cause of CAD in the Introduction. In fact, I believe there are many places in the manuscript where the authors mean atherosclerosis instead of coronary artery disease, especially when presenting and discussing mouse results since the HMDP study did not examine the coronary arteries of mice. I believe the authors should make the appropriate changes throughout the manuscript.

We have made the changes as suggested.

2) The authors state in the introduction, "For example, mice tend to develop atherosclerotic lesions in the aorta and carotids, while humans often develop lesions in coronary arteries (Ma et al., 2012)." This is not entirely correct, so this sentence should be revised. Several models of mice show coronary artery atherosclerosis development, but most researchers study lesions in larger aortas. Further, humans develop lesions throughout the arterial tree, but perhaps what the authors meant was the most consequential plaque development is in the coronary arteries. Please rephrase.

We have rephrased the statement as suggested.

3) Last line of page 5 should read "...which will drive modules and pathways that are more likely..." not "derive"

Typo corrected.

Author Response

We appreciate the editor's and reviewers' time to review our manuscript. We will work on the suggestions and have provided an initial assessment of what we can do for our revised submission.

Reviewer #1 (Public Review):

Summary:

This study aimed to investigate the effects of optically stimulating the A13 region in healthy mice and a unilateral 6-OHDA mouse model of Parkinson's disease (PD). The primary objectives were to assess changes in locomotion, motor behaviors, and the neural connectome. For this, the authors examined the dopaminergic loss induced by 6-OHDA lesioning. They found a significant loss of tyrosine hydroxylase (TH+) neurons in the substantia nigra pars compacta (SNc) while the dopaminergic cells in the A13 region were largely preserved. Then, they optically stimulated the A13 region using a viral vector to deliver the channelrhodopsine (CamKII promoter). In both sham and PD model mice, optogenetic stimulation of the A13 region induced pro-locomotor effects, including increased locomotion, more locomotion bouts, longer durations of locomotion, and higher movement speeds. Additionally, PD model mice exhibited increased ipsilesional turning during A13 region photoactivation. Lastly, the authors used whole-brain imaging to explore changes in the A13 region's connectome after 6-OHDA lesions. These alterations involved a complex rewiring of neural circuits, impacting both afferent and efferent projections. In summary, this study unveiled the pro-locomotor effects of A13 region photoactivation in both healthy and PD model mice. The study also indicates the preservation of A13 dopaminergic cells and the anatomical changes in neural circuitry following PD-like lesions that represent the anatomical substrate for a parallel motor pathway.

Strengths:

These findings hold significant relevance for the field of motor control, providing valuable insights into the organization of the motor system in mammals. Additionally, they offer potential avenues for addressing motor deficits in Parkinson's disease (PD). The study fills a crucial knowledge gap, underscoring its importance, and the results bolster its clinical relevance and overall strength.

The authors adeptly set the stage for their research by framing the central questions in the introduction, and they provide thoughtful interpretations of the data in the discussion section. The results section, while straightforward, effectively supports the study's primary conclusion the pro-locomotor effects of A13 region stimulation, both in normal motor control and in the 6-OHDA model of brain damage.

We thank the reviewer for their positive comments.

Weaknesses:

1) Anatomical investigation. I have a major concern regarding the anatomical investigation of plastic changes in the A13 connectome (Figures 4 and 5). While the methodology employed to assess the connectome is technically advanced and powerful, the results lack mechanistic insight at the cell or circuit level into the pro-locomotor effects of A13 region stimulation in both physiological and pathological conditions. This concern is exacerbated by a textual description of results that doesn't pinpoint precise brain areas or subareas but instead references large brain portions like the cortical plate, making it challenging to discern the implications for A13 stimulation. Lastly, the study is generally well-written with a smooth and straightforward style, but the connectome section presents challenges in readability and comprehension. The presentation of results, particularly the correlation matrices and correlation strength, doesn't facilitate biological understanding. It would be beneficial to explore specific pathways responsible for driving the locomotor effects of A13 stimulation, including examining the strength of connections to well-known locomotor-associated regions like the Pedunculopontine nucleus, Cuneiformis nucleus, LPGi, and others in the diencephalon, midbrain, pons, and medulla.

We considered two approaches initially. The first approach was to look at specific projections to the motor regions, focusing on the MLR. The second approach was to utilize a whole-brain analysis that is presented here. Given what we know about the zona incerta, especially its integrative role, we felt that a reasonable starting point was to examine the full connectome. The value of the whole-brain approach is that it provides a high-level overview of the afferents and efferents to the region. The changes in the brain that occur following Parkinson-like lesions, such as those in the nigrostriatal pathway, are known to be complex and can affect neighbouring regions such as the A13. Therefore, we wished to highlight the A13, which we considered a therapeutic target, and examine changes in connectivity that could occur following acute lesions affecting the SNc. We acknowledge that this study does not provide a causal link, but it presents the fundamental background information for subsequent hypothesis-driven, focused, region-specific analysis.

The terms provided were from the Allen Brain Atlas terminology and were presented as abbreviations. We have looked at other ways to present it, including a greater emphasis on raw numbers and highlighting motor-related subareas. We will rewrite the connectomics section to make it more accessible, reflecting the change in the figures.

Additionally, identifying the primary inputs to A13 associated with motor function would enhance the study's clarity and relevance.

This is a great point and could help simplify the whole-brain results. We can present the motor-related inputs and outputs as part of a new figure in the main paper and add accompanying text in the results section. This will help highlight possible therapeutic pathways. We can also enhance our discussion of these motor-related pathways. We will retain the entire dataset and present it in a supplementary table for those who are interested.

The study raises intriguing questions about compensatory mechanisms in Parkinson's disease and a new perspective on the preservation of dopaminergic cells in A13, despite the SNc degeneration, and the plastic changes to input/output matrices. To gain inspiration for a more straightforward reanalysis and discussion of the results, I recommend the authors refer to the paper titled "Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon from the David Kleinfeld laboratory." This could guide the authors in investigating motor pathways across different brain regions.

Thank you for the advice, and as pointed out, Kleinfeld’s group had a nice, focused presentation of their data. For the connectomic piece, we can certainly adopt their reporting style, which, as you point out, may highlight key motor-related regions. There are a few ideas here that we can explore further, as mentioned above.

2) Description of locomotor performance. Figure 3 provides valuable data on the locomotor effects of A13 region photoactivation in both control and 6-OHDA mice. However, a more detailed analysis of the changes in locomotion during stimulation would enhance our understanding of the pro-locomotor effects, especially in the context of 6-OHDA lesions. For example, it would be informative to explore whether the probability of locomotion changes during stimulation in the control and 6-OHDA groups. Investigating reaction time, speed, total distance, and even kinematic aspects during stimulation could reveal how A13 is influencing locomotion, particularly after 6-OHDA lesions. The laboratory of Whelan has a deep knowledge of locomotion and the neural circuits driving it so these features may be instructive to infer insights on the neural circuits driving movement. On the same line, examining features like the frequency or power of stimulation related to walking patterns may help elucidate whether A13 is engaging with the Mesencephalic Locomotor Region (MLR) to drive the pro-locomotor effects. These insights would provide a more comprehensive understanding of the mechanisms underlying A13-mediated locomotor changes in both healthy and pathological conditions.

Thank you for these suggestions. We will revise as suggested. We will provide additional and/or updated data in revised figures and text. We will also move Supplementary Figures S1 and S2, which present additional locomotor data, into the main text to partly address the reviewers' points.

Reviewer #2 (Public Review):

Summary:

The paper by Kim et al. investigates the potential of stimulating the dopaminergic A13 region to promote locomotor restoration in a Parkinson's mouse model. Using wild-type mice, 6-OHDA injection depletes dopaminergic neurons in the substantia nigra pars compacta, without impairing those of the A13 region and the ventral tegmentum area, as previously reported in humans. Moreover, photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region improves bradykinesia and akinetic symptoms after 6-OHDA injection. Whole-brain imaging with retrograde and anterograde tracers reveals that the A13 region undergoes substantial changes in the distribution of its afferents and projections after 6-OHDA injection. The study suggests that if the remodeling of the A13 region connectome does not promote recovery following chronic dopaminergic depletion, photostimulation of the A13 region restores locomotor functions.

Strengths:

Photostimulation of presumably excitatory (CAMKIIa) neurons in the vicinity of the A13 region promotes locomotion and locomotor recovery of wild-type mice 1 month after 6-OHDA injection in the medial forebrain bundle, thus identifying a new potential target for restoring motor functions in Parkinson's disease patients.

Weaknesses:

Electrical stimulation of the medial Zona Incerta, in which the A13 region is located, has been previously reported to promote locomotion (Grossman et al., 1958). Recent mouse studies have shown that if optogenetic or chemogenetic stimulation of GABAergic neurons of the Zona Incerta promotes and restores locomotor functions after 6-OHDA injection (Chen et al., 2023), stimulation of glutamatergic ZI neurons worsens motor symptoms after 6-OHDA (Lie et al., 2022).

Thank you - we will add this reference. It is useful as Grossman did stimulate the zona incerta in the cat and elicit locomotion, suggesting that stimulation of the area in normal mice has external validity. The area targeted by Chen et al. (2023) is in the lateral aspect of central/medial zona incerta, formed by dorsal and ventral zona incerta, which may account for the differing results. Our data were robust for stimulation of the medial aspect of the rostromedial zona incerta. The thigmotactic behaviour that we observed in our work that focused on CamKII neurons has not been observed with chemogenetic, optogenetic activation or with photoinhibition of GABAergic central/medial ZI (Chen et al. 2023).

Although CAMKIIa is a marker of presumably excitatory neurons and can be used as an alternative marker of dopaminergic neurons, behavioral results of this study raise questions about the neuronal population targeted in the vicinity of the A13 region. Moreover, if YFP and CHR2-YFP neurons express dopamine (TH) within the A13 region (Fig. 2), there is also a large population of transduced neurons within and outside of the A13 region that do not, thus suggesting the recruitment of other neuronal cell types that could be GABAergic or glutamatergic.

We found that CamKII transfection of the A13 region was extremely effective in promoting locomotor activity, which was critical for our work in exploring its possible therapeutic potential. We acknowledge that specific viral approaches that target the GABAergic, glutamatergic, and dopaminergic circuits would be very useful. The range of tools to target A13 dopaminergic circuits is more limited than the SNc, for example, because the A13 region lacks DAT, and TH-IRES-Cre approaches, while useful, are less specific than DAT-Cre mouse models. Intersectional approaches targeting multiple transmitters (glutamate & dopamine, for example) may be one solution as we do not expect that a single transmitter-specific pathway would work, as well as broad targeting of the A13 region. Recent work suggests that GABAergic neuron activation may have more general effects on behaviour rather than control of ongoing locomotor parameters. However, this is in contrast to recent work showing a positive valence effect of dopamine A13 activation on motivated food-seeking behavior, which differs from consummatory behavior observed with GABAergic modulation (Ye, Nunez, and Zhang 2023). Chemogenetic inactivation and ablation of dopaminergic A13 revealed that they contribute to grip strength and prehensile movements, uncoupling food-seeking grasping behavior from motivational factors (Garau et al. 2023). Overall, this suggests differing effects of GABA compared to DA and/or glutamatergic cell types, consistent with our effects of stimulating CamKII.

Regarding the analysis of interregional connectivity of the A13 region, there is a lack of specificity (the viral approach did not specifically target the A13 region), the number of mice is low for such correlation analyses (2 sham and 3 6-OHDA mice), and there are no statistics comparing 6-OHDA versus sham (Fig. 4) or contra- versus ipsilesional sides (Fig. 5). Moreover, the data are too processed, and the color matrices (Fig. 4) are too packed in the current format to enable proper visualization of the data. The A13 afferents/efferents analysis is based on normalized relative values; absolute values should also be presented to support the claim about their upregulation or downregulation.

Generally, papers using tissue-clearing imaging approaches have low sample sizes due to technical complexity and challenges. The technical challenges of obtaining these data were substantial in both collection and analysis. There are multiple technical complexities arising from dual injections (A13 and MFB coordinates) and targeting the area correctly. The A13 region is difficult to target as it spans only around 300 µm in the anterior-posterior axis. While clearing the brain takes weeks, and light-sheet imaging also takes time, the time necessary to analyze the tissue using whole-brain quantification is labor intensive, especially with a lack of a standardized analysis pipeline from atlas registrations, signal segmentations, and quantifications. The field is still relatively new, requiring additional time to refine pipelines.

Correlation matrices are often used in analyzing connectivity patterns on a brain-wide scale, as they can identify any observable patterns within a large amount of data. We used correlation matrices to display estimated correlation coefficients between the afferent and efferent proportions from one brain subregion to another across 251 brain regions in total in a pairwise manner (not for hypothesis testing). We provided descriptive statistics (mean and error bars) in Figure 5C and G. As mentioned in comments for Reviewer 1, we will also present data in a revised Figure 5 and/or a new figure that focuses specifically on motor-related pathways to provide information on possible therapeutic pathways. As suggested, absolute values will be shared in a supplemental table.

In the absence of changes in the number of dopaminergic A13 neurons after 6-OHDA injection, results from this correlation analysis are difficult to interpret as they might reflect changes from various impaired brain regions independently of the A13 region.

We acknowledge that models of Parkinson’s disease, particularly those using 6-OHDA, induce plasticity in various regions, which may subsequently affect A13 connectivity. Our aim is to emphasize the residual, intact A13 pathways that could serve as therapeutic targets in future investigations. This emphasis is pertinent in the context of potential clinical applications, as the overall input and output to the region fundamentally dictate the significance of the A13 region in lesioned nigrostriatal models. We agree with the reviewer that the changes certainly can be independent of A13; however, the fact that there was a significant change in the connectome post-6-OHDA injection and striatonigral degeneration is in and of itself important and important to document.

There is no causal link between anatomical and behavioral data, which raises questions about the relevance of the anatomical data.

This point was also addressed earlier in response to a comment from Reviewer 1. Focusing on specific motor pathways is one avenue to explore. However, given that the zona incerta acts as an integrative hub, we believed it is prudent to initially examine both afferent and efferent pathways using a brain-wide approach. For instance, without employing this methodology, the potential significance of cortical interconnectivity to the A13 region might not have been fully appreciated. As mentioned previously, we will place additional emphasis on motor-related regions in our revised paper, thereby enhancing the relevance of the anatomical data presented. With these modifications, we anticipate that our data will underscore specific motor-related targets for future exploration, employing optogenetic targeting to assess necessity and sufficiency.

Overall, the study does not take advantage of genetic tools accessible in the mouse to address the direct or indirect behavioral and anatomical contributions of the A13 region to motor control and recovery after 6-OHDA injection.

We acknowledge that our study has not specifically targeted neurons that express dopaminergic, glutamatergic, or GABAergic properties (refer to earlier comment for more detail). However, like others, we find that targeting one neuronal population often does not result in a pure transmitter phenotype. For instance, evidence suggests co-localization of dopamine neurons with a subpopulation of GABA neurons in the A13/medial zona incerta (Negishi et al. 2020). In the hypothalamus, research by Deisseroth and colleagues (Romanov et al. 2017) indicates the presence of multiple classes of dopamine cells, each containing different ratios of co-localized peptides and/or fast neurotransmitters. Consequently, we believe our work lays the foundation for the investigations suggested by the reviewer. Furthermore, if one considers this work in the context of a preclinical study to determine whether the A13 might be a target in human Parkinson's disease, the existing technology that could be utilized is deep brain stimulation (DBS) or electrical modulation, which would also affect different neuronal populations in a non-specific manner. While optogenetic stimulation therapy is longer term, using CamKII combined with the DJ hybrid AAV could be a translatable strategy for targeting A13 neuronal populations in non-human primates (Watakabe et al. 2015; Watanabe et al. 2020).

Reviewer #3 (Public Review):

Kim, Lognon et al. present an important finding on pro-locomotor effects of optogenetic activation of the A13 region, which they identify as a dopamine-containing area of the medial zona incerta that undergoes profound remodeling in terms of afferent and efferent connectivity after administration of 6-OHDA to the MFB. The authors claim to address a model of PD-related gait dysfunction, a contentious problem that can be difficult to treat with dopaminergic medication or DBS in conventional targets. They make use of an impressive array of technologies to gain insight into the role of A13 remodeling in the 6-OHDA model of PD. The evidence provided is solid and the paper is well written, but there are several general issues that reduce the value of the paper in its current form, and a number of specific, more minor ones. Also, some suggestions, that may improve the paper compared to its recent form, come to mind.

Thank you for the suggestions and careful consideration of our work - it is appreciated.

The most fundamental issue that needs to be addressed is the relation of the structural to the behavioral findings. It would be very interesting to see whether the structural heterogeneity in afferent/effects projections induced by 6-OHDA is related to the degree of symptom severity and motor improvement during A13 stimulation.

As mentioned in comments for Reviewer 1, we will be highlighting motor-related A13 pathways in a revised Figure 5 and/or a new figure. We hope that our work will provide a roadmap for future studies to disentangle divergent or convergent A13 pathways that are involved in different or all PD-related motor symptoms. Because we could not measure behavioural change in the same animals studied with the anatomic study (essentially because the optrode would have significantly disrupted the connectome we are measuring), we cannot directly compare behaviour to structure.

The authors provide extensive interrogation of large-scale changes in the organization of the A13 region afferent and efferent distributions. It remains unclear how many animals were included to produce Fig 4 and 5. Fig S5 suggests that only 3 animals were used, is that correct? Please provide details about the heterogeneity between animals. Please provide a table detailing how many animals were used for which experiment. Were the same animals used for several experiments?

The behavioral set and the anatomical set were necessarily distinct. In the anatomical experiments, we employed both anterograde and retrograde viral approaches to target the afferent and efferent A13 populations with fluorescent proteins. For the behavioral approach, a single ChR2 opsin was utilized to photostimulate the A13 region; hence combining the two populations was not feasible. We were also concerned that the optrode itself would interfere with connectomics. A lower number of animals were used for the whole-brain work due to technical limitations described earlier. We will provide more details regarding numbers we can identify as a table and text.

While the authors provide evidence that photoactivation of the A13 is sufficient in driving locomotion in the OFT, this pro-locomotor effect seems to be independent of 6-OHDA-induced pathophysiology. Only in the pole test do they find that there seems to be a difference between Sham vs 6-OHDA concerning the effects of photoactivation of the A13. Because of these behavioral findings, optogenic activation of A13 may represent a gain of function rather than disease-specific rescue. This needs to be highlighted more explicitly in the title, abstract, and conclusion.

We agree with the reviewer that this aspect needs to be highlighted more. Optogenetic activation of A13 may represent a gain of function in both healthy and 6-OHDA mice, highlighting a parallel descending motor pathway that remains intact. 6-OHDA lesions have multiple effects on motor and cognitive function. This makes a single pathway unlikely to rescue all deficits observed in 6-OHDA models. We can say that the lack of locomotion observed in 6-OHDA models can be reversed by A13 region stimulation. We have discussed some aspects of the gain of function possibility but will augment this in other areas of the paper as well, as suggested.

The authors claim that A13 may be a possible target for DBS to treat gait dysfunction. However, the experimental evidence provided (in particular the lack of disease-specific changes in the OFT) seems insufficient to draw such conclusions. It needs to be highlighted that optogenetic activation does not necessarily have the same effects as DBS (see the recent review from Neumann et al. in Brain: https://pubmed.ncbi.nlm.nih.gov/37450573/). This is important because ZI-DBS so far had very mixed clinical effects. The authors should provide plausible reasons for these discrepancies. Is cell-specificity, which only optogenetic interventions can achieve, necessary? Can new forms of cyclic burst DBS achieve similar specificity (Spix et al, Science 2021)? Please comment.

Thank you for the useful comments - we will update our discussion accordingly.

Our study highlights a parallel motor pathway provided by the A13 region that remains intact in 6-OHDA mice and can be sufficiently driven to rescue the hypolocomotor pathology observed in the OFT and overcome bradykinesia and akinesia. The photoactivation of ipsilesional A13 also has an overall additive effect on ipsiversive circling, representing a gain of function on the intact side that contributes to the magnitude of overall motor asymmetry against the lesioned side. The effects of DBS are rather complex, ranging from micro-, meso-, to macro-scales, involving activation, inhibition, and informational lesioning, and network interactions. This could contribute to the mixed clinical effects observed with ZI-DBS, in addition to differences in targeting and DBS programming among the studies (see review (Ossowska 2019)). Also the DBS studies targeting ZI have never targeted the rostromedial ZI which extends towards the hypothalamus and contains the A13. Furthermore, DBS and electrical stimulation of neural tissue, in general, are always limited by current spread and lower thresholds of activation of axons (e.g., axons of passage), both of which can reduce the specificity of the true therapeutic target. Optogenetic studies have provided mechanistic insights that could be leveraged in overcoming some of the limitations in targeting with conventional DBS approaches. Spix et al. (2021) provided an interesting approach highlighting these advancements. They devised burst stimulation to facilitate population-specific neuromodulation within the external globus pallidus. Moreover, they found a complementary role for optogenetics in exploring the pathway-specific activation of neurons activated by DBS. To ascertain whether A13 DBS may be a viable therapy for PD gait, it will be necessary to perform many more preclinical experiments, and tuning of DBS parameters could be facilitated by optogenetic stimulation in these murine models.

In a recent study, Jeon et al (Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus, 2022, Cell Reports) provided evidence on the topographically graded organization of STN afferents and McElvain et al. (Specific populations of basal ganglia output neurons target distinct brain stem areas while collateralizing throughout the diencephalon, 2021, Neuron) have shown similar topographical resolution for SNr efferents. Can a similar topographical organization of efferents and afferents be derived for the A13/ ZI in total?

The ZI can be subdivided into four subregions in the antero-posterior axis: rostral (ZIr), dorsal (ZId), ventral (ZIv), and caudal (ZIc) regions. The dorsal and ventral ZI is also referred together as central/medial/intermediate ZI. There are topographical gradients in different cell types and connectivity across these subregions (see reviews: (Mitrofanis 2005; Monosov et al. 2022; Ossowska 2019). Recent work by Yang and colleagues (2022) demonstrated a topographical organization among the inputs and outputs of GABAergic (VGAT) populations across four ZI subregions. Given that A13 region encompasses a smaller portion (the medial aspect) of both rostral and medial/central ZI (three of four ZI subregions) and coexpress VGAT, A13 region likely falls under rostral and intermediate medial ZI dataset found in Yang et al. (2022). With our data, we would not be able to capture the breadth of topographical organization shown in Yang et al (2022).

In conclusion, this is an interesting study that can be improved by taking into consideration the points mentioned above.

Reviewer #1 (Recommendations For The Authors):

1) Figure 2 indeed presents valuable information regarding the effects of A13 region photoactivation. To enhance the comprehensiveness of this figure and gain a deeper understanding of the neurons driving the pro-locomotor effect of stimulation, it would be beneficial to include quantifications of various cell types:

• cFos-Positive Cells/TH-Positive Cells: it can help determine the impact of A13 stimulation on dopaminergic neurons and the associated pro-locomotor effect in the healthy condition and especially in the context of Parkinson's disease (PD) modeling.

• cFos-Positive Cells /TH-Negative Cells: Investigating the number of TH-negative cells activated by stimulation is also important, as it may reveal non-dopaminergic neurons that play a role in locomotor responses. Identifying the location and characteristics of these TH-negative cells can provide insights into their functional significance.

Incorporating these quantifications into Figure 2 would enhance the figure's informativeness and provide a more comprehensive view of the neuronal populations involved in the locomotor effects of A13 stimulation.

Agreed - we will add quantification and create graphs to present the data in Figure 2.

2) Refer to Figure 3. In the main text (page 5) when describing the animal with 6-OHDA the wrong panels are indicated. It is indicated in Fgure 2A-E but it should be replaced with 3A-E. Please do that.

Will be done

Reviewer #2 (Recommendations For The Authors):

Abstract

Page 1: Inhibitory or lesion studies will be necessary to support the claim that the global remodeling of afferent and efferent projections of the A13 region highlights the Zona Incerta's role as a crucial hub for the rapid selection of motor function.

We believe that overall, there is quite a bit of evidence that the zona incerta is a hub for afferent/efferents. Mitrofanis (2005) and, more recently, Wang et al. (2020) summarize some of the evidence. Yang (2022) illustrates that the zona incerta shows multiple inputs to GABAergic neurons and outputs to diverse regions. Recent work suggests that the zona incerta contributes to various motor functions such as hunting, exploratory locomotion, and integrating multiple modalities (Zhao et al. 2019; Wang et al. 2019; Monosov et al. 2022; Chometton et al. 2017). We will update our paper to reflect these references.

Introduction

Page 2, paragraph 2: "However, little attention has been placed on the medial zona incerta (mZI), particularly the A13, the only dopamine-containing region of the rostral ZI" Is the A13 region located in the rostral or medial ZI or both?

It should have been written “rostromedial” ZI. The A13 is located in the medial aspect of rostromedial ZI. We will update the introduction.

Page 2, para 3: Li et al (2021) used a mini-endoscope to record the GCaMP6 signal. Masini and Kiehn, 2022 transiently blocked the dopaminergic transmission; they never used 6-OHDA. Please correct through the text.

We will correct this.

Page 2, para 4: the A13 connectome encompasses the cerebral cortex,... MLR. The MLR is a functional region, correct this for the CNF and PPN.

Thank you, we will correct this.

Page 3, the last paragraph of the introduction could be clarified by presenting the behavioral data first, followed by the anatomy.

We will correct this.

Figure 1 is nice and clear, and well summarizes the experimental design.

Thank you.

Figure 2 shows an example of the extent of the ChR2-YFP expression and the position of an optical fiber tip above the dopaminergic A13 region from a mouse. Without any quantification, these images could be included in Figure 1. Despite a very small volume (36.8nL) of AAV, the extent of ChR2-YFP expression is quite large and includes dopaminergic and unidentified neurons within the A13 region but also a large population of unidentified neurons outside of it, thus raising questions about the volume and the types of neurons recruited.

This is an important consideration. As mentioned previously, we will provide more information on viral spread and optrode location. The issue of viral spread is complex and depends on factors including tissue type, serotype, and promotor of the virus. Li et al. (2021), for example, used different virus serotypes and promotors, injecting 150 nL, whereas we used AAV DJ, injecting 36.8nL. AAV-DJ is a hybrid viral type consisting of multiple serotypes. It has a high transduction efficiency, which leads to greater gene delivery than single-serotype AAV viral constructs (Mao et al. 2016). A secondary consideration regarding translation was that AAV-DJ could effectively transduce non-primate neurons (Watanabe et al. 2020). We have addressed the issue of neurons recruited earlier and will provide c-Fos quantification to illustrate the extent of co-localization with TH.

Anatomical reconstruction of the extent of the ChR2-YFP expression and the location of the tip of the optical fiber will be necessary to confirm that ChR2-YFP expression was restricted to the A13 region.

We will provide additional information regarding viral spread, ferrule tip placement, and c-fos cell counts.

Page 5, 1st para: Double-check the references, as not all of them are 6-OHDA injections in the MLF.

Will correct.

Page 5, 1st para, 4th line: Replace ferrule with optical canula or fiber.

Will correct.

Page 5, 1st para, 9th line: Replace Figure 2 with Figure 3.

Will correct.

Page 5, 2nd para: About the refractory decrease in traveled distance by sham-ChR2 mice: is this significant?

It was not significant (Figure S1, 1-way RM ANOVA: F5,25 = 0.486, P = 0.783)). We will update this.

Figure 3 showing behavioral assessments is nice, but the stats are not always clear. In Fig 3A, are each of the off and on boxes 1 minute long? The figure legend states the test lasts 1 min, but isn't it 4 minutes? In Figure 3B-E and 3J-M, what are the differences? Do the stats identify a significant difference only during the stimulation phase? Fig. 3F-I are nice and could have been presented as primary examples prior to data analysis in Fig. 3B-E. Group labels above the graph would help.

Yes, the off-on boxes are 1 minute long. We will correct the error in the legend. Great suggestion for F-I - we will move them ahead of the summary figures.

Fig. 3L-M, what do PreSur, Post, and Ferrule mean? I assume that Ferrule refers to mice tested with the optical fiber without stimulation, whereas Stim. refers to the stimulation. It would be helpful to standardize the format of stats in Fig. 3B-E and 3-J-M. What are time points a, b, and c referring to?

We will do this.

Figure S2A: the higher variability in 6-OHDA-YFP mice in comparison to 6-OHDA-ChR2 mice prior to stimulation suggests that 6-OHDA-YFP mice were less impaired. Why use boxplots only for these data? Would a pairwise comparison be more appropriate?

Data did not follow a normal distribution and thus, were plotted as box and whiskers with the horizontal line through the box indicating the group median, interquartile range indicated by the limits of the box, and group minimum and maximum indicated by the whiskers. And indeed, a non-parametric equivalent of paired t-test (Wilcoxon signed-rank test) was used.

Fig. S2B: add the statistical marker.

Will do

Page 7, para 1, line 8: to add "in comparison to 6-OHDA-YFP and YFP mice" to during photostimulation... (Figure 3E).

Will do

Page 7, para 3, line 5: about larger improvement, replace "sham ChR2" with "6-OHDA."

Will do

Page 8, para 1, line 4: Perier et al., 2000 reported that 6-OHDA injection increased the firing frequency of the ZI over a month.

We will add that time frame. Agreed, it is shorter than the behavioral work, which was started 3 weeks after 6-OHDA injection.

Page 8, para 2, line 1: Since the results were expected, add some references.

Will do

Page 8, para 3, line 4. Double-check the reference.

Will correct and update

Page 8: About large-scale changes in the A13 region, the relevance of correlation matrices is difficult to grasp. Analysis of local connectivity would have been more informative in the context of GABAergic and glutamatergic neurons of the ZI in the vicinity of the A13 region.

We will explore alternative methods to present the data.

Page 8, para 3, line: given Fig. 2, there is concern about the claim that only the A13 region was targeted. The time of the analysis after 6-OHDA should be mentioned. Some sections of the paragraph could be moved to methods.

As mentioned earlier, we will provide additional information regarding viral spread, ferrule tip placement, and c-fos cell counts. We will mention analysis time after 6-OHDA and update Figure 1a to include this.

Fig. 4: The color code helps the reader visualize distribution differences. However, statistical analyses comparing 6-OHDA versus sham should be included. Quantification per region would greatly help readers visualize the data and support the conclusion. The relationship between the type of correlation (positive or negative) and absolute change (increase or decrease) is unknown in the current format, which limits the interpretation of the data. Moreover, examples of raw images of axons and cells should be presented for several brain regions. The experimental design with a timeline, as in Fig. 1, would be helpful. The legend for Fig. 4 is a bit long. Some sections are very descriptive, whereas others are more interpretive.

We will explore alternative methods of presenting the data, as suggested in a previous comment. Should we retain the correlation matrix, we will incorporate the reviewer’s suggestions.

Page 10, para 1, line 1: add "afferent" to "changes in -afferent and- projection patterns."

Will do

Page 10, para 1, line 9: remove the 2nd "compared to sham" in the sentence.

Will do

10, para 1, line 10: remove "coordinated" in "several regions showed a coordinated reduction in afferent density." We cannot say anything about the timing of events, as there is only info at 1 month.

Will do

Page 10, para 2: the section should be written in the past tense.

Will do

Page 13, para 2, the last sentence is overstated. Please remove "cells" and refer to the A13 region instead.

Will do

About differential remodelling of the A13 region connectome: Figure 5C and 5G: The proportion of total afferents ipsi- and contralateral to 6-OHDA injection argues that the A13 region primarily receives inputs from the cortical plate and the striatum. Unfortunately, there are no statistics.

Due to the small sample size, we provided descriptive statistics (mean and error bars) in Figure 5C and G. As mentioned in comments for Reviewers 1 and 2, we will revise Figure 5 to present data focusing on motor-related pathways to provide clarity. In addition, absolute values will be shared in a supplemental table.

Figure 5 D and 5H: Changes in the proportion of total afferents/projections are relatively modest (less than 10% of the whole population for the highest changes). There is no standard deviation for these data and no statistics. Do they reflect real changes or variability from the injection site?

The changes are relatively modest (less than 10%) since a small brain region usually provides a very small proportion of total input (McElvain et al. 2021; Yang et al. 2022). The changes in the proportions reflect real differences between average proportions observed in sham and 6-OHDA mice. The variability in the total labeling of neurons and fibers was minimized by normalizing individual regional counts against total counts found in each individual animal.

Fig 5F and H: The example in F shows a huge decrease in the striatum, but H indicates only a 2% change, which makes the example not very representative. Absolute values would be helpful.

While a 2% change may seem small, it represents a relatively large change in the A13 efferent connectome. To provide further clarity, we will provide absolute values as suggested in our new supplemental table.

Figure 6 is inaccurate and unnecessary.

Agree - it is too simplistic. We will remove it and replace it with one outlined in comments to Reviewer 1.

Discussion

Although interesting, the discussion is too long.

We will make it more concise in the revised paper.

Page 12: para 2. If the A13 region has a pro-locomotor effect and has therapeutical potential; the claim about its plasticity relies on Fig. 4 and 5, which have a limited scope in the current analysis and presentation (see comments above).

We will revise the paper per the comments above and then update this accordingly.

Methods

Page 17, para 1: include the stereotaxic coordinates of the optical cannula above the A13 region.

We will include this information.

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