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Reply to the reviewers

We thank all the reviewers for their comments on our manuscript. We have attempted to address all the points raised by the reviewers and are happy to note that the manuscript is significantly strengthened with the additional experiments that we have performed and from significant restructuring of the manuscript.

Reviewer #1

Major Comments

1. The choice of cells looks confusing. Drosophila are indeed widely used in research of neurodegeneration mechanisms, since they well reflect the behavioral characteristics of a wide range of brain diseases, but why authors used insect immune cells to study the effect of mHTT on cellular processes? Huntington's disease has a well-established site of origin, in the spiny neurons of the striatum, and they certainly have a different protein context than in insect cells. __Author's response: __We thank the reviewer for this comment. Patients with Huntington's disorder display a variety of symptoms affecting peripheral, non-neuronal cells, including alterations in the function of immune cells. Hemocytes isolated from Drosophila expressing pathogenic forms of Huntingtin also display altered immune responses. Through our manuscript we explore the effect of Huntingtin aggregates on cellular functions of hemocytes. Additionally, we have now included data showing that we are able to observe similar phenotypes in mammalian cells such as neuronal SHSY5Y and HEK293T (Supp. Fig. 3). This is indicative of similar effects being exerted by Huntingtin aggregates across cell types and organisms. Finally, we demonstrate that we are able to rescue neurodegeneration in the fly eye upon overexpression of either Hip1 or components of the Arp2/3 complex (Fig. 4F), further solidifying our results that Huntingtin aggregates alter CME in an actin-dependent manner and that this largely is responsible for the toxicity. This validates our observations that effects on CME appear to be independent of cell type and that non-neuronal cells such as hemocytes can also be used to study the effects of pathogenic aggregates.

The interrelationship between mutant huntingtin and actin cytoskeleton and clathrin-mediated endocytosis that are convincingly demonstrated in other earlier studies in the m/s are described in rather morphological level and there is no description of molecular interactions of proteins belonging to three systems considered, Htt (control vs mutant), actin cytoskeleton and CME. Lack of these data renders the morphological observations unsupported

__Author's response: __Previous data shown in Hosp et al, 2017 indicates that a large number of proteins involved in both actin remodelling and clathrin mediated endocytosis are sequestered within Huntingtin aggregates. While the mechanism of sequestration remains unknown, is has also been observed that loss of Huntingtin results in altered organization of the actin cytoskeleton. We have now added points discussing this in the results section.

Three last figures of total eight demonstrate the effect of proteins, responsible for the initiation of certain neurodegenerative pathologies, on the activity of clathrin-mediated endocytosis, and on the properties of actin cytoskeletal system, however neither in the abstract nor in the introduction there is no any word about these proteins; in the discussion only a few words are devoted to one of these proteins TDP-43. When starting the article, did the authors plan to enter this data into the manuscript?

__Author's response: __We have now amended this by revising the abstract and the text.

It is important to work on the style of the manuscript, the article is difficult to read, it is a collection of data that does not seem related to each other.

__Author's response: __We have reorganized the manuscript and have improved on the flow to make it easier for the reader. We apologize for the rather tedious and confusing flow in the previous draft.

Reviewer #2

This manuscript endeavours to explore the link between mutant Huntingtin, clathrin-mediated membrane transport and the actin cytoskeleton: both its dynamics and overall mechanics. As I read it, it carries the interesting idea that pathogenic protein aggregates alter actin cytoskeletal dynamics by sequestering Arp2/3 nucleator. This has two consequences in the authors' experiments: disruption of clathrin-coated vesicle movement and an increase in cellular stiffness. An interesting question is whether these two effects are related: Is the disruption of vesicular movement due to the change in cytoplasmic stiffness? Or could they be features that both reflect the underlying change in actin dynamics. This may be hard to tease apart and beyond the purview of this manuscript.

I have some suggestions that could strengthen the MS.

Major Comments

1. Further characterizing Arp2/3 sequestration. The notion seems to be that actin nucleators would be sequestered (and inactivated) by mutant protein aggregates, as supported by co-localization studies. In addition, could the authors:

2. a) Test if the dynamics of Arp2/3 are altered, comparing e.g. Arp3-GFP FRAP in the aggregates vs that elsewhere. Author's response: We indeed attempted the FRAP experiment. However due to some technical difficulties we were not convinced by the extent of FRAP in the transgenic fly line. It appeared as an artifact and we were not comfortable including the data in the manuscript. We have instead provided example files for the reviewer to examine.

3. b) Test more directly if actin nucleation is altered in cells that have pathogenic mutant aggregates. This could be done by barbed-end labelling (e.g. measuring incorporation of labelled actin in live cells that are lightly permeabilized with saponin). __Author's response: __We have performed barbed-end labeling for HTT Q15 and HTT Q138 expressing cells. Images and quantification have now been added to the revised manuscript as Figures 2H and 2I. While this was a challenging experiment, it was deeply satisfying to observe such dramatic changes indicating a change in the state of the actin cytoskeleton.

Does manipulating actin nucleation alter cellular mechanics as it does for clathrin-coated vesicle transport? For example, does inhibition of Arp2/3 (e.g. with CK666) increase cellular stiffness and would stiffness be amelioriated in mutant cells if Arp3 is overexpressed?

__Author's response: __We have used LatA to look at whether alteration in the actin cytoskeleton affects cellular stiffness. We found that disruption of the actin cytoskeleton leads to a decrease in cellular stiffness in WT as well as in HTT Q138 expressing cells (shown in Figure 5 and discussed in the results section). We have also now performed AFM on CK666 treated cells and showed that treatment of CK666 leads to a decrease in cellular stiffness similar to LatA treated cells. This further strengthens our hypothesis that a 'Goldilocks' state of actin remodeling and consequently cellular stiffness is required for CME to proceed. We have not performed AFM on cells overexpressing Arp2/3 in HTT Q138 background. However, we believe that it will rescue cellular stiffness as overexpression of Arp2/3 rescues filopodia formation in HTT Q138 expressing cells (Figure 4E) as well as neurodegeneration. AFM data obtained from CK666 treated cells is now added in Supplementary figure 8.

Although it may be difficult to determine if the defect in vesicle transport is due to the change in rheology, I wonder if the authors could reinforce their analysis by showing the overall relationship between the two features. It would be interesting if they could plot CCV velocity against elasticity for all the various conditions that they have tested. Would this cumulative analysis be informative?

__Author's response: __This data is already present across the manuscript as part of different figures. We are not sure whether we can reuse the same data to put it as part of a different figure which plots the relationship between elasticity and CCS velocity. We would be grateful for advice on whether this is allowed and how to mention that the data is also part of different figures.

Focus of the MS. I think that the MS is a little longer and more discursive than it needs to be. I rather struggled to find the focus of the story (which could well be me). There is a deal of repetition that could be profitably cut (the reader may actually find it easier to follow). As well, some anticipation and summaries could be shortened. The final paragraph of the introduction largely summarizes the paper; it could be shortened quite considerably, so that the reader can get directly into the Results themselves. Similarly, the final paragraph of the results is a summary which could work better elsewhere - perhaps, e.g. at the beginning of the discussion.

__Author's response: __We have now trimmed and rearranged the text in the manuscript. We have reorganized the manuscript and have improved on the flow to make it easier for the reader. We apologize for the rather tedious and confusing flow in the previous draft. We are open to further suggestions to improve the writing style.

Specific points

i) Fig 3E. The changes in F-actin flow revealed by PIV are quite dramatic. How reproducible are these changes. (The data presented were from single cells?) __Author's response: __ Changes in F- actin flow obtained from PIV analysis (now figure 2J, 7E in MS) were performed on atleast 5 cells of each type, and the results were observed to be consistent across all. The representative figure is a true representative of the data observed.

1. ii) If TPD43, does it also affect Arp2/3? Author's response: __We thank the reviewer for this comment. Unfortunately, __we could not perform this experiment due to the unavailability of a fluorescently tagged TDP43 fly line which which would enable us to visualize whether Arp3 was sequestered within the aggregates.

Minor points:

1. a) Fig 5a, b - why change the order on the x-axis? __Author's response: __We have fixed this now. We have removed figure 5b, since, in the revised MS we are only talking about stiffness instead of viscoelastic properties of the cells.

Reviewer #2

Overall, I think that the significance of the MS lies in its evidence that sequestration of actin nucleators may be a key effect of mutant protein aggregation, with implications for cellular function. This would provide a useful conceptual framework to understand the cell biological consquences of creating pathogenic protein aggregates.

__Reviewer #3 __

Summary

In the paper, the authors showed that huntingtin aggregates, which play a critical role in initiating neurodegenerative diseases, impair clathrin-mediated endocytosis (CME). Using live cell imaging and AFM, the authors demonstrated that CME is affected by the alteration in actin cytoskeletal organization and cellular viscosity. Further, the authors concluded that there was a strong link between dynamic actin organization and functional CME in the context of neurodegeneration. While the data is interesting and novel, the study in its current form needs major revision before it is accepted.

Major comments:

1) Figure 2: The authors should show the compromised actin cytoskeleton structure after Lat A and cytoD treatment to back up the findings.

__Author's response: __We have included the representative micrographs of compromised cytoskeleton in terms of filopodia formation upon treatment of LatA and CytoD in Supplementary figure 3E.

2) Figure 2g and 2h: Quantification data of filopodia must be supported with representative images.

__Author's response: __Figure number has been changed to 2D and 2E. Representative image for the quantification of filopodia has been now included in supplementary figure 3D.

3) RNAi studies must be performed using control siRNA to check off-target effects.

__Author's response: __Luc VAL10 was used as a control for all the RNAi experiments. However, data for RNAi is not shown as the phenotype for Luc VAL10 was comparable to WT. We have included Luc VAL10 as a control for Profilin RNAi in the FRAP experiment (Supplementary figure 4C).

4) The result section needs to be reorganized to maintain flow. In the current format, the results of a similar set of experiments are spread across different figures, making it a bit difficult to understand.

__Author's response: __We apologize for the inconvenience. This issue has been addressed now.

5) Figure 3d: The expression level and spatial distribution of HTTQ138 transfection were not convincing compared to the httQ15 expression level and the distribution.

Author's response Figure 3D (Figure 3A in this MS) shows the data obtained from hemocytes isolated from third instar larva of the same age. These are not transfected cells and are obtained from Drosophila larvae using the same Gal4 driver, Cg-Gal4. Thus, the level of expression will be same. However, the distribution may show a change due to the aggregating nature of HTT Q138, while HTT Q15 is non-aggregating and therefore remains diffused.

6) Suppl Fig 2a data must be supported using images showing myosin VI distribution in wild-type vs. HTTQ138 transfected cells.

__Author's response: __This data (Supplementary figure 4D in present MS) has been obtained from genetic knockdown of myosin VI. The aim of the experiment was to show that we see similar effects on CCSs movement as we see upon disruption of the actin cytoskeleton.

7) Suppl movie videos are not labeled correctly in the source. It is not possible to locate them and know which videos are referred to in the manuscript.

__Author's response: __We apologize for the inconvenience. This issue has been fixed now.

8) Page 8: How do HTT aggregates sequester the actin-binding proteins? An explanation should be provided in the result section.

__Author's response: __Previous data shown in Hosp et al, 2017 indicates that a large number of proteins involved in both actin remodelling and clathrin mediated endocytosis are sequestered within Huntingtin aggregates. While the mechanism of sequestration remains unknown, the types of proteins involved in actin remodelling are diverse and do not represent specific types or classes. We have now added points discussing this in the results section.

9) Page 10: The authors concluded that "increasing the availability of proteins involved in actin reorganization is capable of restoring CME even in the presence of pathogenic aggregates." Since several actin-associated proteins are involved in actin reorganization, which types/classes of proteins are involved in CME restoration? The authors should expand it in the discussion.

__Author's response: __As we have only investigated the roles of Hip1 and the Arp2/3 complex, we are confident of only reporting their roles in the context of this manuscript. However, previous data shown in Hosp et al, 2017 indicates that a large number of proteins involved in both actin remodelling and clathrin mediated endocytosis are sequestered within Huntingtin aggregates. While the mechanism of sequestration remains unknown, the types of proteins involved in actin remodelling are diverse and do not represent specific types or classes. Therefore this indicates that modulation of actin, through the sequestration of proteins involved in this process is affected in the presence of Huntingtin aggregates. We have added points detailing this in the results and discussion sections.

10) The schematic of the proposed model depicting critical steps by which pathogenic proteins inhibit CME is required. It will help readers to understand the molecular mechanism easily.

Author's response: We have now included a model in the manuscript (Fig. 8B).

Minor:

1) Figure panel referencing in the text needs to be more consistent, for example, fig 3e is referred to before fig 3d., and fig 2 panels are referred to before fig. 3 panels.

__Author's response: __We have reordered the figures and maintained a consistent order throughout.

2) The authors should use similar phrasing throughout the manuscript to avoid confusion. For instance, either use 'HTTQ138' or 'htt Q138'.

__Author's response: __We apologize for this. We have now maintained uniform nomenclature through the text.

3) Page 10: AFM indentation experimental part and its discussion in the result section is unnecessary. Shift it to the 'Materials and Method' section.

__Author's response: __We have now trimmed this portion and we are now only showing elasticity data and not viscoelasticity.

4) This statement looks a bit exaggerated. There is not sufficient evidence to support the statement- "It can be said that the cells in general behave like a soft glass. The presence of aggregates lowers the effective temperature pushing it nearer to the glass transition, affecting transport."

__Author's response: __We have now removed all figures resulting from an analysis that assumes glassy behaviour. Instead, we have now provided a more conventional and well-established analysis to obtain Young's modulus of cells exhibiting different transport properties.

5) Page 12: What is the basis for selecting proteins Aβ-42, FUSR521C, αSynA30P, αSynA53T, and TDP-43 over other proteins? An explanatory sentence must be added to support the selection.

__Author's response: __We have modified the text to clarify this point.

As a student majoring in communication, I am very interested in recommendation algorithms. Because we will find that many times Tik Tok or other software will push us things we like to watch or what we have searched for will always push us relevant content. Sometimes after browsing some clothing, you will even find discounts on the same items in other software. I think this is a good marketing tool but it may have some drawbacks. I am looking forward to studying chapter 12.

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Reply to the reviewers

Reply to Reviewers

We are grateful to the three reviewers for their careful and constructive critiques of our preprint. We will address all of their comments and suggestions, which help to make our paper more precise and understandable. In our replies, we use 'Patterson, eLife (2021)' as shorthand for Patterson, Basu, Rees & Nurse, eLife 2021:10.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Novák and Tyson present a model-based analysis of published data that had claimed to demonstrate bistable activation of CDK at the G2/M transition in fission yeast. They point out that the published data does not distinguish between ultra-sensitive (switch-like, but reversible) and bistable (switch-like, but irreversible) activation. They back up their intuition with robust quantitative modeling. They then point out that, with a simple experimental modification, the published experiments could be repeated in a way that would test between the ultra-sensitive and bistable possibilities.

This is an accurate and concise summary of our paper.

Therefore, this is a rare paper that makes a specific modeling-based prediction and proposes a straightforward way to test it. As such, it will be of interest to a broad range of workers involved in the fields cell cycle and regulatory modeling.

We agree that our work will be of interest to a broad range of scientists studying cell cycle regulation and mathematical modeling of bistable control systems.

Nonetheless, attention to the following points would improve the manuscript. The authors should be more careful about how they describe protein abundance. They often refer to protein level. I believe in every case they mean protein concentration, but this is not explicitly stated; it could be interpreted as number of protein molecules per cell. The authors should either explicitly state that level means concentration or, more simply, use concentration instead of level.

A valid criticism that has been addressed in the revised version.

The authors should explain why they include stoichiometric inhibition of CDK by Wee1 in their model. Is it required to make the model work in the wild-type case, or only in the CDK-AF case? My intuition is it should only be required in the AF case, but I would like to know for sure. Also, they should state if there is any experimental data for such regulation.

Bistability of the Tyr-phosphorylation switch requires 'sufficient' nonlinearity, which may come from the phosphorylation and dephosphorylation reactions that interconvert Cdk1, Wee1 and Cdc25. The easiest way to model these interconversion reactions is to use Hill- or Goldbeter-Koshland functions for the phosphorylation and dephosphorylation of Wee1 and Cdc25, but this approach is not appropriate for Gillespie SSA, which assumes elementary reactions. Both Wee1 and Cdc25 are phosphorylated on multiple sites, which we approximate by double phosphorylation; but this level of nonlinearity is not sufficient to make the switch bistable. In addition, stochiometric inhibition is a well-known source of nonlinearity, and in the Wee1:Cdk1 enzyme:substrate complex, Cdk1 is inhibited because Wee1 binds to Cdk1 near its catalytic site. In our model, stoichiometric inhibition of Cdk1 by Wee1 is required for bistability even in the wild-type case because the regulations of Wee1 and Cdc25 by phosphorylation are not nonlinear enough. There is experimental evidence that stoichiometric inhibition of Cdk1 by Wee1 is significant: mik1D wee1ts double mutant cells at the restrictive temperature (Lundgren, Walworth et al. 1991) are less viable than AF-Cdk1 (Gould and Nurse 1989). Furthermore, Patterson (eLife, 2021) found weak 'bistability' when they used AF-Cdk1 to induce mitosis. This puzzling observation suggests a residual feedback mechanism in the absence of Tyr-phosphorylation. Our model accounts for this weak bistability by assuming that free CDK1 can phosphorylate and inactivate the Wee1 'enzyme' in the Wee1:Cdk1 complex, which makes CDK1 and Wee1 mutual antagonists. This reaction is based on formation of a trimer, Cdk1:Wee1:Cdk1, which is possible since CDK1 phosphorylation of Wee1 occurs in its N-terminal region, which lies outside the C-terminal catalytic domain of Wee1 (Tang, Coleman et al. 1993). These ideas have been incorporated into the text in the subsection describing the model (see lines120-125).

The authors should explicitly state, on line 131, that the fact that "the rate of synthesis of C-CDK molecules is directly proportional to cell volume" results in a size-dependent increase in the concentration of C-CDK.

The accumulation of C-CDK molecules in fission yeast cells is complicated. In general, we may assume that larger cells have more ribosomes and make all proteins faster than do smaller cells. Absent other regulatory effects, the number of protein molecules is proportional to cell volume, and the concentration is constant. But, in Patterson's experiments, the number of C-CDK molecules is zero at the start of induction and rises steeply thereafter (see lines 147-148), and the rate of increase (#molec/time) is proportional to the size of the growing cell.

The authors should explain, on line 100, why they are "quite sure the bistable switch is the correct interpretation".

Line 105-106: "Although we suspect that the mitotic switch is bistable,.."

On line 166, include the units of volume.

Done

On lines 152 and 237, "smaller protein-fusion levels "should be replaced with "lower protein-fusion concentrations".

Done

**Referee cross-commenting** *I concur with the other two reviews. *

Reviewer #1 (Significance (Required)): *The paper is significant in that it points out an alternative interpretation for an important result in an important paper. Specifically, it points out that the published data is consistent with activation of CDK at the G2/M transition in fission yeast could be ultra-sensitive (switch-like, but reversible) instead of bistable (switch-like, but irreversible). The distinction is important because it has been claimed, by the authors of the submitted manuscript among others, that bistability is required for robust cell-cycle directionality. *

We agree with this assessment.

However, activation of CDK at the G2/M transition in other species has been shown to be bistable and the authors state that they are "quite sure the bistable switch is the correct interpretation". So, the paper is more likely an exercise in rigor than an opportunity to overturn a paradigm.

We were the first authors to predict that the G2/M switch is bistable (J. Cell Sci., 1993) and among the first to prove it experimentally in frog egg extracts (PNAS, 2004). Our models (Novak and Tyson 1995, Novak, Pataki et al. 2001, Tyson, Csikasz-Nagy et al. 2002, Gerard, Tyson et al. 2015) of fission yeast cell-cycle control rely on bistability of the G2/M transition; so, understandably, we believe that the transition in fission yeast is a bistable switch. But the 'bistable paradigm' has never been directly demonstrated by experimental observations in fission yeast cells. The Patterson paper (eLife, 2021) claims to provide experimental proof, but we demonstrate in our paper that Patterson's experiments are not conclusive evidence of bistability. Furthermore, we suggest that a simple change to Patterson's protocol could provide convincing evidence that the G2/M switch is either monostable or bistable. We are not proposing that the switch is monostable; we would be quite surprised if the experiment, correctly done, were to indicate a reversible switch. Our point is simply that the published experiments are inconclusive. The point we are making is neither a mere 'exercise in rigor' nor a suggestion to 'overturn a paradigm.' Rather it is a precise theoretical analysis of a central question of cell cycle regulation that should be of interest to both experimentalists and mathematical modelers.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary: The manuscript asks whether the data reported in Patterson et al. (2021) is consistent with a bistable switch controlling the G2/M transition in fission yeast. Patterson et al. (2021) use an engineered system to decouple a non-degradable version of Cyclin-dependent kinase (CDK) from cell growth and concomitantly measure CDK activity (by the nuclear localization of a downstream target, Cut3p). They observe cells with indistinguishable CDK levels but two distinct CDK activities, which they posit shows bistable behavior. In this study, the authors ask if other models can also explain this data. The authors use both deterministic and Gillespie based stochastic simulations to generate relationships between CDK activities and protein levels for various cell sizes. They conclude that the experiments performed in Patterson et al. are insufficient to distinguish between a bistable switch and a reversible ultrasensitive switch. They propose additional experiments involving the use a degradable CDK construct to also measure the inactivation kinetics.

This is an accurate summary of our paper.

They propose that a bistable switch will have different forward (OFF->ON) and backward (ON->OFF) switching rates. A reversible ultrasensitive switch will have indistinguishable switching rates.

Our analysis of Patterson's (2021) experiments is based on the well-known fact that the threshold for turning a bistable switch on is significantly different from the threshold for turning it off (in Patterson's case, the 'threshold' is the level of fusion protein in the cell when CDK is activated), whereas for a reversible, ultrasensitive switch, the two thresholds are nearly indistinguishable. The 'rate' at which the switch is made is a different issue, which we do not address explicitly. In the experiments and in our model, the switching rates are fast, whether the switch is bistable or monostable. The results are interesting and worth publication in a computational biology specific journal, as they might only appeal to a limited audience.

We think our results should also be brought to the attention of experimentalists studying cell cycle regulation, because Patterson's paper (eLife, 2021) presents a serious misunderstanding of the existence and implications of 'bistability' of the G2/M transition in fission yeast. Whereas Patterson's work is an elegant and creative application of genetics and molecular biology to an important problem, it is not backed up by quantitative mathematical modeling of the experimental results. In that sense, Patterson's work is incomplete, and its shortcomings need to be addressed in a highly respected journal, so that future cell-cycle experimentalists will not make the same-or similar-mistakes.

Several ideas need to be clarified and additional information needs to be provided about the specific parameters used for the simulations: Major comments: #1 The parameters need to be made more accessible by means of a supplementary table and appropriate references need to be cited.

Two new supplementary tables (S1 and S2) summarize the dynamic variables and parameter values.

It is not clear why Michaelis Menten kinetics will not be applicable to this system. Has it been demonstrated that the Km s of the enzymes are much greater than the substrate concentrations for all the reactions? If yes, please cite.

MM kinetics are not appropriate for such protein interaction networks because one protein may be both an enzyme and a substrate for a second protein (e.g., Wee1 and CDK, or Cdc25 and CDK). So, the condition for validity of MM kinetics (enzyme concen ≪ substrate concen) cannot be satisfied for both reactions. Indeed, enzyme concen ≈ substrate concen is probably true for most reactions in our network. Hence, it is advisable to stick with mass-action rate laws. Furthermore, MM kinetics are a poor choice for 'propensities' in Gillespie SSA calculations, as has been shown by many authors (Agarwal, Adams et al. 2012, Kim, Josic et al. 2014, Kim and Tyson 2020).

It will not be surprising if the simulation with Michaelis Menten would alter the dynamics shown in this study. A reversible switch with two different enzymes (catalyzing the ON->OFF and OFF->ON transitions) having different kinetics can give asymmetric switching rates. This would directly contradict what has been shown in Figure 7A-D.

We don't follow the reviewer's logic here. The two transitions, off → on and on → off, are already driven by different molecular processes (dephosphorylation of inactive CDK-P by Cdc25 and phosphorylation of active CDK by Wee1, respectively). Positive feedback of CDK activity on Cdc25 and Wee1 (++ and −−, respectively) causes bistability and asymmetric switching thresholds. Switching rates, which are determined by the kinetic rate constants of the up and down processes, are of secondary importance to the primary question of whether the switch is monostable or bistable.

#2 Line 427: The authors use a half-time of 6 hours in their model as Patterson et al. used a non-degradable construct. It is not clear why dilution due to cell growth has not been considered. The net degradation rate of a protein is the sum of biochemical degradation rate and growth dilution rate. The growth dilution rate seems significant (140 mins doubling time or 0.3 h-1 dilution rate) relative to assumed degradation rate (0.12 h-1). Please clarify why was the effect of dilution neglected in the model or show by sensitivity analysis this does not change the predicted CDK activation thresholds.

The reviewer highlights an important effect, but it is not relevant to our calculations. In the deterministic model used to calculate the bifurcation diagrams, both cell volume and the concentration of the non-degradable Cdc13:Cdk1 dimer are kept constant; therefore, there is no dilution effect. The stochastic model deals with changing numbers of molecules per cell; the dilution effect is taken into account by the appearance of cell volume, V(t), at appropriate places in the propensity functions. In other words: in the deterministic model, which is written for concentration changes, the dilution term, −(x/V)(dV/dt), is zero because V=constant; in the stochastic model, written in terms of numbers of molecules, dilution effects are implicit in the propensity functions.

*#3 Line 402 The authors state that the production rate of the Cdk protein is 'assumed' proportional to the cell volume. The word 'assumed' is incorrect here as a simple conversion of concentration-based differential equation (with constant production rate) to molecular numbers would show that production rate is proportional to the volume. This is not an assumption. *

Correct; we modified the text (see line 450-462). The role of cell volume in production rate is more relevant to the case of Cdc25, where we assume that its production rate, Δconcentration/Δt, is proportional to V, because the concentration of Cdc25 in the cell increases as the cell grows. We added two references (Keifenheim, Sun et al. 2017, Curran, Dey et al. 2022) to justify this assumption. In the stochastic code, the propensity for synthesis of Cdc25 molecules is proportional to V2.

#4 Line 423 Please cite the appropriate literature that shows that fission yeast growth during cell division is exponential. If the dynamics are more complicated, involving multiple phases of growth during cell division, please state so.

We now acknowledge that volume growth in fission yeast, rather than exponential, is bilinear with a brief non-growing phase at mitosis (Mitchison 2003). However, we suggest that our simplifying assumption of exponential growth is appropriate for the purposes of these calculations. See line 473-476: "In our stochastic simulations, we assume that cell volume is increasing exponentially, V(t) = V0eμt. Although fission yeast cells actually grow in a piecewise linear fashion (Mitchison 2003), the simpler exponential growth law (with doubling time @ 140 min) is perfectly adequate for our purposes in this paper.."

*#5 Line 250 The authors convert the bistable version of the CDK switch to reversible sigmoidal by assuming that Wee1 and Cdc25 phosphorylation is proportional to the CDK level rather than activity, which seems biochemically unrealistic. This invokes an altered circuit architecture where inactive CDK has enough catalytic activity to phosphorylate the two modifying enzymes (Wee1/Cdc25) but not enough to drive mitosis. This might be possible if the Km of CDK for Wee1/Cdc25 is lower relative to other downstream substrates that drive mitosis. The authors can reframe this section of the paper to state this possibility, which might be interesting to experimentalists. *

The reviewer is correct that the molecular biology underlying our 'reversible sigmoidal' model is biochemically unrealistic. But, in our opinion, this is the simplest way to convert our bistable model into a monostable, ultrasensitive switch while maintaining the basic network structure in Fig. 1. Our purpose is to show that a monostable model-only slightly changed from the bistable model-can account for Patterson's experimental data equally well. If Nurse's group modifies the experimental protocol as we suggest and their new results indicate that the G2/M transition in fission yeast is bistable, then our reversible sigmoidal model, having served its purpose, can be forgotten. If they show that the transition is not bistable, then both experimentalists and theoreticians will have to think about biochemically realistic mechanisms that can account for the new data...and everything else we already know about the G2/M transition in fission yeast.

#6 It is difficult to phenomenologically understand a bistable switch just based on differences in activation and inactivation thresholds. For example, a reversible ultrasensitive switch also shows a difference in activation and inactivation thresholds (Figure 7D). How much of a difference should be expected of a bistable switch versus reversible switch?

We show how much of a difference can be expected by contrasting Fig. 7 to Fig. 8. For the largest cells (panel D of both figures), the difference is small and probably undetectable experimentally. For medium-sized cells (panel C), the difference is larger but probably difficult to distinguish experimentally. Only the smallest cells (panel B) provide an opportunity for clearly distinguishing experimentally between monostable and bistable switching.

*Moreover, as the authors clearly understand (line 275), time-delays in activation and inactivation reactions can inflate these differences. In the future, if the authors can convert the equations to potential energy space as done in Acar et al. 2005 (Nature 435:228) in Figure 3c-d, it will be useful. Also, predicting the distribution of switching rates from the Gillespie simulation might be informative and can be directly compared to experimental measurements in the future (if the Cut3p levels in nucleus and cytosol equilibrates fast enough or other CDK biosensors are developed). *

The famous paper by Acar et al. (2005) is indeed an elegant experimental and theoretical study of bistability ('cellular memory') in the galactose-signalling network of budding yeast. We have included a comparison of Patterson et al. with Acar et al. in our Conclusions section (lines 353-368):

"It is instructive, at this point, to compare the work of Patterson et al. (2021) to a study by Acar et al. (Acar, Becskei et al. 2005) of the galactose-signaling network of budding yeast. Combining elegant experiments with sophisticated modeling, Acar et al. provided convincing proof of bistability ('cellular memory') in this nutritional control system. They measured PGAL1-YFP expression (the response) as a function of galactose concentration in the growth medium (the signal), analogous to Patterson's measurements of CDK activity as a function of C-CDK concentration in fission yeast cells. In Acar's experiments, the endogenous GAL80 gene was replaced by PTET-GAL80 in order to maintain Gal80 protein concentration at a constant value determined by doxycycline concentration in the growth medium. The fixed Gal80p concentration in Acar's cells is analogous to cell volume in Patterson's experiments. In Fig.3b of Acar's paper, the team plotted the regions of monostable-off, monostable-on and bistable signaling in dependence on their two control parameters, external galactose concentration and intracellular Gal80p concentration, analogous to our Fig.4. Because Acar's experiments explored both the off → on and on → off transitions, they could show that their observed thresholds (the red circles) correspond closely to both saddle-node bifurcation curves predicted by their model. On the other hand, Patterson's experiments (as analyzed in our Fig.4) probe only the off → on transition."

The purpose of our paper is to show that Patterson-type experiments can and should be done so as to probe both thresholds, as was done by van Oudenaarden's team. They went further to characterize their bistable switch in terms of 'the concept of energy landscapes'. We think it is premature to pursue this idea in the context of the G2/M transition in fission yeast until there is firm, quantitative data characterizing the nature of the 'presumptive' bistable switch in fission yeast.

Minor comments: #1 Line 2: Please replace "In most situations" to "In favorable conditions"

Done.

**Referee cross-commenting** I agree with Reviewer 1 that this falls more under pointing out an alternative interpretation of a single experiment than challenging widely supported orthodoxy about how the eukaryotic cell cycle leaves mitosis.

As we said earlier, our 1993 paper in J Cell Sci is the source of this orthodox view, and it is widely supported at present because there is convincing experimental evidence for bistability in frog egg extracts, budding yeast cells and mammalian cells. Patterson's paper is not sound evidence for bistability of the G2/M transition in fission yeast cells. It is important for experimentalists to know why the experiments fail to confirm bistability, and important for someone to do the experiment correctly in order to confirm (or, what would be really interesting, to refute) the expectation of bistability at the G2/M transition in fission yeast cells.

Reviewer #2 (Significance (Required)): Suitable for specialist comp bio journal eg PLoS Comp Bio

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

The paper by Novak and Tyson revisits a recent paper from Nurse group on the bistability of mitotic switch in fission yeast using mathematical modelling. The authors extend their older models of mitotic entry check point and implement both deterministic and stochastic version of new model. They show this model does indeed possess bistability and show that combined with stochastic fluctuations the model can show bimodality for the cyclin-CDK activity at a particular cell size consistent with the recent experimental data. However, the authors also show alternative model that has mono-stable ultrasensitivity can also explain the data and suggest experiments that can prove the existence of hysteresis and therefore bistability.

Right on.

While the biological implication of the study is well explained, the authors can improve the presentation of their model and the underlying assumptions. I have the following comments and suggestions for improvement of the paper.

1. • The cartoon of the mathematical model is confusing at places, for example the wee1-CDK complex according to the equations either dissociates back to wee1 and CDK or gives rise to pCDK and wee1, the arrow below is confusing as it implies it can also give rise to wee1p, the CDK phosphorylation of wee1 is already included in the diagram. Also, the PP2A is put on the arrow for all reactions but for wee1p2 to wee1p its action shown with a dashed line. Also, I wondered if wee1p and wee1p2 can also bind CDK and sequester or phosphorylate CDK?* We are sorry for the confusion and have improved Fig. 1.

2. The rates and variables in the ODEs are not fully described. Also sometimes unclear what is parameter and what is a variable, I had to look at the code.*

We now include tables of variables and parameter values, with explanatory notes.

• The model has quite a few parameters, but these are not at all discussed in the paper. How did the authors come up with these particular set of parameters, has there been some systematic fitting, or tuning by hand to produce a good fit to the data? I could only see the value of the parameters in the code, but perhaps a table with the parameters of the model, what they mean and their value (and perhaps how the values is obtained) is missing.*

The parameters were tuned by hand to fit Patterson's data, based, of course, on our extensive experience fitting mathematical models to myriad data sets on the cell division cycles of fission yeast, budding yeast, and frog egg extracts. We now provide a table of parameter values.

• The authors are using the Gillespie algorithm with time varying parameters (as some rates depend on volume and volume is not constant). Algorithm needs to be modified slightly to handle this (see for example Shahrezaei et al Molecular Systems Biology 2008). *

A valid criticism, but the rate of cell volume increase is very slow compared to the propensities of the biochemical reactions. We write (lines 492-498):

"In each step of the SSA, the volume of the cell is increasing according to an exponential function, and, consequently, the propensities of the volume-dependent steps are, in principle, changing with time; and this time-dependence could be taken into account explicitly in implementing Gillespie's SSA (Shahrezaei, Ollivier et al. 2008). However, the step-size between SSA updates is less than 1 s compared to the mass-doubling time (140 min) of cell growth. So, it is warranted to neglect the change in V(t) between steps of the SSA, as in our code."

• The authors correctly point out, ignoring mRNA has resulted in underestimation of noise, however another point is that mRNA life times are short and that also affects the timescale of fluctuations and this may be relevant to the switching rates between the bistable states. *

A valid point, but to include mRNA's would double the size of the model. Furthermore, we have little or no data about mRNA fluctuations in fission yeast cells, so it would be impossible to estimate the values of all the new parameters introduced into the model. Finally, the switching rates between bistable states (or across the ultrasensitive boundary) are not the primary focus of Patterson's experiments or our theoretical investigations. So, we propose to delay this improvement to the model until the relevant experimental data is available.

• In the introduction add, "In this study" to "Intrigued by these results, we investigated their experimental observations with a model of bistability in the activation of cyclin-CDK in fission yeast." *

Done

Reviewer #3 (Significance (Required)): Overall, this is an interesting study that revisits an old question and some recent experimental data. The use of stochastic modelling in explaining variability and co-existence of cell populations in the context of cell cycle and comparison to experimental data is novel and of interest to the communities of cell cycle researchers, systems biologists and mathematical biologists.

We agree. Thanks for the endorsement

References

Acar, M., A. Becskei and A. van Oudenaarden (2005). "Enhancement of cellular memory by reducing stochastic transitions." Nature 435(7039): 228-232.

Agarwal, A., R. Adams, G. C. Castellani and H. Z. Shouval (2012). "On the precision of quasi steady state assumptions in stochastic dynamics." J Chem Phys 137(4): 044105.

Curran, S., G. Dey, P. Rees and P. Nurse (2022). "A quantitative and spatial analysis of cell cycle regulators during the fission yeast cycle." Proc Natl Acad Sci U S A 119(36): e2206172119.

Gerard, C., J. J. Tyson, D. Coudreuse and B. Novak (2015). "Cell cycle control by a minimal Cdk network." PLoS Comput Biol 11(2): e1004056.

Gould, K. L. and P. Nurse (1989). "Tyrosine phosphorylation of the fission yeast cdc2+ protein kinase regulates entry into mitosis." Nature 342(6245): 39-45.

Keifenheim, D., X. M. Sun, E. D'Souza, M. J. Ohira, M. Magner, M. B. Mayhew, S. Marguerat and N. Rhind (2017). "Size-Dependent Expression of the Mitotic Activator Cdc25 Suggests a Mechanism of Size Control in Fission Yeast." Curr Biol 27(10): 1491-1497 e1494.

Kim, J. K., K. Josic and M. R. Bennett (2014). "The validity of quasi-steady-state approximations in discrete stochastic simulations." Biophys J 107(3): 783-793.

Kim, J. K. and J. J. Tyson (2020). "Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy." PLoS Comput Biol 16(10): e1008258.

Lundgren, K., N. Walworth, R. Booher, M. Dembski, M. Kirschner and D. Beach (1991). "mik1 and wee1 cooperate in the inhibitory tyrosine phosphorylation of cdc2." Cell 64(6): 1111-1122.

Mitchison, J. M. (2003). "Growth during the cell cycle." Int Rev Cytol 226: 165-258.

Novak, B., Z. Pataki, A. Ciliberto and J. J. Tyson (2001). "Mathematical model of the cell division cycle of fission yeast." Chaos 11(1): 277-286.

Novak, B. and J. J. Tyson (1995). "Quantitative Analysis of a Molecular Model of Mitotic Control in Fission Yeast." J Theor Biol 173: 283-305.

Patterson, J. O., S. Basu, P. Rees and P. Nurse (2021). "CDK control pathways integrate cell size and ploidy information to control cell division." Elife 10.

Shahrezaei, V., J. F. Ollivier and P. S. Swain (2008). "Colored extrinsic fluctuations and stochastic gene expression." Mol Syst Biol 4: 196.

Tang, Z., T. R. Coleman and W. G. Dunphy (1993). "Two distinct mechanisms for negative regulation of the Wee1 protein kinase." EMBO J 12(9): 3427-3436.

Tyson, J. J., A. Csikasz-Nagy and B. Novak (2002). "The dynamics of cell cycle regulation." Bioessays 24(12): 1095-1109.

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

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Reply to the reviewers

Dear Editor,

We have addressed the points and concerns raised by the reviewers and wish to thank them for their effort and time. We agree with all the comments and suggestions, which resulted in a significant improvement of the manuscript. Below, we provide a point-by-point response to all comments.

Sincerely,

Anders Hofer, corresponding author

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

In their paper Ranjbarian and colleagues provide a tour de force at characterizing dAK from G. intestinalis using both enzymology and structural biology. G. intestinalis does not have RNR and therefore this organism relies on dAK which catalyzes formation of dAMP and ADP from deoxyadenosine and ATP (among other substrate pairs). The authors performed a terrific job at testing this reaction in depth using a recombinant dAK and a battery of various co-substrates (both natural as well as synthetic ones, Table 1). Extensive structural information on dAK was obtained using a combination of X-ray crystallography and cryo-EM. Overall, this work will be paramount aid in better understanding of the reaction mechanism, especially in the context of molecules which can be used as inhibitors of such a crucial enzyme (metabolic vulnerability for this parasite).

This manuscript, in its current form does not require additional experiments but I would like to have a few aspects corrected/clarified, before it can be accepted for publication:

Line 30: "whereas the affinities for deoxyguanosine, deoxyinosine and deoxycytidine were 400-2000 times lower." Better not to use term "affinity" when KM or kcat/KM are implied (unless ITC was used to measure true Kds).

-This is a good point, and we are now using KM values in all instances were actual numbers are implied and only kept the word affinity in cases where it is discussed in more general terms.

Line 31: "Deoxyadenosine analogues halogenated at the 2- and/or 2´-positions were also potent substrates, with comparable EC50 values as the main drug used today, metronidazole, but with the advantage of being usable on metronidazole-resistant parasites." Not sure this sentence is clear as written.

-We have now rewritten the sentence as follows: "Deoxyadenosine analogues halogenated at the 2- and/or 2´-positions were also potent substrates with comparable EC50 values on cultured G. intestinalis cells as metronidazole, the first line treatment today, with the additional advantage of being effective against metronidazole-resistant parasites."

Line 55: "..G. intestinalis (synonymous to G. lamblia and G. duodenalis)..". Very nice that authors provide this information as it is usually a point of confusion i.e. multiple names for the same organism.

-Thanks a lot, we are happy that you liked it.

Line 61 and above as well: "Treatment regimes are mainly based on metronidazole and to a lesser extent other 5-nitroimidazoles...". MT is introduced a bit sporadically, and not completely clear which enzyme it inhibits and its mode of action." Common knowledge is that MT is known for its action in aerobic parasites/bacteria and known as Flagyl, where it is mode of action was linked to "activation" due to microaerophilic conditions. Maybe MT can be introduced after text starting from Line 71?

-The description of metronidazole is adjusted as following: "Metronidazole (Flagyl) is the most commonly used drug to treat giardiasis and selectively kills the parasite and other anaerobic organisms by forming free radicals under oxygen-limited conditions, but it has side effects such as nausea, abdominal pain, diarrhea, and in some cases neurotoxicity reactions."

Line 70: what is "cyst-wall"?

-It is a cell wall consisting of three major cyst wall proteins and N-acetylgalactosamine. We have adjusted the sentence to the following to make the term clearer: The trophozoites can also secrete material to form a cyst wall and go through two rounds of DNA replication to form cysts, which contain four nuclei and a 16N genome per cell (4N in each nucleus).

Line 90: "The reaction is catalyzed by deoxyribonucleoside kinases (dNKs), which are.." I really do not like when in order to find a reaction which is catalyzed by an enzyme in a particular study one needs to dive into the literature, sometimes it requires a lot of time as in most of recent papers on the subject reactions catalyzed are not listed. Please add a Figure or a panel with reactions catalyzed by both dNKs families.

-It is a good idea and we have now added a figure (Fig. 1), which compares the deoxyribonucleotide metabolism of G. intestinalis with mammalian cells. The different deoxyribonucleoside kinases in the parasite and mammalian cells are included in the figure.

Line 96: "..was found to have a ~10-fold higher affinity to thymidine.." as I mentioned above I really do not like the usage of "affinity", when actually low KM is implied.

-It is corrected now (see above).

Line 113: "This does not match the current knowledge that there are three dNKs in total whereof one completely specific for thymidine. The lack of knowledge about these essential enzymes in the parasite has hampered the understanding of Giardia deoxyribonucleoside metabolism and hence its exploitation as a target for antiparasitic drugs." Very good rationale, as I mentioned above, I think a Figure needs to be introduced that depicts different enzymes involved in deoxyribonucleoside metabolism (both TK1 and non- TK1 members) in Giardia with clearly labeled all known paralogs and corresponding enzymatic reactions.

-Thanks a lot for the suggestion. Information about the different dNKs in G. intestinalis with mammalian cells for comparison is included in the new figure (Fig. 1).

Line 132: Odd designation of supplementary figures, usually it is "Fig. S1" etc. The legend for Fig. S1 is not adequate, please add description of species and name of enzymes for all sequences shown. Also each sequences in alignment should start with number (a.a. number) as it is not clear if a full sequence is shown or not. Overall comment about the multiple sequence alignment (relevant to Fig. S1): with such a small number of sequences it is very hard to make any substantial predictions about conserved regions etc.

-Thanks for the suggestions. We have now included more sequences, sequence numbering, and description of species as well as enzyme names. Some other changes are also that we have now used the same G. intestinalis dAK sequence in the alignment as in the experiments (same strain and accession number), and that we have made a realignment using Clustal W instead of Clustal Omega (gives better alignment of the termini). The designation of supplementary figures is according to the style of PLoS journals.

Fig. 1 and elsewhere: I will prefer that all bar graphs show individual values + the error bar (if possible);

-We have now added individual values to the bar graphs.

I do not have any issues with X-ray data and cryo-EM studies (refinement statistics, particles classification etc).

**Referees cross-commenting**

I also agree with all the comments provided by Reviewer 2 and very pleased to see that we were very similar in our evaluations.

Reviewer #1 (Significance (Required)):

In their paper Ranjbarian and colleagues provide a tour de force at characterizing dAK from G. intestinalis using both enzymology and structural biology. G. intestinalis does not have RNR and therefore this organism relies on dAK which catalyzes formation of dAMP and ADP from deoxyadenosine and ATP (among other substrate pairs). The authors performed a terrific job at testing this reaction in depth using a recombinant dAK and a battery of various co-substrates (both natural as well as synthetic ones, Table 1). Extensive structural information on dAK was obtained using a combination of X-ray crystallography and cryo-EM. Overall, this work will be paramount aid in better understanding of the reaction mechanism, especially in the context of molecules which can be used as inhibitors of such a crucial enzyme (metabolic vulnerability for this parasite).

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

Summary:

Ranjbarian et al. investigated a non-TK1-Like deoxyribonucleoside kinase (dNK) found in the protozoan parasite Giardia intestinalis. They used enzyme kinetic assays on heterologously expressed Gi dNK in E. coli to determine which deoxyribonucleotides were most likely physiological substrates for the enzyme. Their characterization revealed that this Gi dNK has a strong affinity to deoxyadenosine. They further investigated the affinity and activity of the dNK on deoxyadenosine analogues, some of which have known pharmaceutical utility. Finally, using a combination of crystallography, cryo-EM, chromatography, and mass photometry, they reveal that unlike other dNKs, Gi dNK forms a tetramer. They characterize important regions required for tetramerization and postulate that this tetramerization evolved to provide Gi dNK with a heightened affinity for deoxyadenosine.

Major comments and questions:

• The claims in this manuscript are well-supported, and I found no major issues with experimental methods. • The authors provide a structure of tetrameric dNK and suggest that this tetramer leads to the increased affinity to substrate compared to non-giardia dNKs. They also show through mutations that removing the novel dimerization regions decreases substrate affinity by 100-fold. However, I was left unclear about why the tetramer would lead to such high affinity for substrate compared to two dimers. This is especially notable, since the authors state that there are no signs of cooperativity, which is a common way that oligomerization may lead to heightened affinity. If the authors have no current evidence explaining this, they can consider adding a short amount of discussion speculating on the mechanism and future directions of study. -Thanks for this suggestion. We have now added a section in the last paragraph of the discussion where we speculate on the subject.

Minor comments and questions:

• The authors state that dATP acts as a mixed inhibitor and not a simple competitive inhibitor, and that previous studies have shown that this is because the dNTP competes in two locations (line 163). Is it also possible that competitive inhibition + allosteric regulation could be causing this behavior instead? -It is true that this can be theoretically explained in many ways. In fact, many allosteric regulators affect both the Vmax and Km values. However, in all studied dNKs, the dNTP acts as a dual competitor and no proper allosteric regulation with a separate allosteric site has ever been observed so far. We have rephrased this part as following to make it clear: "Mixed inhibition is often the result of allosteric regulation but studies of other dNKs have shown that this is not the case [17]. Instead, the far-end dNTP product gives a dual inhibition where the deoxyribonucleoside moiety competes with the substrate and the phosphate groups mimic those of ATP but coming from the opposite direction."

• In the introduction (line 93), non-TK1-like dNKs are described as "not structurally related to TK1-like". This left me unclear, are they still interrelated among themselves? -We have added the following sentence for clarification: "The non-TK1-like dNKs are further subdivided into a monophyletic group of canonical non-TK1-like dNKs and a second group with thymidine kinases from Herpesviridae, which are structurally related to the canonical group but share very little amino acid sequence homology."

• I was left confused by lines 106-116 in the introduction, where the specificities of dNKs in giardia are discussed. This is touched upon again in the discussion, but it was not clear here that there are several deoxyribonucleotides unaccounted for. -We think this should be clear now with the added Fig. 1 where the dNKs are shown.

• When describing enzyme assays (Line 145), the authors say there is no salt dependence, but there looks to be MgCl2 always included in the assays (presumably for the ATP). -This is a good point and something we have overlooked when the sentence was written (Mg2+ is required). We have now corrected the sentence as follows: "Based on initial enzyme activity studies, it was confirmed that the assay did not have any specific requirements regarding K+, Na+, NH4+, acetate or reducing agents, and that it was linear with respect to time (S2 Fig)."

• I was confused by the y-axis of Fig 2. How is enzyme activity lower when dAdo is added? I think I read "enzyme activity" as total substrate depleted, when it is actually referring exclusively to the given non-dAdo substrate in each column. -This is a very good point that we seem to have overlooked. We have now adjusted the y-axis title to "Indicated enzyme activity" and added the following sentence to the figure legend: "The recorded enzyme activities are for the substrates indicated on the x-axis (excluding the activity with the competing substrate)."

• Lines 239 - 255 and Figure 3 were a little unclear to me. Specifically, I was having trouble following in the text which dimer is in the ASU, which is symmetry related, and matching those terms with which are canonical and non-canonical. -We agree with the reviewer and thank them for their comment. In order to improve the presentation of these results, we chose to extensively rearrange the figures and accompanying text. We now present the initial X-ray data together with the cryo-EM data in a new Figure 4 that focuses on the overall architecture of the tetramer. We realize that some of the nomenclature previously used in that figure was, as the reviewer pointed out, confusing and superfluous, and we have now simplified and unified it. The structural details of how the extended N- and C-termini interact with the neighboring subunit have been moved to the new figure 6 in order to present them just before the functional analyses of the consequences of truncating the termini. As a consequence of these changes to the figure layout, we made substantial changes in the organization of the text surrounding these figures, which also led to a clearer presentation. Since the changes to the figures and text are quite substantial, we would like to point out that they are only changes to the presentation, not to the data shown.

• The authors suggest that in the experiment shown in Figure S9 (Line 285), low activity may be caused by minor impurities. I'm not sure why impurities would lower activity significantly. Could there be other differences in experimental conditions that are at play instead? -The sentence refers to a side activity (dATP dephosphorylation) which is not the normal reaction of deoxyadenosine kinase. We have rephrased the sentence to make it clearer: "The dATP-dephosphorylating activity was several orders of magnitude lower than the regular dAK activity (to phosphorylate deoxyadenosine) and was possibly catalyzed by other enzymes present as minor impurities in the protein preparation."

• (Optional) From looking at the crystallography stats, I think the authors can potentially push the resolution more. At higher resolutions, Rmerge may become high, but depending on the data collection strategy, Eiger detectors can lead to high Rmerge just out of sheer data redundancy. Cc 1/2 can be a more useful metric in these contexts. -This is a good point and well spotted by the reviewer. Indeed, a CC1/2 of 0.802 suggests that the resolution can be pushed further. However, due to contaminating spots at higher resolutions the statistics significantly worsen when trying to push the resolution beyond 2.1 A, which is why we did not process the data to a higher resolution.

• For Figure S8, the Polder map feature in Phenix is another option for showing ligand occupancy in an unbiased way. Did the authors try this? -We want to thank the reviewer for suggesting this. We have calculated a polder map using the Polder map feature in Phenix and both the resulting map and correlation coefficients support the presence of a dADP in the active site of monomer I. We added a section to the relative paragraph to include these new findings: "To increase our confidence that dADP was correctly placed within active site I, we calculated a polder map for dADP to test whether the b-phosphate density is correctly attributed or if it rather belongs to the bulk solvent. The resulting polder map and statistics support the placement of dADP in active site I with correlation coefficients of CC1,2=0.7627, CC1,3=0.9424, and CC2,3=0.7423 suggesting that the density does belong to dADP as CC1,3 > CC1,2 and CC1,3 > CC2,3 (S8 Fig.)."

• It's disappointing that the tetramers show so much preferred orientation in the cryo-EM. With that said, while the nominal resolution is 4.8 Å, I think that with the streakiness the EM structure looks to have worse resolution than that. -We agree that the streakiness of the map is substantial. This is simply a result of the severe anisotropy of the map, which means that the resolution is probably worse than 4.8 Å in the "bad directions" of the map. The supplementary material (S9 Fig) clearly shows the preferred orientations leading to this problem. In the course of this study, we tried several methods to lessen the preferred orientation problem such as using graphene oxide-coated grids and collecting tilted data. However, when we got the crystal structure we saw no point in continuing these efforts. To address the comment of the reviewer, we extended the description of the EM map in the main text to say:

"Due to strong preferred orientations, it was not possible to get an isotropic, high-resolution 3D structure of dAK using cryo-EM. The resulting 3D map had a nominal resolution of 4.8 Å, but a clearly anisotropic appearance probably reflecting lower resolution in the poorly resolved direction (S9 Fig)."

**Referees cross-commenting**

Overall, I agree with Reviewer #1's evaluation, and don't have any further suggestions or thoughts at this time.

Reviewer #2 (Significance (Required)):

Medical relevance: G. intestinalis is a parasite that causes 190 million cases of giardiasis per year. While treatable, there is evidence that giardia are developing a resistance to the main treatment at the moment, metronidazole. Thus, the authors provide a compelling case for the medical relevance of their investigation of Gi dNK for further pharmaceutical development. They provide further evidence for this by showing that several deoxyadenosine analogs bind the dNK and inhibit giardia growth. This work represents a very useful first step into a potential avenue for medical development. It's important to note that clinical studies are not within the purview of this research. However, in the discussion, the authors provide several comments on the promise of this avenue for future research.

Conceptual, technical, and mechanistic relevance: Through biochemical and structural study, the authors provide a compelling framework to understand an enzyme that is very important to the unique lifestyle of giardia parasites. From an evolutionary standpoint, the authors provide insight into how giardia can survive even without major components of de novo DNA synthesis. The authors principally use well-established tools and techniques of the enzymology field. but do so to characterize a unique and previously uncharacterized enzyme system. This enzyme proves to be notable not just for its medical significance, but because it is unique among its family (non-TK1-like deoxynucleotide kinases) in its strong affinity for substrate and tetrameric quaternary structure. One relatively novel technique used in the study is mass photometry, which is a relatively new and exciting way to characterize native proteins at very low concentrations. Using this technique helps the authors overcome a common criticism of structural studies in which the high concentrations or crowding conditions of techniques like crystallography and cryo-EM may be inducing non-physiological oligomers.

In summary, this work represents a meaningful addition to the protein structure-function literature. While it will principally be of interest to basic/fundamental researchers who study the mechanistic detail of protein function and evolution, it also provides a foundation for future translational work and antiparasitic drug design.

Reviewer's background: I received my PhD in chemistry studying the structure and function of another enzyme key to DNA metabolism (except in giardia), ribonucleotide reductase. My background is in structural biology and biochemistry. I do not have sufficient expertise to comment on studies performed on G. intestinalis growth and susceptibility to deoxyadenosine analogs.

• *

Author Response

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

Public Reviews:

Roget et al. build on their previous work developing a simple theoretical model to examine whether ageing can be under natural selection, challenging the mainstream view that ageing is merely a byproduct of other biological and evolutionary processes. The authors propose an agent-based model to evaluate the adaptive dynamics of a haploid asexual population with two independent traits: fertility timespan and mortality onset. Through computational simulations, their model demonstrates that ageing can give populations an evolutionary advantage. Notably, this observation arises from the model without invoking any explicit energy tradeoffs, commonly used to explain this relationship.

The model’s results are based on both numerical simulations and formal mathematical analysis.

Additionally, the theoretical model developed here indicates that mortality onset is generally selected to start before the loss of fertility, irrespective of the initial values in the population. The selected relationship between the fertility timespan and mortality onset depends on the strength of fertility and mortality effects, with larger effects resulting in the loss of fertility and mortality onset being closer together. By allowing for a trans-generational effect on ageing in the model, the authors show that this can be advantageous as well, lowering the risk of collapse in the population despite an apparent fitness disadvantage in individuals. Upon closer examination, the authors reveal that this unexpected outcome is a consequence of the trans-generational effect on ageing increasing the evolvability of the population (i.e., allowing a more effective exploration of the parameter landscape), reaching the optimum state faster.

The simplicity of the proposed theoretical model represents both the major strength and weakness of this work. On one hand, with an original and rigorous methodology, the logic of their conclusions can be easily grasped and generalised, yielding surprising results. Using just a handful of parameters and relying on direct competition simulations, the model qualitatively recapitulates the negative correlation between lifespan and fertility without requiring energy tradeoffs. This alone makes this work an important milestone for the rapidly growing field of adaptive dynamics, opening many new avenues of research, both theoretically and empirically.

We thank the reviewers and editor for highlighting the importance of the work presented here.

On the other hand, the simplicity of the model also makes its relationship with living organisms difficult to gauge, leaving open questions about how much the model represents the reality of actual evolution in a natural context.

We presented both in results and discussion how the mathematical trade-offs between fertility and survival time give rise to (xb, xd) configuration representative of existing aging modes.

In particular, a more explicit discussion of how the specifics of the model can impact the results and their interpretation is needed. For example, the lack of mechanistic details on the trans-generational effect on ageing makes the results difficult to interpret.

We discussed the role of the transgenerational Lansing effect played to its function, there is no need for a particular mechanism beyond that function of transgenerational negative effect. We reinforce this in the discussion by adding the following sentence “Regarding the nature of the transgenerational effect, our model is agnostic and the mere transmission of any negative effect would be sufficient to exert the function.“

Even if analytical results are obtained, most of the observations appear derived from simulations as they are currently presented. Also, the choice of parameters for the simulations shown in the paper and how they relate to our biological knowledge are not fully addressed by the authors.

The long time limit of the system with and without the Lansing effect is based on analytical results later confirmed using numerical simulations. The choice of parameters is explained in the introduction as being the minimum ones for defining a living organism. As for the parameters’ values, our numerical analysis gives a solution for any ib, id, xb and xd on R+, making the choice of initial value a mere random decision.

Finally, the conclusions of evolvability are insufficiently supported, as the authors do not show if the wider genotypic variability in populations with the ageing trans-generational effect is, in fact, selected.

We do not show nor claim that evolvability per se is selected for but that the apparent advantage given by this transgenerational effect seems to be mediated by an increased genotypic/phenotypic variability conferred to the lineage that we interpreted as evolvability.

Recommendations for the authors

(1) The authors could use the lineage tracing results for the evolvability aspect. Specifically, within subpopulations featuring the Lansing effect, it would be valuable to explore whether individuals with parental age greater than the mortality onset (a > x_d) demonstrate higher fitness compared to individuals with a < x_d. Additionally, an examination of how this variation evolves over time could provide further insights into the dynamics of the proposed model.

We thank the reviewer for this suggestion. This is an ongoing work in the group, especially in the context of varying environmental conditions.

(2) In all simulations, I_b = I_d = 1, resulting in total fertility (x_b * I_b) equating to x_b, while x_d is proportional to life expectancy. Considering an exploration of the implications of this parameter setting, the authors could frame x_d as a 'lifespan cost', potentially allowing for the model to be conceptualised in terms of energetic tradeoffs. This might offer additional perspectives on the dynamics of the model and its alignment with biological principles.

We discuss how the apparent trade-offs given by the model depending on ib and id values can be related to the interpretation of such trade-offs that has been accepted for most of the past century. Our claim here in the discussion is that one does not need such energetic trade-off for the fertility/longevity trade-offs to appear. Such energetic trade-off is not a “biological principle” but merely an accepted interpretation of a fertility/longevity trade-off that is not even a general mechanism.

(3) Considering the necessity of variation in x_d for the observed patterns, an exploration could be undertaken by the authors to examine a model where x_d is simply variable without inheritance. This could involve centring x_d at some value d with some variance σ_d for all individuals. In such a scenario, it may be observed whether the same convergence of x_b - x_d occurs without requiring x_d to be selected. Furthermore, similar consequences of the Lansing effect could potentially be identified.

This was done early on during our work and did not show any major changes in the model’s behaviour beyond the time of convergence. We did not include it to the final manuscript because of the low added value to an already long and complex manuscript.

(4) While it may not be necessary to alter the model itself, it is suggested that the authors consider acknowledging the potential consequences of certain modelling decisions that might be perceived as biologically unrealistic. Notable examples include assumptions such as fertility from birth and zero mortality prior to x_d. These assumptions, such as infertility from birth, could be viewed as distinctive features, and it might be worth mentioning that parental care of offspring could have co-evolved with such features. This is particularly relevant considering the energy tradeoff hypothesis that has been postulated.

Although inspired from results obtained in Drosophila, mice, nematodes and zebrafish, the model is so far haploid and asexual, thus involving individuals likely more similar to unicellulars. In these conditions, infertility from birth did not seem relevant to us. However, the model and codes are accessible online and we hope that others will use it to address such questions. It is interesting though to notice that ageing appears here without such constraint.

Additionally, the consideration that all organisms face a non-trivial mortality rate at every age, not solely from physiological causes, reflects the reality within which selection operates.

We thought this was the best way to reflect, an environment with a limited carrying capacity. A more complex model is under construction to take into account the fact that older individuals might be more sensitive to it than younger ones.

(5) While acknowledging the technical rigour applied by the authors, it is suggested that further attention be given to conducting a comprehensive 'reality check' associated with the chosen parameters, particularly regarding the biological relevance of the results. For instance, the authors argue that offspring of old organisms do not, on average, live similarly to their parents. However, it is noted that studies in the haploid asexual organism yeast, akin to what the authors model (albeit not necessarily yeast), revealed that the average lifespan of yeast progeny born from young or old mothers is very similar.

We do not claim that progeny of old parents live less long than that of younger parents on average, we say that it happens in the progeny of physiologically old parents, representing at most 10% of the population in our numerical simulations.

The authors cite experimental evolution in Drosophila progeny conceived later in the life of the parent, indicating that the onset of mortality in these progeny occurs late, sometimes even after the end of the fertility period (Burke et al., 2016; Rose et al., 2002). While the authors report their own previous studies with divergent results, independent experiments have suggested an increase of x_d following an artificial increase of x_b (Luckinbill and Clare, 1985; Sgro et al., 2000). A more in-depth consideration of these contrasting observations and their potential implications for the current model could enhance the overall robustness of the study.

The increase of x_d following an artificial increase of x_b is predicted by our model as discussed. The divergence of observations between studies is alas hard to assess.

(6) To enhance readability and maintain consistency, it is suggested that the authors homogenise the description of key parameters, specifically x_b and x_d, throughout the text. This could contribute to improved clarity and rigour. One recommendation is to refer to x_b consistently as the 'fertility span' and x_d as the 'mortality onset' for the sake of uniformity in terminology.

We have modified the text accordingly.

(7) At various points in the text, the assertion is made that observations have indicated a tradeoff between fertility and longevity. It is recommended that the authors provide references or data to substantiate this claim. This addition would contribute to the empirical grounding of the mentioned tradeoff and strengthen the overall support for the assertions made in the study.

We added the following references to the discussion Lemaitre et al., 2015, Kirkwood, 2005 and Rodrigues and Flatt 2016.

(8) The statement claiming that the model is 'able to describe all types of ageing observed in the wild' should be moderated. As the authors themselves acknowledge, the model is referred to as a 'toy model,' and it is made clear that it cannot capture, nor is intended to capture, the entire diversity observed in life. Adjusting this statement to reflect the limited scope and purpose of the model would enhance precision and accuracy in the presentation of its capabilities.

Although a toy model, its possible configurations encompass all the possible configurations described so far across the diversity of ageing throughout the tree of life from negligible senescence with no loss of fertility (x_b and x_d >> 0) to menopause-like configurations (x_b >> x_d) through fast mortality increase post reproduction (x_b = x_d). Replacing our current square functions would allow age-dependant decrease or increase of fertility and/or risks of mortality onsets.

(9) To bolster the biological relevance of the study, it is strongly recommended that the authors cross-check the results of their simulations with previously published experimental findings. This approach would serve to strengthen the alignment between the model outcomes and observed biological phenomena. Additionally, placing greater emphasis on the biological relevance aspects throughout the text would contribute to a more robust and comprehensive exploration of the study's implications.

In the present manuscript we have tried to cite a certain number of results from artificial selection experiments on life history traits in order to strengthen the interpretations of our model’s behaviour. There are numerous other studies, going in the same direction or not, but we do not think that it would be relevant to add an exhaustive list of them. Nevertheless, we added Stearns et al., 2000 that adds extrinsic high mortality to the evolution of life history traits.

(1) For enhanced clarity, it is suggested that the x-axis in Figure 1 be labelled as 'age.' Considering this adjustment could contribute to clearer visual communication of the data.

We agree with the reviewer and modified the figure accordingly.

(!!) The addition of graphical legends is recommended for Figures 3-5, as well as the supplementary figures. Including these legends would provide essential context and improve the interpretability of the figures for readers.

We agree with the reviewer and modified the figure accordingly.

(12) For improved distinction of the ranges indicated by quantiles in Figure 3, it is suggested that the authors consider enhancing visual clarity. One approach could involve making the middle quantile thicker or using a different line type. Additionally, it is recommended to explore the calculation of the highest density 90% intervals rather than the 1-9 deciles. This adjustment could contribute to a clearer representation of the data distribution in the figure.

We named the different deciles directly on the figure to improve readability.

(13) It is observed that the mathematical proofs in Annex 1 are not displaying properly in the PDF. Additionally, there seem to be missing and broken references for the Annex. This issue may be related to LaTeX formatting. The authors could consider revisiting the formatting of Annex 1 to ensure the correct display of mathematical proofs and address the referencing concerns, possibly by checking and rectifying any LaTeX-related issues.

The latex file of the supplementary was not correctly compiled. It is now corrected.

(14) There is inconsistency in the text regarding the reference to the Annex, with both 'Annex' and 'Annexe' being used interchangeably. To maintain uniformity, it is suggested that the authors consistently use either 'Annex' or 'Annexe' throughout the text. This adjustment would contribute to a more polished presentation of the supplementary material.

We corrected them accordingly.

(15)There appears to be a typographical error in the name of Supplementary Figure 3.

We corrected it accordingly.

I think the natural rights framework provide a view to understand human basic rights, but according on universal principles might not explain or solve complex ethical issues in a globalized context. Moreover, seeing everyone's rights as indivisible and equal seems unrealistic to me because it may overlook the impact of social and economic inequalities.

Author Response

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

Reviewer #1 (Public Review):

Wang et al investigated the evolution, expression, and function of the X-linked miR-506 miRNA family. They showed that the miR-506 family underwent rapid evolution. They provided evidence that miR-506 appeared to have originated from the MER91C DNA transposons. Human MER91C transposon produced mature miRNAs when expressed in cultured cells. A series of mouse mutants lacking individual clusters, a combination of clusters, and the entire X-linked cluster (all 22 miRNAs) were generated and characterized. The mutant mice lacking four or more miRNA clusters showed reduced reproductive fitness (litter size reduction). They further showed that the sperm from these mutants were less competitive in polyandrous mating tests. RNA-seq revealed the impact of deletion of miR-506 on the testicular transcriptome. Bioinformatic analysis analyzed the relationship among miR-506 binding, transcriptomic changes, and target sequence conservation. The miR-506-deficient mice did not have apparent effect on sperm production, motility, and morphology. Lack of severe phenotypes is typical for miRNA mutants in other species as well. However, the miR-506-deficient males did exhibit reduced litter size, such an effect would have been quite significant in an evolutionary time scale. The number of mouse mutants and sequencing analysis represent a tour de force. This study is a comprehensive investigation of the X-linked miR-506 miRNA family. It provides important insights into the evolution and function of the miR-506 family.

The conclusions of this preprint are mostly supported by the data except being noted below. Some descriptions need to be revised for accuracy.

L219-L285: The conclusion that X-linked miR-506 family miRNAs are expanded via LINE1 retrotransposition is not supported by the data. LINE1s and SINEs are very abundant, accounting for nearly 30% of the genome. In addition, the LINE1 content of the mammalian X chromosome is twice that of the autosomes. One can easily find flanking LINE1/SINE repeat. Therefore, the analyses in Fig. 2G, Fig. 2H and Fig. S3 are not informative. In order to claim LINE1-mediated retrotransposition, it is necessary to show the hallmarks of LINE1 retrotransposition, which are only possible for new insertions. The X chromosome is known to be enriched for testis-specific multi-copy genes that are expressed in round spermatids (PMID: 18454149). The conclusion on the LINE1-mediated expansion of miR-506 family on the X chromosome is not supported by the data and does not add additional insights. I think that the LINE1 related figure panels and description (L219-L285) need to be deleted. In discussion (L557558), "...and subsequently underwent sequence divergence via LINE1-mediated retrotransposition during evolution" should also be deleted. This section (L219-L285) needs to deal only with the origin of miR506 from MER91C DNA transposons, which is both convincing and informative.

Reply: Agreed, the corresponding sentences were deleted.

Fig. 3A: can you speculate/discuss why the miR-506 expression in sperm is higher than in round spermatids?

Reply: RNAs are much less abundant in sperm than in somatic or spermatogenic cells (~1/100). Spermborne small RNAs represent a small fraction of total small RNAs expressed in their precursor spermatogenic cells, including spermatocytes and spermatids. Therefore, when the same amount of total/small RNAs are used for quantitative analyses, sperm-borne small RNAs (e.g., miR-506 family miRNAs) would be proportionally enriched in sperm compared to other spermatogenic cells. We discussed this point in the text (Lines 550-556).

**Reviewer #2 (Public Review):

In this paper, Wang and collaborators characterize the rapid evolution of the X-linked miR-506 cluster in mammals and characterize the functional reference of depleting a few or most of the miRNAs in the cluster. The authors show that the cluster originated from the MER91C DNA transposon and provide some evidence that it might have expanded through the retrotransposition of adjacent LINE1s. Although the animals depleted of most miRNAs in the cluster show normal sperm parameters, the authors observed a small but significant reduction in litter size. The authors then speculate that the depletion of most miRNAs in the cluster could impair sperm competitiveness in polyandrous mating. Using a successive mating protocol, they show that, indeed, sperm lacking most X-linked miR-506 family members is outcompeted by wild-type sperm. The authors then analyze the evolution of the miR-506 cluster and its predicted targets. They conclude that the main difference between mice and humans is the expansion of the number of target sites per transcript in humans.

The conclusions of the paper are, in most cases, supported by the data; however, a more precise and indepth analysis would have helped build a more convincing argument in most cases.

(1) In the abstracts and throughout the manuscript, the authors claim that "... these X-linked miRNA-506 family miRNA [...] have gained more targets [...] " while comparing the human miRNA-506 family to the mouse. An alternative possibility is that the mouse has lost some targets. A proper analysis would entail determining the number of targets in the mouse and human common ancestor.

Reply: This question alerted us that we did not describe our conclusion accurately, causing confusion for this reviewer. Our data suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis. In other words, mice never lost any targets compared to humans, but per the miR-506 family miRNA tends to target more genes in humans than in mice.

We revised the text to more accurately report our data. The pertaining text (lines 490-508) now reads: “Furthermore, we analyzed the number of all potential targets of the miR-506 family miRNAs predicted by the aforementioned four algorithms among humans, mice, and rats. The total number of targets for all the X-linked miR-506 family miRNAs among different species did not show significant enrichment in humans (Fig. S9C), suggesting the sheer number of target genes does not increase in humans. We then compared the number of target genes per miRNA. When comparing the number of target genes per miRNA for all the miRNAs (baseline) between humans and mice, we found that on a per miRNA basis, human miRNAs have more targets than murine miRNAs (p<0.05, t-test) (Fig. S9D), consistent with higher biological complexity in humans. This became even more obvious for the X-linked miR-506 family (p<0.05, t-test) (Fig. S9D). In humans, the X-linked miR-506 family, on a per miRNA basis, targets a significantly greater number of genes than the average of all miRNAs combined (p<0.05, t-test) (Fig. S9D). In contrast, in mice, we observed no significant difference in the number of targets per miRNA between X-linked miRNAs and all of the mouse miRNAs combined (mouse baseline) (Fig. S9D). These results suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis.”

We also changed “have gained” to “have” throughout the text to avoid confusion.

(2) The authors claim that the miRNA cluster expanded through L1 retrotransposition. However, the possibility of an early expansion of the cluster before the divergence of the species while the MER91C DNA transposon was active was not evaluated. Although L1 likely contributed to the diversity within mammals, the generalization may not apply to all species. For example, SINEs are closer on average than L1s to the miRNAs in the SmiR subcluster in humans and dogs, and the horse SmiR subcluster seems to have expanded by a TE-independent mechanism.

Reply: Agreed. We deleted the data mentioned by this reviewer.

(3) Some results are difficult to reconcile and would have benefited from further discussion. The miR-465 sKO has over two thousand differentially expressed transcripts and no apparent phenotype. Also, the authors show a sharp downregulation of CRISP1 at the RNA and protein level in the mouse. However, most miRNAs of the cluster increase the expression of Crisp1 on a reporter assay. The only one with a negative impact has a very mild effect. miRNAs are typically associated with target repression; however, most of the miRNAs analyzed in this study activate transcript expression.

Reply: Both mRNA and protein levels of Crisp1 were downregulated in KO mice, and these results are consistent with the luciferase data showing overexpression of these miRNAs upregulated the Crisp1 3’UTR luciferase activity. We agree that miRNAs usually repress target gene expression. However, numerous studies have also shown that some miRNAs, such as human miR-369-3, Let-7, and miR-373, mouse miR-34/449 and the miR-506 family, and the synthetic miRNA miRcxcr4, activate gene expression both in vitro (1, 2) and in vivo (3-6). Earlier reports have shown that these miRNAs can upregulate their target gene expression, either by recruiting FXR1, targeting promoters, or sequestering RNA subcellular locations (1, 2, 6). We briefly discussed this in the text (Lines 605-611).

(4) More information is required to interpret the results of the differential RNA targeting by the murine and human miRNA-506 family. The materials and methods section needs to explain how the authors select their putative targets. In the text, they mention the use of four different prediction programs. Are they considering all sites predicted by any method, all sites predicted simultaneously by all methods, or something in between? Also, what are they considering as a "shared target" between mice and humans? Is it a mRNA that any miR-506 family member is targeting? Is it a mRNA targeted by the same miRNA in both species? Does the targeting need to occur in the same position determined by aligning the different 3'UTRs?

Reply: Since each prediction method has its merit, we included all putative targets predicted by any of the four methods. The "shared target" refers to a mRNA that any miR-506 family member targets because the miR-506 family is highly divergent among different species. We have added the information to the “Large and small RNA-seq data analysis” section in Materials and Methods (Lines 871-882).

(5) The authors highlight the particular evolution of the cluster derived from a transposable element. Given the tendency of transposable elements to be expressed in germ cells, the family might have originated to repress the expression of the elements while still active but then remained to control the expression of the genes where the element had been inserted. The authors did not evaluate the expression of transcripts containing the transposable element or discuss this possibility. The authors proposed an expansion of the target sites in humans. However, whether this expansion was associated with the expansion of the TE in humans was not discussed either. Clarifying whether the transposable element was still active after the divergence of the mouse and human lineages would have been informative to address this outstanding issue.

Reply: Agreed. The MER91C DNA transposon is denoted as nonautonomous (7); however, whether it was active during the divergence of mouse and human lineages is unknown. To determine whether the expansion of the target sites in humans was due to the expansion of the MER91C DNA transposon, we analyzed the MER91C DNA transposon-containing transcripts and associated them with our DETs. Of interest, 28 human and 3 mouse mRNAs possess 3’UTRs containing MER91C DNA sequences, and only 3 and 0 out of those 28 and 3 genes belonged to DETs in humans and mice, respectively (Fig. S9E), suggesting a minimal effect of MER91C DNA transposon expansion on the number of target sites. We briefly discussed this in the text (Lines 511-518).

Post-transcriptional regulation is exceptionally complex in male haploid cells, and the functional relevance of many regulatory pathways remains unclear. This manuscript, together with recent findings on the role of piRNA clusters, starts to clarify the nature of the selective pressure that shapes the evolution of small RNA pathways in the male germ line.

Reply: Agreed. We appreciate your insightful comments.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors conducted a comprehensive study of the X-linked miR-506 family miRNAs in mice on its origin, evolution, expression, and function. They demonstrate that the X-linked miR-506 family, predominantly expressed in the testis, may be derived from MER91C DNA transposons and further expanded by retrotransposition. By genetic deletion of different combinations of 5 major clusters of this miRNA family in mice, they found these miRNAs are not required for spermatogenesis. However, by further examination, the mutant mice show mild fertility problem and inferior sperm competitiveness. The authors conclude that the X-linked miR-506 miRNAs finetune spermatogenesis to enhance sperm competition.

Strengths:

This is a comprehensive study with extensive computational and genetic dissection of the X-linked miR506 family providing a holistic view of its evolution and function in mice. The finding that this family miRNAs could enhance sperm competition is interesting and could explain their roles in finetuning germ cell gene expression to regulate reproductive fitness.

Weaknesses:

The authors specifically addressed the function of 5 clusters of X-link miR-506 family containing 19 miRNAs. There is another small cluster containing 3 miRNAs close to the Fmr1 locus. Would this small cluster act in concert with the 5 clusters to regulate spermatogenesis? In addition, any autosomal miR-506 like miRNAs may compensate for the loss of X-linked miR-506 family. These possibilities should be discussed.

Reply: The three FmiRs were not deleted in this study because the SmiRs are much more abundant than the FmiRs in WT mice (Author Response image 1, heatmap version of Fig. 5C). Based on small RNA-seq, some FmiRs, e.g., miR-201 and miR-547, were upregulated in the SmiRs KO mice, suggesting that this small cluster may act in concert with the other 5 clusters and thus, worth further investigation. To our best knowledge, all the miR-506 family miRNAs are located on the X chromosome, although some other miRNAs were upregulated in the KO mice, they don’t belong to the miR-506 family. We briefly discussed this point in the text (Lines 635-638).

Author response image 1.

sRNA-seq of WT and miR-506 family KO testis samples.

Direct molecular link to sperm competitiveness defect remains unclear but is difficult to address.

Reply: In this study, we identified a target of the miR-506 family, i.e. Crisp1. KO of Crisp1 in mice, or inhibition of CRISP1 in human sperm (7, 8), appears to phenocopy the quinKO mice, displaying largely normal sperm motility but compromised ability to penetrate eggs. The detailed mechanism warrants further investigation in the future.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Lines 84-85: "Several cellular events are unique to the male germ cells, e.g., meiosis, genetic recombination, and haploid male germ cell differentiation (also called spermiogenesis)". This statement is not accurate. Please revise. Meiosis and genetic recombination are common to both male and female germ cells. They are highly conserved in both sexes in many species including mouse.

Reply: Agreed. We have revised the sentence and it now reads: “Several cellular events are unique to the male germ cells, e.g., postnatal formation of the adult male germline stem cells (i.e., spermatogonia stem cells), pubertal onset of meiosis, and haploid male germ cell differentiation (also called spermiogenesis) (9)” (Lines 83-86).

Lines 163-164: "we found that Slitrk2 and Fmr1 were syntenically linked to autosomes in zebrafish and birds (Fig. 1A), but had migrated onto the X chromosome in most mammals". This description is not accurate. Chr 4 in zebrafish and birds is syntenic to the X chromosome in mammals. The term "migrated" is not appropriate. Suggestion: Slitrk2 and Fmr1 mapped to Chr 4 (syntenic with mammalian X chromosome) in zebrafish and birds but to the X chromosome in most mammals.

Reply: Agreed. Revised as suggested.

Reviewer #2 (Recommendations For The Authors):

(1) In the significance statement, the authors mention that the mutants are "functionally infertile," although the decrease in competitiveness is partial. I suggest referring to them as "functionally sub-fertile."

Reply: Agreed. Revised as suggested.

(2) I will urge the authors to explain in more detail how some figures are generated and what they mean. Some critical information needs to be included in various panels.

(2a) Figure S1. The phastCons track does not seem to align as expected with the rest of the figure. The highest conservation peak is only present in humans, and the sequence conserved in the sea turtle has the lowest phastCons score. I was expecting the opposite from the explanation.

Reply: The tracks for phyloP and phastCons are the scores for all 100 species, whereas the tracks with the species names on the left are the corresponding sequences aligned to the human genome. We have revised our figure to make it clearer.

(2b) Figure 2A and Figure S2C. Although all the functional analysis of the manuscript has been done in mice, the alignments showing sequence conservation do not include the murine miRNAs. Please include the mouse miRNAs in these panels.

Reply: The mouse has Mir-506-P7 with the conserved miRNA-3P seed region, which was included in the lower panel in Figure S2C. However, mice do not have Mir-506-P6, which may have been lost or too divergent to be recognized during the evolution and thus, were not included in Figure 2A and the upper panel in Figure S2C.

(2c) Figure S7H. The panel could be easier to read.

Reply: Agreed. We combined all the same groups and turned Figure S7H (now Figure S6H) into a heatmap.

(2d) The legend of Figure 6G reads, "The number of target sites within individual target mRNAs in both humans and mice ." Can the author explain why the value 1 of the human "Number of target sites" is connected to virtually all the "Number of target sites" values in mice?

Reply: Sorry for the confusion. For example, for gene 1, we have 1 target site in the human and 1 target site in the mouse; but for gene 2, we have 1 target site in the human and multiple sites in the mouse; therefore, the value 1 is connected to more than one value in the mouse.

Reviewer #3 (Recommendations For The Authors):

CRISP1 and EGR1 protein localization in WT and mutant sperm by immunostaining would be helpful.

Reply: Agreed. We performed immunostaining for CRISP1 on WT sperm, and the new results are presented in Figure S8D. CRISP1 seems mainly expressed in the principal piece and head of sperm.

The detailed description of the generation of various mutant lines should be included in the Methods.

Reply: We added more details on the generation of knockout lines in the Materials and Methods (686701).

References:

(1) S. Vasudevan, Y. Tong, J. A. Steitz, Switching from repression to activation: microRNAs can upregulate translation. Science 318, 1931-1934 (2007).

(2) R. F. Place, L. C. Li, D. Pookot, E. J. Noonan, R. Dahiya, MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105, 1608-1613 (2008).

(3) Z. Wang et al., X-linked miR-506 family miRNAs promote FMRP expression in mouse spermatogonia. EMBO Rep 21, e49024 (2020).

(4) S. Yuan et al., Motile cilia of the male reproductive system require miR-34/miR-449 for development and function to generate luminal turbulence. Proc Natl Acad Sci U S A 116, 35843593 (2019).

(5) S. Yuan et al., Oviductal motile cilia are essential for oocyte pickup but dispensable for sperm and embryo transport. Proc Natl Acad Sci U S A 118 (2021).

(6) M. Guo et al., Uncoupling transcription and translation through miRNA-dependent poly(A) length control in haploid male germ cells. Development 149 (2022).

(7) V. G. Da Ros et al., Impaired sperm fertilizing ability in mice lacking Cysteine-RIch Secretory Protein 1 (CRISP1). Dev Biol 320, 12-18 (2008).

(8) J. A. Maldera et al., Human fertilization: epididymal hCRISP1 mediates sperm-zona pellucida binding through its interaction with ZP3. Mol Hum Reprod 20, 341-349 (2014).

(9) L. Hermo, R. M. Pelletier, D. G. Cyr, C. E. Smith, Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microsc Res Tech 73, 241-278 (2010).

Author Response

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

Reviewer #1 (Public Review):

The present study by Berger et al. analyzes to what extent memory formation is dependent on available energy reserves. This has been dealt with extensively in the case of aversive memory formation, but only very sparsely in the case of appetitive memory formation. It has long been known that an appetitive memory in flies can only be formed by starvation. However, the authors here additionally show that not only the duration of starvation plays a role, but also determines which form of memory (short- or long-term memory) is formed. The authors demonstrated that internal glycogen stores play a role in this process and that this is achieved through insulin-like signaling in octopaminergic reward neurons that integrates internal energy stores into memory formation. Here, the authors suggest that octopamine plays a role as a negative regulator of different forms of memory.

The study sheds light on an old question, to what extent the octopaminergic neuronal system plays a role in the formation of appetitive memory, since in recent years only the dopaminergic system has been in focus. Furthermore, the data are an interesting contribution to the ongoing debate whether insulin receptors play a role in neurons themselves or in glial cells. The experiments are very well designed and the authors used a variety of behavioural experiments, genetic tools to manipulate neuronal activity and state-of-the-art imaging techniques. In addition, they not only clearly demonstrated that octopamine is a negative regulator of appetitive memory formation, but also proposed a mechanism by which the insulin receptor in octopaminergic neurons senses the internal energy status and then controls the activity of those neurons. The conclusions are mostly supported by the data, but some aspects related to the experimental design, some explanations and literature references need more clarification and revision.

(1) Usually, long-term memory (LTM) is tested 24 hours after training. Here, the authors usually refer to LTM as a memory that is tested 6 hours after training. The addition of a control experiment to show that LTM that the authors observe here lasts longer would increase the power of this study immensely.

We thank the reviewer for this comment, as it helped greatly to clarify the matter.

We measured memory of control and mutant flies 24 h after the training and included the data into the manuscript (Figure 1B and summarized in a model in Figure 2C). We show that control flies develop an intermediate type of memory, that is depending on the length of starvation either anesthesia-sensitive or resistant. Mutants lacking octopamine develop either anesthesia-sensitive or resistant long-term memory.

(2) The authors define here another consolidated memory component as ARM, when they applied a cold-shock 2 hours after training. However, some publications showed that LTM is formed after only one training cycle (Krashes et al 2008, Tempel et al 1983). This makes it difficult to determine, whether appetitive ARM can be formed. Furthermore, one study showed that appetitive ARM is absent after massed training (Colomb et al 2009). Therefore, the conclusion could be also, that different starvation protocols, would lead to different stabilities of LTM. Therefore, additional experiments could help to clarify this opposing explanation. From these results, it can then be concluded either that different stable forms of LTM are formed depending on the starvation state, or that two differently consolidated memory phases (LTM, ARM) are formed, as has already been shown for aversive memory. This is also important for other statements in the manuscript, and therefore the authors should address this. For example, the findings about the insulin receptor (is it two opposing memories or different stabilities of LTM).

The flies indeed develop different types of memory depending on the length of starvation and the internal energy supply.

Reviewer #2 (Public Review):

How organism physiological state modulates establishment and perdurance of memories is a timely question that the authors aimed at addressing by studying the interplay between energy homeostasis and food-related conditioning in Drosophila. Specifically, they studied how starvation modulates the establishment of short-term vs long-term memories and clarified the role of the monoamine Octopamine in food-related conditioning, showing that it is not per se involved in formation of appetitive short-term memories but rather gates memory formation by suppressing LTM when energy levels are high. This work clarifies previously described phenotypes and provides insight about interconnections between energy levels, feeding and formation of short-term and long-term food-related memories. In the absence of population-specific manipulation of octopamine signaling, it however does not reach a circuit-level understanding of how these different processes are integrated.

Strengths

• Previous studies have documented the impact of Octopamine on different aspects of food-related behaviors (regulation of energy homeostasis, feeding, sugar sensing, appetitive memory...), but we currently lack a clear understanding of how these different functions are interconnected. The authors have used a variety of experimental approaches to systematically test the impact of internal energy levels in establishment of appetitive memory and the role of Octopamine in this process.

• The authors have used a range of approaches, performed carefully controlled experiments and produced high quality data.

Weaknesses

(1) In the tbh mutant flies, Tyramine -to- Octopamine conversion is inhibited, resulting not only in a lack of Octopamine, but also in elevated levels of Tyramine. If and how elevated levels of Tyramine contributes to the described phenotypes is unclear. In the current version of the manuscript, only one set of experiments (Figure 2) has been performed using Octopamine agonist. This is particularly important in light of recent published data showing that starvation modifies Tyramine levels. (2) Octopamine (and its precursor Tyramine) have been implicated in numerous processes, complicating the analysis of the phenotypes resulting from a general inhibition of tbh.

We thank the reviewer for raising these points. The observed memory defects of the Tbh mutants can be solely explained by loss of octopamine. We included models into the manuscript to illustrate this (Figure 2 C and Figure 7E).

To address whether the elevated levels of tyramine observed in Tbh mutants interfere with food consumption, we analyzed the effect of increased levels of tyramine and octopamine on food consumption. We included the data (Figure S2). An increase in tyramine levels did not result in a change in food intake, rather the increase in octopamine levels reduced food intake. Our data show that the reduction of food intake observed in starved Tbh mutants is due to the increased internal energy supply.

(3) The manuscript explores various aspects of the impact of energy levels on food-related behaviors and the underlying sensing and effector mechanism, both in wild-type and tbh mutants, making it difficult to follow the flow of the results.

We included models illustrating the results to clarify the content of the manuscript.

Reviewer #3 (Public Review):

In this manuscript, Berger et al. study how internal energy storage influence learning and memory. Since in Drosophila melanogaster, octopamine (OA) is involved in the regulation of energy homeostasis they focus on the roles of OA. To do so they use the tyramine-β-hydroxylase (Tbh) mutant that is lacking the neurotransmitter OA and study short term memory (STM), long-term memory (LTM) and anesthesia-resistant memory (ARM). They show that the duration of starvation affects the magnitude of both short- and long-term memory. In addition, they show that OA has a suppressive effect on learning and memory. In terms of energy storage, they show that internal glycogen storage influences how long sucrose is remembered and high glycogen suppresses memory. Finally, they show that insulin-like signaling in octopaminergic neurons, which is also related to internal energy storage, suppresses learning and memory.

This is an important study that extends our knowledge on OA activity in learning and memory and the effects the metabolic state has on learning and memory. The authors nicely use the genetic tools available in flies to try and unravel the complex circuitry of metabolic state level, OA activity and learning and memory.

Nevertheless, I do have some comments that I think require attention:

(1) The authors use RNAi to reduce the level of glycogen synthase or glycogen phosphorylase. These manipulations are expected to affect the level of glycogen. Using specific drivers the authors attempt to manipulate glycogen level at the muscles and fat bodies and examine how this affects learning and memory. The conclusions of the authors arise solely from the manipulation intended (i.e. the genetics). However, the authors also directly measured glycogen levels at these organs and those do not follow the manipulation intended, i.e. the RNAi had very limited effect on the glycogen level. Nevertheless, these results are ignored.

We agreed with the reviewer and repeated the experiments. While we could not detect differences in whole animals, we detected differences in tissues enriched for muscles or fat, e.g. thorax or abdomen. We added the data.

(2) The authors claim in the summary that OA is not required for STM. However, according to one experiment OA is required for STM as Tbh mutants cannot form STM. In another experiment OA is suppressive to STM as wt flies fed with OA cannot form STM. Therefore, it is very difficult to appreciate the actual role of OA on STM.

During mild starvation, the internal energy supply is greater in Tbh mutants than in control flies. This information is integrated into the reward system via insulin receptor signaling. Therefore, the association between the odorant and sucrose is not meaningful to the mutants and no STM is formed. At the same time there is no release of octopamine and therefore no repression of LTM. In starved animals, octopamine suppresses food intake (we added the data). This is consistent with a function of Octopamine as a signal for the presence of food. Depending on when the signal comes, this might suppress the formation of STM or LTM.

(3) The authors use t-test and ANOVA for most of the statistics, however, they did not perform normality tests. While I am quite sure that most datasets will pass normality test, nevertheless, this is required.

Thanks for pointing this out. We have included a description in the “Materials and Methods” section that explains how we tested the data for normal distribution. We corrected the figure legends accordingly.

“We used the Shapiro-Wilk test (significance level P < 0.05) followed by a QQ-Plot chart analysis to determine whether the data were normal distributed. “

(4) While it is logical to assume that OA neurons are upstream to R15A04 DA neurons, I am not sure this really arises from the experiment that is presented here. It is well established that without activity in R15A04 DA neurons there is no LTM. Since OA acts to decrease LTM, can one really conclude anything about the location of OA effect when there is no learning?

Normally control flies did not form memory 6 h after training, only Tbh mutants. We wanted to investigate what kind of memory develops in Tbh mutants. During the experiments of the manuscript, we kept the training procedure constant.

(5) It is unclear how expression of a dominant negative form of insulin receptor (InR) in OA neurons can rescue the lack of OA due to the Tbh mutation. If OA neurons cannot release anything to the presumably downstream DA neurons, how can changing their internal signaling has any effect?

The expression of the dominant negative form of the insulin receptor signals no food or low energy levels and activation of the insulin receptor that there is enough food. The reward is a source of food, but the energy content is not high enough to fill the energy stores. The insulin receptor activation can activate at least three different signaling cascades, one of which might regulate octopamine release.

While I stressed some comments that need to be addressed, the overall take-home message of the manuscript is supported and the authors do show that the metabolic state of the animal affects learning and memory. I do think though, that some more caution is required for some of the conclusions.

We added additional data to address the points raised.

Recommendations for the authors:

We addressed all points raised by the reviewers, clarified the content or added more data.

Reviewer #1 (Recommendations For The Authors):

(1) Throughout the manuscript, the full stop of a sentence is always placed before the references.

We fixed this.

(2) I find the English in the manuscript not yet sufficient for publication. I suggest that the authors carefully revise the manuscript. I think if the sentences are structured a little more clearly, this paper has enormous potential to be read by your broad community.

We agree and revised the manuscript. We hope the manuscript is now clearer.

(3) Sentences l114 to l117 are misleading. The authors imply that they tested the same flies for changes in odor perception or sucrose sensitivity. I assume that the authors meant that they analyzed different groups of animals.

We clarified the sentence as follows:

“To ensure that the observed differences in learning and memory were not due to changes in odorant perception, odorant evaluation or sucrose sensitivity, different fly populations of the same genotypes were tested for their odorant acuity, odorant preference and their sucrose responsiveness (Table S1).”

(4) In the title as well as in the abstract the influence of octopamine on appetitive memory formation is described in more detail, this is also the main focus of this study. However, in the introduction, the influence of the insulin receptor on memory formation is discussed first. Personally, I would describe this later in the manuscript, ideally in the results section. At this point in the manuscript, this leads to an interruption in the flow of reading.

Thanks for the suggestion. We changed the order in the introduction.

(5) The authors could consider, since they only used Drosophila melanogaster, changing "Drosophila melanogaster" to "Drosophila" throughout the manuscript.

We modified the text accordingly.

(6) All evaluations and statistical tests are state of the art. However, I have one comment. For each statistical test, a correction should be made depending on the number of tests. However, I could not determine whether this was also done for the parametric or non-parametric one-sample t-test. From the results and the methods section, I would guess not. Here I would recommend a Bonferroni correction or even better a Sidak-Holm correction. Furthermore, the authors could also go into more detail about which non-parametric one-sample t-test they used.

We described the statistic used in more detail in the material and method section.

“We used the Shapiro-Wilk test (significance level P < 0.05) followed by a QQ-Plot chart analysis to determine whether the data were normal distributed. For normal distributed data, we used the Student’s t test to compare differences between two groups and the one-way ANOVA with Tukey’s post hoc HSD test for differences between more than two groups. For nonparametric data, we used the Mann-Whitney U test for differences between two groups and for more than two groups the Kruskal-Wallis test with post hoc Duenn analysis and Bonferroni correction. The nonparametric one-sample sign test was used to analyze whether behavior was not based on random choice and differed from zero (P < 0.5). The statistical data analysis was performed using statskingdom (https://www.statskingdom.com).”

(7) In nearly all figure legends the sentence "The letter "a" marks a significant difference from random choice as determined by a one-sample sign test (P < 0.05; P< 0.01)" occur. This is correctly indexed in the figures. However, I do not understand here what then P < 0.05; P**< 0.01 means. The significance level should be described here. I would strongly recommend the authors to make the definition clearer.

We corrected this in the figure legends (see also above).

(8) In Fig. 1B the labelling is a bit confusing. I interpret the two right groups as the mutants for octopamine, but there is still w[1118] in front.

We modified the Figure 1B.

Reviewer #2 (Recommendations For The Authors):

Suggestions

(1) Assessing the contribution of Tyramine in the observed phenotypes (for example by reducing the levels of Tyramine or its specific receptor) would help understand the contribution of Tyramine in the observed phenotypes.

See comments above.

We thank the reviewer for raising these points. The observed memory defects of the Tbh mutants can be solely explained by loss of octopamine. We included models into the manuscript to illustrate this (Figure 2 C and Figure 7E).

To address whether the elevated levels of tyramine observed in Tbh mutants interfere with food consumption, we analyzed the effect of increased levels of tyramine and octopamine on food consumption. We included the data (Figure S2). An increase in tyramine levels did not result in a change in food intake, rather the increased octopamine levels reduced food intake. Our data show that the reduction of food intake observed in starved Tbh mutants is due to the increased internal energy supply.

(2) Cell-specific inhibition of octopamine receptors should thus be performed to precisely interpret the observed phenotypes and dissect how interconnected the different phenotypes are, which is the object of this publication.

We observed that the time point and duration of octopamine application changes the behavioral output. The behavior analyzed depends on pulses of octopamine and differences of the internal energy status. A cell-specific inhibition via RNAi knock down of octopamine receptors might not clarify the issue.

(3) Defining of streamline and progressively integrating the different observations into a unifying model would improve the clarity and flow of the manuscript.

We included models explaining the observed results (Figure 2C and Figure 7E).

Minor comments

Line 129: Figure 1B should be mentioned, not 2B.

Figure 1 legend: E should be replaced by C (after A,B).

Figure S5: what are the arrows pointing to? Why are the Inr foci visible in A not seen in B? It should be mCD8-GFP and not mCD on top of the images.

We fixed this.

Reviewer #3 (Recommendations For The Authors):

Major:

(1) Can one really conclude from Figure 2A that OA acts on R15A04 DA neurons? It is well established that without activity in these DA neurons there is no LTM. Since OA acts to decrease learning, how one can conclude anything about the location of OA effect when there is no learning? With STM the situation was opposite, OA supported learning and this was abolished when DA neurons were silenced. I think some supporting experiment are required, i.e. how OA affects DA neurons activity or, alternatively, tone down a bit the writing.

Normally control flies did not form memory 6 h after training, only Tbh mutants. We wanted to investigate what kind of memory develops in Tbh mutants. During the experiments of the manuscript, we kept the training procedure constant. The inhibition of dopaminergic neurons blocks the memory of Tbh mutants. Taken together the duration of the memory, the cold-shock experiments and the inhibition of the dopaminergic neurons, Tbh develops LTM after training. This training does not evoke memory in controls.

The loss of STM in mildly starved Tbh mutants depends on the integration of the high internal energy levels via InR signaling. Reducing the internal energy levels further by extension of starvation result in STM supporting that OA is not directly involved in the formation of STM.

(2) Figure 4 requires some clarifications. In Supplementary Figure S2 the authors show that they could not manipulate glycogen levels in muscles. However, in Figure 4B they show that "Increasing glycogen levels in the muscles did not change short-term memory in 16 h starved flies, but the reduction in glycogen significantly improved memory strength (Figure 4B)" (lines 231-233). How can this be reconciled?

While we could not detect differences in whole animals, we detected differences in glycogen content in body parts enriched with muscles or fat, e.g. thorax or abdomen when using UAS-GlyP-RNAi or UAS-GlyS-RNAi under the control of the respective Gal4 drivers.

We added the data.

Likewise, the authors write that "Increasing or decreasing glycogen levels in the fat bodies had no effect on memory performance (Figure 4C)" Line (233-234). However, in Figure S2 they show that they can only increase glycogen levels but not decrease them.

As explained above the conclusion of Figure 4 "Thus, low levels of glycogen in the muscles upon starvation positively influence appetitive short-term memory, while high levels of glycogen in the muscles and fat body reduce short-term memory" lines 245-246, is not supported by the direct measurements of glycogen presented in Figure S2.

We added the data showing that the reduction or increase can be measured when analyzing the specific body parts enriched in muscles tissue or fat tissue.

(3) In cases where mutant flies do not display learning, a control should be done to see if they ate the sugar (with dye). Especially since the genetic manipulation affects metabolism.

We analyzed how much sucrose the animals consumed in the behavioral test. Tbh and controls fed and there was no difference in feeding behavior between the mutants and the controls.

“We next determined whether differences in preferences influence sucrose intake during training. Therefore, we measured the sucrose intake of starved flies in the behavioral set up. We used a food-colored sucrose solution and evaluated the presence of food in the abdomen of the fly after two 2 min (Table S1). Flies fed sucrose within 2 min and there was no difference between w1118 and TβhnM18 flies. “

(4) The use of t-test requires the data to be normally distributed. If I am not mistaken this was not demonstrated for any of the datasets used. I did a quick check on one of the datasets provided in the excel sheet and it is normally distributed. Therefore, please add normality test for all data sets. If some do not pass normality, please use a suitable non-parametric test.

We added normality test to all data sets and used non-parametric tests for non-normal distributed data. We clarify this in the material and method section and the figure legends.

(5) The authors show that OA suppresses also STM. This result is in contradiction to previous published results. This by itself is not a problem. However, this result also seems to me in contradiction to the authors own results. According to Figure 1B, OA is required for STM as it absence in the tbh mutant results in loss of STM. According to Figure 2C, OA is reducing STM as wt flies fed with OA just prior to learning do not form STM. This appears in other places in the manuscript as well.

In addition, in the text lines 178-180, the authors write "A short pulse of octopamine before the training inhibits the STM. Thus, octopamine is a negative regulator of appetitive dopaminergic neuron-dependent long-term memory and can block STM." But in the summary they write "Octopamine is not required for short-term memory, since octopamine deficient mutants form appetitive short-term memory to sucrose and to other nutrients depending on the internal energy status." So, the take-home message regarding OA and STM is unclear.

The authors need to better clarify this point.

We clarified these points. See comments above. The loss of memory in Tbh mutants is not due to loss of octopamine, but increased energy levels that changes the reward properties of sucrose.

(6) The manuscript is very difficult to follow. The authors constantly change between 16 and 40 hours starvation, short term memory, 3 hour memory and 6 hour memory. I think it would have been better to have a more focused manuscript. However, if this is not possible, I recommend preparing a diagram with the different neurons or signaling pathways (i.e. insulin) and how they affect each other. Also, perhaps add to each figure a panel describing exactly the experimental conditions. I think also simplifying the text and adding more conclusions throughout the results section will help the readers to follow. Finally, I think that it would help understanding the conclusions if the authors can add a diagram of the flow that they think occurs. For example, the authors show that glycogen suppresses learning as its reduction increases learning. They also show that InR activity receptor suppresses learning as its KD also increases learning. If I am not mistaken the link between the two is not straight forward (but I may be wrong here). A diagram of the flow would be very helpful.

We prepared diagrams summarizing and explaining the results.

Minor

(1) I may not have understood correctly as I am not sure that I found Table S1.

Also, there was no legend for Table S1.

Nevertheless, if I understood correctly, the authors write that "Before the experiments, flies were tested to determine whether they perceived the odorants, preferred one odorant over other and responded to the reward similarly to ensure that the observed differences in behavior were not due to changes in odorant perception or sucrose sensitivity (Table S1)." However, according to the Table that I found it seems that following 40h starvation wt flies show preference to OCT whereas this does not occur for the mutant. Also, it seems that at 16h the mutant has a much higher preference to the odors than after 40h. This is a bit odd. I am also not sure what the balance value refers to. Finally, the mutant shows really low 2M sucrose preference after 40h. In general, this set of experiments requires a bit more explanation.

I think it is better to show these experiments using graphs and add this to the supplementary figures.

We clarified the experiments in the result section as follows and added an explanation to the material and method section. We tested the odorant acuity and sucrose preference for all genotypes used in the manuscript and added the data to the Table S1.

“The flies of the different genotypes sensed the odorants and evaluated them as similar salient in comparison. This is important to a avoid a bias in the situation where flies have to choose between the two odorants after training. They also sensed sucrose. We next determined whether differences in preferences influence sucrose intake during training. Therefore, we measured the sucrose intake of starved flies in the behavioral set up. We used a food-colored sucrose solution and evaluated the presence of food in the abdomen of the fly after two 2 min (Table S1). Flies fed sucrose within 2 min and there was no difference between w1118 and TβhnM18 flies.”

(2) Line 129 should be Figure 1B

Is corrected.

(3) Line 133, Figure 1C, how can one explain the negative reinforcement? I can understand no reinforcement, but negative?

The effect of glucose might be doses dependent. 0.15 M sucrose is a much closer to a realistic concentration found in fruits than 2 M sucrose and might therefore elicit aversion. When animals are starved enough they might find any food source attractive, even when the concentrations of sucrose is unrealistic.

(4) Figure 1, why are the graphs different between panel B and C?

Is corrected.

(5) In Figure S1, are the TβhnM18 groups differ significantly from zero? I think they are, so better to state this somewhere. If not, the claims in lines 134-135 are not supported by the data.

We added the significance and added the data to Figure 1.

Figure S1 legend: there is no A panel. Also "below box blots" should be box plots.

Thanks for pointing that out. We corrected it.

(6) It is not clear what is the duration of starvation used in Figure 2A. I assume that 16h and sucrose 2M used were used, but I would state that explicitly.

We added the information to the figure legends.

(7) Figure 2A is missing a control of flies with both the driver and UAS shibirets at the permissive temperature.

We added the controls to the supplement (Figure S1).

(8) It seems to me that Figure 3B, in which the author state that "Only after 40 h of starvation did TβhnM18 mutants show a similar preference to control sucrose consumption" (line 198) is somewhat in contradiction to Table S1 in which I see Sucrose preference for wt 0.36 and for tbh 0.17. I think this comment arise because I did not understand Table S1 correctly, so please better explain.

We rewrote this section.

(9) In Figure 3C, consider not using std as this stands for standard deviation and may be confusing.

We now use the term “food” instead of “std” and explained in the legend that food means standard fly food.

We fixed this.

(10) Please check the Supplementary Figures. I think Figures S2 and S3 are switched.

We fixed this.

(11) There is a mistake in Figure S3A. The right column should have another "+" sign.

Thanks, we fixed this.

(12) I am somewhat puzzled by Figures 4 and 5. If I understand correctly figure 4B w1118 mef2-G4 is exactly the same experiment as Figure 5A w1118 mef2-G4 and yet in Figure 4B performance index is 0.2 and in Figure 5A about 0.4. According to other comparisons it seems to me that these will be significantly different and yet it is the same experiment.

They are two independent experiments done at different times. The controls were independently repeated.

(13) Line 273 should be Figure 5C.

Is corrected.

(14) I don't think this is a correct sentence "Virgin females remembered sucrose significantly better than mated females." Line 274.

Reads now:

“Virgin females remembered the odorant paired with sucrose significantly better than mated females.”

(15) Line 340 there is no Figure 1E

Is fixed (1 C)

(16) The data excel file is difficult to follow. In Figure 2 there are references to Figure 5. The graphs are pointing to other files. Text is not always in English. It is not clear what W stands for. I recommend making it more accessible.

We corrected the data excel files.

(17) The manuscript is difficult to follow. I recommend preparing a diagram with the different neurons or signaling pathways (i.e. insulin) and how they affect each other.

We improved the data presentation by

a) adding a model showing the kinetics of memory formation in controls and mutants (Figure 2C)

b) a model explaining how the internal state is integrated into the formation of memory (Figure 7D).

Setting up and learning a note taking or card index system has a reasonably large up-front cost, but learning it well and being able to rely on it over long periods of time will eventually reap larger and cheaper long-term outcomes and benefits.

Unless changing systems creates dramatically larger improvements, the cost of change will surely swamp the benefits making the switch useless. This advice given by Kaiser is still as true today as it was in 1908, we tend not to think about the efficiency as much now as he may have then however and fall trap to shiny object syndrome.

Author Response

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

This study reports important evidence that infants' internal factors guide children's attention and that caregivers respond to infants' attentional shifts during caregiver-infant interactions. The authors analyzed EEG data and multiple types of behaviors using solid methodologies that can guide future studies of neural responses during social interaction in infants. However, the analysis is incomplete, as several methodological choices need more adequate justification.

Reviewer #1

Public Review:

The authors bring together multiple study methods (brain recordings with EEG and behavioral coding of infant and caregiver looking, and caregiver vocal changes) to understand social processes involved in infant attention. They test different hypotheses on whether caregivers scaffold attention by structuring a child's behavior, versus whether the child's attention is guided by internal factors and caregivers then respond to infants' attentional shifts. They conclude that internal processes (as measured by brain activation preceding looking) control infants' attention, and that caregivers rapidly modify their behaviors in response to changes in infant attention.

The study is meticulously documented, with cutting-edge analytic approaches to testing alternative models; this type of work provides a careful and well-documented guide for how to conduct studies and process and analyze data for researchers in the relatively new area of neural response in infants in social contexts.

We are very pleased that R1 considers our work an important contribution to this developing field, and we hope that we have now addressed their concerns below.

Some concerns arise around the use of terms (for example, an infant may "look" at an object, but that does not mean the infant is actually "attending); collapsing of different types of looks (to people and objects), and the averaging of data across infants that may mask some of the individual patterns.

We thank the reviewer for this feedback and their related comments below, and we feel that our manuscript is much stronger as a result of the changes we have made. Please see blow for a detailed description of our rationale for defining and analysing the attention data, as well as the textual changes made in response to the author’s comments.

Recommendations For The Authors

This paper is rigorous in method, theoretically grounded, and makes an important contribution to understanding processes of infant attention, brain activity, and the reciprocal temporal features of caregiver-infant interactions. The alternative hypothesis approach sets up the questions well (although authors should temper any wording that suggests attention processes are one or the other. That is, certain bouts of infant attention can be guided by exogenous factors such as social input, and others be endogenous; so averaging across all bouts can actually mask the variation in these patterns). I appreciated the focus on multiple types of behavior (e.g., gaze, vocal fluctuations in maternal speech); the emphasis on contingent responding; and the very clear summaries of takeaways after each section. Furthermore, methods and analyses are well described, details on data processing and so on are very thorough, and visualizations aptly facilitate data interpretation. However, I am not an expert on infant neural responses in EEG and assume that a reviewer with such expertise will weigh in on the treatment and quality of the data; therefore, my comments should be interpreted in light of this lack of knowledge.

We thank R1 for these very positive and insightful comments on our analyses which are the result of a number of years of methodological and technical developmental work.

We do agree with R1 that we should more carefully word parts of our argument in the Introduction to make clear the fact that shifts in infant attention could be driven by a combination of interactive and endogenous influences. As a result of this comment, we have made direct changes to parts of the Introduction; removing any wording that suggests that these processes are ‘alternative’ or ‘separate’, and our overall aim states: ‘Here, recording EEG from infants during naturalistic interactions with their caregiver, we examined the (inter)-dependent influences of infants’ endogenous oscillatory neural activity, and inter-dyadic behavioural contingencies in organising infant attention’.

Examining variability between infant attention episodes in the factors that influence the length and timing of the attention episode is an important area for future investigation. We now include a discussion on this on page 38 of the Discussion section, with suggestions for how this could be examined. Investigating different subtypes of infant attention is methodologically challenging, given the number of infant behaviours that would need to inform such an analysis- all of which are time consuming to code. Developing automated methods for performing these kinds of analyses is an important avenue for future work.

Here, I review various issues that require revision or elaboration based on my reading of what I consider to otherwise be a solid and important research paper.

Problem in the use of the term attention scaffolding. Although there may be literature precedent in the use of this term, it is problematic to narrowly define scaffolding as mother-initiated guidance of attention. A mother who responds to infant behaviors, but expands on the topic or supports continued attention, and so on, is scaffolding learning to a higher level. I would think about a different term because it currently implies a caregiver as either scaffolding OR responding contingently. It is not an either-or situation in conceptual meaning. In fact, research on social contingency (or contingent responsiveness), often views the follow-in responding as a way to scaffold learning in an infant.

Yes, we agree with R1 that the term ‘attention scaffolding’ could be confusing given the use of this term in previous work conducted with children and their caregivers in problem-solving tasks, that emphasise modulations in caregiver behaviour as a function of infant behaviour. As a result of this suggestion, we have made direct edits to the text throughout, replacing the term attentional scaffold with terms such as ‘organise’ and ‘structure’ in relation to the caregiver-leading or ‘didactic’ perspective, and terms such as ‘contingent responding’ and ‘dynamic modulation’ in relation to the caregiver-following perspective. We feel that this has much improved the clarity of the argument in the Introduction and Discussion sections.

Do individual data support the group average trends? My concern with unobservable (by definition) is that EEG data averages may mask what's going on in individual brain response. Effects appear to be small as well, which occurs in such conditions of averaging across perhaps very variable response patterns. In the interest of full transparency and open science, how many infants show the type of pattern revealed by the average graph (e.g., do neural markers of infant engagement forward predict attention for all babies? Majority?). Non-parametric tests on how many babies show a claimed pattern would offer the litmus test of significance on whether the phenomenon is robust across infants or pulled by a few infants with certain patterns of data. Ditto for all data. This would bolster my confidence in the summaries of what is going on in the infant brain. (The same applies as I suggest to attention bouts. To what extent does the forward-predict or backward-predict pattern work for all bouts, only some bouts, etc.?). I recognize that to obtain power, summaries are needed across infants and bouts, but I want to know if what's being observed is systematic.

We thank R1 for this comment and understand their concern that the overall pattern of findings reported in relation to the infants’ EEG data might obscure inter-individual variability in the associations between attention and theta power. Averaging across individual participant EEG responses is, however, the gold standard way to perform both event-locked (Jones et al., 2020) and continuous methods (Attaheri et al., 2020) of EEG analysis that are reported in the current manuscript. EEG data, and, in particular, naturalistic EEG data is inherently noisy, and averaging across participants increases the signal to noise ratio (i.e. inconsistent, and, therefore, non-task-related activity is averaged out of the response (Cohen, 2014; Noreika et al., 2020)). Examining individual EEG responses is unlikely to tell us anything meaningful, given that, if a response is not found for a particular participant, then it could be that the response is not present for that participant, or that it is present, but the EEG recording for that participant is too noisy to show the effect. Computing group-level effects, as is most common in all neuroimaging analyses, is, therefore, most optimal to examining our main research questions.

The findings reported in this analysis also replicate previous work conducted by our lab which showed that infant attention to objects significantly forward-predicted increases in infant theta activity during joint table-top play with their caregiver, involving one toy object (compared to our paradigm which involved 3;Wass et al., 2018). More recent work conducted by our lab has also shown continuous and time-locked associations between infant look durations and infant theta activity when infants play with objects on their own (Perapoch Amadó et al., 2023). To reassure readers of the replicability of the current findings, we now reference the Wass et al. (2018) study at the beginning of the Discussion section.

Could activity artifacts lead to certain reported trends? Babies typically look at an object before they touch or manipulate the object, and so longer bouts of attention likely involve a look and then a touch for lengthier time frames. If active involvement with an object (touching for example) amplifies theta activity, that may explain why attention duration forward predicts theta power. That is, baby looks, then touches, then theta activates, and coding would show visual gaze preceding the theta activation. Careful alignment of infants' touches and other such behaviors with the theta peak might help address this question, again to lend confidence to the robustness of the interpretation.

Yes, again this is a very important point, and the removal of movement-related artifact is something we have given careful attention to in the analysis of our naturalistic EEG data (Georgieva et al., 2020; Marriott Haresign et al., 2021). As a result of this comment we have made direct changes to the Results section on page 18 to more clearly signal the reader to our EEG pre-processing section before presenting the results of the cross-correlation analyses.

As we describe in the Methods section of the main text, movement-related artifacts are removed from the data with ICA decomposition, utilising an automatic-rejection algorithm, specially designed for work with our naturalistic EEG data (Marriott Haresign et al., 2021). Given that ICA rejection does not remove all artifact introduced to the EEG signal, additional analysis steps were taken to reduce the possibility that movement artifacts influenced the results of the reported analyses. As explained in the Methods section, rather than absolute theta power, relative theta was used in all EEG analyses, computed by dividing the power at each theta frequency by the summed power across all frequencies. Eye and head movement-related artifacts most often associate with broadband increases in power in the EEG signal (Cohen, 2014): computing relative theta activity therefore further reduces the potential influence of artifact on the EEG signal.

It is also important to highlight that previous work examining movement artifacts in controlled paradigms with infants has shown that limb movements actually associate with a decrease in power at theta frequencies, compared to rest (Georgieva et al., 2020). It is therefore unlikely that limb movement artifacts explain the pattern of association observed between theta power and infant attention in the current study.

That said, examining the association between body movements and fluctuations in EEG activity during naturalistic interactions is an important next step, and something our lab is currently working on. Given that touching an object is most often the end-state of a larger body movement, aligning the EEG signal to the onset of infant touch is not all that informative to understanding how body movements associate with increases and decreases in power in the EEG signal. Our lab is currently working on developing new methods using motion tracking software and arousal composites to understand how data-derived behavioural sub-types associate with differential patterns of EEG activity.

The term attention may be misleading. The behavior being examined is infant gaze or looks, with the assumption that gaze is a marker of "attention". The authors are aware that gaze can be a blank stare that doesn't reflect underlying true "attention". I recommend substitution of a conservative, more precise term that captures the variable being measured (gaze); it would then be fine to state that in their interpretation, gaze taken as a marker for attention or something like that. At minimum, using term "visual attention" can be a solution if authors do not want to use the precise term gaze. As an example, the sentence "An attention episode was defined as a discrete period of attention towards one of the play objects on the table, or to the partner" should be modified to defined as looking at a play object or partner.

We thank the reviewer for this comment, and we understand their concern with the use of the term ‘attention’ where we are referring to shifts in infant eye gaze. However, the use of this term to describe patterns of infant gaze, irrespective of whether they are ‘actually attending’ or not is used widely in the literature, in both interactive (e.g. Yu et al., 2021) and screen-based experiments examining infant attention (Richards, 2010). We therefore feel that its use in our current manuscript is acceptable and consistent with the reporting of similar interaction findings. On page 39 of the Discussion we now also include a discussion on how future research might further investigate differential subtypes of infant looks to distinguish between moments where infants are attending vs. just looking.

Why collapse across gaze to object vs. other? Conceptually, it's unclear why the same hypotheses and research questions on neural-attention (i.e., gaze in actuality) links would apply to looks to a mom's face or to an object. Some rationale would be useful to the reader as to why these two distinct behaviors are taken as following the same principles in ordering of brain and behavior. Perhaps I missed something, however, because later in the Discussion the authors state that "fluctuations in neural markers of infants' engagement or interest forward-predict their attentiveness towards objects", which suggests there was an object-focused variable only? Please clarify. (Again, sorry if I missed something).

This is a really important point, and we agree with R1 that it could have been more clearly expressed in our original submission – for which, we apologise. In the cross-correlation analyses conducted in parts 2 and 3 which examines forwards-predictive associations between infant attention durations and infant endogenous oscillatory activity (part two), and caregiver behaviour (part three), as R1 describes, we include all infant looks towards objects and their partner. Including all infant look types is necessary to produce a continuous variable to cross-correlate with the other continuous variables (e.g. theta activity, caregiver vocal behaviours), and, therefore, does not concentrate only on infant attention episodes towards objects.

We take the reviewers’ point that different attention and neural mechanisms may be associated with looks towards objects vs. the partner, which we now acknowledge directly on page 10 of the Introduction. However, our focus here is on the endogenous and interactive mechanisms that drive fluctuations in infant engagement with the ongoing, free-flowing interaction. Indeed, previous work has shown increases in theta activity during sustained episodes of infant attention to a range of different stimuli, including cartoon videos (Xie et al., 2018), real-life screen-based interactions (Jones et al., 2020), as well as objects (Begus et al., 2016). In the second half of part 2, we go on to address the endogenous processes that support infant attention episodes specifically towards objects.

As a result of this comment, we have made direct changes to the Introduction on page 10 to more clearly explain the looking behaviours included in the cross-correlation analysis, and the rationale behind the analysis being conducted in this way – which is different to the reactive analyses conducted in the second half of parts one and three, which examines infant object looks only. Direct edits to the text have also been made throughout the Results and Methods sections as a result of this comment, to more clearly specify the types of looks included in each analysis. Now, where we discuss the cross-correlation analyses we refer only to infant ‘attention durations’ or infant ‘attention’, whilst ‘object-directed attention’ and ‘looks towards objects’ is clearly specified in sections discussing the reactive analyses conducted in parts 2 and 3. We have also amended the Discussion on page 31so that the cross-correlation analyses is interpreted relative to infant overall attention, rather than their attention towards objects only.

Why are mothers' gazes shorter than infants' gazes? This was the flip of what I'd expect, so some interpretation would be useful to understanding the data.

This is a really interesting observation. Our findings of the looking behaviour of caregivers and infants in our joint play interactions actually correspond to much previous micro-dynamic analysis of caregiver and infant looking behaviour during early table-top interactions (Abney et al., 2017; Perapoch Amadó et al., 2023; Yu & Smith, 2013, 2016). The reason for the shorter look durations in the adult is due to the fact that the caregivers alternate their gaze between their infant and the objects (i.e. they spend a lot of the interaction time monitoring their infants’ behaviours). This can be seen in Figure 2 (see main text) which shows that caregiver looks are divided between looks to their infants and looks towards objects. In comparison, infants spend most of their time focussing on objects (see Figure 2, main text), with relatively infrequent looks to their caregiver. As a result, infant looks are, overall, longer in comparison to their caregivers’.

Minor points

Use the term association or relation (relationships is for interpersonal relationships, not in statistics).

This has now been amended throughout.

I'm unsure I'd call the interactions "naturalistic" when they occur at a table, with select toys, EEG caps on partners, and so on. The term seems more appropriate for studies with fewer constraints that occur (for example) in a home environment, etc.

We understand R1s concern with our use of the term ‘naturalistic’ to refer to the joint play interactions that we analyse in the current study. However, we feel the term is appropriate, given that the interactions are unstructured: the only instruction given to caregivers at the beginning of the interaction is to play with their infants in the way that they might do at home. The interactions, therefore, measure free-flowing caregiver and infant behaviours, where modulations in each individual’s behaviour are the result of the intra- and inter-individual dynamics of the social exchange. This is in comparison to previous work on early infant attention development which has used more structured designs, and modulations in infant behaviour occur as a result of the parameters of the experimental task.

Reviewer #2

Public Review

Summary:

This paper acknowledges that most development occurs in social contexts, with other social partners. The authors put forth two main frameworks of how development occurs within a social interaction with a caregiver. The first is that although social interaction with mature partners is somewhat bi-directional, mature social partners exogenously influence infant behaviors and attention through "attentional scaffolding", and that in this case infant attention is reactive to caregiver behavior. The second framework posits that caregivers support and guide infant attention by contingently responding to reorientations in infant behavior, thus caregiver behaviors are reactive to infant behavior. The aim of this paper is to use moment-to-moment analysis techniques to understand the directionality of dyadic interaction. It is difficult to determine whether the authors prove their point as the results are not clearly explained as is the motivation for the chosen methods.

Strengths

The question driving this study is interesting and a genuine gap in the literature. Almost all development occurs in the presence of a mature social partner. While it is known that these interactions are critical for development, the directionality of how these interactions unfold in real-time is less known.

The analyses largely seem to be appropriate for the question at hand, capturing small moment-to-moment dynamics in both infant and child behavior, and their relationships with themselves and each other. Autocorrelations and cross-correlations are powerful tools that can uncover small but meaningful patterns in data that may not be uncovered with other more discretized analyses (i.e. regression).

We are pleased that R2 finds our work to be an interesting contribution to the field, which utilises appropriate analysis techniques.

Weaknesses

The major weakness of this paper is that the reader is assumed to understand why these results lead to their claimed findings. The authors need to describe more carefully their reasoning and justification for their analyses and what they hope to show. While a handful of experts would understand why autocorrelations and cross-correlations should be used, they are by no means basic analyses. It would also be helpful to use simulated data or even a simple figure to help the reader more easily understand what a significant result looks like versus an insignificant result.

We thank the reviewer for this comment, and we agree that much more detail should be added to the Introduction section. As a result of this comment, we have made direct changes to the Introduction on pages 9-11 to more clearly detail these analysis methods, our rationale for using these methods; and how we expect the results to further our understanding of the drivers of infant attention in naturalistic social interactions.

We also provide a figure in the SM (Fig. S6) to help the reader more clearly understand the permutation method used in our statistical analyses described in the Methods, on page 51, which depicts significant vs. insignificant patterns of results against their permutation distribution.

While the overall question is interesting the introduction does not properly set up the rest of the paper. The authors spend a lot of time talking about oscillatory patterns in general but leave very little discussion to the fact they are using EEG to measure these patterns. The justification for using EEG is also not very well developed. Why did the authors single out fronto-temporal channels instead of using whole brain techniques, which are more standard in the field? This is idiosyncratic and not common.

We very much agree with R2 that the rationale and justification for using EEG to understand the processes that influence infants’ attention patterns is under-developed in the current manuscript. As a result of this comment we have made direct edits to the Introduction section of the main text on pages 7-8 to more clearly describe the rationale for examining the relationship between infant EEG activity and their attention during the play interactions with their caregivers.

As we describe in the Introduction section, previous behavioural work conducted with infants has suggested that endogenous cognitive processes (i.e. fluctuations in top-down cognitive control) might be important in explaining how infants allocate their attention during free-flowing, naturalistic interactions towards the end of the first year. Oscillatory neural activity occurring at theta frequencies (3-6Hz), which can be measured with EEG, has previously been associated with top-down intrinsically guided attentional processes in both adulthood and infancy (Jones et al., 2020; Orekhova, 1999; Xie et al., 2018). Measuring fluctuations in infant theta activity therefore provides a method to examine how endogenous cognitive processes structure infant attention in naturalistic social interactions which might be otherwise unobservable behaviourally.

It is important to note that the Introduction distinguishes between two different oscillatory mechanisms that could possibly explain the organisation of infant attention over the course of the interaction. The first refers to oscillatory patterns of attention, that is, consistent attention durations produced by infants that likely reflect automatic, regulatory functions, related to fluctuations in infant arousal. The second mechanism is oscillatory neural activity occurring at theta frequencies, recorded with EEG, which, as mentioned above, is thought to reflect fluctuations in intrinsically guided attention in early infancy. We have amended the Introduction to make the distinction between the two more clear.

A worrisome weakness is that the figures are not consistently formatted. The y-axes are not consistent within figures making the data difficult to compare and interpret. Labels are also not consistent and very often the text size is way too small making reading the axes difficult. This is a noticeable lack of attention to detail.

This has now been adjusted throughout, where appropriate.

No data is provided to reproduce the figures. This does not need to include the original videos but rather the processed and de-identified data used to generate the figures. Providing the data to support reproducibility is increasingly common in the field of developmental science and the authors are greatly encouraged to do so.

This will be provided with the final manuscript.

Minor Weaknesses

Figure 4, how is the pattern in a not significant while in b a very similar pattern with the same magnitude of change is? This seems like a spurious result.

The statistical analysis conducted for all cross-correlation analyses reported follows a rigorous and stringent permutation-based temporal clustering method which controls for family-wise error rate using a non-parametric Monte Carlo method (see Methods in the main text for more detail). Permutations are created by shuffling data sets between participants and, therefore, patterns of significance identified by the cluster-based permutation analysis will depend on the mean and standard deviation of the cross-correlations in the permutation distribution. Fig. S6 now depicts the cross-correlations against their permutation distributions which should help readers to understand the patterns of significance reported in the main text.

The correlations appear very weak in Figures 3b, 5a, 7e. Despite a linear mixed effects model showing a relationship, it is difficult to believe looking at the data. Both the Spearman and Pearson correlations for these plots should be clearly included in the text, figure, or figure legend.

We thank the reviewer for this comment, and agree that reporting the correlations for these plots would strengthen the findings of the linear mixed effects models reported in text. As a result, we have added both Spearman and Pearson correlations to the legends of Figures 3b, 5a and 7e, corresponding to the statistically significant relationships examined in the linear mixed effects models. The strength of the relationships are entirely consistent with those documented in other previous research that used similar methods (e.g. Piazza et al., 2018). How strong the relationship looks to the observer is entirely dependent on the graphical representation chosen to represent it. We have chosen to present the data in this way because we feel that it is the most honest way to represent the statistically significant, and very carefully analysed, effects that we have observed in our data.

Linear mixed effects models need more detail. Why were they built the way they were built? I would have appreciated seeing multiple models in the supplementary methods and a reasoning to have landed on one. There are multiple ways I can see this model being built (especially with the addition of a random intercept). Also, there are methods to test significance between models and aid in selection. That being said, although participant identity is a very common random effect, its use should be clearly stated in the main text.

We very much agree with R2 that the reporting of the linear mixed effects models needs more detail and this has now been added to the Method section (page 54). Whilst it is true that there are multiple ways in which this model could be built, given the specificity of our research questions, regarding the reactive changes in infant theta activity and caregiver behaviours that occur after infant look onsets towards objects (see pages 9-11 of the Introduction), we take a hypothesis driven approach to building the linear mixed effects models. As a result, random intercepts are specified for participants, as well as uncorrelated by-participant random slopes (Brown, 2021; Gelman & Hill, 2006; Suarez-Rivera et al., 2019). In this way, infant look durations are predicted from caregiver behaviours (or infant theta activity), controlling for between participant variability in look durations, as well as the strength of the effect of caregiver behaviours (or infant theta activity) on infant look durations.

Some parentheses aren't closed, a more careful re-reading focusing on these minor textual issues is warranted.

This has now been corrected.

Analysis of F0 seems unnecessarily complex. Is there a reason for this?

Computation of the continuous caregiver F0 variable may seem complex but we feel that all analysis steps are necessary to accurately and reliably compute this variable in our naturalistic, noisy and free-flowing interaction data. For example, we place the F0 only into segments of the interaction identified as the mum speaking so that background noises and infant vocalisations are not included in the continuous variable. We then interpolate through unvoiced segments (similar to Räsänen et al., 2018), and compute the derivative in 1000ms intervals as a measure of the rate of change. The steps taken to compute this variable have been both carefully and thoughtfully selected given the many ways in which this continuous rate of change variable could be computed (cf. Piazza et al., 2018; Räsänen et al., 2018).

The choice of a 20hz filter seems odd when an example of toy clacks is given. Toy clacks are much higher than 20hz, and a 20hz filter probably wouldn't do anything against toy clacks given that the authors already set floor and ceiling parameters of 75-600Hz in their F0 extraction.

We thank the reviewer for this comment and we can see that this part of the description of the F0 computation is confusing. A 20Hz low pass filter is applied to the data stream after extracting the F0 with floor and ceiling parameters set between 75-600Hz. The 20Hz filter therefore filters modulations in the caregivers’ F0 that occur at a modulation frequency greater than 20Hz. The 20Hz filter does not, therefore, refer to the spectral filtering of the speech signal. The description of this variable has been rephrased on page 48 of the main text.

Linear interpolation is a choice I would not have made. Where there is no data, there is no data. It feels inappropriate to assume that the data in between is simply a linear interpolation of surrounding points.

The choice to interpolate where there was no data was something we considered in a lot of detail, given the many options for dealing with missing data points in this analysis, and the difficulties involved with extracting a continuous F0 variable in our naturalistic data sets. As R2 points out, one option would be to set data points to NaN values where no F0 is detected and/ or the Mum is not vocalising. A second option, however, would be to set the continuous variable to 0s where no F0 is detected and/ or the Mum is not vocalising (where the mum is not producing sound there is no F0 so rather than setting the variable to missing data points, really it makes most objective sense to set to 0).

Either of these options (setting parts where no F0 is detected to NaN or 0) makes it difficult to then meaningfully compute the rate of change in F0: where NaN values are inserted, this reduces the number of data points in each time window; where 0s are inserted this creates large and unreal changes in F0. Inserting NaN values into the continuous variable also reduces the number of data points included in the cross-correlation and event-locked analyses. It is important to note that, in our naturalistic interactions, caregivers’ vocal patterns are characterised by lots of short vocalisations interspersed by short pauses (Phillips et al., in prep), similar to previous findings in naturalistic settings (Gratier et al., 2015). Interpolation will, therefore, have largely interpolated through the small pauses in the caregiver’s vocalisations.

The only limitation listed was related to the demographics of the sample, namely saying that middle class moms in east London. Given that the demographics of London, even east London are quite varied, it's disappointing their sample does not reflect the community they are in.

Yes we very much agree with R2 that the lack of inclusion of caregivers from wider demographic backgrounds is disappointing, and something which is often a problem in developmental research. Our lab is currently working to collect similar data from infants with a family history of ADHD, as part of a longitudinal, ongoing project, involving families from across the UK, from much more varied demographic backgrounds. We hope that the findings reported here will feed directly into the work conducted as part of this new project.

That said, demographic table of the subjects included in this study should be added.

This is now included in the SM, and referenced in the main text.

References

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Author Response

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

We are grateful to the reviewers for their appreciation of our study and thoughtful comments. In response to the main concern raised by all reviewers regarding the potential influences of external noise factors on intuitive inference, such as external disturbances or imperfect observations, we have conducted three new experiments suggested by the reviewers. These experiments were designed to: (1) assess the influence of external forces on humans’ judgments by implementing a wall to block wind disturbances from one direction, (2) examine human accuracy in predicting the landing position of a falling ball when its trajectory is obscured, and (3) evaluate the effect of object geometry on human judgment of stability. The findings from these experiments consistently support our proposal of the stochastic world model on gravity embedded in human mind. Besides, we have also addressed the rest comments from the reviewers in a one-by-one fashion.

Reviewer #1 (Recommendations For The Authors):

As mentioned in the public review, I did not find it entirely convincing that the study shows evidence for a Gaussian understanding of gravity. There are two studies that would bolster this claim: 1. Replicate experiment 1, but also ask people to infer whether there was a hidden force. If people are truly representing gravity as proposed in the paper, you should get no force inferences. However, if the reason the Gaussian gravity model works is that people infer unseen forces, this should come out clearly in this study.

Author response image 1.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

R1: We thank the reviewer for this suggestion. To directly test whether participants’ judgments were influenced by their implicit assumptions about external forces, we duplicated the original experimental setup with the addition of a wall implemented on one side (Supplementary Figure 4A). Before the start of the experiment, we explicitly informed the participants that the wall was designed to block wind, ensuring that any potential wind forces from the direction of the wall would not influence the collapse. If participants’ judgments were affected by external noise, we would expect to observe a skewed angle distribution. Contrary to this prediction, our results showed a normal distribution across all three participants tested (1 female; ages: 24-30), similar to the experiment without the wall (Supplementary Figure 4B). Therefore, the stochastic nature of intuitive inference on objects’ stability is embedded in the mind, not shaped by external forces or explicit instructions.

This new experiment has been added to the revised manuscript

Line 166-168: “…, and remained unchanged with the addition of a wall on one side to block potential external disturbances from wind (Supplementary Figure 4).”

(2) Similarly, you can imagine a simple study where you drop an object behind a floating occluder and you check where people produce an anticipatory fixation (i.e., where do they think the object will come out?). If people have a stochastic representation of gravity, this should be reflected in their fixations. But my guess is that everyone will look straight down.

Author response image 2.

Trajectory experiment to test the stochastic nature of gravity represented in the mind. (a) Experiment design. In this experiment, participants were required to use a mouse to determine the landing point of a parabolic trajectory (marked by the green dot), obscured by a grey rectangle. Note that the parabolic trajectory was determined only by gravity, and no external disturbances were introduced. The parameters used in this experiment are detailed in the upper right corner. (b) Predictive errors from three participants. The predictive errors from all three participants conform to Gaussian distributions with non-negligible variances. These results suggest the notion of an inherent stochastic property of gravity represented in the mind.

R2: We thank the reviewer for suggesting this thought experiment. However, when predicting the landing point of a falling object, participants may rely more on learned knowledge that an unimpeded object continues to fall in a straight line, rather than drawing on their intuitive physics. To avoid this potential confounding factor, we designed a similar experiment where participants were asked to predict the landing point of a parabolic trajectory, obscured by an occluder (Author response image 2A). In each trial, participants used a mouse (clicking the left button) to predict the landing point of each parabolic trajectory, and there were 100 trials in total. This design not only limits the impact of direct visual cues but also actively engages the mental simulation of intuitive physics. All three participants (1 female; ages: 24-30) were unable to accurately predict the landing points of the trajectories, and the predictive errors conformed to Gaussian distributions with different variances (Author response image 2B). Therefore, this new experiment confirms the stochastic nature of intuitive physics.

(3) I believe the correct alternative model should be the one that has uncertainty over unseen forces, which better captures current proposals in the field, and controls for the amount of uncertainty in the models.

R3: We thank the reviewers for the above-mentioned suggestions, and the findings from these two new experiments reinforce our proposal regarding the inherent stochastic characteristic of how the mind represents gravity.

(4) I was not convinced that the RL framework was set up correctly to tackle the questions it claims to tackle. What this shows is that you can evolve a world model with Gaussian gravity in a setup that has no external perturbations. That does not imply that that is how humans evolved their intuitive physics, particularly when creatures have evolved in a world full of external perturbations. Showing that when (1) there are hidden perturbations, and (2) these perturbations are learnable, but (3) the model nonetheless just learns stochastic gravity, would be a more convincing result.

R4: We completely agree with the reviewer that the RL framework serves primarily as a theoretic model to explain the stochastic nature of the world model on gravity, rather than as a demonstration of the developmental origins of intuitive physics abilities. The genesis of such abilities is multifaceted and unlikely to be fully replicated through a simple simulation like RL. Therefore, the purpose of incorporating the RL framework in our study is to demonstrate that external perturbances are not necessary for the development of a stochastic representation of gravity. In fact, introducing additional external noise into the RL framework likely heightens the uncertainty in learning gravity’s direction, potentially amplifying, rather than diminishing, the stochastic nature of mental gravity.

In revision, we have clarified the role of the RL framework

Line 265-277: “While the cognitive impenetrability and the self-consistency observed in this study, without resorting to an external perturbation, favor the stochastic model over the deterministic one, the origin of this stochastic feature of the world model is unclear.

Here we used a reinforcement learning (RL) framework to unveil this origin, because our intelligence emerges and evolves under the constraints of the physical world. Therefore, the stochastic feature may emerge as a biological agent interacts with the environment, where the mismatches between external feedback from the environment and internal expectations from the world model are in turn used to fine-tune the world model (Friston et al., 2021; MacKay, 1956; Matsuo et al., 2022). Note that a key aspect of the framework is determining whether the stochastic nature of the world model on gravity emerges through this interaction, even in the absence of external noise.”

(5) Some comments on the writing:

The word 'normality' is used to refer to people's judgments about whether a tower collapsed looked 'normal'. I was a bit confused by this because normality can also mean 'Gaussian' and the experiments are also sampling from Gaussian distributions. There were several points where it took me a second to figure out which sense of 'normality' the paper was using. I would recommend using a different term.

R5: We are sorry for the confusion. In revision, the term “normality” has been replaced with “confidence level about normal trajectory”.

(6) One small comment is that Newton's laws are not a faithful replica of the "physical laws of the world" they are a useful simplification that only works at certain timescales. I believe some people propose Newtonian physics as a model of intuitive physics in part because it is a rapid and useful approximation of complex physical systems, and not because it is an untested assumption of perfect correspondence.

R6: We are sorry for the inaccurate expression. We have revised our statements in the manuscript Line 15-16: “We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution.”

(7) Line 49-50: Based on Fig 1d, lower bound of possible configurations for 10 blocks is ~17 in log-space, which is about 2.5e7. But the line here says it's 3.72e19, which is much larger. Sorry if I am missing something.

R7: We thank the reviewer to point out this error. We re-calculated the number of possible configurations using the formula (3) in the appendix, and the number of configurations with 10 blocks is:

Thus,

This estimated number is much larger than that in our previous calculation, which has been corrected in the revised text.

Line 827-829: “d) The lower bound of configurations’ possible number and the number of blocks in a stack followed an exponential relationship with a base of 10. The procedure can create at least 1.14×1050 configurations for stacks consisting of 10 blocks.”

Line 49-50: “… but the universal cardinality of possible configurations is at least 1.14×1050 (Supplementary Figure 1), …”

Line 1017-1018: “… the number of configurations can be estimated with formula (9), which is 1.14×1050.”

(8) Lines 77-78: "A widely adopted but not rigorously tested assumption is that the world model in the brain is a faithful replica of the physical laws of the world." This risks sounding like you are asserting that colleagues in the field do not rigorously test their models. I think you meant to say that they did not 'directly test', rather than 'rigorously test'. If you meant rigorous, you might want to say more to justify why you think past work was not rigorous.

R8: We apologize for the inappropriate wording, the sentence has been revised and we illustrate the motivation more comprehensively in the revised text,

Line 76-92: “A prevailing theory suggests that the world model in the brain accurately mirrors the physical laws of the world (Allen et al., 2020; Battaglia et al., 2013; Zhou et al., 2022). For example, the direction of gravity encoded in the world model, a critical factor in stability inference, is assumed to be straight downward, aligning with its manifestation in the physical world. To explain the phenomenon that tall and thin objects are subjectively perceived as more unstable compared to short and fat ones (Supplementary Figure 2), external noise, such as imperfect perception and assumed external forces, is introduced to influence the output of the model. However, when the brain actively transforms sensory data into cognitive understanding, these data can become distorted (Kriegeskorte and Douglas, 2019; Naselaris et al., 2011), hereby introducing uncertainty into the representation of gravity’s direction. In this scenario, the world model inherently incorporates uncertainty, eliminating the need for additional external noise to explain the inconsistency between subjective perceptions of stability and the actual stability of objects. Note that this distinction of these two theories is nontrivial: the former model implies a deterministic representation of the external world, while the latter suggests a stochastic approach.”

(9) Lines 79-84 States that past models encode gravity downward. It then says that alternatively there is consensus that the brain uses data from sensory organs and adds meaning to them. I think there might be a grammatical error here because I did not follow why saying there is 'consensus' on something is a theoretical alternative. I also had trouble following why those two statements are in opposition. Is any work on physics engines claiming the brain does not take data from sensory organs and add meaning to them?

R9: We are sorry for the confusion. Here we intend to contrast the deterministic model (i.e., the uncertainty comes from outside the model) with the stochastic model (i.e., the uncertainty is inherently built into the model). In revision, we have clarified the intention. For details, please see R8.

(10) Lines 85-88: Following on the sentence above, you then conclude that the representation of the world may therefore not be the same as reality. I did not understand why this followed. It seems you are saying that, because the brain takes data from sensory organs, therefore its representations may differ from reality.

R10: Again, we are sorry about the confusion. Please see the revised text in R8.

(11) Lines 190-191: I had trouble understanding this sentence. I believe you are missing an adjective to clarify that participants were more inclined to judge taller stacks as more likely to collapse.

R11: We are sorry for the confusion. What we intended to state here is that participants’ judgment was biased, showing a tendency to predict a collapse for stacks regardless of their actual stability. We have revised this confusing sentence in the revision. Line 202–204: “However, the participants showed an obvious bias towards predicting a collapse for stacks regardless of their actual stability, as the dots in Fig 2b are more concentrated on the lower side of the diagonal line.”

(12) Line 201: I don't think it's accurate to say that MGS "perfectly captured participants' judgments" unless the results are actually perfect.

R12: We agree, and in revision we have toned down the statement Line 213–214: “…, the MGS, in contrast to the NGS, more precisely reflected participants’ judgments of stability …”

Reviewer #2 (Recommendations For The Authors):

I think this is an impressive set of experiments and modeling work. The paper is nicely written and I appreciate the poetic license the authors took at places in the manuscript. I only have clarification points and suggest a simple experiment that could lend further support to their conclusions. 1. In my opinion, the impact of this work is twofold. First, the suggestion that gravity is represented as a distribution of the world and not a result of (inferred) external perturbations. Second, that the distribution is advantageous as it balances speed and accuracy, and lessens computational processing demands (i.e., number of simulations). The second point here is contingent on the first point, which is really only supported by the RL model and potentially the inverted scene condition. I am somewhat surprised that the RL model does not converge on a width much smaller than ~20 degrees after 100,000 simulations. From my understanding, it was provided feedback with collapses based on natural gravity (deterministically downward). Why is learning so slow and the width so large? Could it be the density of the simulated world model distribution? If the model distribution of Qs was too dense, then Q-learning would take forever. If the model distribution was too sparse, then its final estimate would hit a floor of precision. Could the authors provide more details on the distribution of the Qs for the RL model?

Author response image 3.

RL learning curves as a function of θ angle with different sampling densities and learning rates. Learning rates were adjusted to low (a), intermediate (b) and high (c) settings, while sampling densities were chosen at four levels: 5x5, 11x11, 31x31, and 61x61 shown from the left to the right. Two key observations emerged from the simulations as the reviewer predicted. First, higher learning rates resulted in a more rapid decline in learning curves but introduced larger variances. Second, increased sampling density necessitated more iterations for convergence. Note that in all simulations, we limited the iterations to 1,000 times (as opposed to 100,000 times reported in the manuscript) to demonstrate the trend without excessive computational demands.

R1: To illustrate the distribution of the Q-values for the RL model, we re-ran the RL model with various learning rates and sampling densities (Author response image 3). These results support the reviewer’s prediction that higher learning rates resulted in a more rapid decline in learning curves but introduced larger variances, and increased sampling density requires more iterations for convergence.

This simulation also elucidates the slower learning observed in the experiment described in the text, where the force sphere was divided into 61x61 angle pairs, and the learning rate was set to 0.15. This set of parameters ensured convergence within a reasonable brief timeframe while maintaining high-resolution force assessments.

Besides, the width of the Gaussian distribution is mainly determined by the complexity of stacks. As shown in Figure 3c and Supplementary Figure 9, stacks with fewer blocks (i.e., less complex) caused a larger width, whereas those with more blocks resulted in a narrower spread. In the study, we used a collection of stacks varying from 2 to 15 blocks to simulate the range of stacks humans typically encounter in daily life.

In revision, we have incorporated these insights suggested by the reviewer to clarify the performance of the RL framework:

Line 634-639: “The angle density and learning rate are two factors that affect the learning speed. A larger angle density prolongs the time to reach convergence but enables a more detailed force space; a higher learning rate accelerates convergence but incurs larger variance during training. To balance speed and convergence, we utilized 100,000 configurations for the training.”

Line 618-619: “…, separately divided them into 61 sampling angles across the spherical force space (i.e., the angle density).”

(2) Along similar lines, the authors discuss the results of the inverted science condition as reflecting cognitive impenetrability. However, do they also interpret it as support for an intrinsically noisy distribution of gravity? I would be more convinced if they created a different scene that could have the possibility of affecting the direction of an (inferred) external perturbation - a previously held explanation of the noisy world model. For example, a relatively simple experiment would be to have a wall on one side of the scene such that an external perturbation would be unlikely to be inferred from that direction. In the external perturbation account, phi would then be affected resulting in a skewed distribution of angle pairs. However, in the authors' stochastic world model phi would remain unaffected resulting in the same uniform distribution of phi the authors observed. In my opinion, this would provide more compelling evidence for the stochastic world model.

Author response image 4.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

R2: We thank the reviewer for this suggestion. Following the reviewer’s concern, we designed the experiment with the addition of a wall implemented on one side (Supplementary figure 4A). We explicitly informed the participants that the wall was designed to block wind before the start of the experiment, ensuring no potential wind forces from the direction of the wall to influence the collapse trajectory of configurations. Participants need to judge if the trajectory was normal. If participants’ judgments were influenced by external noises, we would expect to observe a skewed angle distribution. However, our results still showed a normal distribution across all participants tested, consistent with the experiment without the wall (Supplementary figure 4B). This experiment suggested the stochastic nature of intuitive inference on objects’ stability is embedded in the mind, rather than shaped by external forces or explicit instructions.

We revised the original manuscript, and added this new experiment

Line 166-168: “…, and remained unchanged with the addition of a wall on one side to block potential external disturbances from wind (Supplementary Figure 4).”

(3) I didn't completely follow the authors' explanation for the taller objects illusion. On lines 229-232, the authors state that deviations from gravity's veridical direction are likely to accumulate with the height of the objects. Is this because, in the stochastic world model account, each block gets its own gravity vector that is sampled from the distribution? The authors should clarify this more explicitly. If this is indeed the author's claim, then it would seem that it could be manipulated by varying the dimensions of the blocks (or whatever constitutes an object).

R3: We are sorry for the confusion caused by the use of the term ‘accumulate’. In the study, there is only one gravity vector sampled from the distribution for the entire structure, rather than each block having a unique gravity vector. The height illusion is attributed to the fact that the center of gravity in taller objects is more susceptible to influence when gravity deviates slightly from a strictly downward direction. This is especially true for objects consisting of multiple blocks stacked atop one another. In revision, we have removed the confusing term ‘accumulate’ for clarification.

Line 242-244: “…, because the center of gravity in taller objects is more susceptible to influence when gravity deviates slightly from a strictly downward direction during humans’ internal simulations.”

(4) The authors refer to the RL simulations as agent-environment interactions, but in reality, the RL model does not interact with the blocks. Would experience-dependent or observation be more apropos?

R4: We completely agree. Indeed, the RL model did not manipulate stacks; rather, it updated its knowledge of natural gravity based on the discrepancies between the RL model’s predictions and observed outcomes. In revision, we have removed the confusing term ‘agent-environment interactions’ and clarified its intended meaning.

Line 19-22: “Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, …”

Reviewer #3 (Public Review):

(1) In spite of the fact that the Mental Gravity Simulation (MGS) seems to predict the data of the two experiments, it is an untenable hypothesis. I give the main reason for this conclusion by illustrating a simple thought experiment. Suppose you ask subjects to determine whether a single block (like those used in the simulations) is about to fall. We can think of blocks of varying heights. No matter how tall a block is, if it is standing on a horizontal surface it will not fall until some external perturbation disturbs its equilibrium. I am confident that most human observers would predict this outcome as well. However, the MSG simulation would not produce this outcome. Instead, it would predict a non-zero probability of the block to tip over. A gravitational field that is not perpendicular to the base has the equivalent effect of a horizontal force applied on the block at the height corresponding to the vertical position of the center of gravity. Depending on the friction determined by the contact between the base of the block and the surface where it stands there is a critical height where any horizontal force being applied would cause the block to fall while pivoting about one of the edges at the base (the one opposite to where the force has been applied). This critical height depends on both the size of the base and the friction coefficient. For short objects this critical height is larger than the height of the object, so that object would not fall. But for taller blocks, this is not the case. Indeed, the taller the block the smaller the deviation from a vertical gravitational field is needed for a fall to be expected. The discrepancy between this prediction and the most likely outcome of the simple experiment I have just outlined makes the MSG model implausible. Note also that a gravitational field that is not perpendicular to the ground surface is equivalent to the force field experienced by the block while standing on an inclined plane. For small friction values, the block is expected to slide down the incline, therefore another prediction of this MSG model is that when we observe an object on a surface exerting negligible friction (think of a puck on ice) we should expect that object to spontaneously move. But of course, we don't, as we do not expect tall objects that are standing to suddenly fall if left unperturbed. In summary, a stochastic world model cannot explain these simple observations.

Author response image 5.

Differentiating Subjectivity from Objectivity. In both Experiment 1 (a) and Experiment 2 (b), participants were instructed to determine which shape appeared most stable. Objectively, in the absence of external forces, all shapes possess equal stability. Yet, participants typically perceived the shape on the left as the most stable because of its larger base area. The discrepancy between objective realities and subjective feelings, as we propose, is attributed to the human mind representing gravity’s direction as a Gaussian distribution, rather than as a singular value pointing directly downward.

R1: We agree with the reviewer that objects will remain stable until disturbed by external forces. However, in many cases, this is a clear discrepancy between objective realities and subjective feelings. For example, electromagnetic waves associated with purple and red colors are the farthest in the electromagnetic space, yet purple and red are the closest colors in the color space. Similarly, as shown in Supplementary Figure 4, in reality all shapes possess equal stability in the absence of external forces. Yet, humans typically perceive the shape on the left as more stable because of its larger base area. In this study, we tried to explore the mechanism underlying this discrepancy by proposing that the human mind represents gravity’s direction as a Gaussian distribution, rather than as a singular value pointing directly downward.

In revision, we have clarified the rationale of this study

Line 76-98: “A prevailing theory suggests that the world model in the brain accurately mirrors the physical laws of the world (Allen et al., 2020; Battaglia et al., 2013; Zhou et al., 2022). For example, the direction of gravity encoded in the world model, a critical factor in stability inference, is assumed to be straight downward, aligning with its manifestation in the physical world. To explain the phenomenon that tall and thin objects are subjectively perceived as more unstable compared to short and fat ones (Supplementary Figure 2), external noise, such as imperfect perception and assumed external forces, is introduced to influence the output of the model. However, when the brain actively transforms sensory data into cognitive understanding, these data can become distorted (Kriegeskorte and Douglas, 2019; Naselaris et al., 2011), hereby introducing uncertainty into the representation of gravity’s direction. In this scenario, the world model inherently incorporates uncertainty, eliminating the need for additional external noise to explain the inconsistency between subjective perceptions of stability and the actual stability of objects. Note that this distinction of these two theories is nontrivial: the former model implies a deterministic representation of the external world, while the latter suggests a stochastic approach. Here, we investigated these two alternative hypotheses regarding the construction of the world model in the brain by examining how gravity’s direction is represented in the world model when participants judged object stability.”

(2) The question remains as to how we can interpret the empirical data from the two experiments and their agreement with the predictions of the stochastic world model if we assume that the brain has internalized a vertical gravitational field. First, we need to look more closely at the questions posed to the subjects in the two experiments. In the first experiment, subjects are asked about how "normal" a fall of a block construction looks. Subjects seem to accept 50% of the time a fall is normal when the gravitational field is about 20 deg away from the vertical direction. The authors conclude that according to the brain, such an unusual gravitational field is possible. However, there are alternative explanations for these findings that do not require a perceptual error in the estimation of the direction of gravity. There are several aspects of the scene that may be misjudged by the observer. First, the 3D interpretation of the scene and the 3D motion of the objects can be inaccurate. Indeed, the simulation of a normal fall uploaded by the authors seems to show objects falling in a much weaker gravitational field than the one on Earth since the blocks seem to fall in "slow motion". This is probably because the perceived height of the structure is much smaller than the simulated height. In general, there are even more severe biases affecting the perception of 3D structures that depend on many factors, for instance, the viewpoint.

R2: We thank the reviewer for highlighting several potential confounding factors in our study. We address each of these concerns point-by-point:

(a) Misinterpretation of the 3D scene and motion. In Response Figure 4 shown above, there is no 3D structure, yet participants’ judgment on stability still deviated from objective realities. In addition, the introduction of 3D motion was to aid in understanding the stacks’ 3D structure. Previous studies without 3D motion have reported similar findings (Allen et al., 2020). Therefore, regardless of whether objects are presented in 2D or 3D, or in static or in motion formats, humans’ judgment on object stability appears consistent.

(b) Errors in perceived height. While there might be discrepancies between perceived and simulated heights, such errors are systematic across all conditions. Therefore, they may affect the width of the Gaussian distribution but do not fundamentally alter its existence.

(c) The viewpoint. In one experiment, we inverted gravity’s direction to point upward, diverging from common daily experience. Despite this change in viewpoint, the Gaussian distribution was still observed. That is, the viewpoint appears not a key factor in influencing how gravity’s direction is represented as a Gaussian distribution in our mental world.

In summary, both our and previous studies (Allen et al., 2020; Battaglia et al., 2013) agree that humans’ subjective assessments of objects’ stability deviate from actual stability due to noise in mental simulation. Apart from previous studies, we suggest that this noise is intrinsic, rather than stemming from external forces or imperfect observations.

(3) Second, the distribution of weight among the objects and the friction coefficients acting between the surfaces are also unknown parameters. In other words, there are several parameters that depend on the viewing conditions and material composition of the blocks that are unknown and need to be estimated. The authors assume that these parameters are derived accurately and only that assumption allows them to attribute the observed biases to an error in the estimate of the gravitational field. Of course, if the direction of gravity is the only parameter allowed to vary freely then it is no surprise that it explains the results. Instead, a simulation with a titled angle of gravity may give rise to a display that is interpreted as rendering a vertical gravitational field while other parameters are misperceived. Moreover, there is an additional factor that is intentionally dismissed by the authors that is a possible cause of the fall of a stack of cubes: an external force. Stacks that are initially standing should not fall all of a sudden unless some unwanted force is applied to the construction. For instance, a sudden gust of wind would create a force field on a stack that is equivalent to that produced by a tilted gravitational field. Such an explanation would easily apply to the findings of the second experiment. In that experiment subjects are explicitly asked if a stack of blocks looks "stable". This is an ambiguous question because the stability of a structure is always judged by imagining what would happen to the structure if an external perturbation is applied. The right question should be: "do you think this structure would fall if unperturbed". However, if stability is judged in the face of possible external perturbations then a tall structure would certainly be judged as less stable than a short structure occupying the same ground area. This is what the authors find. What they consider as a bias (tall structures are perceived as less stable than short structures) is instead a wrong interpretation of the mental process that determines stability. If subjects are asked the question "Is it going to fall?" then tall stacks of sound structure would be judged as stable as short stacks, just more precarious.

R3: Indeed, the external forces suggested by the reviewer certainly influence judgments of objects’ stability. The critical question, however, is whether humans’ judgments on objects’ stability accurately mirror the actual stability of objects in the absence of external forces. To address this question, we designed two new experiments.

Experiment 1: we duplicated the original experimental setup with the addition of a wall implemented on one side (Supplementary Figure 4A). We explicitly informed the participants that the wall could block wind, ensuring that no potential wind from the direction of the wall could influence the configuration. If participants’ judgments were affected by external noise, we would expect to observe a skewed angle distribution. Contrary to this prediction, our results showed a normal distribution across all three participants (Age: 25-30, two females), which is similar to the experiment without the wall (Supplementary Figure 4B).

Author response image 6.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

Experiment 2: The second experiment adopted another paradigm to test the hypothesis of stochastic mental simulation. Consider humans to infer the landing point of a parabolic trajectory that was obscured by an occlude (Author response image 2A), the stochastic mental simulation predicted that humans’ behavior follows a Gaussian distribution. However, if humans’ judgments were influenced by external noise, the landing points could not be Gaussian. The experiment consists of 100 trials in total, and in each trial participants used a mouse to predict the landing point of each trajectory by clicking the left button. Our results found all three participants (1 female; ages: 24-30) were unable to accurately predict the landing points of the trajectories, and the predictive errors conformed to Gaussian distributions with different variances (Author response image 2B). Therefore, this new experiment confirms the stochastic nature of intuitive physics.

Author response image 7.

Trajectory experiment to test the stochastic nature of gravity represented in the mind. (a) Experiment design. In this experiment, participants were required to use a mouse to determine the landing point of a parabolic trajectory (marked by the green dot), obscured by a grey rectangle. Note that the parabolic trajectory was determined only by gravity, and no external disturbances were introduced. The parameters used in this experiment are detailed in the upper right corner. (b) Predictive errors from three participants. The predictive errors from all three participants conform to Gaussian distributions with non-negligible variances. These results suggest the notion of an inherent stochastic property of gravity represented in the mind.

(4) The RL model used as a proof of concept for how the brain may build a stochastic prior for the direction of gravity is based on very strong and unverified assumptions. The first assumption is that the brain already knows about the force of gravity, but it lacks knowledge of the direction of this force of gravity. The second assumption is that before learning the brain knows the effect of a gravitational field on a stack of blocks. How can the brain simulate the effect of a non-vertical gravitational field on a structure if it has never observed such an event?

R4: We agree with the reviewer that the RL framework serves primarily as a theoretic model to explain the stochastic nature of the world model on gravity, rather than as a demonstration of the developmental origins of intuitive physics abilities. The genesis of such abilities is multifaceted and unlikely to be fully replicated through a simple simulation like RL. Therefore, the purpose of incorporating the RL framework in our study is to demonstrate that external perturbances are not necessary for the development of a stochastic representation of gravity.

In revision, we have clarified the role of the RL framework

Line 265-277: “While the cognitive impenetrability and the self-consistency observed in this study, without resorting to an external perturbation, favor the stochastic model over the deterministic one, the origin of this stochastic feature of the world model is unclear.

Here we used a reinforcement learning (RL) framework to unveil this origin, because our intelligence emerges and evolves under the constraints of the physical world. Therefore, the stochastic feature may emerge as a biological agent interacts with the environment, where the mismatches between external feedback from the environment and internal expectations from the world model are in turn used to fine-tune the world model (Friston et al., 2021; MacKay, 1956; Matsuo et al., 2022). Note that a key aspect of the framework is determining whether the stochastic nature of the world model on gravity emerges through this interaction, even in the absence of external noise.”

(5) The third assumption is that from the visual input, the brain is able to figure out the exact 3D coordinates of the blocks. This has been proven to be untrue in a large number of studies. Given these assumptions and the fact that the only parameters the RL model modifies through learning specify the direction of gravity, I am not surprised that the model produces the desired results.

Author response image 8.

Perception Uncertainty in 3D stacks structures. (a) Experimental design. A pair of two stacks with similar placements of blocks were presented sequentially to participants, who were instructed to judge whether the stacks were identical and to rate their confidence in this judgment. Each stack was presented on the screen for 2 seconds. (b) Behavior Performance. Three participants (2 males, age range: 24-30) were recruited to the experiment. The confidence in determining whether a pair of stacks remained unchanged rapidly decreased when each block had a very small displacement, suggesting humans could keenly perceive trivial changes in configurations. The x-axis denotes the difference in block placement between stacks, with the maximum value (0.4) corresponding to the length of a block’s short side. The Y-axis denotes humans’ confidence in reporting no change. The red curve illustrates the average confidence level across 4 runs, while the yellow curve is the confidence level of each run.

R5: Indeed, uncertainty is inevitable when perceiving the external world, because our perception is not a faithful replica of external reality. A more critical question pertains to the accuracy of our perception in representing the 3D coordinates of a stack’s blocks. To address this question, we designed a straightforward experiment (Author response image 5a), where participants were instructed to determine whether a pair of stacks were identical. The position of each block was randomly changed horizontally. We found that all participants were able to accurately identify even minor positional variations in the 3D structure of the stacks (Author response image 5b). This level of perceptual precision is adequate for locating the difference between predictions from mental simulations and actual observations of the external world.

(6）Finally, the argument that the MGS is more efficient than the NGS model is based on an incorrect analysis of the results of the simulation. It is true that 80% accuracy is reached faster by the MGS model than the 95% accuracy level is reached by the NGS model. But the question is: how fast does the NGS model reach 80% accuracy (before reaching the plateau)?

R6: Yes. The NGS model achieved 80% accuracy as rapidly as the MGS model. However, the NGS model required a significantly longer period to reach the plateau crucial for decision-making. In revision, this information is now included.

Line 348-350: “…, while the initial growth rates of both models were comparable, the MGS reached the plateau crucial for decision-making sooner than the NGS.”

We greatly appreciate the thorough and insightful review provided by all three reviewers, which has considerably improved our manuscript, especially in terms of clarity in the presentation of the approach and further validation of the robustness implications of our results.

Reference: Allen KR, Smith KA, Tenenbaum JB. 2020. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences 117:29302–29310.

Battaglia PW, Hamrick JB, Tenenbaum JB. 2013. Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences 110:18327–18332.

Friston K, Moran RJ, Nagai Y, Taniguchi T, Gomi H, Tenenbaum J. 2021. World model learning and inference. Neural Networks 144:573–590.

Kriegeskorte N, Douglas PK. 2019. Interpreting encoding and decoding models. Current opinion in neurobiology 55:167–179.

MacKay DM. 1956. The epistemological problem for automataAutomata Studies.(AM-34), Volume 34. Princeton University Press. pp. 235–252.

Matsuo Y, LeCun Y, Sahani M, Precup D, Silver D, Sugiyama M, Uchibe E, Morimoto J. 2022. Deep learning, reinforcement learning, and world models. Neural Networks.

Naselaris T, Kay KN, Nishimoto S, Gallant JL. 2011. Encoding and decoding in fMRI. Neuroimage 56:400–410.

Zhou L, Smith K, Tenenbaum J, Gerstenberg T. 2022. Mental Jenga: A counterfactual simulation model of physical support.

Author Response

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

eLife assessment:

This important study combines a comparative approach in different synapses with experiments that show how synaptic vesicle endocytosis in nerve terminals regulates short-term plasticity. The data presented support the conclusions and make a convincing case for fast endocytosis as necessary for rapid vesicle recruitment to active zones. Some aspects of the description of the data and analysis are however incomplete and would benefit from a more rigorous approach. With more discussion of methods and analysis, this paper would be of great interest to neurobiologists and biophysicists working on synaptic vesicle recycling and short-term plasticity mechanisms.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study examines the role of release site clearance in synaptic transmission during repetitive activity under physiological conditions in two types of central synapses, calyx of Held and hippocampal CA1 synapses. After the acute block of endocytosis by pharmacology, deeper synaptic depression or less facilitation was observed in two types of synapses. Acute block of CDC42 and actin polymerization, which possibly inhibits the activity of Intersectin, affected synaptic depression at the calyx synapse, but not at CA1 synapses. The data suggest an unexpected, fast role of the site clearance in counteracting synaptic depression.

Strengths:

The study uses an acute block of the molecular targets with pharmacology together with precise electrophysiology. The experimental results are clear-cut and convincing. The study also examines the physiological roles of the site clearance using action potential-evoked transmission at physiological Ca and physiological temperature at mature animals. This condition has not been examined.

Weaknesses:

Pharmacology may have some off-target effects, though acute manipulation should be appreciated. Although this is a hard question and difficult to address experimentally, reagents may affect synaptic vesicle mobilization to the release sites directly in addition to blocking endocytosis.

To acutely block vesicle endocytosis, we utilized two different pharmacological tools, Dynasore and Pitstop-2, after testing their blocking spectra and potencies at the calyx presynaptic terminals and collected data of their common effects on target functions. Since the recovery from STD was faster at the calyx synapses in the presence of both endocytic blockers in physiological 1.3 mM [Ca2+] (Figure 2B), but not in 2.0 mM [Ca2+] (Figure S4), they might facilitate vesicle mobilization in physiological condition.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Mahapatra and Takahashi report on the physiological consequences of pharmacologically blocking either clathrin and dynamin function during compensatory endocytosis or of the cortical actin scaffold both in the calyx of Held synapse and hippocampal boutons in acute slice preparations

Strengths:

Although many aspects of these pharmacological interventions have been studied in detail during the past decades, this is a nice comprehensive and comparative study, which reveals some interesting differences between a fast synapse (Calyx of Held) tuned to reliably transmit at several 100 Hz and a more slow hippocampal CA1 synapse. In particular, the authors find that acute disturbance of the synaptic actin network leads to a marked frequency-dependent enhancement of synaptic depression in the Calyx, but not in the hippocampal synapse. This striking difference between both preparations is the most interesting and novel finding.

Weaknesses:

Unfortunately, however, these findings concerning the different consequences of actin depolymerization are not sufficiently discussed in comparison to the literature. My only criticism concerns the interpretation of the ML 141 and Lat B data. With respect to the Calyx data, I am missing a detailed discussion of the effects observed here in light of the different RRP subpools SRP and FRP. This is very important since Lee et al. (2012, PNAS 109 (13) E765-E774) showed earlier that disruption of actin inhibits the rapid transition of SRP SVs to the FRP at the AZ. The whole literature on this important concept is missing. Likewise, the role of actin for the replacement pool at a cerebellar synapse (Miki et al., 2016) is only mentioned in half a sentence. There is quite some evidence that actin is important both at the AZ (SRP to FRP transition, activation of replacement pool) and at the peri-active zone for compensatory endocytosis and release site clearance. Both possible underlying mechanisms (SRP to FRP transition or release site clearance) should be better dissected.

The concept of FRP and SRP are derived from voltage-clamp step-depolarization experiments at calyces of Held in pre-hearing rodents at RT, which cannot be directly dissected in data of action-potential evoked EPSCs at post-hearing calyces at physiological conditions. However, we dissected as much by referring to related literatures in new paragraphs in Result section (p9-10), particularly on the different effects of Latrunculin application and experimental conditions by adding a new supplementary Figure (now S5). Regarding F-actin role in vesicle replenishment at cerebellar synapses, we added sentences in Discussion section (p14, last paragraph).

Reviewer #3 (Public Review):

General comments:

(1) While Dynasore and Pitstop-2 may impede release site clearance due to an arrest of membrane retrieval, neither Latrunculin-B nor ML-141 specifically acts on AZ scaffold proteins. Interference with actin polymerization may have a number of consequences many of which may be unrelated to release site clearance. Therefore, neither Latrunculin-B nor ML-141 can be considered suitable tools for specifically identifying the role of AZ scaffold proteins (i.e. ELKS family proteins, Piccolo, Bassoon, α-liprin, Unc13, RIM, RBP, etc) in release site clearance which was defined as one of the principal aims of this study.

In this study, we focused our analysis on the downstream activity of scaffold protein intersectin by comparing the common inhibitory effects of CDC42 and actin polymerization, by use of ML141 and Latrunculin B, respectively, on vesicle endocytosis and synaptic depression/ facilitation without addressing diverse individual drug effects. To avoid confusion we removed “AZ” from scaffold protein.

(2) Initial EPSC amplitudes more than doubled in the presence of Dynasor at hippocampal SC->CA1 synapses (Figure S2). This unexpected result raises doubts about the specificity of Dynasor as a tool to selectively block SV endocytosis.

It is possible that Dynasore might have unknown or off-target effects. However, the main conclusion is backed up by Pitstop-2.

(3) In this study, the application of Dynasore and Pitstop-2 strongly decreases 100 Hz steady-state release at calyx synapses while - quite unexpectedly - strongly accelerates recovery from depression. A previous study found that genetic ablation of dynamin-1 actually enhanced 300 Hz steady-state release while only little affecting recovery from depression (Mahapatra et al., 2016). A similar scenario holds for the Latrunculin-B effects: In this study, Latrunculin-B strongly increased steady-state depression while in Babu et al. (2020), Latrunculin-B did not affect steady-state depression. In Mahapatra et al. (2016), Latrunculin-B marginally enhanced steady-state depression. The authors need to make a serious attempt to explain all these seemingly contradicting results.

The latrunculin effect on STD can vary according to the condition of application and external [Ca2+], which we show in a new supplemental Figure S5. The latrunculin effect on the recovery from STD also varies with temperature, [Ca2+], and animal age, which affect Ca2+-dependent fast recovery component from depression. We added paragraphs for this issue in Results section (p9-10).

(4) The experimental conditions need to be better specified. It is not clear which recordings were obtained in 1.3 mM and which (if any?) in 2 mM external Ca. It is also unclear whether 'pooled data' are presented (obtained from control recordings and from separate recordings after pre-incubation with the respective drugs), or whether the data actually represent 'before'/'after' comparisons obtained from the same synapses after washing in the respective drugs. The exact protocol of drug application (duration of application/pre-incubation?, measurements after wash-out or in the continuous presence of the drugs?) needs to be clearly described in the methods and needs to be briefly mentioned in Results and/or Figure legends.

We added methodological explanations and reworded sentences in the text to be clear for pharmacological data derived from non-sequential separate experiments.

(5) The authors compare results obtained in calyx with those obtained in SC->CA1 synapses which they considered examples for 'fast' and 'slow' synapses, respectively. There is little information given to help readers understand why these two synapse types were chosen, what the attributes 'fast' and 'slow' refer to, and how that may matter for the questions studied here. I assume the authors refer to the maximum frequency these two synapse types are able to transmit rather than to EPSC kinetics?

Yes, the “fast and slow” naming features maximum operating frequency these synapses can transmit. We reworded “fast and slow” to “fast-signaling and slow-plastic” and added explanation in the text.

(6) Strong presynaptic stimuli such as those illustrated in Figures 1B and C induce massive exocytosis. The illustrated Cm increase of 2 to 2.5 pF represents a fusion of 25,000 to 30,000 SVs (assuming a single SV capacitance of 80 aF) corresponding to a 12 to 15% increase in whole terminal membrane surface (assuming a mean terminal capacitance of ~16 pF). Capacitance measurements can only be considered reliable in the absence of marked changes in series and membrane conductance. Since the data shown in Figs. 1 and 3 are central to the argumentation, illustration of the corresponding conductance traces is mandatory. Merely mentioning that the first 450 ms after stimulation were skipped during analysis is insufficient.

Conductance trace is shown with a trace of capacitance change induced by a square pulse in our previous paper (Yamashita et al, 2005 Science).

(7) It is essential for this study to preclude a contamination of the results with postsynaptic effects (AMPAR saturation and desensitization). AMPAR saturation limits the amplitudes of initial responses in EPSC trains and hastens the recovery from depression due to a 'ceiling effect'. AMPAR desensitization occludes paired-pulse facilitation and reduces steady-state responses during EPSC trains while accelerating the initial recovery from depression. The use of, for example, 1 mM kynurenic acid in the bath is a well-established strategy to attenuate postsynaptic effects at calyx synapses. All calyx EPSC recordings should have been performed under such conditions. Otherwise, recovery time courses and STP parameters are likely contaminated by postsynaptic effects. Since the effects of AMPAR saturation on EPSC_1 and desensitization on EPSC_ss may partially cancel each other, an unchanged relative STD in the presence of kynurenic acid is not necessarily a reliable indicator for the absence of postsynaptic effects. The use of kynurenic acid in the bath would have had the beneficial side effect of massively improving voltage-clamp conditions. For the typical values given in this MS (10 nA EPSC, 3 MOhm Rs) the expected voltage escape is ~30 mV corresponding to a change in driving force of 30 mV/80 mV=38%, i.e. initial EPSCs in trains are likely underestimated by 38%. Such large voltage escape usually results in unclamped INa(V) which was suppressed in this study by routinely including 2 mM QX-314 in the pipette solution. That approach does, however, not reduce the voltage escape.

Glutamate released during AP-evoked EPSCs does not saturate or desensitize postsynaptic receptors at post-hearing calyces of Held (Ishikawa et al, 2002; Yamashita et al, 2003) although it does in pre-hearing calyces (Yamashita et al, 2009). In fact, as shown in Figure S3, our results are essentially the same with or without kynurenate.

(8) In the Results section (pages 7 and 8), the authors analyze the time course into STD during 100 Hz trains in the absence and presence of drugs. In the presence of drugs, an additional fast component is observed which is absent from control recordings. Based on this observation, the authors conclude that '... the mechanisms operate predominantly at the beginning of synaptic depression'. However, the consequences of blocking or slowing site clearing are expected to be strongly release-dependent. Assuming a probability of <20% that a fusion event occurs at a given release site, >80% of the sites cannot be affected at the arrival of the second AP even by a total arrest of site clearance simply because no fusion has yet occurred. That number decreases during a train according to (1-0.2)^n, where n is the number of the AP, such that after 10 APs, ~90% of the sites have been used and may potentially be unavailable for new rounds of release after slowing site clearance. Perhaps, the faster time course into STD in the presence of the drugs isn't related to site clearance?

Enhanced depression at the beginning of stimulation indicates the block of rapid SV replenishment mechanism, which includes endocytosis-dependent site-clearance and scaffold-dependent vesicle translocation to release sites.

(9) In the Discussion (page 10), the authors present a calculation that is supposed to explain the reduced size of the second calyx EPSC in a 100 Hz train in the presence of Dynasore or Pitstop-2. Does this calculation assume that all endocytosed SVs are immediately available for release within 10 ms? Please elaborate.

We do not assume rapid endocytosed vesicle reuse within 10 ms as it requires much longer time for glutamate refilling (7s at PT; Hori & Takahashi, 2012). Instead, already filled reserved vesicles can rapidly replenish release sites if sites are clean and scaffold works properly. Results shown in Figure S6 also indicate that block of vesicle transmitter refilling has no immediate effect on synaptic responses.

(10) It is not clear, why the bafilomycin/folimycin data is presented in Fig. S5. The data is also not mentioned in the Discussion. Either explain the purpose of these experiments or remove the data.

These v-ATPase blockers, which block vesicular transmitter refilling, are reported to enhance EPSC depression at hippocampal synapses at RT and 2 mM [Ca2+] presumably because of lack of filled vesicles undergoing rapid vesicle recycling (eg Kiss & Run). We thought it important to determine whether these data have physiological relevance since such a mechanism might also regulate synaptic strength during repetitive transmission. However, our results did not support its physiological relevance. Since these results are not within our main questions, the negative results are shown it in supplementary Figure 6 and explained in the last paragraph of Result section (p11), but were not discussed further in Discussion section.

(11) The scheme in Figure 7 is not very helpful.

We updated the scheme to summarize our conclusion that vesicle replenishment through endocytosis-dependent site-clearance and scaffold-dependent mechanism independently co-operate to strengthen synaptic efficacy during repetitive transmission at calyx fast-signaling synapses. However, endocytic site clearance is solely required to support facilitation at slow-plastic hippocampal SC-CA1 synapses.

Recommendations for the authors:

First, my deep apologies for the long delay in reviewing your paper. All reviewers are now in agreement that the paper has valuable new information, but some methods are not described well and some results appear to be incompatible with previous results in the literature. The discussion of previous literature is also incomplete and not well-balanced. With more discussion of methods and literature strengthened this paper would be of great interest to neurobiologists and biophysicists working on synaptic vesicle recycling and short-term plasticity mechanisms. We ask that you address the comments and revise your paper before we can fully recommend the paper as being an important contribution with compelling evidence and a strong data set that supports the conclusions.

We explained methods more explicitly. Apparent incompatibility with previous results is now explained and discussed with new supplementary data.

Major:

(1) In this study, the application of Dynasore and Pitstop-2 strongly decreased 100 Hz steady-state release at calyx synapses while - quite unexpectedly - it strongly accelerated recovery from depression. A previous study found that genetic ablation of dynamin-1 actually enhanced 300 Hz steady-state release while only little affecting recovery from depression (Mahapatra et al., 2016). A similar scenario holds for the Latrunculin-B effects: In this study, Latrunculin-B strongly increased steady-state depression while in Babu et al. (2020), Latrunculin-B did not affect steady-state depression. In Mahapatra et al. (2016), Latrunculin-B marginally enhanced steady-state depression. The authors need to make a serious attempt to explain all these seemingly contradicting results.

Lack of change in the recovery from depression in dynamin-1 knockout mice by Mahapatra et al (2016) is consistent with results in Figure S4 in 2 mM [Ca2+], whereas accelerated recovery by Dynasore (Figure 2B2) is observed in 1.3 mM [Ca2+] suggesting that it is masked in 2 mM [Ca2+] but revealed in physiological [Ca2+] (p7, top paragraph). In both cases, however, recovery from STD is not prolonged unlike Hosoi et al (2009).

The latrunculin issues are discussed in Results section with newly added Supplementary Figure S5 (p9-10).

(2) The experimental conditions need to be better specified. It is not clear which recordings were obtained in 1.3 mM and which (if any?) in 2 mM external Ca. It is also unclear whether 'pooled data' are presented (obtained from control recordings and from separate recordings after pre-incubation with the respective drugs), or whether the data actually represent 'before'/'after' comparisons obtained from the same synapses after washing in the respective drugs. The exact protocol of drug application (duration of application/pre-incubation?, measurements after wash-out or in the continuous presence of the drugs?) needs to be clearly described in the methods and needs to be briefly mentioned in Results and/or Figure legends.

We made these points clearer in Method section and Result section.

(3) Please cite and discuss briefly previous papers that have shown fast endocytosis in the calyx of Held with membrane capacitance measurements like Renden and von Gersdorff, J Neurophysiology, 98:3349, 2007 and Taschenberger et al., Neuron, 2002. These papers first showed exocytosis and endocytosis kinetics in more mature (hearing) mice calyx of Held and at higher physiological temperatures.

One of these literatures relevant to the present study is quoted in p4.

(4) The findings concerning the different consequences of actin depolymerization are not sufficiently discussed in comparison to the literature. My only criticism concerns the interpretation of the ML 141 and Lat B data. With respect to the Calyx data, I am missing a detailed discussion of the effects observed here in light of the different RRP subpools SRP and FRP. This is very important since Lee et al. (2012, PNAS 109 (13) E765-E774) showed earlier that disruption of actin inhibits the rapid transition of SRP SVs to the FRP at the AZ. The whole literature on this important concept is missing. Likewise, the role of actin for the replacement pool at a cerebellar synapse (Miki et al., 2016) is only mentioned in half a sentence. There is quite some evidence that actin is important both at the AZ (SRP to FRP transition, activation of replacement pool) and at the peri-active zone for compensatory endocytosis and release site clearance. Both possible underlying mechanisms (SRP to FRP transition or release site clearance) should be better dissected.

We added discussions on the issue of latrunculin in Result section by quoting previous literatures (p9-10). Since there is no direct evidence (by vesicle imaging) for the presence of FRP and SRP, these definitions derived from voltage clamp step-depolarization studies are difficult to incorporate into the dissection of synaptic depression in physiological conditions.

Reviewer #1 (Recommendations For The Authors):

I have no major comments, but the following issues may be addressed.

(1) The term "fast and slow" synapses may be relative and a bit confusing. I do not think hippocampal synapses are slow synapses.

We have replaced “fast and slow” by “fast-signaling and slow-plastic” to represent their functions and added explanation in the text.

(2) Off-target effects of pharmacological effects may be discussed. In this respect, bafilomycin experiments can be used to argue against the slow effects of vesicle cycling such as endocytosis, and vesicle mobilization. However, the effects on rapid vesicle mobilization cannot be excluded entirely. Because I cannot exclude the absence of off-target effects either (can be addressed by looking at single vesicle imaging at nano-scale, which is hard to do or looking at EM level quantitatively?), I feel this is a matter of discussion.

It is possible that Dynasore might have unknown or off-target effects. However, the main conclusion is backed up by Pitstop-2.

(3) Fig2 A2, B2 and Fig 4 A2 and B2. It is easier to plot the recovery only normalized to the initial value. Subtracting steady-state is somewhat confusing because the recovery looks faster after deeper depression, but this may be just apparent.

We have given values for both types of plots in Table 2, which indicates no essential difference in the recovery parameters.

Reviewer #2 (Recommendations For The Authors):

Line 51: Rajappa et al. (2016) investigated clearance deficits in synaptophysin KO mice (not synaptobrevin).

Corrected.

Line 54: intersectin is introduced as AZ scaffold protein, although in most of the literature, it is referred to as an endocytic scaffold protein (also in the cited one, e.g. Sakaba et al. 2013). At least, this should be discussed.

Since blockers of intersectin downstream protein activity has no effect on vesicle endocytosis (Figure 3 and Sakaba et al, 2013), we called it (presynaptic) scaffold protein instead of endocytic scaffold protein.

Reviewer #3 (Recommendations For The Authors):

Minor comments

Page 1, Title: I don't think the presented data address the role of the presynaptic scaffold in SV replenishment. In addition, 'SV replenishment' and 'site clearance' should not be used synonymously as it seems to be implied here.

In this study our focus was on the downstream activity of scaffold protein intersectin and since block of its downstream effector proteins CDC42 and actin activities do not obstruct the endocytic activity (Fig 3, and Sakaba et al., 2013), instead of naming it as “endocytic scaffold protein”, we adopted “presynaptic scaffold protein”.

We have corrected it in the text.

Page 2, Abstract: Clarify 'physiologically optimized condition' here and elsewhere in the manuscript.

Abstract: in physiologically optimized condition → in physiological temperature and Ca2+.

Page 3, line 62: I don't think 'the site-clearance hypothesis is widely accepted'. There are very few models that implement such a mechanism. Examples would be Pan & Zucker (2009) Neuron and Lin, Taschenberger & Neher 2022 (PNAS) which could be cited.

62: the site-clearance hypothesis is “widely accepted”→ “well supported”

Page 3 line 77: Please clarify 'fast synapses

77: fast synapses→fast-signaling synapses, added clarification in the text.

Page 4, line 100: Please clarify 'in the maximal rate'.

100: in the maxima rate→reached during 1-Hz stimulation.

Page 6, line 136: Please clarify 'to reduce the gap'.

136: To reduce the gap between these different results→To explore the reason for these different results

Page 7, line 157: I don't consider ML141 and Latrunculin-B 'scaffold protein inhibitors'.

157: scaffold protein inhibitors had no effect on→ reworded as “none of these inhibitors affected fast or slow endocytosis”. 　

Page 7, line 162: P-value missing.

162: p < 0.001 added.

Page 8, line 184: "Since both endocytic blockers and scaffold inhibitors enhanced synaptic depression with a similar time course" consider rephrasing. Sounds like you refer to the time course by which these drugs exert their effect after being applied.

184: Since both endocytic blockers and scaffold inhibitors enhance synaptic depression with a similar time course→Since the enhancement of synaptic depression by endocytic blockers or scaffold inhibitor occurred mostly at the early phase of synaptic depression.

Same on page 11, line 250: "At the calyx of Held, scaffold protein inhibitors significantly enhanced synaptic depression with a time course closely matching to that enhanced by endocytic blocker" Please consider rephrasing.

At the calyx of Held, scaffold protein inhibitors significantly enhanced synaptic depression with a time course closely matching to that enhanced by endocytic blocker →the early phase of synaptic depression like endocytic blockers

Page 13, line 318: Please clearly state which experiments were performed at 1.3 mM and which at 2 mM external Ca if two different concentrations were used during recordings.

320: Added text “Unless otherwise noted, EPSCs were recorded in 1.3 mM [Ca2+] aCSF at 37oC” in the methods.

Page 15: line 346: Reference in the wrong format.

346; (25) → (Yamashita et al, 2005)

Page 15: line 351: Do you mean to say every 10 s and every 20 s? Please clarify.

No, averaged at 10 ms and 20 ms, respectively as written.

Page 16, line 369: 1 mM kyn was present in only very few experiments shown in the supplemental figures. Please clarify.

368: In some experiments, to test in the presence of 1 mM kyn, if there is any difference in enhanced STD following endocytic block. However, as shown in Figure S3, our results are essentially the same with or without kynurenate, suggesting glutamate released during AP-evoked EPSCs does not saturate or desensitize postsynaptic receptors at post-hearing calyces of Held (Ishikawa et al, 2002; Yamashita et al, 2003) unlike in pre-hearing calyces (Yamashita et al, 2009).

Page 16, line 387: You cannot simply use multiple t-tests to compare a single control to multiple test conditions which seems to be the scenario here. Please correct or clarify.

Experimental protocols are clarified in Methods as “Experiments were designed as population study using different cells from separate brain slices under control and drug treatment, rather than on a same cell before and after the drug exposure.”

Table S1: 'Endo decay rate'. It's either the 'Endo rate' or the 'Deacy rate of delta Cm'. Please correct.

Corrected as Endocytosis rate (Endo rate).

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

(1) The authors' primary research question revolves around the inquiry of "how far in advance semantic information might become available from parafoveal preview." In contrast to prior studies, the current research seeks to achieve a breakthrough in terms of timing by employing innovative technology. They mention in the manuscript that "most of these studies have been limited to measuring parafoveal preview from fixations to an immediately adjacent word... We tackle these core issues using a new technique that combines the use of frequency tagging and the measurement of magnetoencephalography (MEG)-based signals." However, the argumentation for how this new technology constitutes a breakthrough is not sufficiently substantiated. Specifically, there are two aspects that require further clarification. Firstly, the authors should clarify the importance of investigating the timing of semantic integration in their research question. They need to justify why previous studies focusing on the preview effect during fixations to an immediately adjacent word cannot address their specific inquiry about "how far in advance semantic information might become available from parafoveal preview," which requires examining parafoveal processing (POF). Secondly, in terms of the research methodology, the authors should provide a more comprehensive explanation of the advantages offered by MEG technology in the observation of the timing of semantic integration compared to the techniques employed in prior research. Indeed, the authors have overlooked some rather significant studies in this area. For instance, the research conducted by Antúnez, Milligan, Hernández-Cabrera, Barber, & Schotter in 2022 addresses the same research question mentioned in the current study and employs a similar experimental design. Importantly, they utilize a natural reading paradigm with synchronized ERP and eye-tracking recordings. Collectively, these studies, along with the series of prior research studies employing ERP techniques and RSVP paradigms discussed by the authors in their manuscript, provide ample evidence that semantic information becomes available and integrated from words before fixation occurs. Therefore, the authors should provide a more comprehensive citation of relevant research and delve deeper into explaining the potential contributions of their chosen technology to this field.

We express our gratitude to the reviewer for providing insightful comments. Firstly, we clarify the advantages of the RIFT technique. The revised paragraph is on Page 4 with tracked changes and is copied as follows:

“…… The RIFT technique provides a notable advantage by generating a signal — the tagging response signal — specifically yoked to just the tagged word. This ensures a clear separation in processing the tagged word from the ongoing processing of other words, addressing a challenge faced by eye tracking and ERP/FRP approaches. Moreover, RIFT enables us to monitor the entire dynamics of attentional engagement with the tagged word, which may begin a few words before the tagged word is fixated.”

We also rephase our research questions in the introduction section on Page 5 with tracked changes:

“This paradigm allows us to address three questions. First, we aimed to measure when in the course of reading people begin to direct attention to parafoveal words. Second, we sought to ascertain when semantic information obtained through parafoveal preview is integrated into the sentence context. Modulations of pre-target RIFT responses by the contextual congruity of target words would serve as evidence that parafoveal semantic information has not only been extracted and integrated into the sentence context but that it is affecting how readers allocate attention across the text. Third, we explored whether these parafoveal semantic attention effects have any relationship to reading speed.”

Secondly, we would like to elucidate the significance of investigating the timing of semantic integration and why this complements existing findings of parafoveal processing (POF) during reading. Our manuscript has been revised accordingly, with specific modifications highlighted on Page 2. The revised passage reads as follows:

“…… eye tracking-based evidence for the extraction of parafoveal semantic information …… was eventually extended into English …… For example, Schotter and Jia (2016) showed preview benefits on early gaze measures for plausible compared to implausible words, even for plausible words that were unrelated to the target. These results demonstrate that semantic information can indeed be extracted from parafoveal words. However, due to the limitations of the boundary paradigm, which only assesses effects after target words have been fixated, it is challenging to precisely determine when and how parafoveal semantic processing takes place. Furthermore, it is generally hard to distinguish between the effects of cross-saccade integration (e.g., mismatch between the preview and the word fixated) and the effects of how differing words fit into the context itself (Veldre and Andrews, 2016a, 2016b).”

Thirdly, we now better highlight the contributions of Antúnez et al. paper as they have provided important evidence for parafoveal semantic processing during natural reading. The relevant modifications are highlighted on Page 3. The revised passage is as follows: “Although many of these effects have been measured in the context of unnatural reading paradigms (e.g., the “RSVP flanker paradigm”), similar effects obtain during natural reading. Using the stimuli and procedures from Schotter and Jia (2016), Antúnez et al. (2022) showed that N400 responses, measured relative to the fixation before the target words (i.e., before the boundary change while the manipulated words were in parafoveal preview), were sensitive to the contextual plausibility of these previewed words. These studies suggest that semantic information is available from words before they are fixated, even if that information does not always have an impact on eye fixation patterns.”

References:

Schotter ER, Jia A. 2016. Semantic and plausibility preview benefit effects in English: Evidence from eye movements. J Exp Psychol Learn Mem Cogn 42:1839–1866. doi:10.1037/xlm0000281

Veldre A, Andrews S. 2016a. Is Semantic Preview Benefit Due to Relatedness or Plausibility? J Exp Psychol Hum Percept Perform 42:939–952. doi:10.1037/xhp0000200

Veldre A, Andrews S. 2016b. Semantic preview benefit in English: Individual differences in the extraction and use of parafoveal semantic information. J Exp Psychol Learn Mem Cogn 42:837–854. doi:10.1037/xlm0000212

Antúnez M, Milligan S, Andrés Hernández-Cabrera J, Barber HA, Schotter ER. 2022. Semantic parafoveal processing in natural reading: Insight from fixation-related potentials & eye movements. Psychophysiology 59:e13986. doi:10.1111/PSYP.13986

(2) Further, the authors emphasize semantic integration in their observed results but overlook the intricate relationship between access, priming, and integration. This assertion appears overly confident. Despite using low-constraint sentences and low-predicted targets (lines 439-441), differences between congruent and incongruent conditions may be influenced by word-level factors. For instance, in the first coherent sentence, such as "Last night, my lazy brother came to the party one minute before it was over" (line 1049), replacing the keyword "brother" with an incongruent word could create an incoherent sentence, possibly due to semantic violation, relation mismatch with "lazy," or prediction error related to animate objects. A similar consideration applies to the second example sentence, "Lily says this blue jacket will be a big fashion trend this fall" (line 1050), where the effect might result from a discrepancy between "blue" and an incongruent word. However, the authors do not provide incongruent sentences to substantiate their claims. I recommend that the authors discuss alternative explanations and potentially control for confounding factors before asserting that their results unequivocally reflect semantic integration. My intention is not to dispute the semantic integration interpretation but to stress the necessity for stronger evidence to support this assertion.

We agree with the reviewer that stimulus control is very critical for this kind of work and apologize for the lack of clarity in the original manuscript.

(1) We fully agree that word-level factors can be an important confound, which is why we carefully controlled word-level factors in the experimental design. As detailed in the Appendix of the original manuscript, each pair of target words has been strategically embedded into two sentences, allowing for the creation of both congruent and incongruent sentence pairs through the interchange of these words. We now have explicitly specified this design in all sentences, as reflected in the edited manuscript on Page 38. For example, considering the exemplar pair of “brother/jacket”,

“Last night, my lazy brother/jacket came to the party one minute before it was over.

Lily says this blue jacket/brother will be a big fashion trend this fall.”

In this design, the pair of target words is presented in both congruent and incongruent sentences. Participant A reads “lazy brother” and “blue jacket”, while Participant B reads “lazy jacket” and “blue brother”. This approach ensures that the same target words appear in both congruent and incongruent conditions across participants, serving as an effective control for word-level factors.

(2) We acknowledge that the consideration of word-level information is crucial when making claims about contextual integration in the current study. However, we don’t think there are many cases in the stimulus set where a single feature like animacy is enough to create the mismatch. Instead, the stimuli were written so that it is not possible to strongly predict any word or even a specific semantic feature, so that appreciating the mismatch requires the comprehender to integrate the word into the context (and especially to integrate the word with the immediately preceding one). However, this more local modifier/noun plausibility may behave differently from a more global contextual plausibility, which is a limitation of the stimulus set and has been discussed in the revised manuscript, as indicated by the tracked changes on Page 16, as copied below:

“Two noteworthy limitations exist in the current study. Firstly, the construction of pretarget–target word pairs consistently follows an adjective–noun phrase structure, potentially leading to semantic violations arising from immediate local incongruence rather than a broader incongruence derived from the entire sentential context. While the context preceding target words was deliberately minimized to ensure a pure effect of bottom-up parafoveal processing rather than the confounding impact of top-down prediction, it is essential to recognize that information from both local and global contexts can exert distinct effects on word processing during natural reading (Wong et al., 2022). Future investigations should incorporate more information-rich contexts to explore the extent to which the parafoveal semantic integration effect observed in this study can be generalized.”

References:

Wong R, Veldre A, Andrews S. 2022. Are There Independent Effects of Constraint and Predictability on Eye Movements During Reading? J Exp Psychol Learn Mem Cogn. doi:10.1037/XLM0001206

Reviewer #2 (Public Review):

This MEG study used co-registered eye-tracking and Rapid Invisible Frequency Tagging (RIFT) to track the effects of semantic parafoveal preview during natural sentence reading. Unpredictable target words could either be congruent or incongruent with sentence context. This modulated the RIFT response already while participants were fixating on the preceding word. This indicates that the semantic congruency of the upcoming word modulates visual attention demands already in parafoveal preview.

The quest for semantic parafoveal preview in natural reading has attracted a lot of attention in recent years, especially with the development of co-registered EEG and MEG. Evidence from dynamic neuroimaging methods using innovative paradigms as in this study is important for this debate.

We express our gratitude to the reviewer for recognizing the significance of our research question in the domain of natural reading.

Major points:

(1) The authors frame their study in terms of "congruency with sentence context". However, it is the congruency between adjective-noun pairs that determines congruency (e.g. "blue brother" vs "blue jacket", and examples p. 16 and appendix). This is confirmed by Suppl Figure 1, which shows a significantly larger likelihood of refixations to the pre-target word for incongruent sentences, probably because the pre-target word is most diagnostic for the congruency of the target word. The authors discuss some possibilities as to why there is variability in parafoveal preview effects in the literature. It is more likely to see effects for this simple and local congruency, rather than congruency that requires an integration and comprehension of the full sentence. I'm not sure whether the authors really needed to present their stimuli in a full-sentence context to obtain these effects. This should be explicitly discussed and also mentioned in the introduction (or even the abstract).

We have addressed this limitation of the study explicitly in the revised manuscript. The modifications can be found in the tracked changes on Page 16, and is copied as follows:

“Two noteworthy limitations exist in the current study. Firstly, the construction of pretarget–target word pairs consistently follows an adjective–noun phrase structure, potentially leading to semantic violations arising from immediate local incongruence rather than a broader incongruence derived from the entire sentential context. While the context preceding target words was deliberately minimized to ensure a pure effect of bottom-up parafoveal processing rather than the confounding impact of top-down prediction, it is essential to recognize that information from both local and global contexts can exert distinct effects on word processing during natural reading (Wong et al., 2022). Future investigations should incorporate more information-rich contexts to explore the extent to which the parafoveal semantic integration effect observed in this study can be generalized.”

References:

Wong R, Veldre A, Andrews S. 2022. Are There Independent Effects of Constraint and Predictability on Eye Movements During Reading? J Exp Psychol Learn Mem Cogn. doi:10.1037/XLM0001206

(2) The authors used MEG and provided a source estimate for the tagging response (Figure 2), which unsurprisingly is in the visual cortex. The most important results are presented at the sensor level. This does not add information about the brain sources of the congruency effect, as the RIFT response probably reflects top-down effects on visual attention etc. Was it necessary to use MEG? Would EEG have produced the same results? In terms of sensitivity, EEG is better than MEG as it is more sensitive to radial and deeper sources. This should be mentioned in the discussion and/or methods section.

Source estimation was exclusively provided for the tagging response rather than the congruency effect because we posit that this conditional contrast would emanate from the same brain regions exhibiting the tagging responses in general. As depicted in the following figure, source localization for the congruency effect was identified in the left association cortex (Brodmann area 18), the same area as the source localization for the tagging response (the negative cluster observed here is due to the incongruent minus congruent contrast). While we agree with the Reviewer that the RIFT result might indicate a top-down effect on visual attention, it is important to note that, due to the low-pass filter property of synapses, observing a tagging response at a high frequency beyond the visual cortex is challenging.

Author response image 1.

We discussed the necessity of using MEG in the edited manuscript with tracked changes on Page 20, and is copied as follows:

“While the current study was conducted using MEG, these procedures might also work with EEG. If so, this would make our approach accessible to more laboratories as EEG is less expensive. However, there are currently no studies directly comparing the RIFT response in EEG versus MEG. Therefore, it would be of great interest to investigate if the current findings can be replicated using EEG.”

(3) The earliest semantic preview effects occurred around 100ms after fixating the pre-target word (discussed around l. 323). This means that at this stage the brain must have processed the pre-target and the target word and integrated their meanings (at some level). Even in the single-word literature, semantic effects at 100 ms are provocatively early. Even studies that tried to determine the earliest semantic effects arrived at around 200 ms (e.g. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382728/, https://psycnet.apa.org/record/2013-17451-002). The present results need to be discussed in a bit more detail in the context of the visual word recognition literature.

We have incorporated this valuable suggestion into the discussion section to enhance the clarity of our key result regarding the timing of parafoveal semantic integration. The revised manuscript with tracked changes can be found on Page 14, and the relevant passage is provided below:

“Our results also provide information about the time course of semantic integration …… by as early as within 100 ms after fixating on the pre-target word. The timing of this parafoveal semantic effect appears remarkably early, considering that typical semantic access for a single word occurs no earlier than around 200 ms, as demonstrated in the visual word recognition literature (Carreiras et al., 2014). For instance, in a Go/NoGo paradigm, the earliest distinguishable brain activity related to category-related semantic information of a word occurs at 160 ms (Amsel et al., 2013; Hauk et al., 2012). Therefore, the RIFT results presented here suggest that natural reading involves parallel processing that spans multiple words. The level of (covert) attention allocated to the target word, as indexed by the significant difference in RIFT responses compared to the baseline interval, was observed even three words in advance (see Figure 2C). This initial increase in RIFT coincided with the target entering the perceptual span (McConkie and Rayner, 1975; Rayner, 1975; Underwood and McConkie, 1985), likely aligning with the initial extraction of lower-level perceptual information about the target. The emerging sensitivity of the RIFT signal to target plausibility, detected around 100 ms after the fixation on the pre-target word, suggests that readers at that time had accumulated sufficient semantic information about the target words and integrated that information with the evolving sentence context. Therefore, it is plausible that the initial semantic processing of the target word commenced even before the pre-target fixation and was distributed across multiple words. This parallel processing of multiple words facilitates rapid and fluent reading.”

References:

Carreiras M, Armstrong BC, Perea M, Frost R. 2014. The what, when, where, and how of visual word recognition. Trends Cogn Sci 18:90–98. doi:10.1016/j.tics.2013.11.005

Amsel BD, Urbach TP, Kutas M. 2013. Alive and grasping: Stable and rapid semantic access to an object category but not object graspability. Neuroimage 77:1–13. doi:10.1016/J.NEUROIMAGE.2013.03.058

Hauk O, Coutout C, Holden A, Chen Y. 2012. The time-course of single-word reading: Evidence from fast behavioral and brain responses. Neuroimage 60:1462. doi:10.1016/J.NEUROIMAGE.2012.01.061

McConkie GW, Rayner K. 1975. The span of the effective stimulus during a fixation in reading. Percept Psychophys 17:578–586. doi:10.3758/BF03203972

Rayner K. 1975. The perceptual span and peripheral cues in reading. Cogn Psychol 7:65–81.

Underwood NR, McConkie GW. 1985. Perceptual Span for Letter Distinctions during Reading. Read Res Q 20:153. doi:10.2307/747752

(4) As in previous EEG/MEG studies, the authors found a neural but no behavioural preview effect. As before, this raises the question of whether the observed effect is really "critical" for sentence comprehension. The authors provide a correlation analysis with reading speed, but this does not allow causal conclusions: Some people may simply read slowly and therefore pay more attention and get a larger preview response. Some readers may hurry and therefore not pay attention and not get a preview response. In order to address this, one would have to control for reading speed and show an effect of RIFT response on comprehension performance (or vice versa, with a task that is not close to ceiling performance). The last sentence of the discussion is currently not justified by the results.

We acknowledge that the correlation analysis between the RIFT effect and reading speed on the group level lacks causality, making it less ideal for addressing this question. We have incorporated this acknowledgment as one of the limitations of the current study in the revised manuscript on Page 16, as indicated by the tracked changes, and the relevant passage is provided below:

“Two noteworthy limitations exist in the current study. …… Secondly, the correlation analysis between the pre-target RIFT effect and individual reading speed (Figure 5) does not establish a causal relationship between parafoveal semantic integration and reading performance. Given that the comprehension questions in the current study were designed primarily to maintain readers’ attention and the behavioural performance reached a ceiling level, employing more intricate comprehension questions in future studies would be ideal to accurately measure reading comprehension and reveal the impact of semantic parafoveal processing on it.”

We reformulated the last sentence:

“These results support the idea that words are processed in parallel and suggest that early and deep parafoveal processing may be important for fluent reading.”

(5) L. 577f.: ICA components were selected by visual inspection. I would strongly recommend including EOG in future recordings when the control of eye movements is critical.

We appreciate the reviewer for providing this valuable suggestion. We acknowledge that EOG recordings were not included in the current study due to restrictions on MEG data collection from the University of Birmingham during the COVID-19 pandemic. In our future studies, we will follow the reviewer's suggestion to incorporate EOG recordings in data collection. This addition will facilitate optimal eye movement-related artifact rejection through ICA, as recommended by Dimigen in his methodological paper:

Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage, 207, 116117.

(6) The authors mention "saccade planning" a few times. I would suggest looking at the SWIFT model of eye movement control, which is less mechanistic than the dominant EZ-Reader model (https://psycnet.apa.org/record/2005-13637-003). It may be useful for the framing of the study and interpretation of the results (e.g. second paragraph of discussion).

In the revised manuscript, we have provided a more comprehensive explanation eye movements/saccade planning, aligning it with the SWIFT model. Please refer to Page 15 with tracked changes, and the updated passage is provided below:

“The results of the present study are aligned with the SWIFT model of eye movement control in natural reading (Engbert et al., 2005), wherein the activation field linked to a given word is hypothesized to be both temporally and spatially distributed. Indeed, we found that the initial increase in covert attention to the target word occurred as early as three words before, as measured by RIFT responses (Figure 2C). These covert processes enable the detection of semantic incongruity (Figure 3B and Figure 3C). However, it may occur at the non-labile stage of saccade programming, preventing its manifestation in fixation measures of the currently fixated pre-target word (Figure 1B). Therefore, the RIFT technique’s capacity to yoke patterns to a specific word offers a unique opportunity to track the activation field of word processing during natural reading.”

References:

Engbert R, Nuthmann A, Richter EM, Kliegl R. 2005. Swift: A dynamical model of saccade generation during reading. Psychol Rev 112:777–813. doi:10.1037/0033-295X.112.4.777

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

While the manuscript is well-written and presents a structured analysis of the data, it requires further clarification and substantiation regarding the originality of the research questions, the advantages of the proposed methodology, and the interpretation of the results related to semantic integration. Additional references and a more thorough discussion of related research are needed to strengthen the manuscript's contribution to the field.

We appreciate the reviewer's kind words about this manuscript and the insightful comments and suggestions provided. In the revised manuscript, we have now placed additional emphasis on the importance of investigating semantic integration within the realm of parafoveal processing in natural reading. We have clarified the advantages of employing MEG and RIFT and expanded upon our results in the context of Antúnez et al.'s 2022 paper, as suggested by the reviewer.

Reviewer #2 (Recommendations For The Authors):

(1) L. 59: The "N400" has been linked to much more than "semantic access". I think it is widely accepted that "access" happens (or at least begins) earlier, and that the N400 reflects high-level integration processes etc.

Earlier debates about whether the N400 is more linked to access or integration have resolved in favour of an access account, but with a growing appreciation of the blurred boundaries between constructions like access, priming, and integration, as Reviewer 1 also pointed out in comment #2.

(2) L. 177: I wasn't sure about the selection of sensors. Were the same sensors used for all participants (whether they had a tagging response or not)?

We appreciate the reviewer for highlighting the confusion regarding the sensor selection procedure in the study. In response, we have added further clarifications about this procedure in the Method section of the revised manuscript. The relevant changes can be found on Page 25 with tracked changes, and the modified passage is reproduced below:

"Please note that the tagging response sensors may vary in number across participants (7.9 ± 4.5 sensors per participant, M ± SD). Additionally, they may have a different but overlapping spatial layout, primarily over the visual cortex. For the topography of all tagging response sensors, please refer to Figure 2A."

(3) Ll. 247ff.: I don't understand the idea of a "spill-over effect". The future cannot spill into the past. Or does this refer to possible artefacts or technical problems?

In the revised manuscript, we have rephrased this passage with tracked changes on Page 11, and the updated version is provided below:

“We conducted a similar analysis of the coherence measured when participants fixated the target word and found no significant modulations related to the contextual congruity of that target word. …… Thus, the parafoveal semantic integration effect identified during the pre-target intervals cannot be attributed to signal contamination from fixations on the target word induced by the temporal smoothing of filters.”

(4) I struggled to follow the "internal attention" explanation for the paradoxical RIFT effect (p. 11/12).

We appreciate the reviewer for pointing out the confusion, and we have rephrased the passage in the revised manuscript with tracked changes on Page 13. The revised version is provided below:

"Previous work has demonstrated that tagging responses decrease as attention shifts from an external task (e.g., counting visual targets) to an internal task (e.g., counting heartbeats) (Kritzman et al., 2022). Similarly, in a reading scenario, visually perceiving the flickering word constitutes an external task, while the internal task involves the semantic integration of previewed information into the context. If more attentional resources are internally directed when faced with the challenge of integrating a contextually incongruent word, fewer attentional resources would remain for processing the flickering word. This may be the kind of shift reflected in the reduction in RIFT responses."

References:

Kritzman L, Eidelman-Rothman M, Keil A, Freche D, Sheppes G, Levit-Binnun N. 2022. Steady-state visual evoked potentials differentiate between internally and externally directed attention. Neuroimage 254:119133.

(5) L. 572: Why was detrending necessary on top of a 0.5 Hz high-pass filter? Was detrending applied to the continuous raw data, or to epochs? Was it just the linear trend or other polynomial terms?

We agree with the Reviewer that, given the prior application of a 0.5Hz high-pass filter to the data, the detrending does not alter the data. Nonetheless, we included this procedure in the manuscript for the sake of completeness. In the revised manuscript, we have provided additional clarification on this point, as indicated by the tracked changes on Page 23. The modified passage is presented below:

"Subsequently, detrending was applied individually to each channel of the continuous raw data to factor out the linear trend."

(6) Source analysis, p. 25f.: How was the beamformer regularized?

This information was already included in the original manuscript on Page 26. The original text is provided below for reference:

“No regularisation was performed to the CSD matrices (lambda = 0).”

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Why does stimulation at 0.15 Hz show a third harmonic signal (Figure 5A) but 0.25 Hz does not show a second harmonic signal?

Second and third harmonic signals were sometimes observed in 0.15 Hz and also in 0.25 Hz and other frequency stimulations. The second harmonic signal is easier to understand as vasomotion may be reacting to both directions of oscillating stimuli. The reason for the emergence of the third harmonics was totally unknown. These harmonic signals were not always observed, and the magnitude of these signals was variable. The frequency-locked signal was robust, thus, in this manuscript, we decided to describe only this signal. These observations are mentioned in the revised manuscript (Results, page 9, paragraph 2).

References for the windows are missing. Closed craniotomy: (Morii, Ngai, and Winn 1986). Thinned skull: (Drew et al. 2010).

These references were incorporated into the revised manuscript.

An explanation of, or at least a discussion on, why a flavoprotein or other intrinsic signal from the parenchyma might follow vasomotion with high fidelity would be most helpful.

We spend a large part of the Results describing that any fluorescence signal from the brain parenchyma follows the vasomotion because the blood vessels largely lack fluorescence signals within the filter band that we observe. This is described as “shadow imaging”. What was rather puzzling was that flavoprotein or other intrinsic signals were phase-shifted in time. This suggests that these autofluorescence signals have an anti-phase “shadow imaging” component and another component that is phase-shifted in time. This is described in the manuscript as the following.

(Results, page 13, paragraph 2)

“Production and degradation of flavin and other metabolites may be induced by the fluctuation in the blood vessel diameter with a fixed delay time. The phase shift in the autofluorescence could be due to the additive effect of “shadow” imaging of the vessel and to the concentration fluctuation of the autofluorescent metabolite”

Glucose and oxygen are likely to be abundantly delivered during the vasodilation phase compared to the vasoconstriction phase of vasomotion. These molecules will trigger cell metabolism and endogenous fluorescent molecules such as NADH, NADPH, and FAD may increase or decrease with a certain delay, which is required for the chemical reactions to occur. Therefore, the concentration fluctuation of these metabolites could lag in time to the changes in the blood flow. These discussions are added in the revised manuscript (Discussions, page 19, paragraph 2).

Reviewer #2 (Recommendations For The Authors):

Minor corrections to the text and figures:

(1) Figures 1 and 2- The single line slice basal and dilated traces are larger in Figure 2 (intact skull) than in Figure 1 (thinned skull)- have these been mixed up, as the authors state in the text that larger dilations are detected in the thinned skull preparation?

The example vessel described for the thinned skull (Figure 1) happened to be larger than that shown for the intact skull (Figure 2). We did not describe that larger dilations are observed in the thinned skull preparation. What was described was that the vessel profiles were shallower in the intact skull. This is because the presence of the intact skull blurs the fluorescence image.

(2) Figure 3- I think the lower panel of the amplitude spectrums from 3 individual animals included in D would benefit from being in its own panel within this Figure (i.e. E). The peak ratio is also used in this figure, but the equation to calculate this is not displayed until Figure 4.

We thank the reviewer for recommending making the figure more comprehensible. We have divided panel D into D and E and shifted the panel character accordingly. The manuscript text was also updated.

As the reviewer describes, the peak ratio of 0.25 Hz is used in Figure 3E (original). However, the equation to calculate this figure is described in the appropriate location within the main text of the manuscript (Results, page 10, paragraph 2) as well as in the figure legend.

(3) Figure 5- In the visual stimulation traces displayed in C you have included a 10-degree scale bar, which looks similar in amplitude to the trace but the text states these are 17-degree amplitude traces.

We thank the reviewer for noticing this mistake of labeling in the figure. We have corrected the error in the revised figure.

(4) Figure 6- For the Texas red fluorescence traces and image scales displayed in F, you have shown the responding traces on the right and non-responding on the left, but the figure legend states the amplitude is strong on the left and weak on the right.

We thank the reviewer for noticing the error in the figure legend text. We have corrected the error in the revised manuscript.

(5) Figure 6- It would be helpful for the reader if the r value was displayed on the graph in G.

We thank the reviewer for the suggestion. We have indicated the r value in Figure 6G as the reviewer recommended.

Reviewer #3 (Recommendations For The Authors):

Major

It is unclear to me if the authors are studying vasomotion per se. Vasomotion is an intrinsic, natural rhythm of blood vessel diameter oscillation that is entrained by endogenous rhythmic neural activity. Importantly, if you take neural activity away, the blood vessel (with flow and pressure) should still be capable of oscillating due to an intrinsic mechanism within the vessel wall. In contrast, if one increases neural activity by way of sensory stimulation and blood flow increases, this is the basis of functional hyperemia. If one stimulates the brain over and over again at a particular frequency, it is expected that blood flow will increase whenever neural activity increases to the stimulus, up to a particular frequency until the blood vessel cannot physically track the stimulus fast enough. Functional hyperemia does not depend on an intrinsic oscillator mechanism. It occurs when the brain becomes active above endogenous resting activity due to sensory or motor activity.

We thank the reviewer for stressing the importance of the distinction between “vasomotion” and functional “hyperemia”.

We recognized that the terminology used in our paper was not explicitly explained. Traditionally, “vasomotion” is defined as the dilation and constriction of the blood vessels that occurs spontaneously at low frequencies in the 0.1 Hz range without any apparent external stimuli. Sensory-induced changes in the blood flow are usually called “hyperemia”. However, in our paper, we used the term, vasomotion, literally, to indicate both forms of “vascular” “motion”. Therefore, the traditional vasomotion was called “spontaneous vasomotion” and the hyperemia, with both vasoconstriction and vasodilation, induced with slow oscillating visual stimuli was called “visually induced vasomotion”. This distinction in the terminology is now explicitly introduced in the revised manuscript (Introduction, page 3, paragraph 2-3; page 4, paragraph 1-2).

Using our newly devised methods, we show the presence of “spontaneous vasomotion”. However, this spontaneous vasomotion was often fragmented and did not last long at a specific frequency. With visual stimuli that slowly oscillated at temporal frequencies close to the frequency of spontaneous vasomotion, oscillating hyperemia, or “visually induced vasomotion” was observed. Importantly, this visually induced vasomotion is not observed in novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to neuronal activity in the primary visual cortex. We also do not know how the synchronized vasomotion can spread throughout the whole brain. Where the plasticity for vasomotion entrainment occurs is also unknown. How much of the visually induced vasomotion relies on the mechanisms of intrinsic spontaneous vasomotion is also undetermined. Discussion about the future directions of understanding the mechanisms of visually induced vasomotion and entrainment is described in better detail in the revised manuscript (Discussions, page 19, paragraph 1).

To me, one would need to silence the naturally occurring vasomotion to study it. As soon as one activates the brain with an external stimulus, functional hyperemia is being studied. One idea that would be interesting to look at is whether a single or perhaps a double stimulus, in an untrained vs trained mouse, shows vasodilation that occurs across the cortex and in the cerebellum. In other words, is there something special about repeating the signal over and over again that results in brain-wide synchronization, or does a single or double oscillation of the same frequency (0.25Hz) also transiently synchronize the brain? My guess is that a short stimulus would give you the same thing (especially in a trained mouse) and that there is nothing special about oscillating the signal over and over again (except for the learning component).

We thank the reviewer for the ideas of new experiments to understand whether the visually induced vasomotion shares the same mechanisms for creating spontaneous vasomotion or not.

We would like to emphasize again that the visually induced vasomotion is not observed in the Novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to the visual stimuli. Entrainment with repeated presentation of visual stimuli is required for this global synchronization phenomenon to occur.

We would also like to emphasize that, even in Expert animals, the visually induced vasomotion that is frequency-locked to the presented stimulus does not always occur immediately. As shown in Figure 3D lower panel (Figure 3E in the revised figure), the vasomotion did not always immediately frequency-lock. The vasomotion was also not always stable throughout the 15 min of visual stimulation presentation. These characteristics are emphasized in the revised manuscript (Results, page 10, paragraph 1).

Therefore, we would assume that a single or double frequency of the visual stimulation would not always be sufficient to transiently frequency-lock the visually induced vasomotion.

An alternative idea is to test frequencies lower than vasomotion. Vasomotion typically oscillates around a wide range of very low frequencies averaging around 0.1Hz, yet here the authors entrain blood vessel oscillations towards the top end of vasomotion, at 0.25Hz. What would happen if the authors tried synchronizing brain activity with 0.025Hz? Would the natural vasomotion frequency still be there, or would it be gone, dominated by the 0.025Hz entrainment?

We would assume that visually induced vasomotion will not be induced with 0.025 Hz visual stimuli. This is too slow to induce smooth pursuit of the visual stimuli with eye movement. We show that, even if smooth eye pursuit occurs, the visually induced vasomotion may or may not occur (Figure 6F). However, visually induced vasomotion does not largely occur without eye movement. Therefore, the proposed experiment by the reviewer is likely not doable.

Finally, perhaps the authors can see if there is a long-lasting change in natural vasomotion occurring after the animal has been trained to 0.25Hz. For example, is there greater power in the endogenous fluctuation at either 0.25Hz (or perhaps 0.1Hz) with no visual stimulation given but after the animal has been trained? These ideas would be interesting to test and could help clarify whether this is plasticity in functional hyperemia or plasticity in vasomotion.

It should also be mentioned that the frequency-locked vasomotion quickly dissipates as soon as the visual stimulation is halted (Figure 3D upper panel, middle). However, we agree with the reviewer that it would be interesting to see whether the fragmentation of the spontaneous vasomotion is observed less in the Trained or Expert mice compared to the Novice mice, to understand whether the entrainment effect would propagate to the properties of the spontaneous vasomotion.

This issue I have raised is not a fundamental flaw in the paper, it pertains more to the wording, phrasing, and pitch of the paper i.e. is this really entrained and plastic vasomotion? I am skeptical. Nevertheless, I think the authors should try some of these suggestions to better characterize this effect.

We agree that the phrasing used in the original manuscript was rather confusing, as “vasomotion” normally refers to spontaneous vascular movement. However, functional “hyperemia” may not adequately express the phenomenon that we observe either. The phenomenon that we observe is slowly oscillating vasodilation and vasoconstriction that is induced with visual stimuli with a temporal frequency similar to the spontaneously occurring “vasomotion”. This phenomenon is not a direct hyperemia response to the visual stimuli as it requires entrainment and it spreads globally throughout the whole brain. We revised our manuscript to define the terminology that we use.

An important question is if neural activity is entraining the CBF responses. The authors should do one experiment in a pan-neural GCaMP line to test if neural activity in the visual cortex (and other areas captured in the widefield microscope) shows a progressive and gradual synchronization (or not) to the vasomotion responses with training. It is possible to do this through a thinned skull window. This important to know if/how synchronized population neural activity scales with training. Perhaps they will not correlate and there is something more subtle going on.

In our paper, we mainly studied visually induced vasomotion (or visual stimulus-triggered vasomotion). Therefore, visual stimulation must first activate the neurons and, through neurovascular coupling, the initial drive for vasomotion is likely triggered. However, visually induced vasomotion is not observed in novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to neuronal activity in the primary visual cortex.

An important point that should be pointed out is that the neuronal visual response in the primary visual cortex could potentially decrease with repeated visual stimulation presentation as the adaptive movement of the eye should decrease the retinal slip. With repeated training sessions, a more static projection of the presented image will likely be shown to the retina. The neurovascular coupling could be enhanced with increased responsiveness of the vascules and vascular-to-vascular coupling could also be potentiated. This argument is now incorporated in the revised manuscript (Discussions, page 19, paragraph 1).

We agree with the reviewer that, to identify the extent of the neuronal contribution to the vasomotion triggering, whole brain synchronization, and vasomotion entrainment, simultaneous neuronal calcium imaging would be ideal. However, due to the fact that fluorescent Ca2+ indicators expressed in neurons would also be distorted by the “shadow” effect from the vasomotion, exquisite imaging techniques would be required. We recognize this “shadow” effect and we are currently developing methods to take out the “shadow” effect and the intracellular pH fluctuation effect from the fluorescence traces.

The authors nicely show that plasticity in vasomotion coincides with the mouse learning the HOKR task and that as eye movement tracks the stimulus, CBF gets entrained. However, there could also be a stress effect going on in the early trials, and as the mouse gets used to the procedure and stress comes down, the vasomotion entrainment can be seen. It could be the case that the vasomotion process is there on the first trial, but masked by stress-induced effects on neural and/or vascular activity. I did not see anything in the methods about how the mouse was habituated to head restraint. Was the first visual stim trial the first time the mouse was head restrained? If so, there could be a strong stress effect. The authors should address this either by clarifying that habituation to head restraint was done, or by doing a control experiment where each animal receives at least 1week of progressive and gradual head restraint before doing the same HOKR experiment using multiple trials.

We agree with the reviewer that stress could well affect spontaneous vasomotion as well as visually induced vasomotion (or visual stimulus-triggered vasomotion). As the reviewer suggested, we could have compared the habituated and non-habituated mice to the initial visually induced vasomotion response. In addition, whether the experimentally induced increase in stress would interfere with the vasomotion or not could also be studied. With the TexasRed experiments, we observed that tail-vein injection stress appeared to interfere with the HOKR learning process. In the experiments presented in Fig. 3, TexasRed was injected before session 1. Vasomotion entrainment likely progressed with sessions 2 and 3 training. Before session 4, TexasRed was injected again to visualize the vasomotion. The vasomotion was clearly observed in session 4, indicating that the stress induced by tail-vein injection could not interfere with the generation of visually induced vasomotion. This argument is included in the revised manuscript (Discussions, page 20, paragraph 2).

Minor

The first sentence of the introduction requires citations. It is also a somewhat irrelevant comparison to make.

Necessary citation was made in the revised manuscript, as the reviewer suggested. We think that describing how the energy is distributed in the brain would provide one of the most important breakthroughs to the understanding of how efficient information processing in the brain works. Therefore, we would like to keep this introduction.

The third and fourth sentence of the introduction equates vasodilation/vasoconstriction with vasomotion and it is not this simple. Vasomotion is a specific physiological process involving rhythmic changes to artery diameter. Also, the frequency of these slow oscillations needs to be stated. The authors only say they are slower than 10Hz.

The definition of spontaneous vasomotion with indication of typical temporal frequency is described in the revised manuscript, as the reviewer suggested.

More than half of the introduction is describing the paper itself, rather than setting the stage for the findings. The authors need a more thorough account of what is known and what is not known in this area. Some of this information is in the discussion, which should be moved up to the intro.

We have revised the introduction to include the definition of spontaneous vasomotion and visually induced vasomotion or functional hyperemia, as the reviewer suggested.

In the first paragraph of the results section, the authors should state in what way the mice are awake. Are they freely mobile? Are they head-restrained? Are they resting or moving or doing both at different times? This is clarified later but it should come up front as someone reads through the paper.

As the reviewer suggested, we clarified that the experiments were done in awake and head-restrained mice within the first paragraph for the Results section.

The authors say "As shown later, blood vessels on the surface...". There is no need to say "as shown later".

This is deleted as the reviewer suggested.

The use of "full width at 10% maximum" of the Texas red intensity for the diameter measure is a little odd, as it may actually overestimate the diameter, but I see what the authors were trying to do. A full-width half max is standard here and that is likely more appropriate. Also, the line profiles of intensity are not raw data. The authors say the trace is strongly filtered/smoothed. If so, this creates a somewhat artificial platform to make the diameter measurement. The authors should show raw data from a single experiment and make the measurement from that. The raw line profile should look almost square, where a full-width half-max would work well.

Contrary to what the reviewer observed, the raw line profile was not almost square. Even if there were almost no blur in the XY dimension in the optical imaging system, one would not expect to see a square line profile, as the thickness of the vessel increases in the Z dimension towards the center, as this is not a confocal or two-photon microscope image, and an ideal optical section was not created. Therefore, the full-width half-maximum value would definitely be an underestimate of the actual vessel diameter. It may be possible to equate an ideal value for cutoff if we have the 3D point spread function of the imaging. 10% is an arbitrary number but we think 10% is the minimum intensity that we can distinguish from the background intensity fluctuations. We did not attempt to derive the “true” diameter of the vessel and full-width at 10% maximum is just an index of the actual diameter. In most of the manuscript, we only deal with the change of the vessel diameter relative to the basal diameter, therefore, we considered that careful derivation of the absolute diameter estimate is not necessary. This argument is detailed in the Materials and Methods section in the revised manuscript (page 31, paragraph 2).

The raw line profile before filtering is shown overlaid in Figure 1C, as the reviewer suggested.

In Figures 1 and 2, state/label what brain region this is.

The blood vessels between the bregma and lambda on the cortex were observed and described in Figures 1 and 2. This is described in the revised manuscript, as the reviewer suggested.

Can the authors also show what a vein or venule looks like using their quantification method in Figures 1 and 2? This would be a helpful comparison to a static vein.

The methods shown in Figures 1 and 2 would not allow us to distinguish between vein and venule in our study. Methods that allow quantification of the relative blood vessel diameter fluctuation due to spontaneous or visually induced vasomotion activities are shown in Figures 1 and 2. Later in the manuscript, the whole intensity fluctuation of TexasRed or autofluorescence in the brain parenchyma is studied, and in this case, no distinction between vein and venules could be made.

Statements such as this are not necessary: "Later in the manuscript, we will be dealing with vasomotion dynamics observed with the optical fiber photometry methods, in which the blood vessel type under the detection of the fiber could not be identified". Simply talk about this data when you get to it.

We have deleted this statement in this part of the manuscript, as the reviewer suggested.

Same as this, please consider deleting: "Spontaneous vasomotion dynamic differences between different classes of blood vessels would be of interest to study using a more sophisticated in vivo two-photon microscope which we do not own." Just describe the data you have from the methods you have. There is no need to lament.

We deleted this sentence, as the reviewer suggested.

Figure 3 D the light blue boxes showing the time period of visual stimulation physically overlay with the frequency-time spectrograms. They should not overlay with this graph because it makes them more light blue, distorting the figure which also uses light blue in the heat map.

Figure 3D was modified, as the reviewer suggested.

The authors say: "The reason why the vasomotion detected in our system through the intact skull in awake in vivo mice was less periodic was unknown." Yes, but you are imaging an awake mouse. Many spontaneous behaviours such as whisking, grooming, twitching, and struggling will manifest as increased artery diameter. These will be functional hyperemia occurring events on top of rhythmic vasomotion. This can be briefly discussed.

As the reviewer comments, the vasomotion detected in awake mice was likely to be less periodic because the spontaneous animal behavior induces functional hyperemia and interrupts spontaneous vasomotion. This interpretation was included in the revised manuscript (Results, page 8, paragraph 1).

The authors say "extremely tuned" on page 8. They should not use words like "extremely". Perhaps say "more strongly tuned" or equivalent.

We have changed “extremely” to “more strongly”, as the reviewer suggested.

The authors say "First, the Texas Red fluorescence images were Gaussian filtered in the spatial XY dimension to take out the random noise presumably created within the imaging system." It is inadvisable to alter the raw data in this way unless there is a sound reason to do so. If there is random noise this should not affect the Fast Fourier Transform analysis. If there is regular noise caused by instrumentation artefact, which is picked up by the analysis then perhaps this could be filtered out. A static Texas red sample in a vial can be used to determine if there is artefactual noise.

We mainly used the Gaussian filter for better presentation of the imaged data. The TexasRed fluorescence was low in intensity and the acquired images were Gaussian filtered in the spatial XY dimesion to reduce the pixelated noise at the expense of spatial resolution reduction. This filter should not affect the temporal frequency of the observed vasomotion. This is now more clearly indicated in the revised manuscript (Results, page 10, paragraph 2).

There are endogenous fluorescent molecules in cell metabolism that change dynamically to neural activity: NADH, NADPH, and FAD. These are almost certainly a fraction of the auto-fluorescent signal the authors are measuring and it would be expected to see small fluctuations in these metabolites with neural activity. Perhaps this can be discussed, and the authors can likely argue that metabolic signals are much smaller than the change caused by vasodilation.

We found that the autofluorescence signal was phase-shifted in time relative to the vasomotion, which was visualized with TexasRed. This suggests that these autofluorescence signals have an anti-phase “shadow imaging” component and another component that is phase-shifted in time. Glucose and oxygen are likely to be abundantly delivered during the vasodilation phase compared to the vasoconstriction phase of vasomotion. These molecules will trigger cell metabolism and endogenous fluorescent molecules such as NADH, NADPH, and FAD may increase or decrease with a certain delay, which is required for the chemical reactions to occur. Therefore, the concentration fluctuation of these metabolites could lag in time to the changes in the blood flow. It is also expected that these metabolites may fluctuate according to the neuronal activity that triggers visually induced vasomotion or functional hyperemia. These discussions are added in the revised manuscript (Discussions, page 19, paragraph 2).

The authors say "however, we found that, if Texas Red had to be injected before every training session, the mouse did not learn very well." This is interesting. Why do the authors suppose this was the case? Stress from the injection? Or perhaps some deleterious effect on blood vessel function caused by the dye itself? Either way, I think this honest statement should remain. Others need to know about it.

We think that the stress from the injection interferes with the HOKR learning. However, as shown, TexasRed injection after the mouse had learned did not interfere with the eye movement or with the visually induced vasomotion. We do not know whether the injection stress directly interferes with the blood vessel function and affects the plastic vasomotion entrainment. These arguments are now described in the revised manuscript (Discussions, page 20, paragraph 2). The statement above remains as is, as the reviewer suggested.

YCnano50 is a calcium sensor and not really appropriate for the use employed by the authors. They are exciting YFP at 505nm but unless the authors are using a laser line, there is some bandwidth of excitation light that is likely exciting the CFP too which still absorbs light up to ~490nm. Here, calcium signalling may affect the YFP signal. This can be discussed.

Multiband-pass filter (Chroma 69008x with the relevant band of 503 nm / 19.5 nm (FWHM)) was used for direct excitation of YFP. Negligible light is passed below 490 nm. CFP excitation above 490 nm is assumed to be negligible and usually not defined in literature. We assume that with our optical system, fluorescence by direct YFP excitation dominates the effect from the minor CFP excitation effect. We explicitly describe this in the revised manuscript (Materials and Methods, page 28, paragraph 2).

The discussion is interesting but does not actually discuss much of the data or measurements in the paper. Most of the discussion reads more like a topical review, rather than a critical analysis of the effects/measurements and why the authors' interpretations are likely correct. This can be improved.

As the reviewer suggests, we have improved the discussion by starting with the summary of the results (Discussion, page 19, paragraph 1). We also included the possibility of stress affecting visually induced vasomotion (Discussion, page 20, paragraph 2).

Author Response

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

eLife assessment

This valuable paper presents a thoroughly detailed methodology for mesoscale-imaging of extensive areas of the cortex, either from a top or lateral perspective, in behaving mice. While the examples of scientific results to be derived with this method are in the preliminary stages, they offer promising and stimulating insights. Overall, the method and results presented are convincing and will be of interest to neuroscientists focused on cortical processing in rodents.

Authors’ Response: We thank the reviewers for the helpful and constructive comments. They have helped us plan for significant improvements to our manuscript. Our preliminary response and plans for revision are indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors introduce two preparations for observing large-scale cortical activity in mice during behavior. Alongside this, they present intriguing preliminary findings utilizing these methods. This paper is poised to be an invaluable resource for researchers engaged in extensive cortical recording in behaving mice.

Strengths:

-Comprehensive methodological detailing:

The paper excels in providing an exceptionally detailed description of the methods used. This meticulous documentation includes a step-by-step workflow, complemented by thorough workflow, protocols, and a list of materials in the supplementary materials.

-Minimal movement artifacts:

A notable strength of this study is the remarkably low movement artifacts. To further underscore this achievement, a more robust quantification across all subjects, coupled with benchmarking against established tools (such as those from suite2p), would be beneficial.

Authors’ Response: This is a good suggestion. We have records of the fast-z correction applied by the ScanImage on microscope during acquisition, so we have supplied the online fast-z motion correction .csv files for two example sessions on our GitHub page as supplementary files:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/online_fast_z_correction

These files correspond to Figure S3b (2367_200214_E210_1) and to Figures 5 and 6 (3056_200924_E235_1). These are now also referenced in the main text. See lines ~595, pg 18 and lines ~762, pg 24.

We have also made minor revisions to the main text of the manuscript with clear descriptions of methods that we have found important for the minimization of movement artifacts, such as fully tightening all mounting devices, implanting the cranial window with proper, evenly applied pressure across its entire extent, and mounting the mouse so that it is not too close or far from the surface of the running wheel. See Line ~309, pg 10.

Insightful preliminary data and analysis:

The preliminary data unveiled in the study reveal interesting heterogeneity in the relationships between neural activity and detailed behavioral features, particularly notable in the lateral cortex. This aspect of the findings is intriguing and suggests avenues for further exploration.

Weaknesses:

-Clarification about the extent of the method in the title and text:

The title of the paper, using the term "pan-cortical," along with certain phrases in the text, may inadvertently suggest that both the top and lateral view preparations are utilized in the same set of mice. To avoid confusion, it should be explicitly stated that the authors employ either the dorsal view (which offers limited access to the lateral ventral regions) or the lateral view (which restricts access to the opposite side of the cortex). For instance, in line 545, the phrase "lateral cortex with our dorsal and side mount preparations" should be revised to "lateral cortex with our dorsal or side mount preparations" for greater clarity.

Authors’ Response: We have opted to not change the title of the paper, because we feel that adding the qualifier, “in two preparations,” would add unnecessary complexity. In addition, while the dorsal mount preparation allows for imaging of bilateral dorsal cortex, the side mount preparation does indeed allow for imaging of both dorsal and lateral cortex across the right hemisphere (a bit of contralateral dorsal cortex is also imageable), and the design can be easily “flipped” across a mirror-plane to allow for imaging of left dorsal and lateral cortex. Taken together, we do show preparations that allow for pan-cortical 2-photon imaging.

We do agree that imprecise reference to the two preparations can sometimes lead to confusion. Therefore, we made several small revisions to the manuscript, including at ~line 545, to make it clearer that we used two imaging preparations to generate our combined 2-photon mesoscope dataset, and that each of those two preparations had both benefits and limitations.

-Comparison with existing methods:

A more detailed contrast between this method and other published techniques would add value to the paper. Specifically, the lateral view appears somewhat narrower than that described in Esmaeili et al., 2021; a discussion of this comparison would be useful.

Authors’ Response: The preparation by Esmaeili et al. 2021 has some similarities to, but also differences from, our preparation. Our preliminary reading is that their through-the-skull field of view is approximately the same as our through-the-skull field of view that exists between our first (headpost implantation) and second (window implantation) surgeries for our side mount preparation, although our preparation appears to include more anterior areas both near to and on the contralateral side of the midline. We have compared these preparations more thoroughly in the revised manuscript. (See lines ~278.)

Furthermore, the number of neurons analyzed seems modest compared to recent papers (50k) - elaborating on this aspect could provide important context for the readers.

Authors’ response: With respect to the “modest” number of neurons analyzed (between 2000 and 8000 neurons per session for our dorsal and side mount preparations with medians near 4500; See Fig. S2e) we would like to point out that factors such as use of dual-plane imaging or multiple imaging planes, different mouse lines, use of different duration recording sessions (see our Fig S2c), use of different imaging speeds and resolutions (see our Fig S2d), use of different Suite2p run-time parameters, and inclusion of areas with blood vessels and different neuron cell densities, may all impact the count of total analyzed neurons per session. We now mention these various factors and have made clear that we were not, for the purposes of this paper, trying to maximize neuron count at the expense of other factors such as imaging speed and total spatial FOV extent.

We refer to these issues now briefly in the main text. (See ~line 93, pg 3).

-Discussion of methodological limitations:

The limitations inherent to the method, such as the potential behavioral effects of tilting the mouse's head, are not thoroughly examined. A more comprehensive discussion of these limitations would enhance the paper's balance and depth.

Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this configuration (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript at ~line 235, pg. 7.

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it is possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the Thorlabs mesoscope objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use ultrasound gel instead (which we found to be, to some degree, optically inferior to water), but without the horizontal light shield, light from the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult under these conditions because the camera would need the same optical access angle as the 2-photon objective, or would need to be moved downward toward the air table and rotated up at an angle of 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

-Preliminary nature of results:

The results are at a preliminary stage; for example, the B-soid analysis is based on a single mouse, and the validation data are derived from the training data set.

Authors’ Response: In this methods paper, we have chosen to supply proof of principle examples, without a complete analysis of animal-to-animal variance.

The B-SOiD analysis that we show in Figure 6 is based on a model trained on 80% of the data from four sessions taken from the same mouse, and then tested on all of a single session from that mouse. Initial attempts to train across sessions from different mice were unsuccessful, probably due to differences in behavioral repertoires across mice. However, we have performed extensive tests with B-SOiD and are confident that these sorts of results are reproducible across mice, although we are not prepared to publish these results at this time.

We now clarify these points in the main text at ~line 865, pg 27.

An additional comparison of the results of B-SOiD trained on different numbers of sessions to that of keypoint-MOSEQ (Weinreb et al, 2023, bioRxiv) trained on ~20 sessions can now be found as supplementary material on our GitHub site:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/Figure_SZZ_BSOID_MOSEQ_align.pdf

The discrepancy between the maps in Figures 5e and 6e might indicate that a significant portion of the map represents noise. An analysis of variability across mice and a method to assign significance to these maps would be beneficial.

Authors’ Response: After re-examination of the original analysis output files, we have indeed discovered that some of the Rastermap neuron density maps in Figure 6e were incorrectly aligned with their respective qualitative behaviors due to a discrepancy in file numbering between the images in 6e and the ensembles identified in 6c (each time that Rastermap is run on the same data, at least with the older version available at the time of creation of these figures, the order of the ensembles on the y-axis changes and thus the numbering of the ensembles would change even though the neuron identities within each group stayed the same for a given set of parameters).

This unfortunate panel alignment / graphical display error present in the original reviewed preprint has been fixed in the current, updated figure (i.e. twitch corresponds to Rastermap groups 2 and 3, whisk to group 6, walk to groups 5 and 4, and oscillate to groups 0 and 1), and in the main text at ~line 925, pg 29. We have also changed the figure legend, which also contained accurate but misaligned information, for Figure 6e to reflect this correction.

One can now see that, because the data from both figures is from the same session in the same mouse, as you correctly point out, Fig 5d left (walk and whisk) corresponds roughly to Fig 6e group R7, “walk”, and that Fig 5d right (whisk) corresponds roughly to Fig 6e group R4, “twitch”.

We have double-checked the identity of other CCF map displays of Rastermap neuron density and of mean correlations between neural activity and behavioral primitives in all other figures, and we found no other such alignment or mis-labeling errors.

We have also added a caveat in the main text at ~lines 925-940, pg. 30, pointing out the preliminary nature of these findings, which are shown here as an example of the viability of the methods. Analysis of the variability of Rastermap alignments across sessions is beyond the scope of the current paper, although it is an issue that we hope to address in upcoming analysis papers.

-Analysis details:

More comprehensive details on the analysis would be beneficial for replicability and deeper understanding. For instance, the statement "Rigid and non-rigid motion correction were performed in Suite2p" could be expanded with a brief explanation of the underlying principles, such as phase correlation, to provide readers with a better grasp of the methodologies employed.

Authors’ Response: We added a brief explanation of Suite2p motion correction at ~line 136, pg 4. We have also added additional details concerning CCF / MMM alignment and other analysis issues. In general we cite other papers where possible to avoid repeating details of analysis methods that are already published.

Reviewer #2 (Public Review):

Summary:

The authors present a comprehensive technical overview of the challenging acquisition of large-scale cortical activity, including surgical procedures and custom 3D-printed headbar designs to obtain neural activity from large parts of the dorsal or lateral neocortex. They then describe technical adjustments for stable head fixation, light shielding, and noise insulation in a 2-photon mesoscope and provide a workflow for multisensory mapping and alignment of the obtained large-scale neural data sets in the Allen CCF framework. Lastly, they show different analytical approaches to relate single-cell activity from various cortical areas to spontaneous activity by using visualization and clustering tools, such as Rastermap, PCA-based cell sorting, and B-SOID behavioral motif detection.

Authors’ Response: Thank you for this excellent summary of the scope of our paper.

The study contains a lot of useful technical information that should be of interest to the field. It tackles a timely problem that an increasing number of labs will be facing as recent technical advances allow the activity measurement of an increasing number of neurons across multiple areas in awake mice. Since the acquisition of cortical data with a large field of view in awake animals poses unique experimental challenges, the provided information could be very helpful to promote standard workflows for data acquisition and analysis and push the field forward.

Authors’ Response: We very much support the idea that our work here will contribute to the development of standard workflows across the field including those for multiple approaches to large-scale neural recordings.

Strengths:

The proposed methodology is technically sound and the authors provide convincing data to suggest that they successfully solved various problems, such as motion artifacts or high-frequency noise emissions, during 2-photon imaging. Overall, the authors achieved their goal of demonstrating a comprehensive approach for the imaging of neural data across many cortical areas and providing several examples that demonstrate the validity of their methods and recapitulate and further extend some recent findings in the field.

Weaknesses:

Most of the descriptions are quite focused on a specific acquisition system, the Thorlabs Mesoscope, and the manuscript is in part highly technical making it harder to understand the motivation and reasoning behind some of the proposed implementations. A revised version would benefit from a more general description of common problems and the thought process behind the proposed solutions to broaden the impact of the work and make it more accessible for labs that do not have access to a Thorlabs mesoscope. A better introduction of some of the specific issues would also promote the development of other solutions in labs that are just starting to use similar tools.

Authors’ Response: We have edited the motivations behind the study to clarify the general problems that are being addressed. However, as the 2-photon imaging component of these experiments were performed on a Thorlabs mesoscope, the imaging details necessarily deal specifically with this system.

We briefly compare the methods and results from our Thorlabs system to that of Diesel-2p, another comparable system, based on what we have been able to glean from the literature on its strengths and weaknesses. See ~lines 206-213, pg 6.

Reviewer #3 (Public Review):

Summary

In their manuscript, Vickers and McCormick have demonstrated the potential of leveraging mesoscale two-photon calcium imaging data to unravel complex behavioural motifs in mice. Particularly commendable is their dedication to providing detailed surgical preparations and corresponding design files, a contribution that will greatly benefit the broader neuroscience community as a whole. The quality of the data is high, but it is not clear whether this is available to the community, some datasets should be deposited. More importantly, the authors have acquired activity-clustered neural ensembles at an unprecedented spatial scale to further correlate with high-level behaviour motifs identified by B-SOiD. Such an advancement marks a significant contribution to the field. While the manuscript is comprehensive and the analytical strategy proposed is promising, some technical aspects warrant further clarification. Overall, the authors have presented an invaluable and innovative approach, effectively laying a solid foundation for future research in correlating large-scale neural ensembles with behaviour. The implementation of a custom sound insulator for the scanner is a great idea and should be something implemented by others.

Authors’ Response: Thank you for the kind words.

We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

The data is located here: https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with in-depth analysis papers that are currently in preparation.

This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other. This is described in the methods, but a visual representation would greatly benefit the readers looking to implement something similar.

Authors’ Response: This is an excellent suggestion. We have included a workflow diagram in the revised manuscript, in the form of a 3-part figure, for the methods (a), data collection (b and c), and analysis (d). This supplementary figure is now located on the GitHub page at the following link:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/pancortical_workflow_diagrams.pdf

We now reference this figure on ~lines 190-192, pg 6 of the main text, near the beginning of the Results section.

The authors should cite sources for the claims stated in lines 449-453 and cite the claim of the mouse's hearing threshold mentioned in lines 463.

Authors’ Response: For the claim stated in lines 449-453:

“The unattenuated or native high-frequency background noise generated by the resonant scanner causes stress to both mice and experimenters, and can prevent mice from achieving maximum performance in auditory mapping, spontaneous activity sessions, auditory stimulus detection, and auditory discrimination sessions/tasks”

,we can provide the following references: (i) for mice: Sadananda et al, 2008 (“Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain”, Neuroscience Letters, Vol 435, Issue 1, p 17-23), and (ii) for humans: Fletcher et al, 2018 (“Effects of very high-frequency sound and ultrasound on humans. Part I: Adverse symptoms after exposure to audible very-high frequency sound”, J Acoust Soc A, 144, 2511-2520). We will include these references in the revised paper.

For the claim stated on line 463:

“i.e. below the mouse hearing threshold at 12.5 kHz of roughly 15 dB”

,we can provide the following reference: Zheng et al, 1999 (“Assessment of hearing in 80 inbred strains of mice by ABR threshold analyses”, Vol 130, Issues 1-2, p 94-107).

We have included these two new references in the new, revised version of our paper. Thank you for identifying these citation omissions.

No stats for the results shown in Figure 6e, it would be useful to know which of these neural densities for all areas show a clear statistical significance across all the behaviors.

Authors’ Response: It would be useful if we could provide a statistic similar to what we provide for Fig. S6c and f, in which for each CCF area we compare the observed mean correlation values to a null of 0, or, in this case, the population densities of each Rastermap group within each CCF area to a null value equal to the total number of CCF areas divided by the total number of recorded neurons for that group (i.e. a Rastermap group with 500 neurons evenly distributed across ~30 CCF areas would contain ~17 neurons, or ~3.3% density, per CCF area.) Our current figure legend states the maximums of the scale bar look-up values (reds) for each group, which range from ~8% to 32%.

However, because the data in panel 6e are from a single session and are being provided as an example of our methods and not for the purpose of claiming a specific result at this point, we choose not to report statistics. It is worth pointing out, perhaps, that Rastermap group densities for a given CCF area close to 3.3% are likely not different from chance, and those closer to ~40%, which is our highest density (for area M2 in Rastermap group 7, which corresponds to the qualitative behavior “walk”), are most likely not due to chance. Without analysis of multiple sessions from the same mouse we believe that making a clear statement of significance for this likelihood would be premature.

We now clarify this decision and related considerations in the main text at ~line 920, pg 29.

While I understand that this is a methods paper, it seems like the authors are aware of the literature surrounding large neuronal recordings during mouse behavior. Indeed, in lines 178-179, the authors mention how a significant portion of the variance in neural activity can be attributed to changes in "arousal or self-directed movement even during spontaneous behavior." Why then did the authors not make an attempt at a simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc). These models are straightforward to implement, and indeed it would benefit this work if the model extracts information on par with what is known from the literature.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the current methods paper. We are following up with an in depth analysis of neural activity and corresponding behavior across the cortex during spontaneous and trained behaviors, but this analysis goes well beyond the scope of the present manuscript.

Here, we prefer to present examples of the types of results that can be expected to be obtained using our methods, and how these results compare with those obtained by others in the field.

Specific strengths and weaknesses with areas to improve:

The paper should include an overall cartoon diagram that indicates how the various modules are linked together for the sampling of both behaviour and mesoscale GCAMP. This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other.

Authors’ Response: This is an excellent suggestion. We have included a workflow diagram in the revised manuscript, in the form of a 3-part figure, for the methods (a), data collection (b and c), and analysis (c). This supplementary figure is now located on the GitHub page at the following link:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/pancortical_workflow_diagrams.pdf

The paper contains many important results regarding correlations between behaviour and activity motifs on both the cellular and regional scales. There is a lot of data and it is difficult to draw out new concepts. It might be useful for readers to have an overall figure discussing various results and how they are linked to pupil movement and brain activity. A simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc) may help in this regard.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the present methods paper. Such an analysis is a significant undertaking with such large and heterogeneous datasets, and we provide proof-of-principle data here so that the reader can understand the type of data that one can expect to obtain using our methods. We will provide a more complete analysis of data obtained using our methodology in the near future in another manuscript.

Previously, widefield imaging methods have been employed to describe regional activity motifs that correlate with known intracortical projections. Within the authors' data it would be interesting to perhaps describe how these two different methods are interrelated -they do collect both datasets. Surprisingly, such macroscale patterns are not immediately obvious from the authors' data. Some of this may be related to the scaling of correlation patterns or other factors. Perhaps there still isn't enough data to readily see these and it is too sparse.

Authors’ Response: Unfortunately, we are unable to directly compare 1-photon widefield GCaMP6s activity with mesoscope 2-photon GCaMP6s activity. During widefield data acquisition, animals were stimulated with visual, auditory, or somatosensory stimuli (i.e. “passive sensory stimulation”), while 2-photon mesoscope data collection occurred during spontaneous changes in behavioral state, without sensory stimulation. The suggested comparison is, indeed, an interesting project for the future.

In lines 71-71, the authors described some disadvantages of one-photon widefield imaging including the inability to achieve single-cell resolution. However, this is not true. In recent years, the combination of better surgical preparations, camera sensors, and genetically encoded calcium indicators has enabled the acquisition of single-cell data even using one-photon widefield imaging methods. These methods include miniscopes (Cai et al., 2016), multi-camera arrays (Hope et al., 2023), and spinning disks (Xie et al., 2023).

Cai, Denise J., et al. "A shared neural ensemble links distinct contextual memories encoded close in time." Nature 534.7605 (2016): 115-118.

Hope, James, et al. "Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton." bioRxiv (2023).

Xie, Hao, et al. "Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution." Nature Biomedical Engineering (2023): 1-14.

Authors’ Response: We have corrected these statements and incorporated these and other relevant references. There are advantages and disadvantages to each chosen technique, such as ease of use, field of view, accuracy, and speed. We will reference the papers you mention without an extensive literature review, but we would like to emphasize the following points:

Even the best one-photon imaging techniques typically have ~10-20 micrometer resolution in xy (we image at 5 micrometer resolution for our large FOV configuration, but the xy point-spread function for the Thorlabs mesoscope is 0.61 x 0.61 micrometers in xy with 970 nm excitation) and undefined z-resolution (4.25 micrometers for Thorlabs mesoscope). A coarser resolution increases the likelihood that activity related fluorescence from neighboring cells may contaminate the fluorescence observed from imaged neurons. Reducing the FOV and using sparse expression of the indicator lessens this overlap problem.

We do appreciate these recent advances, however, particularly for use in cases where more rapid imaging is desired over a large field of view (CCD acquisition can be much faster than that of standard 2-photon galvo-galvo or even galvo-resonant scanning, as the Thorlabs mesoscope uses). This being said, there are few currently available genetically encoded Ca2+ sensors that are able to measure fluctuations faster than ~10 Hz, which is a speed achievable on the Thorlabs 2-photon mesoscope with our techniques using the “small, multiple FOV” method (Fig. S2d, e).

We have further clarified our discussion of these issues in the main text at ~lines 76-80, pg 2.

The authors' claim of achieving optical clarity for up to 150 days post-surgery with their modified crystal skull approach is significantly longer than the 8 weeks (approximately 56 days) reported in the original study by Kim et al. (2016). Since surgical preparations are an integral part of the manuscript, it may be helpful to provide more details to address the feasibility and reliability of the preparation in chronic studies. A series of images documenting the progression optical quality of the window would offer valuable insight.

Authors’ Response: As you suggest, we now include brief supplementary material demonstrating the changes in the window preparation that we observed over the prolonged time periods of our study, for both the dorsal and side mount preparations. The following link to this material is now referenced at ~line 287, pg 9, and at the end of Fig S1:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/window_preparation_stability.pdf

We have also included brief additional details in the main text that we found were useful for facilitating long term use of these preparations. These are located at ~line 287-290, pg 9.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Sharing raw data and code:

I strongly encourage sharing some of the raw data from your experiments and all the code used for data analysis (e.g. in a github repository). This would help the reader evaluate data quality, and reproduce your results.

Authors’ Response: We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

The data is located here: https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

Our existing GitHub repository, already referenced in the paper, is located here:

https://github.com/vickerse1/mesoscope_spontaneous

We have added an additional reference in the main text to the existence of these publicly available resources, including the appropriate links, located at ~lines 190-200, pg 6.

(2) Use of proprietary software:

The reliance on proprietary tools like LabView and Matlab could be a limitation for some researchers, given the associated costs and accessibility issues. If possible, consider incorporating or suggesting alternatives that are open-source, to make your methodology more accessible to a broader range of researchers, including those with limited resources.

Authors’ Response: We are reluctant to recommend open source software that we have not thoroughly tested ourselves. However, we will mention, when appropriate, possible options for the reader to consider.

Although LabView is proprietary and can be difficult to code, it is particularly useful when used in combination with National Instruments hardware. ScanImage in use with the Thorlabs mesoscope uses National Instruments hardware, and it is convenient to maintain hardware standards across the integrated rig/experimental system. Labview is also useful because it comes with a huge library of device drivers that makes addition of new hardware from basically any source very convenient.

That being said, there are open source alternatives that could conceivably be used to replace parts of our system. One example is AutoPilot (author: Jonny Saunders), for control of behavioral data acquisition: https://open-neuroscience.com/post/autopilot/.

We are not aware of an alternative to Matlab for control of ScanImage, which is the supported control software for the ThorLabs 2-photon mesoscope.

Most of our processing and analysis code (see GitHub page: https://github.com/vickerse1/mesoscope_spontaneous) is in Python, but some of the code that we currently use remains in Matlab form. Certainly, this could be re-written as Python code. However, we feel like this is outside the scope of the current paper. We have provided commenting to all code in an attempt to aid users in translating it to other languages, if they so desire.

(3) Quantifying the effect of tilted head:

To address the potential impact of tilting the mouse's head on your findings, a quantitative analysis of any systematic differences in the behavior (e.g. Bsoid motifs) could be illuminating.

Authors’ Response: We have performed DeepLabCut analysis of all sessions from both preparations, across several iterations with different parameters, to extract pose estimates, and we have also performed BSOiD of these sessions. We did not find any obvious qualitative differences in the number of behavioral motifs identified, the dwell times of these motifs, and similar issues, relating to the issue of tilting of the mouse’s head in the side mount preparation. We also did not find any obvious differences in the relative frequencies of high level qualitative behaviors, such as the ones referred to in Fig. 6, between the two preparations.

Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this configuration (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript. (See ~line 235, pg. 7)

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the Thorlabs mesoscope objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

(4) Clarification in the discussion section:

The paragraph titled "Advantages and disadvantages of our approach" seems to diverge into discussing future directions, rather than focusing on the intended topic. I suggest revisiting this section to ensure that it accurately reflects the strengths and limitations of your approach.

Authors’ Response: We agree with the reviewer that this section included several potential next steps or solutions for each advantage and disadvantage, which the reviewer refers to as “future directions” and are thus arguably beyond the scope of this section. Therefore we have retitled this section as, “Advantages and disadvantages of our approach (with potential solutions):”.

Although we believe this to be a logical organization, and we already include a section focused purely on future directions in the Discussion section, we have refocused each paragraph of the advantages/disadvantages subsection to concentrate on the advantages and disadvantages per se. In addition, we have made minor changes to the “future directions” section to make it more succinct and practical. These changes can be found at lines ~1016-1077, pg 33-34.

Reviewer #2 (Recommendations For The Authors):

Below are some more detailed points that will hopefully help to further improve the quality and scope of the manuscript.

• While it is certainly favorable for many questions to measure large-scale activity from many brain regions, the introduction appears to suggest that this is a prerequisite to understanding multimodal decision-making. This is based on the argument that combining multiple recordings with movement indicators will 'necessarily obscure the true spatial correlation structures'. However, I don't understand why this is the case or what is meant by 'true spatial correlation structures'. Aren't there many earlier studies that provided important insights from individual cortical areas? It would be helpful to improve the writing to make this argument clearer.

Authors’ Response: The reviewer makes an excellent point and we have re-worded the manuscript appropriately, to reflect the following clarifications. These changes can be found at ~lines 58-71, pg. 2.

We believe you are referring to the following passage from the introduction:

“Furthermore, the arousal dependence of membrane potential across cortical areas has been shown to be diverse and predictable by a temporally filtered readout of pupil diameter and walking speed (Shimoaka et al, 2018). This makes simultaneous recording of multiple cortical areas essential for comparison of the dependence of their neural activity on arousal/movement, because combining multiple recording sessions with pupil dilations and walking bouts of different durations will necessarily obscure the true spatial correlation structures.”

Here, we do not mean to imply that earlier studies of individual cortical areas are of no value. This argument is provided as an example, of which there are others, of the idea that, for sequences or distributed encoding schemes that simultaneously span many cortical areas that are too far apart to be simultaneously imaged under conventional 2-photon imaging, or are too sparse to be discovered with 1-photon widefield imaging, there are some advantages of our new methods over conventional imaging methods that will allow for truly novel scientific analyses and insights.

The general idea of the present example, based on the findings of Shimoaka et al, 2018, is that it is not possible to directly combine and/or compare the correlations between behavior and neural activity across regions that were imaged in separate sessions, because the correlations between behavior and neural activity in each region appear to depend on the exact time since the behavior began (Shimoaka et al, 2018), in a manner that differs across regions. So, for example, if one were to record from visual cortex in one session with mostly brief walk bouts, and then from somatosensory cortex in a second session with mostly long walk bouts, any inferred difference between the encoding of walk speed in neural activity between the two areas would run the risk of being contaminated by the “temporal filtering” effect shown in Shimoaka et al, 2018. However, this would not be the case in our recordings, because the distribution of behavior durations corresponding to our recorded neural activity across areas will be exactly the same, because they were recorded simultaneously.

• The text describes different timescales of neural activity but is an imaging rate of 3 Hz fast enough to be seen as operating at the temporal dynamics of the behavior? It appears to me that the sampling rate will impose a hard limit on the speed of correlations that can be observed across regions. While this might be appropriate for relatively slow behaviors and spontaneous fluctuations in arousal, sensory processing and decision formation likely operate on faster time scales below 100ms which would even be problematic at 10 Hz which is proposed as the ideal imaging speed in the manuscript.

Authors’ Response: Imaging rate is always a concern and the limitations of this have been discussed in other manuscripts. We will remind the reader of these limitations, which must always be kept in mind when interpreting fluorescence based neural activity data.

Previous studies imaging on a comparable yet more limited spatial scale (Stringer et al, 2019) used an imaging speed of ~1 Hz. With this in view, our work represents an advance both in spatial extent of imaged cortex and in imaging speed. Specifically, we believe that ~1 Hz imaging may be sufficient to capture flip/flop type transitions between low and high arousal states that persist in general for seconds to tens of seconds, and that ~3-5 Hz imaging likely provides additional information about encoding of spontaneous movements and behavioral syllables/motifs.

Indeed, even 10 Hz imaging would not be fast enough to capture the detailed dynamics of sensory processing and decision formation, although these speeds are likely sufficient to capture “stable” encodings of sensory representations and decisions that must be maintained during a task, for example with delayed match-to-sample tasks.

In general we are further developing our preparations to allow us to perform simultaneous widefield imaging and Neuropixels recordings, and to perform simultaneous 1.2 x 1.2 mm 2-photon imaging and visually guided patch clamp recordings.

Both of these techniques will allow us to combine information across both the slow and fast timescales that you refer to in your question.

We have clarified these points in the Introduction and Discussion sections, at ~lines ~93-105, pg 3, and ~lines 979-983, pg 31 and ~lines 1039-1045, pg 33, respectively.

• The dorsal mount is very close to the crystal skull paper and it was ultimately not clear to me if there are still important differences aside from the headbar design that a reader should be aware of. If they exist, it would be helpful to make these distinctions a bit clearer. Also, the sea shell implants from Ghanbari et al in 2019 would be an important additional reference here.

Authors’ Response: We have added brief references to these issues in our revised manuscript at ~lines 89-97, pg 3:

Although our dorsal mount preparation is based on the “crystal skull paper” (Kim et al, 2016), which we reference, the addition of a novel 3-D printable titanium headpost, support arms, light shields, and modifications to the surgical protocols and CCF alignment represent significant advances that made this preparation useable for pan-cortical imaging using the Thorlabs mesoscope. In fact, we were in direct communication with Cris Niell, a UO professor and co-author on the original Kim et al, 2016 paper, during the initial development of our preparation, and he and members of his lab consulted with us in an ongoing manner to learn from our successful headpost and other hardware developments. Furthermore, all of our innovations for data acquisition, imaging, and analysis apply equally to both our dorsal mount and side mount preparations.

Thank you for mentioning the Ghanbari et al, 2019 paper on the transparent polymer skull method, “See Shells.” We were in fact not aware of this study. However, it should be noted that their preparation seems to, like the crystal skull preparation and our dorsal mount preparation, be limited to bilateral dorsal cortex and not to include, as does our cranial window side mount preparation and the through-the-skull widefield preparation of Esmaeili et al, 2021, a fuller range of lateral cortical areas, including primary auditory cortex.

• When using the lateral mount, rotating the objective, rather than the animal, appears to be preferable to reduce the stress on the animal. I also worry that the rather severe head tilt could be an issue when training animals in more complex behaviors and would introduce an asymmetry between the hemispheres due to the tilted body position. Is there a strong reason why the authors used water instead of an imaging gel to resolve the issue with the meniscus?

Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this situation (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript. (See ~line 235, pg. 7)

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

• In parts, the description of the methods is very specific to the Thorlabs mesoscope which makes it harder to understand the general design choices and challenges for readers that are unfamiliar with that system. Since the Mesoscope is very expensive and therefore unavailable to many labs in the field, I think it would increase the reach of the manuscript to adjust the writing to be less specific for that system but instead provide general guidance that could also be helpful for other systems. For example (but not exclusively) lines 231-234 or lines 371 and below are very Thorlabs-specific.

Authors’ Response: We have revised the manuscript so that it is more generally applicable to mesoscopic methods.

We will make revisions as you suggest where possible, although we have limited experience with the other imaging systems that we believe you are referring to. However, please note that we already mentioned at least one other comparable system in the original eLife reviewed pre-print (Diesel 2p, line 209; Yu and Smith, 2021).

Here are a couple of examples of how we have broadened our description:

(1) On lines ~231-234, pg 7, we write:

“However, if needed, the objective of the Thorlabs mesoscope may be rotated laterally up to +20 degrees for direct access to more ventral cortical areas, for example if one wants to use a smaller, flat cortical window that requires the objective to be positioned orthogonally to the target region.”

Here have modified this to indicate that one may in general rotate their objective lens if their system allows it. Some systems, such as the Thorlabs Bergamo microscope and the Sutter MOM system, allow more than 20 degrees of rotation.

(2) On line ~371, pg 11, we write:

“This technique required several modifications of the auxiliary light-paths of the Thorlabs mesoscope”

Here, we have changed the writing to be more general such as “may require…of one’s microscope.”

Thank you for these valuable suggestions.

• Lines 287-299: Could the authors quantify the variation in imaging depth, for example by quantifying to which extent the imaging depth has to be adjusted to obtain the position of the cortical surface across cortical areas? Given that curvature is a significant challenge in this preparation this would be useful information and could either show that this issue is largely resolved or to what extent it might still be a concern for the interpretation of the obtained results. How large were the required nominal corrections across imaging sites?

Authors’ Response: This information was provided previously (lines 297-299):

“In cases where we imaged multiple small ROIs, nominal imaging depth was adjusted in an attempt to maintain a constant relative cortical layer depth (i.e. depth below the pial surface; ~200 micrometer offset due to brain curvature over 2.5 mm of mediolateral distance, symmetric across the center axis of the window).”

This statement is based on a qualitative assessment of cortical depth based on neuron size and shape, the density of neurons in a given volume of cortex, the size and shape of blood vessels, and known cortical layer depths across regions. A ground-truth measurement of this depth error is beyond the scope of the present study. However, we do specify the type of glass, thickness, and curvature that we use, and the field curvature characterization of the Thorlabs mesoscope is given in Fig. 6 of the Sofroniew et al, 2016 eLife paper.

In addition, we have provided some documentation of online fast-z correction parameters on our GitHub page at:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/online_fast_z_correction

,and some additional relevant documentation can be found in our publicly available data repository on FigShare+ at: https://doi.org/10.25452/figshare.plus.c.7052513

• Given the size of the implant and the subsequent work attachments, I wonder to which extent the field of view of the animal is obstructed. Did the authors perform receptive field mapping or some other technique that can estimate the size of the animals' remaining field of view?

Authors’ Response: The left eye is pointed down ~22.5 degrees, but we position the mouse near the left edge of the wheel to minimize the degree to which this limits their field of view. One may view our Fig. 1 and Suppl Movies 1 and 6 to see that the eyes on the left and right sides are unobstructed by the headpost, light shields, and support arms. However, other components of the experimental setup, such as the speaker, cameras, etc. can restrict a few small portions of the visual field, depending on their exact positioning.

The facts that mice responded to left side visual stimuli in preliminary recordings during our multimodal 2-AFC task, and that the unobstructed left and right camera views, along with pupillometry recordings, showed that a significant portion of the mouse’s field of view, from either side, remains intact in our preparation.

We have clarified these points in the text at ~lines 344-346, pg. 11.

• Line 361: What does movie S7 show in this context? The movie seems to emphasize that the observed calcium dynamics are not driven by movement dynamics but it is not clear to me how this relates to the stimulation of PV neurons. The neural dynamics in the example cell are also not very clear. It would be helpful if this paragraph would contain some introduction/motivation for the optogenetic stimulation as it comes a bit out of the blue.

Authors’ Response: This result was presented for two reasons.

First, we showed it as a control for movement artifacts, since inhibition of neural activity enhances the relative prominence of non-activity dependent fluorescence that is used to examine the amplitude of movement-related changes in non-activity dependent fluorescence (e.g. movement artifacts). We have included a reference to this point at ~lines 587-588, pg 18.

Second, we showed it as a demonstration of how one may combine optogenetics with imaging in mesoscopic 2-P imaging. References to this point were already present in the original version of the manuscript (the eLife “ reviewed preprint”).

• Lines 362-370: This paragraph and some of the following text are quite technical and would benefit from a better description and motivation of the general workflow. I have trouble following what exactly is done here. Are the authors using an online method to identify the CCF location of the 2p imaging based on the vessel pattern? Why is it important to do this during the experiment? Wouldn't it be sufficient to identify the areas of interest based on the vessel pattern beforehand and then adjust the 2p acquisition accordingly? Why are they using a dial, shutter, and foot pedal and how does this relate to the working distance of the objective? Does the 'standardized cortical map' refer to the Allen common coordinate framework?

Authors’ Response: We have revised this section to make it more clear.

Currently, the general introduction to this section appears in lines 349-361. Starting in line 362, we currently present the technical considerations needed to implement the overall goals stated in that first paragraph of this section.

In general we use a post-hoc analysis step to confirm the location of neurons recorded with 2-photon imaging. We use “online” juxtaposition of the multimodal map image with overlaid CCF with the 2-photon image by opening these two images next to each other on the ScanImage computer and matching the vasculature patterns “by eye”. We have made this more clear in the text so that the interested reader can more readily implement our methods.

By use of the phrase “standardized cortical map” in this context, we meant to point out that we had not decided a priori to use the Allen CCF v3.0 when we started working on these issues.

• Does Fig. 2c show an example of the online alignment between widefield and 2p data? I was confused here since the use of suite2p suggests that this was done post-recording. I generally didn't understand why the user needed to switch back and forth between the two modes. Doesn't the 2p image show the vessels already? Also, why was an additional motorized dichroic to switch between widefield and 2p view needed? Isn't this the standard in most microscopes (including the Thorlabs scopes)?

Authors’ Response: We have explained this methodology more clearly in the revised manuscript, both at ~lines 485-500, pg 15-16, and ~lines 534-540, pg 17.

The motorized dichroic we used replaced the motorized mirror that comes with the Thorlabs mesoscope. We switched to a dichroic to allow for near-simultaneous optogenetic stimulation with 470 nm blue light and 2-photon imaging, so that we would not have to move the mirror back and forth during live data acquisition (it takes a few seconds and makes an audible noise that we wanted to avoid).

Figure 2c shows an overview of our two step “offline” alignment process. The image at the right in the bottom row labeled “2” is a map of recorded neurons from suite2p, determined post-hoc or after imaging. In Fig. 2d we show what the CCF map looks like when it’s overlaid on the neurons from a single suite2p session, using our alignment techniques. Indeed, this image is created post-hoc and not during imaging. In practice, “online” during imaging, we would have the image at left in the bottom row of Fig. 2c (i.e. the multimodal map image overlaid onto an image of the vasculature also acquired on the widefield rig, with the 22.5 degree rotated CCF map aligned to it based on the location of sensory responses) rotated 90 degrees to the left and flipped over a horizontal mirror plane so that its alignment matches that of the “online” 2-photon acquisition image and is zoomed to the same scale factor. Then, we would navigate based on vasculature patterns “by-eye” to the desired CCF areas, and confirm our successful 2-photon targeting of predetermined regions with our post-hoc analysis.

• Why is the widefield imaging done through the skull under anesthesia? Would it not be easier to image through the final window when mice have recovered? Is the mapping needed for accurate window placement?

Authors’ Response: The headpost and window surgeries are done 3-7 days apart to increase success rate and modularize the workflow. Multimodal mapping by widefield imaging is done through the skull between these two surgeries for two major reasons. First, to make efficient use of the time between surgeries. Second, to allow us to compare the multimodal maps to skull landmarks, such as bregma and lambda, for improved alignment to the CCF.

Anesthesia was applied to prevent state changes and movements of the mouse, which can produce large, undesired effects on neural responses in primary sensory cortices in the context of these mapping experiments. We sometimes re-imaged multimodal maps on the widefield microscope through the window, roughly every 30-60 days or whenever/if significant changes in vasculature pattern became apparent.

We have clarified these points in the main text at ~lines 510-522, pg 20-21, and we added a link to our new supplementary material documenting the changes observed in the window preparation over time:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/window_preparation_stability.pdf

Thank you for these questions.

• Lines 445 and below: Reducing the noise from resonant scanners is also very relevant for many other 2p experiments so it would be helpful to provide more general guidance on how to resolve this problem. Is the provided solution only applicable to the Thorlabs mesoscope? How hard would it be to adjust the authors' noise shield to other microscopes? I generally did not find many additional details on the Github repo and think readers would benefit from a more general explanation here.

Authors’ Response: Our revised Github repository has been modified to include more details, including both diagrams and text descriptions of the sound baffle, respectively:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_for_noise_reduction_on_resonant_scanner_devices.pdf

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_methodology_summary.pdf

However, we can not presently disclose our confidential provisional patent application. Complete design information will likely be available in early 2025 when our full utility patent application is filed.

With respect to your question, yes, this technique is adaptable to any resonant scanner, or, for that matter, any complicated 3D surface that emits sound. We first 3D scan the surface, and then we reverse engineer a solid that fully encapsulates the surface and can be easily assembled in parts with bolts and interior foam that allow for a tight fit, in order to nearly completely block all emitted sound.

It is this adaptability that has prompted us to apply for a full patent, as we believe this technique will be quite valuable as it may apply to a potentially large number of applications, starting with 2-photon resonant scanners but possibly moving on to other devices that emit unwanted sound.

• Does line 458 suggest that the authors had to perform a 3D scan of the components to create the noise reduction shield? If so, how was this done? I don't understand the connection between 3D scanning and printing that is mentioned in lines 464-466.

Authors’ Response: We do not want to release full details of the methodology until the full utility patent application has been submitted. However, we have now included a simplified text description of the process on our GitHub page and included a corresponding link in the main text:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_methodology_summary.pdf

We also clarified in the main text, at the location that you indicate, why the 3D scanning is a critical part of our novel 3D-design, printing, and assembly protocol.

• Lines 468 and below: Why is it important to align single-cell data to cortical areas 'directly on the 2-photon microscope'? Is this different from the alignment discussed in the paragraph above? Why not focus on data interpretation after data acquisition? I understand the need to align neural data to cortical areas in general, I'm just confused about the 'on the fly' aspect here and why it seems to be broken out into two separate paragraphs. It seems as if the text in line 485 and below could also be placed earlier in the text to improve clarity.

Authors’ Response: Here by “such mapping is not routinely possible directly on the 2-photon mesoscope” what we mean is that it is not possible to do multimodal mapping directly on the mesoscope - it needs to be done on the widefield imaging rig (a separate microscope). Then, the CCF is mapped onto the widefield multimodal map, which is overlaid on an image of the vasculature (and sometimes also the skull) that was also acquired on the widefield imaging rig, and the vasculature is used as a sort of Rosetta Stone to co-align the 2-photon image to the multimodal map and then, by a sort of commutative property of alignment, to the CCF, so that each individual neuron in the 2-photon image can be assigned a unique CCF area name and numerical identifier for subsequent analysis.

We have clarified this in the text, thank you.

The Python code for aligning the widefield and 2-photon vessel images would also be of great value for regular 2p users. It would strongly improve the impact of the paper if the repository were better documented and the code would be equally applicable for alignment of imaging data with smaller cranial windows.

Authors’ Response: All of the code for multimodal map, CCF, and 2-photon image alignment is, in fact, already present on the GitHub page. We have made some minor improvements to the documentation, and readers are more than welcome to contact us for additional help.

Specifically, the alignment you refer to starts in cell #32 of the meso_pre_proc_1.ipynb notebook. In general the notebooks are meant to be run sequentially, starting with cell #1 of meso_pre_proc_1, then going to the next cell etc…, then moving to meso_pre_proc_2, etc… The purpose of each cell is labeled at the top of the cell in a comment.

We now include a cleaned, abridged version of the meso_pre_proc_1.pynb notebook that contains only the steps needed for alignment, and included a direct link to this notebook in the main text:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/python_code/mesoscope_preprocess_MMM_creation.ipynb

Rotated CCF maps are in the CCF map rotation folder, in subfolders corresponding to the angle of rotation.

Multimodal map creation involves use of the SensoryMapping_Vickers_Jun2520.m script in the Matlab folder.

We updated the main text to clarify these points and included direct links to scripts relevant to each processing step.

• Figure 4a: I found it hard to see much of the structure in the Rastermap projection with the viridis colormap - perhaps also because of a red-green color vision impairment. Correspondingly, I had trouble seeing some of the structure that is described in the text or clearer differences between the neuron sortings to PC1 and PC2. Is the point of these panels to show that both PCs identify movement-aligned dynamics or is the argument that they isolate different movement-related response patterns? Using a grayscale colormap as used by Stringer et al might help to see more of the many fine details in the data.

Authors’ Response: In Fig. 4a the viridis color range is from blue to green to yellow, as indicated in the horizontal scale bar at bottom right. There is no red color in these Rastermap projections, or in any others in this paper. Furthermore, the expanded Rastermap insets in Figs. S4 and S5 provide additional detailed information that may not be clear in Fig 4a and Fig 5a.

We prefer, therefore, not to change these colormaps, which we use throughout the paper.

We have provided grayscale png versions of all figures on our GitHub page:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/grayscale_figures

In Fig 4a the point of showing both the PC1 and PC2 panels is to demonstrate that they appear to correspond to different aspects of movement (PC1 more to transient walking, both ON and OFF, and PC2 to whisking and sustained ON walk/whisk), and to exhibit differential ability to identify neurons with positive and negative correlations to arousal (PC1 finds both, both PC2 seems to find only the ON neurons).

We now clarify this in the text at ~lines 696-710, pg 22.

• I find panel 6a a bit too hard to read because the identification and interpretation of the different motifs in the different qualitative episodes is challenging. For example, the text mentions flickering into motif 13 during walk but the majority of that sequence appears to be shaped by what I believe to be motif 11. Motif 11 also occurs prominently in the oscillate state and the unnamed sequence on the left. Is this meaningful or is the emphasis here on times of change between behavioral motifs? The concept of motif flickering should be better explained here.

Authors’ Response: Here motif 13 corresponds to a syllable that might best be termed “symmetric and ready stance”. This tends to occur just before and after walking, but also during rhythmic wheel balancing movements that appear during the “oscillate” behavior.

The intent of Fig. 6a is to show that each qualitatively identified behavior (twitch, whisk, walk, and oscillate) corresponds to a period during which a subset of BSOiD motifs flicker back and forth, and that the identity of motifs in this subset differs across the identified qualitative behaviors. This is not to say that a particular motif occurs only during a single identified qualitative behavior. Admittedly, the identification of these qualitative behaviors is a bit arbitrary - future versions of BSOiD (e.g. ASOiD) in fact combine supervised (i.e. arbitrary, top down) and unsupervised (i.e. algorithmic, objective, bottom-up) methods of behavior segmentation in attempt to more reliably identify and label behaviors.

Flickering appears to be a property of motif transitions in raw BSOiD outputs that have not been temporally smoothed. If one watches the raw video, it seems that this may in fact be an accurate reflection of the manner in which behaviors unfold through time. Each behavior could be thought of, to use terminology from MOSEQ (B Datta), as a series of syllables strung together to make a phrase or sentence. Syllables can repeat over either fast or slow timescales, and may be shared across distinct words and sentences although the order and frequency of their recurrence will likely differ.

We have clarified these points in the main text at ~lines 917-923, pg 29, and we added motif 13 to the list of motifs for the qualitative behavior labeled “oscillate” in Fig. 6a.

• Lines 997-998: I don't understand this argument. Why does the existence of different temporal dynamics make imaging multiple areas 'one of the keys to potentially understanding the nature of their neuronal activity'?

Authors’ Response: We believe this may be an important point, that comparisons of neurobehavioral alignment across cortical areas cannot be performed by pooling sessions that contain different distributions of dwell times for different behaviors, if in fact that dependence of neural activity on behavior depends on the exact elapsed time since the beginning of the current behavioral “bout”. Again, other reasons that imaging many areas simultaneously would provide a unique advantage over imaging smaller areas one at a time and attempting to pool data across sessions would include the identification of sequences or neural ensembles that span many areas across large distances, or the understanding of distributed coding of behavior (an issue we explore in an upcoming paper).

We have clarified these points at the location in the Discussion that you have identified. Thank you for your questions and suggestions.

Minor

Line 41: What is the difference between decision, choice, and response periods?

Authors’ Response: This now reads “...temporal separation of periods during which cortical activity is dominated by activity related to stimulus representation, choice/decision, maintenance of choice, and response or implementation of that choice.”

Line 202: What does ambulatory mean in this context?

Authors’ Response: Here we mean that the mice are able to walk freely on the wheel. In fact they do not actually move through space, so we have changed this to read “able to walk freely on a wheel, as shown in Figs. 1a and 1b”.

Is there a reason why 4 mounting posts were used for the dorsal mount but only 1 post was sufficient for the lateral mount?

Authors’ Response: Here, we assume you mean 2 posts for the side mount and 4 posts for the dorsal mount.

In general our idea was to use as many posts as possible to provide maximum stability of the preparations and minimize movement artifacts during 2-photon imaging. However, the design of the side mount headpost precluded the straight-forward or easy addition of a right oriented, second arm to its lateral/ventral rim - this would have blocked access of both the 2-photon objective and the right face camera. In the dorsal mount, the symmetrical headpost arms are positioned further back (i.e. posterior), so that the left and right face cameras are not obscured.

When we created the side mount preparation, we discovered that the 2 vertical 1” support posts were sufficient to provide adequate stability of the preparation and minimize 2-photon imaging movement artifacts. The side mount used two attachment screws on the left side of the headpost, instead of the one screw per side used in the dorsal mount preparation.

We have included these points/clarifications in the main text at ~lines 217-230, pg 7.

Figure S1g appears to be mislabeled.

Authors’ Response: Yes, on the figure itself that panel was mislabeled as “f” in the original eLife reviewed preprint. We have changed this to read “g”.

Line 349 and below: Why is the method called pseudo-widefield imaging?

Authors’ Response: On the mesoscope, broad spectrum fluorescent light is passed through a series of excitation and emission filters that, based on a series of tests that we performed, allow both reflected blue light and epifluorescence emitted (i.e. Stokes-shifted) green light to reach the CCD camera for detection. Furthermore, the CCD camera (Thorlabs) has a much smaller detector chip than that of the other widefield cameras that we use (RedShirt Imaging and PCO), and we use it to image at an acquisition speed of around 10 Hz maximum, instead of ~30-50 Hz, which is our normal widefield imaging acquisition speed (it also has a slower readout than what we would consider to be a standard or “real” 1-photon widefield imaging camera).

For these 3 reasons we refer to this as “pseudo-widefield” imaging. We would not use this for sensory activity mapping on the mesoscope - we primarily use it for mapping cortical vasculature and navigating based on our multimodal map to CCF alignment, although it is actually “contaminated” with some GCaMP6s activity during these uses.

We have briefly clarified this in the text.

Figures 4d & e: Do the colors show mean correlations per area? Please add labels and units to the colorbars as done in panel 4a.

Authors’ Response: For both Figs 4 and 5, we have added the requested labels and units to each scale bar, and have relabeled panels d to say “Rastermap CCF area cell densities”, and panels e to say “mean CCF area corrs w/ neural activity.”

Thank you for catching these omissions/mislabelings.

Line 715: what is superneuron averaging?

Authors’ Response: This refers to the fact that when Rastermap displays more than ~1000 neurons it averages the activity of each group of adjacent 50 neurons in the sorting to create a single display row, to avoid exceeding the pixel limitations of the display. Each single row representing the average activity of 50 neurons is called a “superneuron” (Stringer et al, 2023; bioRxiv).

We have modified the text to clarify this point.

Line 740: it would be good to mention what exactly the CCF density distribution quantifies.

Authors’ Response: In each CCF area, a certain percentage of neurons belongs to each Rastermap group. The CCF density distribution is the set of these percentages, or densities, across all CCF areas in the dorsal or side mount preparation being imaged in a particular session. We have clarified this in the text.

Line 745: what does 'within each CCF' mean? Does this refer to different areas?

Authors’ Response: The corrected version of this sentence now reads: “Next, we compared, across all CCF areas, the proportion of neurons within each CCF area that exhibited large positive correlations with walking speed and whisker motion energy.”

How were different Rastermap groups identified? Were they selected by hand?

Authors’ Response: Yes, in Figs. 4, 5, and 6, we selected the identified Rastermap groups “by hand”, based on qualitative similarity of their activity patterns. At the time, there was no available algorithmic or principled means by which to split the Rastermap sort. The current, newer version of Rastermap (Stringer et al, 2023) seems to allow for algorithmic discretization of embedding groups (we have not tested this yet), but it was not available at the time that we performed these preliminary analyses.

In terms of “correctness” of such discretization or group identification, we intend to address this issue in a more principled manner in upcoming publications. For the purposes of this first paper, we decided that manual identification of groups was sufficient to display the capabilities and outcomes of our methods.

We clarify this point briefly at several locations in the revised manuscript, throughout the latter part of the Results section.

Reviewer #3 (Recommendations For The Authors):

In "supplementary figures, protocols, methods, and materials", Figure S1 g is mislabeled as Figure f.

Authors’ Response: Yes, on the figure itself this panel was mislabeled as “f” in the original reviewed preprint. We have changed this to read “g”.

In S1 g, the success rate of the surgical procedure seems quite low. Less than 50% of the mice could be imaged under two-photon. Can the authors elaborate on the criteria and difficulties related to their preparations?

Authors’ Response: We will elaborate on the difficulties that sometimes hinder success in our preparations in the revised manuscript.

The success rate indicated to the point of “Spontaneous 2-P imaging (window) reads 13/20, which is 65%, not 50%. The drop to 9/20 by the time one gets to the left edge of “Behavioral Training” indicates that some mice do not master the task.

Protocol I contains details of the different ways in which mice either die or become unsuitable or “unsuccessful” at each step. These surgeries are rather challenging - they require proper instruction and experience. With the current protocol, our survival rate for the window surgery alone is as high as 75-100%. Some mice can be lost at headpost implantation, in particular if they are low weight or if too much muscle is removed over the auditory areas. Finally, some mice survive windowing but the imageable area of the window might be too small to perform the desired experiment.

We have added a paragraph detailing this issue in the main text at ~lines 287-320, pg 9.

In both Suppl_Movie_S1_dorsal_mount and Suppl_Movie_S1_side_mount provided (Movie S1), the behaviour video quality seems to be unoptimized which will impact the precision of Deeplabcut. As evident, there were multiple instances of mislabeled key points (paws are switched, large jumps of key points, etc) in the videos.

Many tracked points are in areas of the image that are over-exposed.

Despite using a high-speed camera, motion blur is obvious.

Occlusions of one paw by the other paws moving out of frame.

As Deeplabcut accuracy is key to higher-level motifs generated by BSOi-D, can the authors provide an example of tracking by exclusion/ smoothing of mislabeled points (possibly by the median filtering provided by Deeplabcut), this may help readers address such errors.

Authors’ Response: We agree that we would want to carefully rerun and carefully curate the outputs of DeepLabCut before making any strong claims about behavioral identification. As the aim of this paper was to establish our methods, we did not feel that this degree of rigor was required at this point.

It is inevitable that there will be some motion blur and small areas of over-exposure, respectively, when imaging whiskers, which can contain movement components up to ~150 Hz, and when imaging a large area of the mouse, which has planes facing various aspects. For example, perfect orthogonal illumination of both the center of the eye and the surface of the whisker pad on the snout would require two separate infrared light sources. In this case, use of a single LED results in overexposure of areas orthogonal to the direction of the light and underexposure of other aspects, while use of multiple LEDs would partially fix this problem, but still lead to variability in summated light intensity at different locations on the face. We have done our best to deal with these limitations.

We now briefly point out these limitations in the methods text at ~lines 155-160, pg 5.

In addition, we have provided additional raw and processed movies and data related to DeepLabCut and BSOiD behavioral analysis in our FigShare+ repository, which is located at:

https://doi.org/10.25452/figshare.plus.c.7052513

In lines 153-154, the authors mentioned that the Deeplabcut model was trained for 650k iterations. In our experience (100-400k), this seems excessive and may result in the model overfitting, yielding incorrect results in unseen data. Echoing point 4, can the authors show the accuracy of their Deeplabut model (training set, validation set, errors, etc).

Authors’ Response: Our behavioral analysis is preliminary and is included here as an example of our methods, and not to make claims about any specific result. Therefore we believe that the level of detail that you request in our DeepLabCut analysis is beyond the scope of the current paper. However, we would like to point out that we performed many iterations of DeepLabCut runs, across many mice in both preparations, before converging on these preliminary results. We believe that these results are stable and robust.

We believe that 650k iterations is within the reasonable range suggested by DLC, and that 1 million iterations is given as a reasonable upper bound. This seems to be supported by the literature for example, see Willmore et al, 2022 (“Behavioral and dopaminergic signatures of resilience”, Nature, 124:611, 124-132). Here, in a paper focused squarely on behavioral analysis, DLC training was run with 1.3 million iterations with default parameters.

We now note, on ~lines 153-154, pg 5, that we used 650K iterations, a number significantly less than the default of 1.03 million, to avoid overfitting.

In lines 140-141, the authors mentioned the use of slicing to downsample their data. Have any precautions, such as a low pass filter, been taken to avoid aliasing?

Authors’ Response: Most of the 2-photon data we present was acquired at ~3 Hz and upsampled to 10 Hz. Most of the behavioral data was downsampled from 5000 Hz to 10 Hz by slicing, as stated. We did not apply any low-pass filter to the behavioral data before sampling. The behavioral variables have heterogeneous real sampling/measurement rates - for example, pupil diameter and whisker motion energy are sampled at 30 Hz, and walk speed is sampled at 100 Hz. In addition, the 2-photon acquisition rate varied across sessions.

These facts made principled, standardized low-pass filtering difficult to implement. We chose rather to use a common resampling rate of 10 Hz in an unbiased manner. This downsampled 10 Hz rate is also used by B-SOiD to find transitions between behavioral motifs (Hsu and Yttri, 2021).

We do not think that aliasing is a major factor because the real rate of change of our Ca2+ indicator fluorescence and behavioral variables was, with the possible exception of whisker motion energy, likely at or below 10 Hz.

We now include a brief statement to this effect in the methods text at ~lines 142-146, pg. 4.

Line 288-299, the authors have made considerable effort to compensate for the curvature of the brain which is particularly important when imaging the whole dorsal cortex. Can the authors provide performance metrics and related details on how well the combination of online curvature field correction (ScanImage) and fast-z "sawtooth"/"step" (Sofroniew, 2016)?

Authors’ Response: We did not perform additional “ground-truth” experiments that would allow us to make definitive statements concerning field curvature, as was done in the initial eLife Thorlabs mesoscope paper (Sofroniew et al, 2016).

We estimate that we experience ~200 micrometers of depth offset across 2.5 mm - for example, if the objective is orthogonal to our 10 mm radius bend window and centered at the apex of its convexity, a small ROI located at the lateral edge of the side mount preparation would need to be positioned around 200 micrometers below that of an equivalent ROI placed near the apex in order to image neurons at the same cortical layer/depth, and would be at close to the same depth as an ROI placed at or near the midline, at the medial edge of the window. We determined this by examining the geometry of our cranial windows, and by comparing z-depth information from adjacent sessions in the same mouse, the first of which used a large FOV and the second of which used multiple small FOVs optimized so that they sampled from the same cortical layers across areas.

We have included this brief explanation in the main text at ~lines 300-311, pg 9.

In lines 513-515, the authors mentioned that the vasculature pattern can change over the course of the experiment which then requires to re-perform the realignment procedure. How stable is the vasculature pattern? Would laser speckle contrast yield more reliable results?

Authors’ Response: In general the changes in vasculature we observed were minimal but involved the following: i) sometimes a vessel was displaced or moved during the window surgery, ii) sometimes a vessel, in particular the sagittal sinus, enlarged or increased its apparent diameter over time if it is not properly pressured by the cranial window, and iii) sometimes an area experiencing window pressure that is too low could, over time, show outgrowth of fine vascular endings. The most common of these was (i), and (iii) was perhaps the least common. In general the vasculature was quite stable.

We have added this brief discussion of potential vasculature changes after cranial window surgery to the main text at ~lines 286-293, pg 9.

We already mentioned, in the main text of the original eLife reviewed preprint, that we re-imaged the multimodal map (MMM) every 30-60 days or whenever changes in vasculature are observed, in order to maintain a high accuracy of CCF alignment over time. See ~lines 507-511, pg 16.

We are not very familiar with laser speckle contrast, and it seems like a technique that could conceivably improve the fine-grained accuracy of our MMM-CCF alignment in some instances. We will try this in the future, but for now it seems like our alignments are largely constrained by several large blood vessels present in any given FOV, and so it is unclear how we would incorporate such fine-grained modifications without applying local non-rigid manipulations of our images.

In lines 588-598, the authors mentioned that the occasional use of online fast-z corrections yielded no difference. However, it seems that the combination of the online fast-z correction yielded "cleaner" raster maps (Figure S3)?

Authors’ Response: The Rastermaps in Fig S3a and b are qualitatively similar. We do not believe that any systematic difference exists between their clustering or alignments, and we did not observe any such differences in other sessions that either used or didn’t use online fast-z motion correction.

We now provide raw data and analysis files corresponding to the sessions shown in Fig S3 (and other data-containing figures) on FigShare+ at:

https://doi.org/10.25452/figshare.plus.c.7052513

Ideally, the datasets contained in the paper should be available on an open repository for others to examine. I could not find a clear statement about data availability. Please include a linked repo or state why this is not possible.

Authors’ Response: We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

The data is located here:

Vickers, Evan; A. McCormick, David (2024). Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice. Figshare+. Collection:

https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

this was written sometime i think around 2016. it's hard to recall the exact date; but if you check in the original gitlog there is one that has an original commit.

SONYe

R  O  C  K    O  F   .   .   .    S   A   G  E   S  ?

**\ **

H  E  A  R    D  E  R  O  R

I T  R E A L L Y  D O E S  M E A N   "FREEDOM"   B R E A D   I S   L I F E

Tying up loose eadds, in a similar vain to the connection between the Burning Bush and universal voting now etched by-stone, there exists a similar missing Link connecting the phrase "it's not a a gam" to Mary Magdeline to a pattern that shows us that the Holy Trinity and our timelines are narrated by a series of names of video game systems and their manufacturers from "Nintendo" to Genesis and the rock of SEGA.  Through a "kiss" and the falling of a wallthe words bread and read are tied up and twisted with the story of this Revelation and the heart of the word Creation, "be the reason it's A.D."  It's a strong connection between the idea that virtual reality and Heaven are linked by more than simply "technology" but that this message that shows us that these tools for understanding have fallen from the sky in order to help us understand why it is so important, why I call it a moral mandate, that we use this information to follow the map delivered to us in the New Testament and literally end world hunger, and literally heal the sick; because of the change in circumstance revealed to us.  These simple things, these few small details that might seem like nothing, or maybe appear to be "changing everything" they are not difficult things to do, in light of Creation, and few would doubt that once we do see them implementied here... the difference between Heaven and Hell will be ever so clear.

A while ago, in a place called Kentucky... this story began with a sort of twisted sci-fi experience that explained a kind of "God machine" that could manipulate time and reality, and in that story, in that very detailed and interesting story that I lived through, this machine was keyed to my DNA, in something like the "Ancient technology" of Stargate SG-1 and Atlantis mythology.  My kind brother Seth made a few appearances in the story, not actually in person but in fairly decent true to life holograms that I saw and spoke to every once in awhile.  He looked a little different, he had long hair; but that's neither here nor there, and he hasn't really had long hair since I was a little boy.  He happens to be a genetic engineer, and I happen to be a computer person (although he's that too, now; just nowhere near as good as me... with computers) so the story talked a little bit about how I would probably not have used DNA as a key, since I'm not a retard, and he probably wouldn't either, because works in that field (cyclone, huracan, tornado).  So then the key we imagined was something ... well, Who cares what the key is, right?

**\ **

o back to the task at hand, not so long ago, in a place called Plantation I was struck by lightning, literally (well not literally) the answer to a question that nobody knew was implanted in my mind, and it all came from asking a single simple question.  I was looking for more chemistry elements in the names of the books of the Holy Bible, after seeing Xenon at the "sort of beginning" of Exodus, where it screams "let there be light" in Linux and chemistry (and I've told you that a hundred times by now).  So it didn't take long to follow the light of that word and read Genesis backwards, and see, at the very beginning of that book, Silicon... in reverse.

*\ *

So, what about God's DNA, anyway*?  *

What's he really made of?

SIM MON S              WILD ER             ROD DEN BERRY

o after seeing Silicon, and connecting that to the numerous attempts I've made to show a message connecting The Matrix to the Fifth Element (as Silicon) describing what it is that God believes we should do with this knowledge--and see that it is narrated as the miracles of Jesus Christ in the New Testament... these names came to me in quick succession, an answer to the question.  I suppose any Gene will do, these three though, have a very important tie to the message that connects Joshua's Promised Land of flowing Milk and Honies to ... a kiss that begins the new day (I hope) ... and a message about exactly how we might go about doing magical things like ending world hunger and healing the sick using technology described ... in Star Trek and Stargate.  A "religion of the Stars" is being born.

That's great... it starts with an earthquake. R.E.M. and a band ... 311.  Oooh, I can see it coming down... The Petty Reckless.  An evening's love starts with a kiss.  Dave Matthews Band.  I wanna rock and roll all night and party every day.  Adam.  I mean Kiss.  Are you starting to see a pattern form?  Birds, snakes, and aeroplanes?  It's that, it's the end of the world as we know it, and I feel fine.

*\ *

 *\ *

*\ *

In that song we see clues that more than just the Revelation of Christ is narrated by John on an island called Patmos.  There yet another Trinity, starting with "Pa" and hearting Taylor Momsen's initials... most likely for a reason... and the Revelation ends with a transition that I hope others will agree with me turns "original sin" into something closer to "obviously salvation" when we finally understand the character that is behind the message of da i of Ra... and begin to see the same design in the names of Asmodai and in this Revelation focusing on freedom and truth that really does suggest Taylor can't talk to me in any way other than "letting freedom sing" in this narrative of kismet and fate and free will and ... then we see that narrative continue in the names of bands, just like the 3/11/11 earthquake is narrated in not just R.E.M.'s song but in the name 311.  Just like the 9/11 attack is narrated not just in that same song (released in 1987) and  "Inside Job" (released in 2000) but also in "Fucked up world."

Dear all of you walking dumb and blind, this same quake is narrated in Taylor's Zombie; waiting for the day to shake, all very similar to Cairo and XP, perhaps a "fad" of doublethink in the minds of the authors singing about a clear prophesy in the Bible; this connection between the day, 3/11 though, and the name of a band and the day of an arrest and the verse Matthew that tells you clearly you have now been baptized in water and fire... it shows us the design of a story whose intent and purpose is to ensure that we no longer allow for things like hurricanes and earthquakes and murder and rape to be "simulated" that we build a better system, that doesn't allow for 'force majeure" to take lives for no reason at all.

Not just in band names, but in the angels names too, in all of our names; we see this narration continue.  The Holy Water that is central to the baptism of Christ is etched into Taylor's name, between "sen" and "mom" the key to the two Mary's whose names contain the Spanish for "sea" in a sort of enlightenment hidden in plain sight.  In "Simmons" the key connection between today, this Biblical Monday, and the word "simulation" that ties to Simpsons and simians and keep it simple stupid*, and in Simmons the missing "s" of Kismet, finally completing the question.***

***\

 *\

*\ *

It's a song and dance that started a long time ago, as you can see from the ancient Hebrew word for "fate" and in more recent years a connection to the ballroom of Atlantis in the Doors 5 to 1 and Dave sang about it in Rapunzel and then Taylor shook a tambourine on the beach only minutes away from me--but never said "hi."  The battle of the bands continues tying some door knocking to a juxtaposition between "Sweet Things" and "Knocking on Heavens door" all the way to a Gossip Girl episode where little J asked a question that I can't be sure she knew was related, she said... "who's that, at the door?"

*\ *

What it really all amounts to, though, is the whole world witnessing the Creation of Adam and Eve from a little girl stuttering out "the the" at the sight of the Grinch himself, and then later not even able to get those words off her lips... about seeing how Creation and modern art are inextricably tied to religion, to heaven, and to freedom.

*\ *

 *\ *

*\ *

The bottom line here, hopefully obvious now, is that you can't keep this message "simple" it's a Matrix woven between more points of light than I can count, and many more that I'm sure you will find.  It's a key to seeing how God speaks to me, and to you; and how we are, we really are that voice.  Tay, if you don't do something just because God called it "fate" you are significantly more enslaved than if you do--and you wanted to.  "Now I see that you and me, were never meant, never meant to be..." she sang before I mentioned her, and before she ever saw me... in a song she calls "Nothing Left to Lose" and I see is not really just another word for freedom.

We have plenty to lose by not starting the fire, not the least of which is Heaven itself.  Understand what "force majeure" really means to you and I.  Ha, by the way.

IN CASE YOU FORGOT YESTERDAY'S MESSAGE

**\ **

*\ *

*\ *

"DADDY, I WANT IT NOW."

VERUKA SALT. whose name means "to see (if) you are the Body of Christ" whined, in the story of Will Why Won Ka, about nothing more or less than Heaven on Hearth, than seeing an end to needless torture and pain.   To see if you are the "Salt of the Earth" warming the road to Heaven; honestly to see if you can break through this inane lie of "I don't understand" and realize that breaking this story and talking about what is being presented not just by me and you but by history and God himself is the key to the car that drives us home.  To see how Cupid you really are.

STOP NODDING, TURN AROUND AND CALL A REPORTER.

The story of Willy Wonka ties directly to the Promised Land of Flowing Milk and Honey to me; by showing us a river of chocolate and a the everlasting God starter, (er is it guardian of B stopper) that opens the doors of perception about exactly what kinds of mistake may have been made in the past in this transition to Heaven that we are well on the way of beginning.  Here, in the Land of Nod, that is also Eden and also the Heart of the Ark we see warnings about "flowing milk and honey" being akin to losing our stable ecosystem, to losing the stuff of life itself, biology and evolution, and if we don't understand--this is probably exactly the mistake that was made and the cause of the story of Cain and Abel.  So here we are talking about genetic engineering and mind uploading and living forever, and hopefully seeing that while all things are possible with God--losing the wisdom of the message of religion is akin to losing life in the Universe and with that any hope of eternal longevity.\ With some insight into religion, you can connect the idea that without bees our stable ecosystem might collapse, to the birds and the bees, and a message about stability and having more than one way to pollinate the flowers  and trees and get some.   Janet and Nanna, by the way, both have pretty brown eyes, but that probably comes as no surprise to you.\ Miss Everything, on the other hand (I hear, does not have brown eyes), leads us to glimpse how this message about the transition of our society might continue on in the New Testament, and suggest that we do need to eat, and have dinner conversation, and that a Last Supper might be a little bit more detrimental to our future than anyone had ever thought, over and over and over again.  To see how religion really does make clear that this is what the message is about, to replace the flowing milk we have a "Golden Cow" that epitomizes nothing less than "not listening to Adam" and we have a place that believes the Hammer of Judah Maccabee should be ... extinct.  You are wrong.

*\ *

*\ *

Of course the vibrating light here ties this Gene to another musical piece disclosing something... "Wild Thing" I make your heart sing.  You can believe the Guitar Man is here to steal the show and deliver bread for the hungry and for the wise.  Here's some, it's not just Imagine Dragons telling you to listen to the radio but Jefferson Starship*too, and Live.  *

*\ *

When you wake up, you can hear God "singing" to you on the radio every single day; many of us already do.  He's telling you to listen to me, and I do not understand why you do not.  You don't look very Cupid, if you ask me.**

***\

WHAT DO YOU THINK YOU ARE,

DAN RE Y NO LDS?

**\ **

I think we all know what the Rod of Jesus Christ is by now.

​

It is a large glowing testament to freedom and truth, and a statement about blindness and evil that is unmistakable.   To say that seeing it is the gateway to Heaven would be an understatement of it's worth, of the implication that not seeing it is obvious Hell when it is linked to everything from nearly every story of the Holy Bible from Isaac to Isaiah to "behold he is to coming" and if you weren't sure if the Hand of God were in action here--it's very clear that it is; that linking Tricky Dick and Watergate to Seagate ... really delivering crystal clear understanding that the foundation of Heaven is freedom and that you have none today because you refuse to see the truth.

It is the doorway to seeing that what has been going on in this place hasn't been designed to hide me, but to hide a prosperous future from you--to hide the truth about our existence and the purpose of Creation--that all told, you are standing at the doorstep of Heaven and stammering your feet, closing your eyes, and saying "you don't want to help anyone."

If delivering freedom, truth, and equality  to you does not a den make,

well, you can all suck it

... from God, to you.

**\ **

Between Stargate and Star Trek it's pretty easy to see a roadmap to very quickly and easily be able to end world hunger and heal the sick without drastically changing the way our society works, it's about as simple as a microwave, or a new kind of medicine--except it's not so easy to see why it is that you are so reluctant to talk about the truth that makes these things so easy to do.  You see, your lack of regard for anyone anywhere has placed you in a position of weakness, and if you do nothing today, you will not be OK tomorrow.\ It's pretty easy to see how Roddenberry's name shows that this message comes from God, that he's created this map that starts with an Iron Rod throughout our history proving Creation, whose heart is a Den of Family who care about the truth, and about freedom, and about helping each other--not what you are--you are not that today.  Today you are sick, and I'd like you to look at the mirror he's made for you, and ***be eshamden (or asham). ***

Realize, realize... what you are.  What you've become, just as I have... the devil in a sweet, sweet kiss.**

***\

-Dave J. Matthews

Unless otherwise indicated, this work was written between the Christmas and Easter seasons of 2017 and 2020(A). The content of this page is released to the public under the GNU GPL v2.0 license; additionally any reproduction or derivation of the work must be attributed to the author, Adam Marshall Dobrin along with a link back to this website, fromthemachine dotty org.

That's a "." not "dotty" ... it's to stop SPAMmers. :/

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sneak preview

now linking to the next page ... in the discussion:

https://fromthemachine.org/2017/08/waiting-for-that-green-light.html

Author Response

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

Reviewer #1

Public Review

Summary:

(1) This work describes a simple mechanical model of worm locomotion, using a series of rigid segments connected by damped torsional springs and immersed in a viscous fluid.

(2) It uses this model to simulate forward crawling movement, as well as omega turns.

Strengths:

(3) The primary strength is in applying a biomechanical model to omega-turn behaviors.

(4) The biomechanics of nematode turning behaviors are relatively less well described and understood than forward crawling.

(5) The model itself may be a useful implementation to other researchers, particularly owing to its simplicity.

Weaknesses:

(6) The strength of the model presented in this work relative to prior approaches is not well supported, and in general, the paper would be improved with a better description of the broader context of existing modeling literature related to undulatory locomotion.

(7) This paper claims to improve on previous approaches to taking body shapes as inputs.

(8) However, the sole nematode model cited aims to do something different, and arguably more significant, which is to use experimentally derived parameters to model both the neural circuits that induce locomotion as well as the biomechanics and to subsequently compare the model to experimental data.

(9) Other modeling approaches do take experimental body kinematics as inputs and use them to produce force fields, however, they are not cited or discussed.

(10) Finally, the overall novelty of the approach is questionable.

(11) A functionally similar approach was developed in 2012 to describe worm locomotion in lattices (Majmudar, 2012, Roy. Soc. Int.), which is not discussed and would provide an interesting comparison and needed context.

9-11: The paper you recommended and our manuscript have some similarities and differences.

Similarities

Firstly, the components constituting the worm are similar in both models. ElegansBot models the worm as a chain of n rods, while the study by Majmudar et al. (2012) models it as a chain of n beads. Each bead in the Majmudar et al. model has a directional vector, making it very similar to ElegansBot's rod. However, there's a notable difference: in the Majmudar et al. model, each bead has an area for detecting contact between the obstacle and the bead, while in ElegansBot, the rod does not feature such an area.

Secondly, the types of forces and torques acting on the components constituting the worm are similar. Each rod in ElegansBot receives frictional force, muscle force, and joint force. Each bead in the Majmudar et al. model receives a constraint force, viscous force, and a repulsive force from obstacles. Each rod in ElegansBot receives frictional torque, muscle torque, and joint torque. Each bead in the Majmudar et al. model receives elastic torque, constraint torque, drive torque, and viscous torque. The Majmudar et al. model's constraint force and torque are similar to ElegansBot's joint force and torque in that they prevent two connected components of the worm from separating. The Majmudar et al. model's viscous force and torque are similar to ElegansBot's frictional force and torque in that they are forces exchanged between the worm and its surrounding environment (ground surface). The Majmudar et al. model's drive torque is similar to ElegansBot's muscle force and muscle torque as a cause of the worm's motion. However, unlike ElegansBot, the Majmudar et al. model did not consider the force generating the drive torque, and there are differences in how each force and torque is calculated. This will be discussed in more detail below.

Differences

Firstly, the medium in which the worm locomotes is different. ElegansBot is a model describing motion in a homogeneous medium like agar or water without obstacles, while the Majmudar et al. model describes motion in water with circular obstacles fixed at each lattice point. This is because the purposes of the models are different. ElegansBot analyzes locomotion patterns based on the friction coefficient, while the Majmudar et al. model analyzes locomotion patterns based on the characteristics of the obstacle lattice, such as the distance between obstacles. Also, for this reason, the Majmudar et al. model's bead, unlike ElegansBot's rod, receives a repulsive force from obstacles.

Secondly, the specific methods of calculating similar types of forces differ. ElegansBot calculates joint forces by substituting frictional forces, muscle forces, frictional torques, and muscle torques into an equation derived from differentiating a boundary condition equation twice over time, where two neighboring rods always meet at one point. This involves determining the process through which various forces and torques are transmitted across the worm. Specifically, it entails calculating how the frictional forces and torques, as well as the muscle forces and torques acting on each rod, are distributed throughout the entire length of the worm. In contrast, The Majmudar et al. model uses Lagrange multipliers method based on a boundary condition that the curve length determined by each bead's tangential angle does not change, to calculate the constraint force and torque before calculating the drive torque and viscous force. This implies that the Majmudar et al. model did not consider the mechanism by which the drive torque and viscous force received by one bead are distributed throughout the worm. ElegansBot's rod receives an anisotropic Stokes frictional force from the ground surface, while the Majmudar et al. model considered the frictional force according to the Navier-Stokes equation for incompressible fluid, assuming the fluid velocity at the bead's location as the bead's velocity.

Thirdly, unlike the Majmudar et al. model, ElegansBot considers the inertia of the worm components. Therefore, ElegansBot can simulate regardless of how low or high the ground surface's friction coefficient is. the Majmudar et al. model is not like this.

(12) The idea of applying biomechanical models to describe omega turns in C. elegans is a good one, however, the kinematic basis of the model as used in this paper (the authors do note that the control angle could be connected to a neural model, but don't do so in this work) limits the generation of neuromechanical control hypotheses.

8, 12: We do not agree with the claim that ElegansBot could limit other researchers in generating neuromechanical control hypotheses. The term θ_("ctrl" ,i)^((t) ) used in our model is designed to be replaceable with neuromechanical control in the future.

(13) The model may provide insights into the biomechanics of such behaviors, however, the results described are very minimal and are purely qualitative.

(14-1) Overall, direct comparisons to the experiments are lacking or unclear.

14-1: If you look at the text explaining Fig. 2 and 5 (Fig. 2 and 4 in old version), it directly compares the velocity, wave-number, and period as numerical indicators representing the behavior of the worm, between the experiment and ElegansBot.

(14-2) Furthermore, the paper claims the value of the model is to produce the force fields from a given body shape, but the force fields from omega turns are only pictured qualitatively.

13, 14-2: We gratefully accept the point that our analysis of the omega-turn is qualitative. Therefore, we have conducted additional quantitative analysis on the omega-turn and inserted the results into the new Fig. 4. We have considered the term 'Force field' as referring to the force vector received by each rod. We have created numerical indicators representing various behaviors of the worm and included them in the revised manuscript.

(15) No comparison is made to other behaviors (the force experienced during crawling relative to turning for example might be interesting to consider) and the dependence of the behavior on the model parameters is not explored (for example, how does the omega turn change as the drag coefficients are changed).

Thank you for the great idea. To compare behaviors, first, a clear criterion for distinguishing behaviors is needed. Therefore, we have created a new mathematical definition for behavior classification in the revised manuscript (“Defining Behavioral Categories” in Method). After that, we compared the force and power (energy consuming rate) between each forward locomotion, backward locomotion, and omega-turn (Fig. 4). And in the revised manuscript, we newly analyzed how the turning behavior changes with variations in the friction coefficients in Figs. S4-S7.

(16) If the purpose of this paper is to recapitulate the swim-to-crawl transition with a simple model, and then apply the model to new behaviors, a more detailed analysis of the behavior of the model variables and their dependence on the variables would make for a stronger result.

In our revised manuscript, we have quantitatively analyzed the changes occurring in turning behavior from water to agar, and the results are presented in Figs. S9 and S10.

(17) In some sense, because the model takes kinematics as an input and uses previously established techniques to model mechanics, it is unsurprising that it can reproduce experimentally observed kinematics, however, the forces calculated and the variation of parameters could be of interest.

(18) Relatedly, a justification of why the drag coefficients had to be changed by a factor of 100 should be explored.

(19) Plate conditions are difficult to replicate and the rheology of plates likely depends on a number of factors, but is for example, changes in hydration level likely to produce a 100-fold change in drag? or something more interesting/subtle within the model producing the discrepancy?

18, 19: As mentioned in the paper, we do not know if the friction coefficients in the study of Boyle et al. (2012) and the friction coefficients in the experiment of Stephens et al. (2016) are the same. In our revised manuscript, we have explored more in detail the effects of the friction coefficient's scale factor, and explained why we chose a scale factor of 1/100 (“Proper Selection of Friction Coefficients” in Supplementary Information). In summary, we analyzed the changes in trajectory due to scaling of the friction coefficient, and chose the scale factor 1/100 as it allowed ElegansBot to accurately reproduce the worm's trajectory while also being close to the friction coefficients in the Boyle et al. paper.

(20) Finally, the language used to distinguish different modeling approaches was often unclear.

(21) For example, it was unclear in what sense the model presented in Boyle, 2012 was a "kinetic model" and in many situations, it appeared that the term kinematic might have been more appropriate. Thank you for the feedback. As you pointed it out, we have corrected that part to 'kinematic' in the revised manuscript.

(22) Other phrases like "frictional forces caused by the tension of its muscles" were unclear at first glance, and might benefit from revision and more canonical usage of terms.

We agree that the expression may not be immediately clear. This is due to the word limit for the abstract (the abstract of eLife VOR should be under 200 words, and our paper's abstract is 198 words), which forced us to convey the causality in a limited number of words. Therefore, although we will not change the abstract, the expression in question means that the muscle tension, which is the cause of the worm's locomotion, ultimately generates the frictional force between the worm and the ground surface.

Recommendations For The Authors

(23) As I stated in my public review, I think the paper could be made much stronger if a more detailed exploration of turning mechanics was presented.

(24) Relatedly, rather than restricting the analysis to individual videos of turning behaviors, I wonder if a parameterized model of the turning kinematics would be fruitful to study, to try to understand how different turning gaits might be more or less energetically favorable.

We thank the reviewer once again for their suggestion. Thanks to their proposal, we were able to conduct additional quantitative analysis on turning behavior.

Reviewer #2

Public Review

Summary:

(1) Developing a mechanical model of C. elegans is difficult to do from basic principles because it moves at a low (but not very small) Reynolds number, is itself visco-elastic, and often is measured moving at a solid/liquid interface.

(2) The ElegansBot is a good first step at a kinetic model that reproduces a wide range of C. elegans motiliy behavior.

Strengths: (3) The model is general due to its simplicity and likely useful for various undulatory movements.

(4) The model reproduces experimental movement data using realistic physical parameters (e.g. drags, forces, etc).

(5) The model is predictive (semi?) as shown in the liquid-to-solid gait transition.

(6) The model is straightforward in implementation and so likely is adaptable to modification and addition of control circuits.

Weaknesses:

(7) Since the inputs to the model are the actual shape changes in time, parameterized as angles (or curvature), the ability of the model to reproduce a realistic facsimile of C. elegans motion is not really a huge surprise. (8) The authors do not include some important physical parameters in the model and should explain in the text these assumptions.

(9. 1) The cuticle stiffness is significant and has been measured [1].

(10. 2) The body of C. elegans is under high hydrostatic pressure which adds an additional stiffness [2].

(11. 3) The visco-elasticity of C. elegans body has been measured. [3]

Thank you for asking. The stiffness of C. elegans is an important consideration. We took this into account when creating ElegansBot, but did not explain it in the paper. The detailed explanation is as follows. C. elegans indeed has stiffness due to its cuticle and internal pressure. This stiffness is treated as a passive elastic force (elastic force term of lateral passive body force) in the paper of Boyle et al. (2012). However, the maximum spring constant of the passive elastic force is 1/20 of the maximum spring constant of the active elastic force. If we consider this fact in our model, the elastic term of the muscle torque is as follows: ( is the active torque elasticity coefficient, is the passive torque elasticity coefficient)

where

Therefore, there is no need to describe the active and passive terms separately in

Furthermore, since , assuming , then and .

(12) There is only a very brief mention of proprioception.

(13) The lack of inclusion of proprioception in the model should be mentioned and referenced in more detail in my opinion.

As you emphasized, proprioception is an important aspect in the study of C. elegans' locomotion. In our paper, its importance is briefly introduced with a sentence each in the introduction and discussion. However, our research is a model about the process of the creation of body motion originated from muscle forces, and it does not model the sensory system that senses body posture. Therefore, there is no mention of using proprioception in our paper's results section. What is mentioned in the discussion is that ElegansBot can be applied as the kinetic body model part in a combination model of a kinetic body model and a neuronal circuit model that receives proprioception as a sensory signal.

(14) These are just suggested references.

(15) There may be more relevant ones available.

The papers you provided contain specific information about the Young's modulus of the C. elegans body. The first paper (Rahimi et al., 2022) measured the Young's modulus of the cuticle after chemically isolating it from C. elegans, while the second paper (Park et al., 2007) and third paper (Backholm et al., 2013) measured the elasticity and Young's modulus of C. elegans without separating the cuticle. Based on the Young's modulus provided in each paper (although the second and third papers did not measure stiffness in the longitudinal direction), we derived the elastic coefficient (assuming a worm radius of 25 μm, cuticle thickness of 0.5 μm, and 1/25 of longitudinal length of the cuticle of 40 μm). The range was quite broad, from 9.82ⅹ1011 μg/sec2 (from the first paper) to 2.16 ⅹ 108 μg / sec2 (from the third paper). Although the elastic coefficient value in our paper falls within this range, since the range of the elastic coefficient is wide, we think we can modify the elastic coefficient in our paper and will be able to reapply our model if more accurate values become known in the future.

Reviewer #3

Public Review

Summary:

(1) A mechanical model is used with input force patterns to generate output curvature patterns, corresponding to a number of different locomotion behaviors in C. elegans

Strengths:

(2) The use of a mechanical model to study a variety of locomotor sequences and the grounding in empirical data are strengths.

(3) The matching of speeds (though qualitative and shown only on agar) is a strength.

Weaknesses:

(4) What is the relation between input and output data?

ElegansBot takes the worm's body control angle as the input, and produces trajectory and force of each segment of the worm as the output.

(5) How does the input-output relation depend on the parameters of the model?

If 'parameter' is understood as vertical and horizontal friction coefficients, then the explanation for this can be found in Fig. 5 (Fig. 4 in the old version).

(6) What biological questions are addressed and can significant model predictions be made?

Equation of motion deciphering locomotion of C. elegans including turning behaviors which were relatively less well understood.

Recommendations For The Authors

(7) The novelty and significance of the paper should be clarified.

We have added quantitative analyses of turning behavior in the revised manuscript, and we hope this will be helpful to you.

(8) Previously much more detailed models have been published, as compared to this one.

We hope the reviewer can point out any previous model that we may have missed.

(9) The mechanics here are simplified (e.g. no information about dorsal/ventral innervation but only a bending angle) setting limitations on the capacity for model predictiveness.

(10) Such limitations should be discussed.

We view the difference between dorsal/ventral innervation and bending angle not as a matter of simplification, but rather as a reflection of the hierarchy that our model implements. Our model does not consider dorsal/ventral innervation, but it uses the bending angle to reproduce behavior in various input and frictional environments, which signifies the strong predictiveness of ElegansBot (Figure 2, 3, 5 (2, 3, 4 in the old version)). Moreover, if the midline of C. elegans is incompressible, then modeling by dividing into dorsal/ventral, as opposed to modeling solely with the bending angle, does not increase the degree of freedom of the worm model, and therefore does not increase its predictiveness.

(11) The aims of the paper and results need to be supported quantitatively and analyzed through parameter sweeps and intervention.

We have conducted additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

(12) The methods are given only in broad brushstrokes, and need to be much more clear (and ideally sharing all code).

We have thoroughly detailed every aspect of this research, from deriving the physical constants of C. elegans, agar, and water to developing the formulas and proofs necessary for operating ElegansBot and its applications. This comprehensive information is all presented in the Results, Methods, and Supplementary Information sections, as well as in the source code. Moreover, we have already ensured that our research can be easily reproduced by providing detailed explanations and by making ElegansBot accessible through public software databases (PyPI, GitHub). To further aid in its application and understanding, especially for those less familiar with the subject, we have also included minimal code as examples in the database. This code is designed to simplify the process of reproducing the results of the paper, thereby making our research more accessible and understandable. Therefore, we believe that readers will easily gain significant assistance from the extensive information we have provided. Should readers require further help, they can always contact us, and we will be readily available to offer support.

(13) The supporting figures and movies need to include a detailed analysis to evidence the claims.

We have conducted and provided additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

Author Response

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

Public Reviews:

Reviewer #1:

Summary:

This manuscript provides some valuable findings concerning the hippocampal circuitry and the potential role of adult-born granule cells in an interesting long-term social memory retrieval. The behavior experiments and strategy employed to understand how adult-born granule cells contribute to long-term social discrimination memory are interesting.

We thank the reviewer for the positive evaluation.

I have a few concerns, however with the strength of the evidence presented for some of the experiments. The data presented and the method described is incomplete in describing the connection between cell types in CA2 and the projections from abGCs. Likewise, I worry about the interpretation of the data in Figures 1 and 2 given the employed methodology. I think that the interpretation should be broadened. This second concern does not impact the interest and significance of the findings.

In response to this concern, we have removed the data concerning abGC projections to PCP4+ and PV-GFP+ cell bodies from Figure 1 and have focused this analysis on dendrites. We now provide high magnification images of dendrites and expand on the methodology, results, and interpretations in the manuscript. We also broaden the interpretation throughout the manuscript to address the reviewer’s concern.

Strengths:

The behavior experiments are beautifully designed and executed. The experimental strategy is interesting.

We appreciate these positive comments.

Weaknesses:

The interpretation of the results may not be justified given the methods and details provided.

We have addressed this concern by providing more methodological details and broadening our interpretation of the results.

Reviewer #2:

Summary:

Laham et al. investigate how the projection from adult-born granule cells into CA2 affects the retrieval of social memories at various developmental points. They use chemogenetic manipulations and electrophysiological recordings to test how this projection affects hippocampal network properties during behavior. I find the study to be very interesting, the results are important for our understanding of how social memories of different natures (remote or immediate) are encoded and supported by the hippocampal circuitry. I have some points that I added below that I think could help clarify the conclusions:

We appreciate the positive assessment and have addressed the more specific points below.

My major concern with the manuscript was that making the transitions between the different experiments for each result section is not very smooth. Maybe they can discuss a bit in a summary conclusion sentence at the end of each result section why the next set of experiments is the most logical step.

In response, we have added summary conclusion sentences at the end of each result section.

In line 113, the authors say that "the DG is known to influence hippocampal theta-gamma coupling and SWRs". Another recent study Fernandez-Ruiz et al. 2021, examined how various gamma frequencies in the dentate gyrus modulate hippocampal dynamics.

We cite this paper in the revised manuscript.

Having no single cells in the electrophysiological recordings makes it difficult to interpret the ephys part. Perhaps having a discussion on this would help interpret the results. If more SWRs are produced from the CA2 region (perhaps aided by projections from abGC), more CA2 cells that respond to social stimuli (Oliva et al. 2020) would reactivate the memories, therefore making them consolidate faster/stronger. On the other hand, the projections from abGC that the authors see, also target a great deal of PV+ interneurons, which have been shown to pace the SWRs frequency (Stark et al 2014, Gan et al 2017), which further suggests that this projection could be involved in SWRs modulation.

We discuss these possibilities and cite Gan et al 2017, Schlingloff et al., 2014, and Stark et al., 2014 in the revised manuscript.

The authors should cite and discuss Shuo et al., 2022 (A hypothalamic novelty signal modulates hippocampal memory).

We mention Chen et al (A hypothalamic novelty signal modulates hippocampal memory.) in the revised manuscript. “Shuo” is the first name of the first author on this paper, so we believe that this is the same paper to which the reviewer refers.

I think the authors forgot to refer to Fig 3a-f, maybe around lines 163-168.

We thank the reviewer for pointing out this error. In the revised manuscript, we refer to all figure panels. Since Fig 3 is now broken into two figures (Fig 3 and 4), the panel lettering has changed in the revised manuscript.

Are the SWRs counted only during interaction time or throughout the whole behavior session for each condition?

The SWRs are counted throughout the whole behavior session for each condition. This is now stated in the revised manuscript.

Figure 3t shows a shift in the preferred gamma phase within theta cycles as a result of abGC projections to CA2 ablation with CNO, especially during Mother CNO condition. I think this result is worth mentioning in the text.

We now mention this finding in the revised manuscript.

Figure 3u in the legend mention "scale bars = 200um", what does this refer to?

The scale bar refers to that shown in Figure 3b, which is now indicated in the legend.

What exactly is calculated as SWR average integral? Is it a cumulative rate? Please clarify.

The integral measure provides information regarding the average total power of SWR events. It sums z-scored amplitude values from beginning to the end of each SWR envelope, and then takes the average across all summed envelopes. SWR integral has been shown to influence SWR propagation (De Filippo and Schmitz, 2023). This is now described in the text.

Alexander et al 2017, "CA2 neuronal activity controls hippocampal oscillations and social behavior", examined some of the CA2 effects in the hippocampal network after CNO silencing, and the authors should cite it.

Alexander et al., 2018, which we believe is the relevant paper, is now cited in the revised manuscript.

Strengths:

Behavioral experiments after abGC projections to CA2 are compelling as they show clearly distinct behavioral readout.

We thank the reviewer for this positive assessment.

Weaknesses:

Electrophysiological experiments are difficult to interpret without additional quantifications (single-cell responses during interactions etc.)

We have addressed this concern by expanding the interpretation of our results.

Reviewer #3:

Laham et al. present a manuscript investigating the function of adult-born granule cells (abGCs) projecting to the CA2 region of the hippocampus during social memory. It should be noted that no function for the general DG to CA2 projection has been proposed yet. The authors use targeted ablation, chemogenetic silencing, and in vivo ephys to demonstrate that the abGCs to CA2 projection is necessary for the retrieval of remote social memories such as the memory of one's mother. They also use in vivo ephys to show that abGCs are necessary for differential CA2 network activity, including theta-gamma coupling and sharp wave-ripples, in response to novel versus familiar social stimuli.

The question investigated is important since the function of DG to CA2 projection remained elusive a decade after its discovery. Overall, the results are interesting but focused on the social memory of the mother, and their description in the manuscript and figures is too cursory. For example, raw interaction times must be shown before their difference. The assumption that mice exhibit social preference between familiar or novel individuals such as mother and non-mother based on social memory formation, consolidation, and retrieval should be better explained throughout the manuscript. Thus, when describing the results, the authors should comment on changes in preference and how this can be interpreted as a change in social memory retrieval. Several critical experimental details such as the total time of presentation to the mother and non-mother stimulus mice are also lacking in the manuscript. The in vivo e-phys results are interesting as well but even more succinct with no proposed mechanism as to how abGCs could regulate SWR and PAC in CA2.

In response to these comments, we provide raw interaction times in a new Figure (Fig. S1). We also provide more information about the experiments and figures in the revision. We explain the rationale for our behavioral interpretations and discuss proposed mechanisms for how abGCs regulate SWR and PAC.

The manuscript is well-written with the appropriate references. The choice of the behavioral test is somewhat debatable, however. It is surprising that the authors chose to use a direct presentation test (presentation of the mother and non-mother in alternation) instead of the classical 3-chamber test which is particularly appropriate to investigate social preference. Since the authors focused exclusively on this preference, the 3-chamber test would have been more adequate in my opinion. It would greatly strengthen the results if the authors could repeat a key experiment from their investigation using such a test. In addition, the authors only impaired the mother's memory. An additional experiment showing that disruption of the abGCs to CA2 circuit impairs social memory retrieval would allow us to generalize the findings to social memories in general. As the manuscript stands, the authors can only conclude the importance of this circuit for the memory of the mother. Developmental memory implies the memory of familiar kin as well.

We selected the direct social interaction test because it allows for more naturalistic social behaviors than measuring investigation times toward social stimuli located inside wire mesh containers. We also decided to focus our studies on the retrieval of mother memories because these are likely the first social memories to be formed. We emphasize that our results cannot be generalized to memories of other social stimuli but given studies on recent social memory formation and retrieval in adults that manipulate abGCs and CA2 separately, we feel that it is likely that this circuit is involved in these functions as well. However, we specify throughout the manuscript that our experiments can only tell us about mother memories. We have also changed the title to reflect this.

The in vivo ephys section (Figure 3) is interesting but even more minimalistic and it is unclear how abGCs projection to CA2 can contribute to SWR and theta-gamma PAC. In Figure 1, the authors suggest that abGCs project preferentially to PV+ neurons in CA2. At a minimum, the authors should discuss how the abGCs to PV+ neurons to CA2 pyramidal neurons circuit can facilitate SWR and theta-gamma PAC.

We have divided Figure 3 into two figures (Figures 3 and 4) and revised the electrophysiology section of the results section. In the revised paper, we now discuss how abGC projections to PV+ interneurons may facilitate SWR and PAC.

Finally, proposing a function for 4-6-week-old abGCs projecting to CA2 begs two questions: What are abGCs doing once they mature further, and more generally, what is the function of the DG to CA2 projection? It would be interesting for the authors to comment on these questions in the discussion.

In response to these comments, we discuss possible answers to these interesting questions.

Recommendations for the authors:

Reviewer #1:

Specifically, in Figure 1, for the analysis of the synapses formed between abGCs and CA2 PNS (as identified by PCP4 expression) and CA2 PV+ cells (as identified by cre-dependent AAV-mCherry expression) in PV-cre line. In panels c and d the soma of a CA2 PN cell is shown, as well as the soma of a PV cell is shown. Why was the soma analyzed? What relevance is there for this? It is my understanding that synapses form on dendrites- this would be much more relevant to show, in my opinion. Also, the methods for panels e and f state that the 3R-Tau+ intensity was analyzed only in stratum lucidum. (There was a normalization for the overall 3R-Tau intensity in SL of CA2 that was obtained by dividing the 3R-Tau intensity of corpus callosum). I don't understand then how a comparison of 3RTau intensity could have been done for CA2 PN soma. There are no CA2 PN soma in stratum lucidum. (This is fairly clearly shown in Figure 1aiii, with the PCP4 staining showing the soma in the somatic layer... not in stratum lucidum). What is being analyzed here?

If the 3R-Tau intensity for dendrites is higher for PV cell dendrites, an example image of dendrites would be very helpful. How was the CA2 PV cell dendrite delimited from the CA2 PN dendrites at 40x magnification for the 3R-Tau intensity? Why were pre-synaptic puncta not examined? Is it possible to determine the post-synaptic target with these methods? This result could be particularly interesting, but I find it very difficult to understand the quantification or the justification behind it. To truly know if a cell is getting a connection, the best method would be to perform whole-cell patch clamp recordings of the post-synpatic target cells and use optogenetics of the abGCs. I understand that perhaps this may be beyond the scope of the paper, but it is a severe limitation for these results.

We have eliminated the cell body measures from Figure 1 and focus instead on the dendrite measures, which we agree are more relevant. We now provide high magnification example images of pyramidal cell (PCP4+) and PV+ interneuron (GFP+) dendrites in Figure 1. We thank the reviewer for pointing out the error about the stratum lucidum as some of the dendrites analyzed are located in the pyramidal cell layer. In addition, neither PCP4 nor GFP label the full extent of dendrites emanating from CA2 pyramidal cells or PV+ interneurons respectively. We mention this in the revised manuscript because abGC projections to more distal dendrites might show a different pattern than that which was observed for proximal dendrites. We also provide more details about how the dendrites were delimited for the analysis, and mention that these results cannot definitively inform us about whether functional synaptic connections have been formed.

Canulation over CA2 is potentially not specific to CA2 terminals. It would be optimal if the authors had some histology demonstrating specific cannula placement, as these surgeries are really tough to get perfectly centered over CA2. Even if it is perfectly centered, how much would the CNO diffuse into CA3? I think that given the methodology, the authors really need to consider that the behavioral results are not only a result of blocking abGC terminals in CA2 alone. Would it really change much if the abGC terminals are also silenced in CA3a/b as well? The McHugh lab has shown that area CA3 is also playing a role in social memory (Chiang, M.-C., Huang, A. J. Y., Wintzer, M. E., Ohshima, T. & McHugh, T. J. A role for CA3 in social recognition memory. Behav Brain Res 354, 2018). It may be that both areas CA2 and CA3 are important for the phenomenon being demonstrated in Figure 2. I think the impact of the study is just as interesting, as this examination of early social memories is very interesting and nicely done. In fact, areas CA2 and CA3 may be acting together (please see Stöber, T. M., Lehr, A. B., Hafting, T., Kumar, A. & Fyhn, M. Selective neuromodulation and mutual inhibition within the CA3-CA2 system can prioritize sequences for replay. Hippocampus 30, 1228-1238, 2020).

We agree that it is possible that CNO infusions targeted at the CA2 would also influence CA3a/b and have revised the paper to include this possible interpretation. We also cite the suggested paper on CA3 involvement in social memory (Chiang et al., 2018) and the paper on CA2-CA3 interactions (Stöber et al, 2020).

Figure 3 is packed with information, but not communicated in a reasonable way. Much more information and a description of the experimental protocol need to be presented. Furthermore, why are there no example traces for the SWRs recorded? There should be more analysis than just a difference score and frequency. How is j, k, and l analyzed and interpreted? Why no example traces there? Also, the n's seem way too small for Figure 3mr. Are there only 32 or three animals used for some of these conditions? This is insufficient in my opinion to conclude much for a 5-minute interaction.

In response to this concern, we have divided Figure 3 into 2 figures – Figure 3 and Figure 4. In Figure 3, we provide example traces for SWRs, with additional SWR data presented in Figures S3 and S4, including data to complement the difference score data in Figure 3. In Figure 4, we include traces of phase amplitude coupling. We also provide more information in the methods about how the phase amplitude coupling data were analyzed. For Figure 4, we used methods described by Tort et al., 2010 to produce a modulation index, which is a measure of the intensity of coupling between theta phase and gamma amplitude. This method additionally allows for visualization of how gamma amplitude is modified across individual theta phase cycles. Regarding the question about n sizes in the 10-12 week abGC group (Fig. 3), the numbers are lower than in the 4-6 week abGC group because by 6 weeks after the first set of recordings, the electrodes in some of the mice were no longer usable. The n sizes for this specific study are 4-5 per group for Nestin-cre mice; 7-8 for Nestin-cre:Gi. This is now clarified in the figure legend.

The discussion section of this paper does not put these results into a broader context with the field. There are other studies examining abGCs and their roles in novelty and memory formation (the work from Juna Song's lab, for example). These should be properly mentioned and discussed.

In response, we have added discussion on the roles of abGCs in nonsocial novelty and memory formation and have cited papers from the Song lab.

In the figure legend for Figure 2, there is no specific explanation for panel h. Perhaps the label is missing in the legend.

We thank the reviewer for noting this error and now include a description in the revised manuscript.

Reviewer #2:

Adding more quantifications (single cells, isolating data during interactions versus noninteraction times) would help understand the results better. In the lack of this, adding a more clear rationale (even if only through the presentation of hypotheses) in between the transitions of the different results sections would make the study easier to read.

In response to this comment, we have added transition sentences between results sections to clarify the rationale and make the manuscript easier to understand.

Reviewer #3:

Line 110: "Hippocampal phase-amplitude coupling (PAC) and generation of sharp waveripples (SWRs) have been linked to novel experience, memory consolidation, and retrieval (Colgin, 2015; Fernandez Ruiz et al., 2019; Meier et al., 2020; Joo and Frank, 2018; Vivekananda et al., 2021). The DG is known to influence hippocampal theta-gamma coupling and SWRs (Bott et al, 2016; Meier et al., 2020), yet no studies have examined the influence of abGCs on these oscillatory patterns." This information comes too early in the result section and is somewhat confusing.

In response to this comment, we have moved this information and provided a better description.

Line 118: "we found that mice with normal levels of abGCs can discriminate between their own mother and a novel mother." Be more descriptive of the results (present the raw interaction times with the statistical test to compare them), this is the conclusion.

In response to this comment, we provide the raw interaction times in a new Figure (Fig. S1) and describe the results in more detail.

Line 121: "These effects were not due to changes in physical activity". Be more specific. Did you subject the mice to a specific test? If not, how did you calculate locomotion? The data presented in the supplementary figure 1a only states the % locomotion.

Locomotion was manually scored whenever an animal moved in the testing apparatus. Speed was not recorded. Total locomotion was divided by trial duration to create a % locomotion measure. We have added these details to the methods.

Line 124: "Coinciding with the recovery of adult neurogenesis, GFAP-TK animals regained the ability to discriminate between their mother and a novel mother". Explain how the difference in interaction time can be interpreted as the ability to discriminate. You could also compute the discrimination index used by several other laboratories (difference of interaction normalized by the total interaction time).

In response to this comment, we describe how the difference in interaction time can be interpreted as the ability to discriminate between novel and familiar mice.

Line 133: "Targeted CNO infusion in Nestin-Cre:Gi mice enabled the inhibition of GiDREADD+ abGC axon terminals present in CA2." Provide data or references to support this claim. Injection of a dye of comparable size to CNO would help. Otherwise, mention that nearby CA3a could be affected as well.

We cannot rule out that nearby CA3a was affected by our cannula infusions of CNO into CA2. Furthermore, since dyes likely diffuse at different rates than CNO, we believe that a dye injection would not eliminate this concern completely. Therefore, we have revised the paper to acknowledge the likelihood that the CNO infusion affected parts of CA3 in addition to CA2. We also changed the title to focus more on the CA2 electrophysiological recordings, which we know were obtained only from the CA2.

Line 150: "When reintroduced to the now familiar adult mouse 6 hours later, after the effects of CNO had largely worn off". Provide data or references supporting this claim.

In response, we cite articles that show behavioral effects of CNO DREADD activation are returned to baseline 6 hrs later.

Line 165: "We found that SWR production is increased during social interaction, with more SWRs produced during novel mouse investigation, presumably during encoding social memories, than during familiar mouse investigation, presumably during retrieval of developmental social memories". How does this compare to the results in Oliva et al, Nature 2021?

The Oliva et al 2021 paper recorded CA2 SWRs during home cage and during post-social stimulus exposure periods of sleep. The timing of the study does not coincide with the measures we made, but we cite the paper.

Line 168: "Inhibition of abGCs in the presence of a social stimulus". How does silencing abGC impact CA2 pyramidal neurons' firing rate?

The direct answer to this question is unknown because we did not measure single units, but based on studies done in the CA3, it is likely that firing rate in CA2 would increase.

Line 203: "abGCs possess a time-sensitive ability to support retrieval of developmental social memories." Can you speculate on the function of the cells later on?

In the revised paper, we speculate about the function of abGCs after they mature and no longer support retrieval of developmental social memories.

Line 229: "GFAP-TK mice were group housed by genotype". Why not housed them with CD1 littermates?

We housed these mice according to genotype to avoid having mice with different levels of abGCs (GFAP-TK + VGCV and CD1 + VGCV) living together in social groups. We did this to avoid potential differences that might emerge in social behavior.

Line 237: "Adult TK, Nestin-cre, and Nestin-cre:Gi offspring underwent a social interaction test in which they directly interacted with the mother". Specify how long was the social interaction time.

In the revised manuscript, we specify that mice interacted with each social stimulus for 5 minutes.

Line 240: "After a 1-hour delay spent in the home cage". Were the mice single-housed or with their littermates during this delay?

In the revised manuscript, we indicate that mice were put back into the home cage with their cagemates during the 1 hr delay period.

Line 241: "The order of stimulus exposure was counterbalanced in all tests." Can you show some data to confirm that the order of presentation did not impair the interaction? Have you considered using your own version of the classical 3-chamber test in order to assess directly the preference for one or the other female mouse?

Our data suggest that the order of testing is not responsible for the observed results. Across all experimental groups without an abGC manipulation (i.e., all direct social interaction assays excluding VGCV+ GFAP-TK trials and CNO+ Nestin-cre:Gi trials), ~84.4% of animals demonstrate a social preference for the novel mother over the mother (CD1 + GFAP-TK VGCV- cohort: 28/33; CD1 VGCV+ cohort: 17/17; CD1 and TK recovery cohort: 24/31; Nestin-cre and Nestin-cre:GI 4-6-week-old abGC cohort: 77/95; 10-12-week-old abGC cohort: 49/55; Total = 195/231 mice with an investigation preference for the novel mother). If stimulus presentation order were to bias social investigation preference toward the first stimulus presented, we would expect the percentage of animals demonstrating a social preference for each stimulus to be around 50%, as roughly half the animals were first exposed to the mother with the other half first exposed to the novel mother. The social novelty preference percentage reported above is comparable to percentages we observe in our lab's novel to familiar social interaction experiments, in which all animals are first exposed to a novel conspecific. We have yet to conduct experiments testing adults using the modified 3-chamber assay described in Laham et al., 2021.

Statistics: The statistical tests used throughout the paper are appropriate but their description is too cursory. Please provide F values and specify the name of the tests used in the figure legends before giving the exact p values.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors were attempting to determine the extent that CIH altered swallowing motor function; specifically, the timing and probability of the activation of the larygneal and submental motor pools. The paper describes a variety of different motor patterns elicited by optogenetic activation of individual neuronal phenotypes within PiCo in a group of mice exposed to CIH. They show that there are a variety of motor patterns that emerge in CIH mice; this is apparently different than the more consistent motor patterns elicited by PiCo activation in normoxic mice (previously published).

Strengths:

The preparation is technically challenging and gives valuable information related to the role of PiCo in the pattern of motor activation involved in swallowing and its timing with phrenic activity. Genetic manipulations allow for the independent activation of the individual neuronal phenotypes of PiCo (glutamatergic, cholinergic) which is a strength.

We thank the reviewers for acknowledging and summarizing the strengths of this study.

Weaknesses:

(1) The data presented are largely descriptive in terms of the effect of PiCo activation on the probability of swallowing and the pattern of motor activation changes following CIH. Comparisons made between experimental data acquired currently and those obtained in a previous cohort of animals (possibly years before) are extremely problematic, with the potential confounding influence of changing environments, genetics, and litter effects. The statistical analyses (i.e. comparing CIH with normoxic) appear insufficiently robust. Exactly how the data were compared is not described.

Yes, we agree the data are descriptive in terms of characterizing the effect of CIH on PiCo activation. However, we would like to emphasize that the data are also mechanistic because they characterize the effects of specifically, optogenetically manipulating PiCo neurons after being exposed to CIH.

Thank you for this comment and for pointing out our misleading description in the paper. This manuscript is meant to independently characterize the effects of CIH to the response of PiCo stimulation. We are not making direct comparisons between the previously published manuscript where mice were exposed to room air. There has been no statistical analysis made between previously published control and current CIH data, since we are not making a direct comparison, only an observational comparison.

To make this clearer, and to address the reviewers concern, we have removed the room air data from figures 1E, 2C and 3A. However, we believe it is important to keep the data from mice exposed to room air in Figure 2B since we did not include this information in the previously published manuscript. It is important to point out that all mice exposed to CIH have some form of submental activity during laryngeal activation in response to PiCo stimulation. This is not the case when mice are exposed to room air only. In this figure, only descriptive analysis are presented. We adjusted our wording throughout the text, particularly in the discussion, to eliminate any confusion that we are making direct comparisons between the two studies. The following sentence has been added to the discussion “While we do not intend to make direct quantitative comparisons between the previously published PiCo-triggered swallows in control mice exposed to room air (Huff et al 2023) and the data presented here for mice exposed to CIH, we believe it is important to compare the conclusions made in these two studies.” This was the motivation for using the eLife Advance format. Since the present study demonstrates that PiCo affects swallow patterning which was not observed in the control data.

(2) There is limited mechanistic insight into how PiCo manipulation alters the pattern and probability of motor activation. For example, does CIH alter PiCo directly, or some other component of the circuit (NTS)? Techniques that silence or activation projections to/from PiCo should be interrogated. This is required to further delineate and define the swallowing circuit, which remains enigmatic.

We agree with the reviewer that our study raises many more questions than we are able to answer at the moment. This however applies to most scientific studies. Even though swallowing has been studied for many decades, the underlying circuitry remains largely enigmatic. We will continue to investigate the role of PiCo and its interaction with the NTS, in healthy and diseased states. These investigations require many different techniques, and approaches, some of which are still in development. For example, we are currently conducting experiments that silence portions of the NTS related to swallow and PiCo: ChAT/Vglut2 neurons using novel unpublished viral approaches. However, these are separate and ongoing studies beyond the scope of the current one.

To address the reviewer’s comment, we have added to the following to the limitation section: “In addition, this preparation does not allow for recording of PiCo neurons to evaluate the direct effects of CIH in PiCo neuronal activity”. The following has also been added to the discussion: “Rather, our data reveal CIH disrupts the swallow motor sequence which is likely due to changes in the interaction between PiCo and the SPG, presumably located in the cNTS. While it has previously been demonstrated that PiCo is an important region in swallow-breathing coordination (Huff et al., 2023), previous studies did not demonstrate that PiCo is involved in swallow motor patterning itself. Here we show for the first time that CIH leads to disturbances in the generation of the swallow motor pattern that is activated by stimulating PiCo. This suggests that PiCo is not only important for coordinating swallow and breathing, but also modulating swallow motor patterning. Further studies are necessary to directly evaluate the presumed interactions between PiCo and the cNTS.”

(3) The functional significance of the altered (non-classic) patterns is unclear.

Like in our original study, the preparation used to stimulate PiCo does not allow to simultaneously characterize the functional significance of swallowing. Therefore, we have included this as a limitation in the limitation section: “In this preparation we are unable to directly determine the functionality of the variable swallow motor pattern seen after CIH. Different experimental techniques, such as videofluoroscopy would need to be used to directly evaluate functional significance. This technique is beyond the scope of this study and not possible to perform in this preparation. We acknowledge this limits our ability to make direct comparisons between dysphagic swallows in OSA patients.”

Reviewer #1 (Recommendations For The Authors):

(1) A more rigorous experimental approach is required. Littermates should be separated and exposed to either room air or CIH at the same (or close to the same) time.

As stated above, we did not directly compare mice exposed to room air with mice exposed to CIH. Hence, we believe this is not necessary, and it would have meant repeating all the experiments already published in the original eLife paper.

(2) Robust statistical analyses are required to determine whether the effects of CIH on the pattern/probability of motor activation are required.

Since control and CIH group were not compared in this study, statistical hypothesis testing is not appropriate or applicable.

(3) Use a combination of retrograde, Cre- AAVs and Cre-dependent approaches to interrogate the circuitry to/from PiCO that forms the swallowing network. This is what is needed to push this area forward, in my view.

Thank you for this suggestion, we will consider this suggestion as we plan for future experiments. Indeed, we are in the process of developing novel approaches. However, in this context we would like to emphasize that further network investigations are exponentially more complicated given that we need to use a Flpo/Cre approach to specifically characterize the glutamatergic-cholinergic PiCo neurons. Most other laboratories that have studied PiCo have avoided this experimental complication and used only a “cre-dependent” approach. This approach is much simpler, but the data are much less specific and the conclusions sometimes misleading. Stimulating for example cholinergic neurons in the PiCo area will also activate Nucleus ambiguus neurons, stimulating glutamatergic neurons will also activate glutamatergic neurons that are not necessarily the glutamatergic/cholinergic neurons that we use to define PiCo specifically. Readers that are unfamiliar with these different approaches often miss this important difference. Hence, compared to stimulating other areas, stimulating the cholinergic-glutamatergic neurons in PiCo is much more specific than e.g. stimulating preBötzinger complex neurons. There are no markers that will specifically stimulate only preBötzinger complex neurons or neurons in the parafacial Nucleus. Unfortunately, this difference is often overlooked.

(4) It should be made more clear how each of the "non-classic" swallowing patterns could cause dysfunction - especially to the reader who is not completely familiar with the neural control of swallowing.

We agree that it would be helpful to understand the functional implications of these alterations in swallow-related motor activation, however since our approach does not allow us to use any tools to measure or evaluate functional activity it would be inappropriate to make suggestions of this type without any data to back up our conclusion. This is why we have not speculated on the functional implications. We have added the following to the discussion section of this manuscript. “While fine wire EMG studies are an excellent evaluation tool to observe temporal motor pattern of sequential swallow related muscles; it must be combined with tools such as videofluoroscopic swallow study (VFSS) and/or high resolution manometry (HRM) in order to characterize the functional significance of these alterations to the swallow motor pattern shown in this study (Park et al., 2017). Since the preparation in this study utilizes only fine wire EMGs we are not able to evaluate or comment on the functional significance of the variable swallow motor patterns. ”

Minor:

The Results should be written in a way that better conveys the neurophysiological effects of the manipulations. As it stands, it reads like a statistical report on how activation of each neuronal phenotype is statistically different from each other. As such it is difficult to read and understand the salient findings.

Thank you for this insight. We have adjusted the language in the results section.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigated the role of a medullary region, named Postinspiratory Complex (PiCo), in the mediation of swallow/laryngeal behaviours, their coordination with breathing, and the possible impact on the reflex exerted by chronic intermittent hypoxia (CIH). This region is characterized by the presence of glutamatergic/cholinergic interneurons. Thus, experiments have been performed in single allelic and intersectional allelic recombinase transgenic mice to specifically excite cholinergic/glutamatergic neurons using optogenetic techniques, while recording from relevant muscles involved in swallowing and laryngeal activation. The data indicate that in anaesthetized transgenic mice exposed to CIH, the optogenetic activation of PiCo neurons triggers swallow activity characterized by variable motor patterns. In addition, these animals show an increased probability of triggering a swallow when stimulation is applied during the first part of the respiratory cycle. They conclude that the PiCo region may be involved in the occurrence of swallow and other laryngeal behaviours. These data interestingly improve the ongoing discussion on neural pathways involved in swallow-breathing coordination, with specific attention to factors leading to disruption that may contribute to dysphagia under some pathological conditions.

The Authors' conclusions are partially justified by their data. However, it should be acknowledged that the impact of the study is to a certain extent limited by the lack of knowledge on the source of excitatory inputs to PiCo during swallowing under physiological conditions, i.e. during water-evoked swallowing. Also the connectivity between this region and the swallowing CPG, a structure not well defined, or other brain regions involved in the reflex is not known.

We thank the reviewer for the comments and the strength of the paper. However, with regards to the “lack of knowledge”, we would like to emphasize that PiCo was first described in 2016, while e.g. the preBötzinger complex was described in 1991. Thus, it is not fair to assume the same level of anatomical and physiological understanding for PiCo as we became accustomed to for the preBötzinger complex. We are fairly confident that in 25 years from now, our knowledge of the in- and outputs of PiCo will be much less limited than it currently is.

Strengths:

Major strengths of the manuscript:

• The methodological approach is refined and well-suited for the experimental question. The in vivo mouse preparation developed for this study takes advantage of selective optogenetic stimulation of specific cell types with the simultaneous EMG recordings from upper airway muscles involved in respiration and swallowing to assess their motor patterns. The animal model and the chronic intermittent hypoxia protocol have already been published in previous papers (Huff et al. 2022, 2023).

• The choice of the topic. Swallow disruption may contribute to the dysphagia under some pathological conditions, such as obstructive sleep apnea. Investigations aimed at exploring and clarifying neural structures involved in this behaviour as well as the connectivity underpinning muscle coordination are needed.

• This study fits in with previous works. This work is a logical extension of previous studies from this group on swallowing-breathing coordination with further advances using a mouse model for obstructive sleep apnea.

We thank the reviewers for acknowledging and summarizing the strengths of this study.

Weaknesses:

Major weaknesses of the manuscript:

• The Authors should be more cautious in concluding that the PiCo is critical for the generation of swallowing itself. It remains to demonstrate that PiCo is necessary for swallowing and laryngeal function in a more physiological situation, i.e. swallow of a bolus of water or food. It should be interesting to investigate the effects of silencing PiCo cholinergic/glutamatergic neurons on normal swallowing. In this perspective, the title should be slightly modified to avoid "swallow pattern generation" (e.g. Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production).

Thank you for pointing out that this manuscript suggest PiCo is necessary for swallow generation. We agree further interventions to silence specifically PiCo ChAt/Vglut2 neurons will be necessary to investigate this claim. Which we have begun to evaluate for a future study by developing a novel as yet unpublished approach. We have altered language throughout the text to limit the perception that PiCo is the swallow pattern generator. We have also changed the title to say: Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production

• The duration of swallows evoked by optogenetic stimulation of PiCo is considerably shorter in comparison with the duration of swallows evoked by a physiological stimulus (water). This makes it hard to compare the timing and the pattern of motor response in CIH-exposed mice. In Figure 1, the trace time scale should be the same for water-triggered and PiCo-triggered swallows. In addition, it is not clear if exposure to CIH alters the ongoing respiratory activity. Is the respiratory rhythm altered by hypoxia? If a disturbed or irregular pattern of breathing is already present in CIH-exposed mice, could this alteration interfere with the swallowing behaviour?

Thank you. We have changed the time scale so that all representative traces are on the same time scale.

We explained in the original paper (Huff et al 2023) that the significant decrease in PiCo-evoked swallow duration compared to water evoked is likely due to the absence of oral/upper airway feedback. We are not making comparisons of the effects of CIH on swallow motor pattern between water-evoked and PiCo-evoked. Rather, we are only characterizing the effects of CIH on the swallow motor pattern in PiCo-evoked swallows. The purpose of Figure 1A is to show that the rostocaudal submental-laryngeal sequence in water-evoked swallows is preserved in “canonical” PiCo-evoked swallow like is shown in the original study. While we did not measure the effects of CIH on breathing and the respiratory pattern in this study, it has been established, by others, that CIH causes respiratory muscle weakness, impaired motor control of the upper airway and variable respiratory rhythm and rhythm generation. However, when characterizing the timing of swallow in relation to inspiration (Figure 1 Figure Supplement 1) and the reset of the respiratory rhythm (Figure 3 figure supplement 1) and by observationally comparing these results with mice exposed to room air (Huff et al 2023) we do not observe any obvious differences in swallow-breathing coordination. However, a separate study in wild-type mice focusing on a characterization of swallowing via water after CIH would be better suited to achieve a better understanding of the physiological changes of swallowing after CIH. We would like to point out that this has shown in Huff et al 2022 that altering respiratory rate/pattern via activation of various preBötzinger Complex neurons does not change swallow behavior. Except in the case of Dbx1 PreBötC neuron activation, which was independent of CIH. Increasing or decreasing respiratory rate via activation of PreBötC Vgat and SST neurons did not change the swallow pattern rather it changed the timing of when swallows occurred. It has been reported before by others that swallow has a hierarchical control over breathing and has the ability to shut breathing down. We believe that the swallowing behavior is independent of respiratory pattern and alterations in breathing pattern does not necessarily affect the swallow motor pattern rather could affect the swallow timing.

Reviewer #2 (Recommendations For The Authors):

Abstract

Lines 37-41 "Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly the generation of swallow motor pattern was significantly disturbed."

It should be better:

"Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly triggers variable swallow motor patterns".

Thank you, this has been changed

Lines 41-43 "This suggests, glutamatergic-cholinergic neurons in PiCo are not only critical for the gating of postinspiratory and swallow activity but also play important roles in the generation of swallow motor pattern." I suggest removing any language claiming PiCo is swallow gating and change "generation" in "modulation"

"This suggests that glutamatergic-cholinergic neurons in PiCo are not only critical in regulating swallow-breathing coordination but also play important roles in the modulation of swallow motor pattern."

Thank you, this has been changed

Introduction:

Line 88-90: Actually, in Huff et al. 2023 it is said "PiCo acts as an interface between the swallow pattern generator and the preBötzinger complex to coordinate swallow and breathing". Please, change accordingly. Please, remove Toor et al., 2019 since their conclusions are quite different.

Line 100-101: Please, change the sentence according to the comments reported above.

Thank you, this has been changed

Results:

Lines 104-105: Did you mean: "We confirmed that optogenetic stimulation of PiCo neurons in ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH triggers swallow and laryngeal activation similar to the control mice exposed to room air (Huff et al., 2023)." Otherwise, the sentence is not clear.

Thank you, this has been changed

Lines 129-130: This finding is not surprising since similar results have been reported in Huff et al. 2023.

Thank you, we wanted to confirm that CIH did not alter this characteristic, which it did not. We believe that it is important to include this as it is a criterion for characterizing laryngeal activation.

Lines 219: The number of water swallows is considerably lower than stimulation-evoked swallows. Why?

We inject water into the mouth three times. Typically, there is one swallow in response to each water injection. Pico is stimulated 25 times at each duration. If we were to stimulate swallow with water as many times as optogenetic stimulation there would be an adaptive response to the water stimulation and the mouse would not respond. This does not seem to be the case with PiCo stimulation. Simple answer is, there are many more PiCo stimulations than water stimulation.

Lines 228-232: "PiCo-triggered swallows are characterized by a significant decrease in duration compared to swallows evoked by water in ChATcre:Ai32 mice (265 {plus minus} 132ms vs 144 {plus minus} 101ms; paired t-test: p= 0.0001, t= 5.21, df= 8), Vglut2cre:Ai32 mice (308 {plus minus} 184ms vs 125 {plus minus} 44ms; paired t-test: p= 0.0003, t= 6.46, df= 7), and ChATcre:Vglut2FlpO:ChR2 mice (230 {plus minus} 67ms vs 130 {plus minus} 35ms; paired t-test: p= 0.0005, t= 5.62, df= 8) exposed to CIH (Table S1).".

Thank you, this has been changed

Line 252 and 254: remove SEM.

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Discussion

Line 267: ...(Figure 1Bi), while 28% of PiCo-triggered swallows...

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Lines 283-290: "Thus, CIH does not alter PiCo's ability to coordinate the timing for swallowing and breathing. Rather, our data reveals that CIH disrupts the swallow motor sequence likely due to changes in the interaction between PiCo and the SPG, presumably the cNTS.

While it has previously been demonstrated that PiCo is an important region in swallow-breathing coordination (Huff et al., 2023), previous studies did not demonstrate that PiCo is involved in swallow pattern generation itself. Thus, here we show for the first time that CIH resulted in the instability of the swallow motor pattern activated by stimulating PiCo, suggesting PiCo plays a role in its modulation.".

Thank you, this has been changed

Could the observed effects be due to a non-specific effect of hypoxia on neuronal excitability? In addition, it should be considered that PiCo-triggered swallows lack the behavioural setting of water-evoked swallows and do not activate the sensory component of the SPG to the same extent as the water-evoked swallows.

Yes, this is very possible. We stated in our first manuscript that the decrease in PiCo-triggered swallow duration, as compared to water-triggered swallow duration, is likely because oral sensory components are not being activated to the same extent (Huff et al. 2023). Since we do not directly measure neuronal excitability, it is not known (in this study) whether CIH causes changes in the excitability to swallow related areas. However, others have shown increased excitability and activity of Vglut2 neurons after CIH exposure (Kline et al 2007,2010), and we have shown e.g. changes in the excitability of preBötC neurons (Garcia et al. 2016, 2017).

Lines 293-300: The sentence is not clear. Is there any evidence indicating that glutamatergic neurons are differently affected by hypoxia than cholinergic neurons?

Thank you, these sentences have been changed to increase clarity. The section now reads: There was no statistical difference in the probability of triggering a swallow during optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32 and ChATcre:Vglut2FlpO:ChR2 neurons in mice exposed to room air (Huff et al 2023). However, when exposed to CIH, ChATcre:Ai32 and Vglut2:Ai32 mice have a lower probability of triggering a swallow -- in some mice swallow was never triggered via PiCo activation, while water-triggered swallows remained – compared to the ChATcre:Vglut2FlpO:ChR2 mice. While it is possible that portions of the presumed SPG remain less affected by CIH, which could offset these instabilities to produce functional swallows, our data suggest that PiCo targets microcircuits within the SPG that are highly affected by CIH. The NTS is a primary first site for upper airway and swallow-related sensory termination in the brainstem (Jean, 1984). CIH induces changes to the cardio-respiratory Vglut2 neurons, resulting in an increase in cNTS neuronal activity (Kline, 2010; Kline et al., 2007), as well as changes to preBötzinger neurons (Garcia et al., 2017; Garcia et al., 2016) and ChAT neurons in the basal forebrain (Tang et al., 2020). It is reasonable to suggests that CIH has differential effects on neurons that only express ChATcre and Vglut2cre versus the PiCo-specific interneurons that co-express ChATcre and Vglut2FlpO, emphasizing the importance of targeting and manipulating these PiCo-specific interneurons.”

Lines 372-374: "Here we show that PiCo, a neuronal network which is critical for the generation of postinspiratory activity (Andersen et al. 2016) and implicated in the coordination of swallowing and breathing (Huff et al., 2023), is severely affected by CIH.".

Thank you, this has been changed.

Methods

Line 398: Did you mean Slc17a6-IRES2-FlpO-D?

Thank you, this has been changed.

Line 399: were.

Thank you, this has been changed.

Line 403: ... expressing both ChAT and Vglut2 and will be reported as ChATcre:Vglut2FlpO.

Thank you, this has been changed.

Line 437: Mice of the ChATcre:Ai32, Vglut2cre:Ai32 and ChATcre:Vglut2FlpO:ChR2 lines were kept in collective cages with food and water ad libitum placed inside custom-built chambers.

Thank you, this has been changed.

Line 479: (Figure 6a in Huff et al., 2022).

Line 497: What does Fig 7 refer to?

This should say Figure 1- figure supplement 2, This has been changed

Lines 501-506: "First, swallow was stimulated by injecting 0.1cc of water into the mouth using a 1.0 cc syringe connected to a polyethylene tube. Second, 25 pulses of each 40ms, 80ms, 120ms, 160ms and 200ms continuous TTL laser stimulation at PiCo was repeated, at random, throughout the respiratory cycle. The lasers were each set to 0.75mW and triggered using Spike2 software (Cambridge Electronic Design, Cambridge, UK). These stimulation protocols were performed in all ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2." .

Thank you, this has been changed.

Line 526 and 540: (Fig.6 in Huff et al., 2022) and (Fig.6d in Huff et al., 2022).

Thank you, this has been fixed

Line 594: Figure 5 doesn't exist. Please, change the sentence.

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Line 595 and 609: The reference Kirkcaldie et al. 2012 is referred to the neocortex and doesn't seem appropriate. Please, quote the atlas of Paxinos and Franklin.

Thank you, this has been changed.

Reference:

Please, correct throughout the text editing of references by removing e.g J.M. or A. or David D. and so on. Only surnames should be mentioned.

Thank you, this has been changed.

Figures:

Figure 1. A and B as well as the purple arrow are lacking. In addition, optogenetic stimulation is applied during different periods of inspiratory activity and this could impact the swallow motor pattern. In Bv, Non-LAR seems very similar to LAR. In panel E, please add the number of animals.

Thank you, this has been fixed.

We used the same optogenetic protocols in the original paper (Huff et al. 2023) and did not observe any changes to the swallow motor patter in relation to the time PiCo was stimulated. The only phase dependent response seen in both control and CIH is when PiCo Is stimulated during inspiration and a swallow is triggered, inspiration will be inhibited. Therefore, we do not believe variability in swallow motor pattern is dependent on the phase of breathing in which PiCo is stimulated.

Biv LAR has a pause in EMG activity before the swallow begins (red arrow pointing to the pause). While Bv Non-LAR does not have this pause, rather the two behaviors converge (red arrow). In order for something to be considered an LAR the pause must be present which is why we separated these two motor patterns.

Figure 1 - Figure Supplement 1. Why do the Authors call the lines "histograms"?

Thank you, this has been fixed. This is a line graph of swallow frequency in relation to inspiration.

Tables:

In tables, data are provided as means and standard deviation. Please, specify this in the Method section.

Thank you, the following is listed in the methods section: “All data are expressed as mean ± standard deviation (SD), unless otherwise noted.”

Reviewer #3 (Public Review):

In the present study, the authors investigated the effects of CIH on the swallowing and breathing responses to PICO stimulation. Their conclusion is that glutamatergic-cholinergic neurons from PICO are not only critical for the gating of post-inspiratory and swallow activity, but also play important roles in the generation of swallow motor patterns. There are several aspects that deserve the authors' attention and comments, mainly related to the study´s conclusions.

• The authors refer to PICO as the generator of post-inspiratory rhythm. However, evidence points to this region as a modulator of post-inspiratory activity rather than a rhythmogenic site (Toor et al., 2019 - 10.1523/JNEUROSCI.0502-19.2019; Oliveira et al., 2021 - 10.1016/j.neuroscience.2021.09.015). For example, sustained activation of PICO for 10 s barely affected the vagus or laryngeal post-inspiratory activity (Huff et al., 2023 - 10.7554/eLife.86103).

Yes, we did refer to PiCo as the postinspiratory rhythm generator as defined as Anderson et al. 2016. We base this statement on the following criteria and experiments: In Anderson et al. 2016, we demonstrate that PiCo can be isolated in vitro, that PiCo neurons are activated in phase with postinspiration, and that they are inhibited during inspiration by preBötC neurons via GABAergic mechanisms and not glycinergic mechanisms. We also demonstrate that optogenetically stimulating cholinergic neurons in the PiCo area resets the inspiratory rhythm both in vivo and in vitro. We also show that PiCo when isolated in transverse slices is autorhythmic and that PiCo, like the preBötC in transverse slices can generate respiratory rhythmic activity in vitro and independent of the preBötC. We also demonstrate that PiCo neurons are an order of magnitude more sensitive to opioids (DAMGO) than the preBötC and that local injections of DAMGO into the PiCo area in vivo abolishes postinspiration, and also abolishes the phase delay of the respiratory rhythm. None of these specific rhythmogenic properties have been studied by the Toor study or the Oliveira et al study. Hence, we do not understand why the reviewer cites these studies as evidence for modulation as opposed to rhythmogenic properties. The fact that PiCo is rhythmogenic should not be considered as an “exclusive property”. Specifically, this does not mean that PiCo is also “modulating” the swallow-breathing coordination as we have demonstrated more specifically in the Huff et al study. In the same sentence we also referred to the PreBӧtzinger complex as the inspiratory rhythm generator as defined by Smith et al 1991, and it seems that the reviewer did not object to this reference. But we would like to point out that the same criteria were used to define the preBötzinger complex as we used for PiCo, except that PiCo neurons are better defined than preBötzinger complex neurons. Dbx1 neurons are often used to characterize the PreBötC, but these neurons form a rostrocaudal and ventrodorsal column which involves also glia cells and transcends the preBötC. Glutamatergic neurons are everywhere, and so are Somatostatin or Neurokinin neurons. Moreover, the 1991 study was only performed in vitro, and did not include a histochemical analysis. We would also like to point out that the present manuscript is investigating the role of PiCo in swallow and laryngeal behaviors, and not specifically postinspiration. Thus, we are not entirely sure how this comment relates to this manuscript.

• The optogenetic activation of glutamatergic and cholinergic neurons from PICO evoked submental and laryngeal responses, and CIH changed these motor responses. Therefore, the authors proposed that PICO is directly involved in swallow pattern generation and that CIH disrupts the connection between PICO and SPG (swallow pattern generator). However, the experiments of the present study did not provide evidence about connections between these two regions nor their possible disruption after CIH, or even whether PICO is part of SPG.

We have edited the text to suggest PiCo modulates swallow motor sequence in addition to the coordination of swallow and breathing. We have also added that further experiments will be necessary to further investigate the connections between PiCo and SPG. But, unfortunately, compared to PiCo, the SPG is much less defined. As already stated above, it cannot be expected that a single study can address all possible open questions. Clearly, more work needs to be done outside of this study to answer all of these questions, which makes this an exciting area of research.

• CIH affects several brainstem regions which might contribute to generating abnormal motor responses to PICO stimulation. For example, Bautista et al. (1995 - 10.1152/japplphysiol.01356.2011) documented that intermittent hypoxia induces changes in the activity of laryngeal motoneurons by neural plasticity mechanisms involving serotonin.

Yes, we thank the reviewer for this comment and we agree that CIH effects multiple brainstem regions. We stated in the manuscript that we are measuring changes in two muscle complexes which spread among three motor neuron pools: hypoglossal nucleus, trigeminal nucleus, and nucleus ambiguus. We have added a discussion on laryngeal activity in the presence of acute bouts of extreme hypoxia, acute intermittent hypoxia, as well as chronic intermittent hypoxia.

• To support the hypothesis that PICO is directly involved in swallow pattern generation the authors should perform the inhibition of Vglut2-ChAT neurons from PICO and then evoke swallow motor responses. If swallow is abolished when the neurons from this region are inhibited, it would indicate that PICO is crucial to generate this behavior.

Thank you. We would like to clarify: “involvement” does not mean “necessary for”. Confusing this difference has caused much confusion and debate in the field. Just as an example: We can argue in great length whether inhibition is necessary for respiratory rhythmogenesis in vivo, but I think there is no question that inhibition is involved in respiratory rhythmogenesis in vivo. But to avoid any confusion, we have changed the text to suggest PiCo is involved in the modulation of swallow motor sequence. We agree various additional inhibition experiments are necessary to explain if PiCo is also a necessary component of the SPG, but this is not the question we have set out to address in this study. To specifically target PiCo we must not only inhibit Vglut2 neurons but neurons that express both ChAT and Vglut2. To our knowledge there are no inhibitory DREADD or opsin techniques for cre/FlpO to specifically target these neurons. As stated above, non-experts in the field do not appreciate this technical nuance. However, we have begun to develop novel techniques necessary to inhibit these specific neurons which will be published in the future.

• In almost all the data presented, the authors observed different patterns of changes in the motor submental and laryngeal responses to PICO activation, including that animals submitted to CIH (6%) presented a "normal" motor response. However, the authors did not discuss the possible explanations and functional implications of this variability.

We agree that it would be helpful to understand the functional implications of these alterations in swallow-related motor activation, however since we are not using any tools to measure or evaluate functional activity it would be inappropriate to make suggestions of this type without any data to back up our conclusion. This is why we have not included any functional implications. We have added the following to the manuscript. “While fine wire EMG studies are an excellent evaluation tool to observe temporal motor pattern of sequential swallow related muscles; it must be combined with tools such as videofluoroscopic swallow study (VFSS) and/or high resolution manometry (HRM) in order to characterize the functional significance of these alterations to the swallow motor pattern shown in this study (Park et al., 2017). Since the preparation in this study utilizes only fine wire EMGs we are not able to evaluate or comment on the functional significance of the variable swallow motor patterns.”

• In Figure 4, the authors need to present low magnification sections showing the PICO transfected neurons as well as the absence of transfection in the ventral respiratory column. The authors could also check the scale since the cAmb seems very small.

Thank you, added different histology images to have a more comparable cAmb. As well as added lower magnification to show absence of transfection in the VRC.

• Finally, the title does not reflect the study. The present study did not demonstrate that PICO is a swallow pattern generator.

We have also changed the title to say: Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production

Summary

Author Response

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

eLife assessment:

This study presents a valuable finding on the possible use of vilazodone in the management of thrombocytopenia through regulating 5-HT1A receptor signaling. The evidence supporting the claims of the authors is solid, with the combined use of computational methods and biochemical assays. The work will be of broad interest to scientists working in the field of thrombocytopenia.

Public Review:

Reviewer #1 (Public Review):

Summary:

This is well-performed research with solid results and thorough controls. The authors did a good job of finding the relationship between the 5-HT1A receptor and megakaryocytopoiesis, which demonstrated the potential of vilazodone in the management of thrombocytopenia. The paper emphasizes the regulatory mechanism of 5-HT1A receptor signaling on hematopoietic lineages, which could further advance the field of thrombocytopenia for therapeutic purposes.

Strengths:

This is comprehensive and detailed research using multiple methods and model systems to determine the pharmacological effects and molecular mechanisms of vilazodone. The authors conducted in vitro experiments using HEL and Meg-01 cells and in vivo experiments using Zebrafish and Kunming-irradiated mice. The experiments and bioinformatics analysis have been performed with a high degree of technical proficiency. The authors demonstrated how vilazodone binds to 5-HTR1A and regulates the SRC/MAPK pathway, which is inhibited by particular 5-HTR1A inhibitors. The authors determined this to be the mechanistic underpinning for the effects of vilazodone in promoting megakaryocyte differentiation and thrombopoiesis.

Weaknesses:

(1) Which database are the drug test sets and training sets for the creation of drug screening models obtained from? What criteria are used to grade the results?

Response: Thank you for your thoughtful comment. The database is built by our laboratory. Firstly, we collected 39 small molecule compounds that can promote MK differentiation or platelet formation and 691 small molecule compounds that have no obvious effect on MK differentiation or platelet formation to buiid the datbase. Then, the data of the remaining 713 types of small molecule compounds were utilized as the Training set, and the Molecular Descriptors of 2 types of active and 15 types of inactive small molecule compounds were randomly picked as the Validation set. With regard to the activity evaluation criteria, the prediction score for each molecule was between 0 and 1, and the model decision was made with a threshold of 0.5. The molecule with a score above the 0.5 threshold was identified as a megakaryopoiesis inducer (1).

Reference：

(1) Mo Q, Zhang T, Wu J, et al. Identification of thrombopoiesis inducer based on a hybrid deep neural network model. Thromb Res. 2023;226:36-50. doi:10.1016/j.thromres.2023.04.011

(2) What is the base of each group in Figure 3b for the survival screening of zebrafish? The positivity rate of GFP-labeled platelets is too low, as indicated by the quantity of eGFP+ cells. What gating technique was used in Figure 3e?

Response: We are deeply grateful for the insightful feedback you have provided regarding Figure 3 and the assessment of zebrafish model. We used 50 zebrafish embryos per group to evaluate VLZ toxicity, and we think this is a suitable and fair baseline. Our gating procedure is clearly depicted in the resulting diagram. Since our goal was to evaluate the fluorescence intensity quantitatively, we isolated the entire zebrafish cell. Since the amount of eGFP+ in various zebrafish tissues found in other literature is likewise quite low and we are unsure of the typical eGFP+ threshold for zebrafish (1, 2), we think this finding should be fair given that each group's activities in the experiment were conducted in parallel.

Reference：

(1) Yang L, Wu L, Meng P, et al. Generation of a thrombopoietin-deficient thrombocytopenia model in zebrafish. J Thromb Haemost. 2022; 20(8): 1900-1909. doi:10.1111/jth.15772

(2) Fallatah W, De Silva IW, Verbeck GF, Jagadeeswaran P. Generation of transgenic zebrafish with 2 populations of RFP- and GFP-labeled thrombocytes: analysis of their lipids. Blood Adv. 2019;3(9):1406-1415. doi:10.1182/bloodadvances.2018023960

(3) In Figure 4C, the MPV values of each group of mice did not show significant downregulation or upregulation. The possible reasons for this should be explained.

Response: Thank you for your thoughtful comment. Megakaryocytes build pseudopodia, which form extensions that release proplatelets into the bone marrow sinusoids. Proplatelets convert into barbell-shaped proplatelets to form platelets in an integrin αIIbβIII mediated process (1-2). Platelet size is established by microtubule and actin-myosin-sceptrin cortical forces which determine platelet size during the vascular formation of barbell proplatelets (3). Conversion is regulated by the diameter and thickness of the peripheral microtubule coil. Proplatelets can also be formed from proplatelets in the circulation (4). Megakaryocyte ploidy correlates with platelet volume following a direct nonlinear relationship to mean platelet volumes (5). Usually there is an equilibrium between platelet generation and clearance from the circulation (normal turnover) controlled by thrombopoietin. When healthy humans receive thrombopoietin, their platelet size decreases (6). Proplatelet formation is dynamic and influenced by platelet turnover (7) which increases upon increased platelet consumption and/or sequestration. In our study, the MPV values of each group of mice did not show significant downregulation or upregulation, from our point of view, there are several possible reasons for these results.

(1) Mice in a radiation-damaged state may result in a decrease in platelet count, but at the same time stimulate the bone marrow to release young and larger platelets, thus keeping the MPV relatively stable.

(2) After radiation injury, bone marrow cells were suppressed, resulting in a decrease in the number of platelets produced, but MPV remained unchanged, possibly because the direct effects of radiation on the bone marrow caused thrombocytopenia, but not necessarily the average platelet size.

Reference：

(1) Thon JN, Italiano JE. Platelet formation. Semin Hematol. 2010(3):220-226. doi: 10.1053/j.seminhematol.2010.03.005.

(2) Larson MK, Watson SP. Regulation of proplatelet formation and platelet release by integrin alpha IIb beta3. Blood. 2006(5):1509-1514. doi: 10.1182/blood-2005-11-011957.

(3) Thon JN, Macleod H, Begonja AJ, et al., Microtubule and cortical forces determine platelet size during vascular platelet production. Nat. Commun. 2012(3):852. doi: 10.1038/ncomms1838.

(4) Machlus KR, Thon JN, Italiano JE Jr. Interpreting the developmental dance of the megakaryocyte: a review of the cellular and molecular processes mediating platelet formation. Br. J. Haematol. 2014(2):227-36. doi: 10.1111/bjh.12758.

(5) Bessman JD. The relation of megakaryocyte ploidy to platelet volume. Am. J. Hematol. 1984(2):161-170. doi: 10.1002/ajh.2830160208.

(6) Harker LA, Roskos LK, Marzec UM, et al., Effects of megakaryocyte growth and development factor on platelet production, platelet life span, and platelet function in healthy human volunteers. Blood. 2000(8):2514-2522. doi: 10.1182/blood.V95.8.2514.

(7) Kowata S, Isogai S, Murai K, et al., Platelet demand modulates the type of intravascular protrusion of megakaryocytes in bone marrow. Thromb. Haemost. 2014(4):743-756. doi: 10.1160/TH14-02-0123.

(4) The PPI diagram and the KEGG diagram in Figure 6 both provide a possible mechanism pathway for the anti-thrombocytopenia effect of vilazodone. How can the authors analyze the differences in their results?

Response: We are appreciated your valuable comments. PPI (Protein-Protein Interaction) refers to the interaction between proteins. Inside cells, proteins interact with each other to perform various biological functions, influencing cell signaling, metabolic pathways, cell cycle, and more. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that integrates information on genomes, chemicals, and biological systems. In pharmacoinformatic, KEGG pathways are often used to understand the molecular mechanisms of specific diseases or biological processes. KEGG contains the interrelationships between genes, proteins, and metabolites, helping to reveal key nodes in biological processes. PPI information can be integrated with data from KEGG pathways, such as metabolic and signaling pathways, to gain a more comprehensive understanding of the role of protein-protein interactions in cellular processes and biological functions. For example, by analyzing nodes in the PPI network, proteins associated with a specific disease can be identified, and further examination of these proteins' locations in KEGG pathways can reveal molecular mechanisms underlying the onset and development of the disease. However, this method also has some limitations：

Uncertainty (1): The construction of protein-protein interaction networks and drug interaction networks involves many assumptions and speculations. The edges of these networks may be based on experimental data but can also rely on bioinformatics predictions. Therefore, the accuracy of predictions is limited by the quality and reliability of the data used during network construction.

Insufficient data (2): Despite the availability of a large amount of bioinformatics data for network construction, interactions between some proteins and drugs may still lack sufficient experimental data. This data insufficiency can result in inaccuracies in network predictions.

Dynamics and temporal-spatial changes (3): The dynamics and temporal-spatial changes in biological systems are crucial for drug effects. Pharmacoinformatic may struggle to capture these changes as it often relies on static network representations, overlooking the temporal and dynamic nature of biological systems.

Reference：

(1) Fernando PC, Mabee PM, Zeng E. Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinformatics. 2020(1):442. doi: 10.1186/s12859-020-03773-2.

(2) Zhang S, Zhao H, Ng MK. Functional module analysis for gene coexpression networks with network integration. IEEE/ACM Trans. Comput. Biol. Bioinform. 2015(5):1146-1160. doi: 10.1109/TCBB.2015.2396073.

(3) Cinaglia P, Cannataro M. A method based on temporal embedding for the pairwise alignment of dynamic networks. Entropy (Basel). 2023(4):665. doi: 10.3390/e25040665.

(5)-HTR1A protein expression is measured only in the Meg-01 cells assay. Similar quantitation through western blot is not shown in other cell models.

Response: Your insightful criticism and recommendation to use different cell models in order to obtain a more accurate depiction of 5-HTR1A protein expression are greatly appreciated. We completely concur that using this strategy would greatly increase the validity of our research. However, establishing a primary megakaryocyte model requires specialized expertise and technical resources, which unfortunately are not readily available to us within the given timeframe. Nevertheless, we acknowledge the limitations of Meg-01 cells, which may exhibit distinct properties compared to true megakaryocytes. To mitigate this concern, we have ensured robust experimental design and rigorous data analysis to interpret our findings within the context of these model cell lines. We believe our results still provide valuable insights into megakaryocyte differentiation and address an important biological question.

Reviewer #2 (Public Review):

Summary:

The authors tried to understand the mechanism of how a drug candidate, VLZ, works on a receptor, 5-HTR1A, by activating the SRC/MAPK pathway to promote the formation of platelets.

Strengths:

The authors used both computational and experimental methods. This definitely saves time and funds to find a useful drug candidate and its therapeutic marker in the subfield of platelets reduction in cancer patients. The authors achieved the aim of explaining the mechanism of VLZ in improving thrombocytopenia by using two cell lines and two animal models.

Weaknesses:

Only two cell lines, HEL and Meg-01 cells, were evaluated in this study. However, using more cell lines is really depending on the workflow and the grant situations of the current research team.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. We fully agree that CD34+ hematopoietic stem/progenitor cells or primary megakaryocytes would provide a more accurate representation of in vitro megakaryopoiesis compared to HEL and Meg-01 cells, which possess limited potential for this process. We acknowledge that our current study did not include experiments with these preferred cell models. This is because our laboratory is still actively developing the technical expertise and resources required for establishing and maintaining primary megakaryocyte and CD34+ cell cultures. Despite the limitations of the current study, we believe the results using HEL and Meg-01 cells provide valuable preliminary insights into the potential effects of VLZ on megakaryocyte differentiation. We are actively working to overcome these limitations and plan to incorporate these more advanced models in our future investigations.

Reviewer #1 (Recommendations For The Authors):

I think the authors can enhance the mechanism study by developing more reliable models and methodologies. The connection to clinical research should be strengthened at the same time.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. Despite the limitations, we are committed to expanding our research in the future by incorporating your suggestion and establishing a primary megakaryocyte model to further validate our findings and strengthen our conclusions. At the same time, we wholeheartedly concur with your suggestion to combine clinical research. Unfortunately, VLZ is not a first-line treatment for depression in China, and getting blood samples from the matching number of patients for analysis is a challenge. To give additional experimental support for the medication, we have attempted to improve the data in vivo as much as feasible, including by implementing the intervention in normal mice. Our findings should also contribute to the theoretical underpinnings of this medication and aid in its practical application.

Reviewer #2 (Recommendations For The Authors):

Issues the authors need to address:

Figure 7: Why the band intensity of GAPDH in b or e is much greater than that in f, g, or h?

Response: Thank you for your careful observation and insightful comment regarding Figure 7. Because the concentration of each batch of protein samples is different, sometimes the GAPDH band strength is increased by the large loading volume. Other factors that may influence the GAPDH band strength include the instrument's contrast adjustment during exposure and the use of different numbers of holes for electrophoresis. Meanwhile, the original three replicate results of all WB results will be provided in the supplementary materials.

Finally, we sincerely thank you for providing us with this opportunity to make a further revision and modification of our manuscript, and your valuable and scientific comments are useful for the great improvement of our manuscript!

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Reply to the reviewers

Reviewer_01

Major comments:

1. The authors cite that acetylated and tyrosinated microtubules have different spatial and compartmental distribution in dendrites and axons and investigate the distribution in the AIS of nonAcD cells and AcD cells, as well as the stem dendrites. However, they just show one example of two different cells (Figure 2D and E) without any statistical analysis. Either, they should remove this part or provide a thorough quantification. Reply: The spatial and compartmentalized distribution of stable and dynamic MTs in the dendrites and axons of nonAcD neurons has been extensively studied and reviewed (see Kapitein & Hoogenraad, 2011; Katrukha et al., 2021; Tas et al., 2017 for reference). However, the organization of the MT cytoskeleton in AcD neurons is still unknown. Here, we provide the very first evidence on the distribution of tyrosinated and acetylated MTs in AcD neurons, as well as data on MT orientations. We agree with the reviewer that to make our results on the spatial organization of these post-translational modifications in AcD neurons more complete, we need to provide a more thorough quantification analysis.

To achieve this, we plan to perform immunostainings on DIV10 neurons using antibodies against tyrosinated (tyr) and acetylated (ac-) tubulin to label dynamic and stable MTs, respectively. Subsequently, we will conduct high-resolution 3D confocal imaging and measure fluorescent intensity to illustrate the abundance and staining patterns of tyr- and ac- MTs in the axons and dendrites of AcD neurons. Since the spatial distribution of tyr- and ac-MTs is distinguishable with confocal microscopy, we will retain STED examples in the figures but conduct new analyses on confocal imaging data. We will measure the total fluorescent intensity of tyr- and ac- MTs in different compartments of AcD neurons and normalize it to the size of the measured area. We will then compare the normalized intensity values between the axons and dendrites of AcD neurons to examine whether there is a specific distribution pattern of stable and dynamic MTs. We will analyse at least 3 independent primary culture preparations with a minimum of 30 cells. Using the same dataset, we will also quantify the percentage of AcD neurons with ac-MTs specifically elongating into the axon compared to AcD.

The authors use EGFP-Rab3A vesicle to investigate anterograde transport at the axon and dendrites. They find a slightly faster transport of these vesicles at the AIS of AcD cells and conclude the axonal cargos in general are transported faster across the AIS in AcD cells. In my opinion, this generalization based on one type of vesicle is too farfetched.

Reply: The Rab3A protein is associated with pre-synaptic vesicles that are transported by KIF1A and KIF1Bβ, members of the kinesin-3 family, towards pre-synaptic buttons (see Guedes-Dias & Holzbaur, 2019; Niwa et al., 2008 for reference). Since KIF1A and KIF1Bβ are common motor proteins that mediate MT-based transport of different types of vesicles (e.g., synaptic vesicles and dense-core vesicles, see Carabalona et al., 2016; Helmer & Vallee, 2023 for reference), we reasoned that Rab3A should be a representative marker for an axonal cargo. However, this indeed does not rule out whether the faster trafficking effect we saw is specific to presynaptic vesicles, as different types of vesicles tend to recruit different modulators that could lead to different trafficking features.

To address this question, we will perform a live-imaging experiment including two additional organelle marker proteins, Neuropeptide Y (NPY) and Lysosome-associated membrane protein 1 (Lamp1). NPY is transported into the axon via KIF1A and KIF1Bβ-mediated dense-core vesicles (see Helmer & Vallee, 2023; Lipka et al., 2016 for reference). Lamp1 is associated with lysosomes and a range of endocytic organelles that recruit both kinesin-1 and kinesin-3, and are transported into both axons and dendrites (as reviewed in Cabukusta & Neefjes, 2018). By introducing two additional types of vesicles, we should be able to answer whether AcD neurons, in general, tend to transport cargoes into the axon faster than nonAcD neurons.

__Minor comments: __

In the introduction, the authors describe how synaptic inputs are received at the dendrites and propagated to the soma in the form of membrane depolarizations. They should add 'excitatory' to synaptic inputs or also describe the impact of inhibitory synaptic inputs at the dendrites.

In my opinion, Figure 2 could be presented in a slightly better way. The lower part of panel A better fits to panel B, which is next to the upper part of panel A. I understand that the authors systematically present their data first for nonAcD cells and then for AcD cells. However, in this special case it is a little bit more difficult to read the current figure in that order. The results displayed in Figure 4 are presented in a slightly confusing order. The authors jump from 4D to 4G, then to 4I and 4E, 4H, 4F. Similarly, 4M and N are addressed before 4O and P to finally get to 4K and L. It would be beneficial to present and address the data in a stringent way.

Reply: Thank you for the suggestions on how to improve the data representation in the figures. We will change Figures 2 and 4 and make adjustments in the text upon revision since we also plan to include additional data.

Reviewer_02

Major comments:

1. The authors suggest that there is reduced Na+ channel density at AcD AIS compared to other AIS arising from the cell body. This is not convincing. Immunostaining for Na+ channels is notoriously difficult and sensitive to fixation since the epitopes of the anti-Pan Nav antibodies are highly sensitive to fixation. In addition, this is based on immunofluorescence intensity quantification. Since the mechanism of localization is through binding to AnkG, the authors should also measure other AIS proteins like AnkG, b4 spectrin, and Nfasc. Do these change? If all uniformly change I would be much more inclined to accept the conclusion. If they do not change, it still doesn't rule out the concern about fixation conditions and slight differences in the cultures. The authors indicate there is about a 40% reduction in fluorescence intensity. That is quite large. This big difference should also be confirmed in brain sections. Reply: The potential fixation issue and antibody sensitivity on Na+ channel staining are indeed valid considerations, and we are aware of them. However, it should be noted that we used pan-Na+ channel antibodies that were previously characterised and widely used in literature (see Solé et al., 2019; Yang et al., 2020 for references). Furthermore, our samples underwent the same fixation and staining protocol, and comparable numbers of AcD and nonAcD neurons were imaged from the same preparation and coverslip for each experiment. Imaging settings were also kept constant. Any loss of Na+ channel staining at the AIS due to fixation should affect both neuron types and therefore our conclusion is justified. Nevertheless, the reviewer's point regarding other AIS components is valid and will be investigated further in the revised manuscript.

Following the reviewer's suggestion to further strengthen our conclusion, we will measure the intensity of AnkG, βIV-spectrin, and neurofascin in DIV21 AcD and nonAcD neurons. We will compare a minimum of 3 independent cultures, each containing at least 10 cells of each type per culture.

We agree with the reviewer that confirming observed differences in Na+ channel staining using brain slices would be beneficial. However, conducting such experiments presents several challenges. Firstly, one approach could involve immunostaining with antibodies against AIS marker AnkG, in combination with somatodendritic marker MAP2 and pan-Nav. However, this method lacks the advantage of clearly identifying neuronal morphology as seen in dissociated cultures, making the outcome unclear and difficult for analysis and interpretation. Alternatively, the use of Thy1-GFP rats, where a subset of neurons is labelled with GFP, could allow for morphological studies. Unfortunately, we do not have access to this rat line, and the process of importing it, obtaining permits, and establishing a colony is beyond the timeframe for manuscript revision. Additionally, while pan-Nav antibodies have shown reliability in dissociated cultures, their efficacy in tissue staining is less certain. We could provide example images upon request. Secondly, endogenously labelling of Na+ channels is another option, but remains a significant challenge. Recent developments in endogenous labelling, such as the CRISPR/Cas9-based method using pORANGE by Fréal et al. (Fréal et al., 2023), and the generation of Scn1a-GFP transgenic mice by Yamagata et al. (Yamagata et al., 2023), offer potential solutions. However, the labelling efficiency of pORANGE is uncertain, and both methods are time-consuming and cannot be completed within the three-month revision period.

As an alternative, we propose emphasising that our results are based on in vitro experiments and discussing the advantages and limitations of this approach in the discussion section.

The analysis of inhibitory synapse differences at the AIS are also not compelling - this is a limitation of the culture system. The authors have no control over the density of inhibitory neurons in the culture well. This interaction is not intrinsic to the AcD neuron, but rather a feature of neuron-neuron interactions which should only be modelled in the animal.

Reply: The reviewer is correct in pointing out that establishing inhibitory synapses at the AIS is not an intrinsic feature of AcD neurons; it depends on the network and should be modelled in animals. We will include this limitation of the cell culture model in the discussion section in the revised manuscript. We also understand the reviewer's concern that the lower amount of inhibitory synapses at AcD neuron AIS might be due to uneven density of inhibitory neurons between cultures. Nonetheless, assuming that the number of inhibitory neurons is constant between preparations, it is an interesting observation that AcD neurons form fewer inhibitory synapses at the AIS. This may be related to the features of the AIS and its morphology and should be further investigated.

To make our study more comprehensive and also address the reviewer's concern regarding the presence of inhibitory neurons, we will perform immunostainings in dissociated cultures (40.000 cells per 18 mm coverslip, same as in experiments with synapse quantification) with antibodies against pCaMKIIa, an excitatory neuron marker, and GAD1, a marker for inhibitory neurons. Then, we will quantify the density of inhibitory neurons in the culture. We will perform measurements from 3-6 independent cultures by analysing large fields of view in different areas of a coverslip (20-30 neurons per area) to determine if the density of inhibitory neurons varies between cultures as well as preparations. Furthermore, as also requested by reviewer 4, we will perform new immunostainings where pre- and post-synaptic markers (VGAT and Gephyrin) will be included in the same sample together with the AIS (AnkG or Neurofascin) and dendritic marker (MAP2). Synapses that contain pre- and post-synaptic components will be analysed and included in the revised version of the manuscript.

Finally, the major limitation of this study is that it is performed in vitro. Surprisingly, the authors actually argue this is a feature of their system. While it is true some of the questions can be addressed perfectly well in vitro, many cannot. In the first paragraph of the results the authors state an advantage of their system is that there are no microenvironments to influence the development of the AcDs. I'm afraid I view this as a drawback. The authors suggest this is an opportunity to examine intrinsic mechanisms of development - true, but it also foregoes the opportunity to determine if the outcomes are different from what occurs in vivo. To this point, the authors report that only 15-20% of the population of hippocampal neurons in culture are AcD neurons. But in their introduction they cite other literature indicating 50% of hippocampal neurons in vivo are AcD neurons - this suggests that the environment of the hippocampus in vivo influences whether a neuron becomes an AcD neuron or not.

Reply: The reviewer is right in pointing that the in vivo environment could indeed affect AcD neuron development, and we also find this to be a very interesting topic to investigate in the future. Even more intriguingly, as shown in a preprint by Lehmann et al. (doi: https://doi.org/10.1101/2023.07.31.551236), network activity stimulates neurons to acquire AcD morphology. While it is true that the impact of the microenvironment on AcD neuron development cannot be studied in dissociated cultures, our in vitro data undoubtedly support the fact that hippocampal neurons can intrinsically develop into AcD morphology independent of the in vivo environment. As also mentioned in the next point, our statement "...their development must be driven by genetically encoded factors rather than specific..." might sound too definitive and therefore eliminate possible effects from the microenvironment. We will revise this part. Although it is highly desirable to move cell biological studies from neuronal cell cultures to tissue, to date, it is still very challenging to perform many of experiments which we did in this study in slices or living animals due to a lack of appropriate technologies and tools. We are convinced that many basic biological questions can be and should be studied in simplified culturing models because they are truly fundamental, they should also be reproducible in these models.

To address the reviewer's question regarding the percentage difference between our data and the previous study by Thome et al. (2014), several factors should be considered. First, as noted by the reviewer, our results were obtained from an in vitro system, which is not directly comparable to the in vivo model system used in Thome et al.'s study (Thome et al., 2014). Second, the age of the neurons quantified in our developmental experiments is DIV5 and DIV7. This young age disparity could contribute to the percentage difference, as Thome et al. analyzed neurons from P28-35 adult animals, where 50% of the AcD neuron population was observed, specifically in the CA1 region. Third, it's important to note that in other hippocampal regions, the percentage of AcD neurons is lower (approximately 20-30%). Since our hippocampal primary cultures contain neurons from all hippocampal regions, this may have averaged out our quantification of AcD neuron percentage. Additionally, in the study by Benavides-Piccione et al. (Benavides-Piccione et al., 2020), they reported 20% AcD neurons in the CA1 region of hippocampi isolated from 8-week-old mouse pups, a number similar to what we observed in vitro. Interestingly, Thome et al. reported that in P8 pups, AcD neuron population in hippocampal CA1 region is 30%. This number increased to 50% in adult animals at age of P28-35, suggesting there is perhaps an age dependent increase of AcD neuron population. This could be an additional reason of why we only saw 15-20% of AcD neurons in our in vitro system, regardless of the in vivo environment.

In the revised version, we will clarify these points in the introduction and discussion sections. Additionally, we will quantify the proportion of AcD neurons in mature DIV21 dissociated hippocampal cultures and compare it to DIV7 cultures to assess whether there is an increase in the AcD population over time. We believe that this experiment, combined with the explanations provided above, will sufficiently address the reviewer's question. However, it is important to acknowledge that the establishment of neuronal networks in vitro differ from those in vivo. Therefore, there may be potential differences in the outcomes.

I appreciated the balanced discussion of whether this is a stochastic or genetically programmed process. This could have been emphasized earlier in the results since the authors invoke the concept that "...their development must be driven by genetically encoded factors rather than specific...". The authors have not shown this and cannot show it in this system. Indeed, as stated in point 4 above, I think their data argue against a simple genetic program.

Reply: As suggested by the reviewer and noted in point 4, we will revise the section on AcD neuron development in our manuscript to emphasize that hippocampal neurons may adopt AcD morphology through genetic or stochastic mechanisms. While we acknowledge that environmental and activity factors may also influence this process, particularly in mature neurons, our study focuses on developing neurons where genetic and stochastic factors are likely to be predominant. This conclusion is supported by the observation that neurons develop into AcD morphology in vitro, where environmental and activity patterns do not mimic those of in vivo systems.

Indeed, our current manuscript does not explore genetic factors involved in AcD neuron development. To address this question, one approach could be to label AIS markers endogenously in dissociated cultures using the PORANGE method (see Willems et al., 2020 for reference) or utilize AnkG-GFP transgenic mice (Fréal et al., 2023; Thome et al., 2023) along with a volume marker like mRuby or GFP. This would allow for the identification of AcD and nonAcD neurons in vivo and in vitro, followed by single-cell transcriptomics analysis to uncover potential genetic factors. Subsequently, candidate genes could be manipulated to demonstrate their essential role in AcD neuron development. However, such experiments require significant time and resources beyond the scope of our current revision timeframe. Nonetheless, this question presents an exciting direction for future research.

Reviewer 3

Major comments:

1. The authors classify neurons into axon-carrying dendrite (AcD) and non-AcD neurons by measuring the stem dendrite length (> 3 µm). I could not find the validity for this cut-off. The non-AcD neurons in Fig. 6B appear more AcD to this reviewer, and, in addition, other researchers have proposed a third category of 'shared root' neurons (doi: 10.7554/eLife.76101). For purposes of reproducibility and transparency, please provide first a comprehensive overview of the entire population of morphologies (i.e. all cells in control conditions). The distances from the soma could be plotted in histogram (etc.) and authors may want to think about independent supporting evidence for the cut-off to classify AcD and non-AcD neurons. Reply: Concerning the validity of AcD neuron classification, we did measure the length of the stem dendrite, as shown in Figure S4G, with an average distance of around 10 µm. However, we admit that this information is presented relatively late in the manuscript. To address the reviewer's criticism, in the revised version, we will include a supplementary figure displaying a gallery of representative images of both AcD and nonAcD neurons analyzed in our study (please refer to Hodapp et al., 2022; Fig S1 C&D; Fig S3 as an example). Given the sample size of AcD and nonAcD neurons in our study, including all images would result in a very large figure (for example, Figure 1: DIV5: 83 AcD neurons out of 427 cells, DIV7: 47 AcD neurons out of 387 cells). We will only show representative examples of AcD neurons in the gallery. Additionally, as suggested, we will plot the length of the stem dendrite (or axon distance) of AcD neurons as a histogram to demonstrate that the AcD neurons included in our study indeed have a stem dendrite longer than 3 µm. To further validate the used classification method, we will measure the diameter of the stem dendrite in all analyzed AcD neurons and then compare the distance between the soma and the start of the axon in each analyzed AcD neuron to the diameter of its stem dendrite. As described by Hodapp et al. (Hodapp et al., 2022; Fig S1A), AcD neurons are expected to have a stem dendrite longer than their diameter.

We have considered having independent evidence to support the classification of nonAcD and AcD neurons. However, the method used by Thome et al. and Wahle et al. for AcD and nonAcD neuron classification is well established and widely accepted (see Thome et al., 2014; Wahle et al., 2022 for references). Similar standards were also employed by Benavides-Piccione et al. (Benavides-Piccione et al., 2020). Introducing independent evidence could potentially raise further doubts, so we have chosen to maintain consistency with previous studies.

As for the "shared root" neurons described by Wahle et al., we did not analyze this category separately and included them in the nonAcD subtype. Nonetheless, it is an interesting direction to explore in the future. For completeness, we will discuss this point in the revised manuscript.

Related to point #1 the primary hippocampal neuron system is excellent for cell biological questions but comes with the drawback of imaginative morphologies including neurons with multiple axons and AISs. It is not mentioned here but literature indicates up to 20% of neurons have two axons (e.g. doi: 10.1007/s12264-017-0169-3, 10.1083/jcb.200707042). How did the authors classify the double axon cells? Since the main hypothesis is the existence of an intrinsic program for AcD neurons (p. 5 top), the two axons from one neuron should develop similarly. The authors can easily test this with the data.

Reply: We appreciate the reviewer's comment regarding the choice of the model system for this type of study. Indeed, as they pointed out, in primary cultures, some neurons develop more than one axon. Since we did not find any supporting evidence from the literature reporting that hippocampal neurons have multiple axons in vivo, we only analyzed neurons with one axon for both AcD and nonAcD neurons. We will clarify this in our method section of the revised manuscript.

Some interpretations about function are not correct and the authors should reconsider these. A role of cisternal organelles on neuronal excitability remains to be demonstrated (and see doi.org/10.1002/cne.21445 showing there is none). In addition, the statement that lower fluorescence intensity of Pan-Nav1 is indicating reduced excitability is flawed. Antibody staining does not scale linearly with voltage-gated sodium channel density and since the AIS of AcD neurons is further from the soma it is most likely smaller in diameter which may account for apparent fluorescent differences. For biophysical reasons (for details I refer to 10.3389/fncel.2019.00570, 10.1016/j.conb.2018.02.016 and 10.7554/eLife.53432) smaller diameter axons will be easier to depolarize by depolarizing voltage-gated channels or excitatory synapses. Finally, in AcD neurons the AIS distance from the soma poses all sorts of interesting cable properties with the soma and the local dendritic membrane and the electrotonic properties alone suffice to make these neurons more excitable.

Reply: The reviewer brings up very valid and important points that we will address in the revised manuscript. First, we will rephrase and adjust our interpretations regarding the functions of the cisternal organelle in the AIS. As also mentioned by reviewer #2, we are aware that antibody staining does not properly reflect Na+ channel density. As discussed above, we will also measure other AIS proteins that anchor Na+ channels to see if there are any correlations in fluorescence intensity between them and Nav1. We agree with the reviewer that AcD neuron's AIS could have a smaller diameter, resulting in fewer Na+ channels. Indirect evidence is already available in the study of Benavides-Piccione et al., showing a smaller axon diameter in AcD neurons compared to nonAcD neurons in both human and mouse brain sections (Figure S4). To test this in our model system, we propose to measure the AIS diameter in AcD neurons. If this is indeed the case, we will indicate it in our revised manuscript and edit the section on Na+ channels.

Exploring the biophysical properties of the AIS and axons of AcD neurons is indeed a highly interesting direction to pursue and is the project in its own. It would necessitate the use of computational modeling approaches, which require considerable time and resources that are not feasible within the timeframe of this revision.

Comparing AcD and non-AcD neurons for AIS plasticity is an excellent idea but the present statistical design is not suitable for answering this question. The authors should directly compare non-AcD and AcD neurons within a two-way ANOVA design, asking the question whether the independent variable axon type is significantly different and interacts with plasticity.

Related points: 'AIS distance' in Figure 7 seems to refer to something else than distance from soma (Figure 1). Please clarify. What were the absolute distances from the soma for the AcD neurons and was this dependent on treatment?

Reply: We appreciate reviewer's comment and in the revised version we will perform the analysis using two-way ANOVA.

Regarding the terminology and definitions used in our manuscript, the "AIS distance" refers to the measurement between the start of the AIS and the axon initiating point, as depicted in Figure S4 of the manuscript. We adopted this parameter from the previous study by Grubb et al. (Grubb & Burrone, 2010), ensuring consistency in our investigation of AIS plasticity. For AcD neurons, where the axon branches out from the dendrite, we defined the AIS distance as the length between the start of the AIS and the border of the stem dendrite, as illustrated in Figure S4B.

In Figure 1, the term "distance from soma" represents the length of stem dendrite and used for AcD and nonAcD neuron classification. As shown in Figure S4G, the absolute distance from the soma for AcD neurons is approximately 10 µm and remains consistent across treatments. We will explain these points more clearly in the revised manuscript.

Minor comments:

1. At p. 7 is stated that "The percentage of none-AcD forming collaterals at DIV1 is much lower than for AcD neurons" but statistical support is lacking. The conclusion in the next line is that "AcD neurons follow consensus development". That is puzzling given the difference just mentioned before. Please clarify. Reply: We will provide statistical support for comparing collateral formation between nonAcD and AcD neurons at DIV1.

Regarding the second point concerning consensus development, we were referring to the general developmental sequence of AcD neurons, as described by Dotti et al. (see Dotti et al., 1988 for reference), where neurons typically first establish an axon and then dendrites. This sequence is not necessary related to collateral formation, which indeed differs between nonAcD and AcD neurons. The ability to form collaterals may come from local differences in microtubule (MT) and actin dynamics at AcD neuron precursor axons, but it does not alter the fact that AcD neurons initially establish an axon and subsequently dendrites. We will clarify it in the revised manuscript.

A study not cited in this manuscript showed distinct dendritic morphologies (doi: 10.1073/pnas.1607548113) and AcD interneurons are different for their axonal arborization (doi: 10.1242/dev.202305). Differences in growth of branch arborization could hint to subtypes. Are the AcD and non-AcD neurons different in their adult morphology? A detailed account of the axonal and dendritic trees would strengthen the data.

Reply: Thank you for pointing this out. We will include this citation. In the study by Hodapp et al., it was shown that AcD and nonAcD neurons exhibit similar dendritic morphology and do not differ in spine density, number of dendritic branches, and total dendritic length. However, in hippocampal AcD neurons, the AcD occupies 35% of the total basal dendrite length, which is larger than basal dendrites in nonAcD neurons, suggesting that AcD neurons do possess specific features in their dendritic trees.

Regarding the axons of AcD neurons, there is currently no detailed study available, and it would be more appropriate to investigate neuronal connectivity through tracing studies in animals rather than in primary cultures. Therefore, this question falls outside the scope of the current manuscript.

Some key references are not included here, and a number of these are mentioned above. In the context of the detailed MT and Rab3A vesicle and cargo transport studies, please acknowledge some of the pioneering work of Alan Peters revealing the ultrastructure of axons emerging from dendrites. See Figs. 5-7 in Peters, Proskauer and Kaiserman-Abramof IR., J Cell Biol 39:604 (1968). What is the identity of the neurons? It makes a difference if the cells are interneurons or pyramidal neurons, CA1 or CA3-like. For plasticity experiments the authors uses cells as independent measurements, but this is inflating the power. How many cultures were used?

Reply: Thank you for pointing this out; we will include the suggested references in the revised manuscript. In our study, we focused on excitatory neurons from the hippocampus. We distinguished neuron types morphologically or with the inhibitory neuron marker GAD1. Identifying CA1, CA2, CA3, and DG subtypes in dissociated culture is more challenging, and this would be an interesting avenue to explore in an in vivo system. Here, we focused on fundamental cell biology aspects related to the AIS structure and its trafficking barrier function, which should be similar in all these neuron types. While there may be subtype-specific differences in AIS plasticity, investigating this is beyond the scope of our manuscript.

For the plasticity experiments, we used a total of 3 independent cultures, from which we collected a comparable number of neurons. In response to the reviewer's concern, we will also plot the mean of each culture to illustrate the variability of our data points.

Reviewer 4

Major comments:

1. A general limitation of this study is the low N for some critical experiments. In several experiments, individual cells become an N, therefore boosting the power of the analysis when in reality, due to the known heterogeneity of AIS length, position, and general cell morphology in vitro, the aim should be to compare means across animals / preparations, each consisting of a comparable number of individual cells. This is especially important for the analyses of COs, axo-axonic synapses and channel expression at the AIS. Reply: We would like to mention that this is a cell biological study where neurons are grown in dissociated cultures. To prepare one such culture, we typically use hippocampi from 6-8 E18 rat embryos, which are then mixed in one suspension before plating. The cells are then plated on coverslips in a 12-well plate format. When referring to replicates, for all experiments except for the longitudinal study of 5-day-long time-lapse imaging of developmental sequences (Figure 1), we used between 3 to 6 independent preparations. From each preparation, we took a comparable number of cells derived from 4-6 different coverslips. For each experiment, we measured more than a hundred cells, which is standard practice in the field. To address the issue with individual measurements, in the revised manuscript, we will additionally plot the means of each independent preparation.

Such critical parameters as e.g. synaptic innervation at the AIS are investigated in a way that does not support the clear statements given, e.g. "The AIS of AcD neurons receives fewer inhibitory inputs" (Highlights statement) or "AcD neurons have less inhibitory synapses at the AIS" (header of Fig. 6). The overall number of analyzed cells is low (3 and 4 preparations, respectively and approximately 50-cells for each marker). The combination of a pre- and postsynaptic marker for inhibitory / excitatory neurons is a solid decision, but the analysis is not done based on the close approximation of these markers, in 3D, along an AIS, but rather in maxIPs and without any regard of whether pre-and postsynaptic markers are actually close to each other not. The expression of these markers alone just points towards the epitopes being expressed, but are they localized to each other in such a manner that they could form bona fide synapses? The methods are not totally clear on the image depth (tile scans with 5 µm in z will not provide the detail of information to resolve synapses, so how did the authors address the subcellular analysis here and for the CO and VGSCs?). And generally, were Nyquist conditions taken into consideration throughout the study? This can be clarified in text and does not require additional experiments.

Reply: The overall number of cells for quantifying inhibitory synapses along the AIS was approximately 80 cells for each synaptic marker. To clarify this, we will indicate the number of cells in the figure legend of our revised manuscript and will additionally plot mean values across independent preparations.

In the current manuscript, our main goal was to provide an initial quantitative measurement of AIS features in AcD neurons to see if they differ from nonAcD neurons. Hence, maxIPs are sufficient for this purpose as they summarize the 3D information. To make our study more comprehensive, following the reviewer's suggestion, we will conduct additional experiments to co-label pre- and post-inhibitory synapses at the AIS with VGAT and gephyrin, respectively. Then, we will image samples in 3D to measure the density as well as the distance between pre- and post-synapses at the AIS of AcD neurons and compare them to nonAcD neurons.

The Nyquist condition was taken into consideration throughout the study. The pixel size of our data collection was 0.081 µm for the laser scanning microscope, as indicated in our methods section. Given the optical setup of our microscope and the fluorophores used to label target proteins (information available in the methods section of our manuscript), the acceptable Nyquist lateral sampling size (or pixel size, in other words) for confocal images is between 0.083 to 0.093 µm and 0.2 µm in the z-plane. In our data collection for laser scanning confocal images, the z-step size was 0.5 µm (see methods section of our manuscript), which is indeed undersampling the data. However, this should not significantly affect our analysis based on maxIPs. The new stainings with matched pre- and post-synaptic markers will be imaged with a smaller z-step (0.2 µm) and then reconstructed in 3D.

The chapter on AIS plasticity is certainly an interesting addition to the study, but is a bit superficial, yet reaches strong conclusions ("More importantly, it further indicates that the AIS of AcD neurons is insensitive to activity changes"). This is based on un-physiological concentrations of KCl, and certainly not on network manipulation that truly tests synaptic activity. It also comes back to the 1st point above. A suggestion would be to edit the conclusion.

Reply: KCl treatment globally depolarizes the membrane potential of neurons, leading to an increase in intracellular calcium via voltage-sensitive calcium channels as well as NMDA and AMPA receptors (Rienecker et al., 2020). This protocol has been used in several initial studies describing the plasticity of the AIS (see Evans et al., 2013, 2017; Grubb & Burrone, 2010; Jamann et al., 2021; Muir & Kittler, 2014; Wefelmeyer et al., 2015 for references). Moreover, as shown by Evans et al. and Grubb et al. (see Evans et al., 2013; Grubb & Burrone, 2010 for references), AIS plasticity is not abolished by TTX, which blocks Na+ channels, but is prevented by L-type calcium channel blockers. This suggests that the occurrence of AIS plasticity is independent of action potentials but more sensitive to calcium-related pathways downstream of membrane potential depolarization and post-synaptic activation. Hence, we believe our results are indicative of how the AIS would react when calcium signaling pathways are altered by activity levels. To address the reviewer's concern, we will focus our conclusion more on membrane potential depolarization and calcium signalling and edit out statements.

As discussed above in response to reviewer #3, the quantification of AIS plasticity includes 3 independent preparations, comprising approximately 200 neurons in total. To prevent inflation of statistical power in the analysis, we will also plot the means and standard error of the mean (SEM) for each independent experiment and assess whether any differences persist.

The rationale behind looking at the cisternal organelle (CO) in this study is outlined in the Introduction, where the authors state that "...... and is responsible for calcium handling". What is "calcium-handling" and where is the evidence cited? Furthermore, in the Results, they state that "...both compounds (VGSCs and COs) are critical for the AIS to regulate neuronal excitability". While this is the case for VGSCs, there is no conclusive evidence in the literature whether of not the CO is "critical" for neuronal excitability. In fact, a number of neurons have no CO in the AIS (as much as 50% of all AIS in mouse primary visual cortex for example do not express synpo at the AIS at all, Schlüter et al., 2017). The CO can therefore not be as critical for AP initiation as the authors state. Furthermore, the authors state that "AIS plasticity in excitatory neurons is triggered by calcium signaling". While certainly shown and adequately cited here, other factors (independent of calcium) can also play a role, therefore this statement is a bit absolute and should be edited accordingly.

Reply: Thank you for constructive editorial suggestions. Regarding the first question on calcium handling, we were referring to Ca2+ storage and release mechanisms. Benedeczky et al. already showed the existence of SERCA-type Ca2+ pumps at the membrane of the cisternal organelle (CO) to demonstrate the involvement of Ca2+ sequestering/storage by the CO at the AIS (Benedeczky et al., 1994). Although indirect, Sánchez-Ponce et al. showed the presence of IP3R, which promotes Ca2+ release from internal storage, at the AIS and partially colocalizes with synaptodin (Sánchez-Ponce et al., 2011). This is also the same case for the Ca2+-binding protein annexin 6. Together, this evidence indicates a putative role of the CO in regulating Ca2+ dynamics (storage/release) at the AIS. Since Ca2+ levels have a significant impact on action potential generation and timing at the AIS (see Bender & Trussell, 2009; Yu et al., 2010 for references), and therefore should be strictly regulated, it is likely that the CO at the AIS is important for regulating neuronal excitability by controlling Ca2+ dynamics. However, as mentioned by the reviewer, there are no conclusive pieces of evidence showing the relationship between the CO and neuron excitability regulation. We will edit our statement accordingly.

In contrast to the findings of Schlüter et al. (Schlüter et al., 2019), which were conducted in the mouse primary visual cortex, Sánchez-Ponce et al. showed that nearly 90% of hippocampal neurons contain synaptopodin, the CO marker protein, at the AIS. Furthermore, Schlüter et al. also demonstrated that in the other 50% of neurons containing COs at the AIS, the COs change size during visual deprivation, and their presence correlates with AIS length changes as well as eye-opening. These observations do suggest that COs are related to neuronal activity. However, this correlation and the formation of COs may be specific to neuro subtypes or require certain triggers. This is another interesting direction to explore, and we will include it in the discussion of the revised manuscript.

Regarding the last point on Ca2+ and AIS plasticity, we were not excluding other factors that could potentially participate in AIS plasticity and will also discuss it in the revised version.

The Introduction ends with the rationale of the study, namely that the authors seek to ....."provide a detailed characterization of the AIS, including its structural and functional properties....". Structure is investigated, but function is limited to the barrier function of the AIS. Since the authors provide no electrophysiology that would really dissect AIS function, I suggest to rephrase this part and focus on transport.

Reply: As suggested, we will certainly emphasize the cargo barrier function of the AIS in AcD neurons in our introduction. But we would like to keep the term "AIS function", because it has already been nicely demonstrated electrophysiologically by previous studies that the plasticity effect of the AIS is very important for maintaining cellular homeostasis.

The Discussion is more a list of future plans than a context to current data. The authors could move some of the new questions they identify into an "outlook" section at the end? Also, again have a critical look at the literature that is cited and which statements are accurate.

For example, the 2nd phrase in the Discussion states that is was shown that AcD neurons have a "role in memory consolidation", referenced to Hodapp et al., 2022. However, that paper does not provide direct evidence of such a role for AcD neurons. The statement "Collectively, our data provide new insights into the development of AcD neurons and demonstrate that there are differences in AIS functionality between AcD and nonAcD neurons", is not correct. AIS function was not investigated outside of the axonal barrier, and here, the AcD and nonAcD cells do not differ. Also, although the Discussion is geared towards excitatory / glutamatergic neurons, it has been shown by others that interneurons show an even stronger trend to exhibit AcD morphology (work by the Wahle lab and others). This is not clear from the current text (also compare "...AcD neurons being a different subtype if pyramidal neuron").

Further original publications should be included in the paragraph highlighting patch-clamp recordings (see above). In the same context, the statement "...showed that rapid AID plasticity occurs mainly in hippocampal dentate gyrus cells but not in principal excitatory neurons" is not accurate (see Kim, Kuba, Jamann and others). Generally, the Introduction and Discussion would benefit from a very clear distinction between studies done in vitro versus those done ex vivo or in vivo. This needs to be stated in the Abstract as well.

Methods: For the imaging of synapses, the CO and VGSCs, it is not clear to me from the methods whether Nyquist conditions were applied to produce data that can support the quantification of nanoscale structures. Basing the analysis and interpretation of channel expression on fluorescence intensity profiles is problematic (variance in staining quality from samples to sample, lack of an internal standard). This should be noted in the text. In the text, the first two references given for "Induction of plasticity" do not reference the correct papers.

Reply: Thank you for the valuable suggestions; we will incorporate them into the revised version of the manuscript. The structure will undoubtedly benefit from these improvements. We will also have a further look into our interpretation of the literatures as well as citations during our revision time frame.

Regarding methods, as stated in response to the second point raised by this reviewer, we ensured that the Nyquist condition was adhered to throughout the study. The pixel size, z-step size, and optical setup of the microscopes used were already indicated in our methods section. With respect to Na+ channel staining, we were indeed aware of the potential issues posed by the experimental setup, and we will explicitly mention this in our revised manuscript. Additionally, we plan to measure other AIS scaffolding and membrane proteins that anchor Na+ channels to assess for potential changes, which could indirectly support our Na+ channel staining results.

Finally, the text is lacking a discussion of limitations of the study, especially from a methodological point of view. In the Abstract/Summary already, the authors could point out that this is a pure in vitro study. Interestingly, to this day, AIS relocation during plasticity events has only been shown in cell culture systems, and not in vivo. Therefore, this needs to be put into context here - the chosen system is great for the type of imaging approach presented here, but may look at a type of AIS plasticity that is not seen in vivo.

Reply: These are very good points. We will include the limitations of the study in the discussion. Indeed, due to technical and methodological challenges, the relocation of the AIS has not yet been demonstrated using animal models. However, in the study by Wefelmeyer et al. (Wefelmeyer et al., 2015), a similar relocation of the AIS resulting from chronic stimulation was observed in hippocampal organotypic slices, and it was accompanied by reduced excitability of neurons. Furthermore, in the same study, neurons with axons/AIS originating from basal dendrites were also mentioned. However, the measurement of chronic AIS plasticity in their study was not performed based on different classes of neuron types. Hence, our work complements their results. Given that the network connectivity of organotypic slices is much closer to real physiological conditions, it is likely that similar plastic adaptations could occur in vivo.

__Minor comments __

1. How does intrinsic neuronal activity play into developmental programs in vitro? Electrical activity in maturing neurons is a major part of how networks are shaped, and cells differentiate. This is not genetically encoded per se, but has been shown to be a major driving force of neuronal development in vivo. Is this reflected in the culture setting in any way? And have the authors considered testing early changes in activity patterns in their cultures to see whether AcDs and nonAcDs develop in similar percentages? To clarify, I am not asking for additional experiments. Reply: It is indeed a valid point that activity can influence neuronal morphology. Lehmann et al. (pre-print, doi: https://doi.org/10.1101/2023.07.31.551236) have recently demonstrated that increased network activity leads to more excitatory principal neurons adopting AcD morphology. However, our developmental data were collected from DIV0 to DIV5, an age at which dissociated neurons do not yet form functional excitatory synapses. Therefore, it is highly unlikely that network activity plays a role in shaping AcD neuron development during this early stage.

The authors may want to add a bit of a technical discussion on the choice of KCl and TTX as triggers for plasticity, especially at the non-physiological concentrations offered here and elsewhere (15 mM KCl).

Reply: We appreciate the reviewer for pointing this out. We will add this in our revised manuscript.

Some key statements would benefit from citing the appropriate original literature (some examples would be the original work by Kole, Bender and Brette on the role of the AIS in AP initiation; original work by D'Este and Letterier on the dendritic and axonal scaffold using nanoscopy; work by Kim, Kuba and Jamann on AIS plasticity in vitro and in vivo that is critical for a more informed discussion of AIS plasticity here, and others)

Reply: These are very good points, we will make suggested edits in the revised version.

In the Introduction, the authors word their text explicitly for excitatory neurons. However, AIS plasticity has also been observed in interneurons (work by the Grubb lab for example), and axo-axonic synapses are in fact not all inhibitory - this is in important factor to consider given the embryonic state of the culture material. Does the DIV maturation reflect how axo-axonic synapses "switch" from excitatory to inhibitory in vivo (also see work of the Burrone lab)? Can the conclusions form the paper really be drawn based on this type of system?

Reply: The AIS plasticity was indeed also observed in inhibitory interneurons (see Chand et al., 2015 for reference) and show opposite phenotypes compared to excitatory neurons. Also related to major comment #5, we did take the potential influence of AcD interneurons on the outcome of AIS plasticity experiment into consideration. Therefore, we also did a control experiment where inhibitory interneurons were labelled with GAD1 after chronic KCl treatment and these neurons were excluded from the analysis. Consistently, we got the same results that excitatory AcD neurons do not undergo chronic AIS plasticity. We will include this data in our revised manuscript. Further, in our current manuscript, we decided to focus on excitatory AcD neurons not only because they are the major functional unit in neuronal circuits, but also because the majority of the electrophysiological features were studied in excitatory AcD neurons. But we agree with the reviewer that AcD interneuron is definitely an interesting subject for follow up research in the future.

As mentioned by the reviewer, Pan-Vazquez et al. (Pan-Vazquez et al., 2020) nicely showed that axo-axonic synapses made by GABAergic Chandelier cells (ChCs) depolarise neurons in brain slices obtained from P12-18 animals. But this effect is reversed in slices obtained from older animals (>>P40). Of note, their results were based on cortical neurons but not hippocampal neurons, hence cell type specificity should be considered. More importantly, previous study reported that this conversion or switch of GABAergic interneurons from excitatory to inhibitory occurs on hippocampal neurons in P12-13 animals (Leinekugel et al., 1995). In dissociated hippocampal neurons from E18 rat embryos, this switch of GABAergic interneurons takes place on DIV9-11 and completes on DIV19, which should have a comparable neuronal developmental stage as the P12-13 in in vivo system (see Ganguly et al., 2001 for reference). Therefore, the conclusion could be drawn in an in vitro system, but it certainly needs to be validated in in vivo system.

The authors state that "less COs account for higher intrinsic excitability". Why is that the case?

Reply: According to Yu et al. and Bender et al., Ca2+ transient at the AIS regulates the generation of action potentials (APs). For instance, reducing Ca2+ transient at the AIS by blocking Ca2+ channels with either mibefradil (a T-type Ca2+ channel antagonist) or Ni2+ (which blocks R- and T-type channels) decreased the number of spikelets evoked by EPSP-like current injection and delayed the timing of spike generation (please see Bender & Trussell, 2009 for details). Therefore, we speculate that Ca2+ transients are less affected when there are fewer cisternal organelles (COs) at the AIS, which could have a more direct impact on AP initiation. However, this is just our hypothesis, and there is indeed no direct evidence showing that COs regulate Ca2+ dynamics. We will discuss this in the revised manuscript.

Last but not least, some very recent studies on AcD biology (Stevens, Thome, Lehmann, Wahle) is available online also on preprint servers and may provide additional support for the current study.

Reply: We will check these pre-prints and include relevant information into the revised version.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The author should evaluate the possibility of naturally occurring arrhythmia due to the geometry of the tissues, by using voltage or calcium dye.

Answer: We thank the reviewer for this suggestion. We have performed new experiments using a voltage-sensitive fluorescent dye (i.e. FluoVolt) with data reported in the new Figure 4 + new results section “arrhythmia analysis”. Briefly, we found that our ring-shaped tissues are compatible with live fluorescence imaging. We were then able to show that our cardiac tissues beat regularly, without naturally occurring arrhythmias or extra beats. We could not detect any re-entrant waves in our tissues in the conditions offered by the speed of our camera. A specific paragraph has also been added to the discussion.

(2) There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. We are currently expanding to other cell lines with improvement in survival (see https://insight.jci.org/articles/view/161356). We confirm that the rings do not detach. The pillar was specifically designed to avoid this (See figure 1B).

As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance.

Answer: We propose a system and a measurement method that do not need calibration. Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A). There is thus no influence of the distance of the camera lens.

In order to evaluate the consistency of the mechanical properties of the hydrogel, we reproduced the experiment pictured in Figure1-Supplement 1, and measured the Young’s Modulus of three different gel solutions on different days. In the three experiments performed, we found values of 10.0-12.2 kPa, resulting in a final average value of 11.2 (+/- 0.6) kPa, coherent with the value reported in the article. We are therefore confident that the mechanical properties are consistent across and within wells. More extensive mechanical characterization of the molded gels would require the access to an Atomic Force Microscope (AFM), and is considered in the future.

The author should address the longevity and reproducibility issues, by working on the calibration of camera lens position/distance to tissues and further optimizing the seeding conditions with hydrogels such as collagen or fibrin, and/or making sure the PEG gels have high reproducibility and consistency.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. This platform (including the design, approach and choice of polymers) allows a fast and reproducible formation of an important number of cardiac tissues (up to 21 per well in a 96-well format, meaning a potential total of about 2,000 tissues) with a limited number of cells.

(3) The evaluation of the arrhythmia should be more extensively explained and demonstrated.

Answer : See answer to comment 1

(4) The results of isoproterenol should be checked as non-paced tissues should have increased beating frequency with increasing dosages. Dofetilide does not typically have a negative inotropic effect on the tissues. Please check on the cell viability before and after dosing

Answer : We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training. We agree that above a concentration of 10nM, dofetilide shows cardiotoxicity in our tissues as tissues completely stop beating.

Reviewer #2 (Recommendations For The Authors):

In addition to the general comments in the public review, I have the following specific suggestions to the authors, that would help improve the manuscript.

(1) Please describe the protocol for preparation of cardiac rings (shown in Figure 1C) in more detail. In particular, please describe how the tissues were transferred from the mold into the 96-well plate and how are they positioned and characterized during the study.

Answer: There is no transfer of the tissues as they directly form in the well, that is pre-equipped with the molded PEG gel (See Figure 1B and methods section). The in situ analysis is a strong asset of this platform.

(2) Please clarify the timepoints in this study. The overall schematic in Figure 1 C shows that the rings were formed on day 22 and then studied for 14 days, while Figure 2B shows data over 20 days following seeding, and Figure 3 shows data 14 days after seeding. It appears that these were separate studies (optimization of myocyte/fibroblast ratio followed by the main study.

Answer: Figure 1C is showing the timeline including the cardiomyocytes differentiation. hiPSC-CMs are indeed seeded in the wells 22 days after starting the differentiation, which represent the Day0 for tissue formation. We apologize for the confusion.

(3) Please explain if the number of rings per well (Figure 2) was used as the only criterion for selecting the myocyte/fibroblast ratio, and if so, why. Were these rings also characterized for their structural and contractile properties?

Answer: Figure 2 supplement 1 report the contractility data according to the different tested ratios, and show no differences. The number for generated ring-shaped tissues was indeed the only criterion retained.

(4) Please provide rationale for using the dermal rather than cardiac fibroblasts.

Answer: We had previous experience generating EHTs using dermal fibroblasts which are easier to obtain commercially. Our approach could in theory also work using cardiac fibroblasts, which we have not tested in the present study.

(5) Figure 2 panels C-E show an interesting segregation of cardiomyocytes into a thin cylindrical layer that does not appear to contain fibroblasts and a shorter and thicker cylinder containing fibroblasts mixed with occasional myocytes. Please specify at which time point this structure forms, and how does it change over time in culture? At which time point were the images taken? It would be helpful to include serial images taken over 1-14 days of study.

Answer: We thank the reviewer for this interesting comment. We have performed additional immunostainings (reported in Figure 2 supplement 3) on tissues at Day 1 and day 7 after seeding. The segregation appears in the 7 first days. It appears that 1 day after seeding the fibroblasts are not yet attached, although the cardiac fiber has already started to be formed. Seven days after seeding, fibroblasts are fully spread and attached, and the contractile ring is formed and well-aligned. Brightfield images are reported in Figure 1E.

(6) In the cardiomyocyte region (Figure 2D) the cells staining for troponin seem to be only at the surfaces. The thickness of the layer is only about 30-40 µµ, so one would assume that cell viability was not an issue. Please specify and discuss the composition of this region.

Answer: We agree but we think this is a technical issue as at the center of the tissue, tissue thickness will limit laser penetration, although at the surface (inner our outer), the laser infiltrates easily between the tissue and the PEG. Moreover, we see on the zoomed view of the tissue in Figure 2 Supplement 2 that we have a staining inside the cardiac fiber, which just appears less strong due to tissue thickness.

(7) Please also discuss segregation in terms of possible causes and the implications of apparently very limited contact between the two cell types, i.e., how representative is this two-region morphology of native heart tissue. Also, it would be interesting to know how the segregation has changed with the change in myocyte/fibroblast ratio.

Answer: We are not sure there is a very limited contact as the use of fibroblasts is critical to ensure the formation of tissues (i.e. no tissues can be formed if we avoid the use of fibroblasts). We agree that these ring-shaped cardiac tissues are not especially representative of a native heart tissue in terms of interactions between several cell types. They were developed as a surrogate for physiopathological and pharmacological experiments (see a recent application in https://insight.jci.org/articles/view/161356)

(8) There is interest and demonstrated ability to culture engineered cardiac tissues over longer periods of time. Please comment what was the rationale for selecting 14-day culture and if the system allows longer culture durations.

Answer: In line with this comment, we have studied the contractile parameters of our rings 28 days after seeding and compared to their contractile parameters at D14. We found a slight increase for all the parameters, which is significant for the maximum contraction speed. Nevertheless, the data is much more variable and the number of tissues is lower (29 for D14 against 17 for D28). Therefore, we demonstrated that long-term culture of our tissues is possible, however not yet optimized. Hence, the following physiological and pharmacological tests have been done at D14.

(9) Figure 3 documents the development of contractile parameters over 14 days of culture. Would it be possible to replace the arbitrary units with the actual values? Also, would it be possible to include the corresponding images of the rings taken at the same time points, to show the associated changes in ring morphologies.

Answer: Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A): it is a ratio, thus without unit. Corresponding images can be seen in Figure 1 E.

(10) The measured contraction stress, strain, and the speeds of contraction and relaxation improve from day 1 to day 7 and then plateau (Figure 3, Supplemental Figure 3. Please discuss this result.

Answer: The new immunostainings performed on tissues at Day 1 and Day 7 show the progressive alignment of the cardiomyocytes and the muscular fibers, with an almost complete organization at Day 7.

(11) The beating frequency does not appear to markedly change over time, while Figure 3B shows strong statistical significance (***) throughout the 14-day period. Please check/confirm.

Answer: We confirm this result.

(12) Please comment on the lack of effect of isoproterenol on beating frequency.

Answer: We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training.

(13) Please compare the contractile function of cardiac tissues measured in this study with data reported for other iPSC-derived tissue models.

Answer : A specific paragraph tackles this aspect in the discussion

Author Response

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

Response to Reviewers’ Public Comments

We are grateful for the reviewers’ comments. We have modified the manuscript accordingly and detail our responses to their major comments below.

(1) Reviewer 2 was concerned that transformation of continuous functional data into categorical form could reduce precision in estimating the genetic architecture.

We agree that transforming continuous data into categories may reduce resolution, but it also improves accuracy when the continuous data are affected by measurement noise. In our dataset, many genotypes are at the lower bound of measurement, and the variation in measured fluorescence among these genotypes is largely or entirely caused by measurement noise. By transforming to categorical data, we dramatically reduced the effect of this noise on the estimation of genetic effects. We modified the results and discussion sections to address this point.

(2) Reviewer 2 asked about generalizability of our findings.

Because our paper is the first use of reference-free analysis of a 20-state combinatorial dataset, generalizability is at this point unknown. However, a recent manuscript from our group confirms the generality of the simplicity of genetic architecture: using reference-free methods to analyze 20 published combinatorial deep mutational scans, several of which involve 20-state libraries, we found that main and pairwise effects account for virtually all of the genetic variance across a wide variety of protein families and types of biochemical functions (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057). Concerning the facilitating effect of epistasis on the evolution of new functions, we speculate that this result is likely to be general: we have no reason to think that the underlying cause of this observation – epistasis brings genotypes with different functions closer in sequence space to each other and expands the total number of functional sequences – arises from some peculiarity of the mechanisms of steroid receptor DBD folding or DNA binding. However, we acknowledge that our data involve sequence variation at those sites in the protein that directly mediate specific protein-DNA contact; it is plausible that sites far from the “active site” may have weaker epistatic interactions and therefore have weaker effects on navigability of the landscape. We have addressed these issues in the discussion.

(3) Reviewer 3 asked “in which situation would the authors expect that pairwise epistasis does not play a crucial role for mutational steps, trajectories, or space connectedness, if it is dominant in the genotype-phenotype landscape?”

The question addressed in our paper is not whether epistasis shapes steps, trajectories or connectedness in sequence space but how it does so and what its particular effects are on the evolution of new functions. The dominant view in the field has been that the primary role of epistasis is to block evolutionary paths. We show, however, that in multi-state sequence space, epistasis facilitates rather than impedes the evolution of new functions. It does this by increasing the number of functional genotypes and bringing genotypes with different functions closer together in sequence space. This finding was possible because of the difference in approach between our paper and prior work: most prior work considered only direct paths in a binary sequence space between two particular starting points – and typically only considering optimization of a single function – whereas we studied the evolution of new functions in a multi-state amino acid space, under empirically relevant epistasis informed by complete combinatorial experiments. The result is a clear demonstration that the net effect of real-world levels of epistasis on navigability of the multidimensional sequence landscape is to make the evolution of new functions easier, not harder.

(4) Reviewer 3 asked for “an explanation of how much new biological results this paper delivers as compared with the paper in which the data were originally published.”

Starr 2017 did not use their data to characterize the underlying genetic architecture of function by estimating main and epistatic effects of amino acid states and combinations; it also did not evaluate the importance of epistasis in generating functional variants, determining the transcription factor’s specificity, or shaping evolutionary navigability on the landscape.

(5) Reviewer 3 requested an explanation of how the results would have been (potentially) different if a reference-based approach were used, and how reference-based analysis compares with other reference-free approaches to estimating epistasis.

This topic has been covered in detail in a recent manuscript from our group (Park et al. Biorxiv 2023.09.02.556057). Briefly, reference-free approaches provide the most efficient explanation of an entire genotype-phenotype map, explaining the maximum amount of genetic variance and reducing sensitivity to experimental noise and missing genotypes compared to reference-based approaches. Reference-based approaches tend to infer much more epistasis, especially higher-order epistasis, because measurement error and local idiosyncrasy near the wild-type sequence propagate into spurious high-order terms. Reference-based analyses are appropriate for characterizing only the immediate sequence neighborhood of a particular “wild-type” protein of interest. Reference-free approaches are therefore best suited to understanding genotype-phenotype landscapes as a whole. We have clarified these issues in the revised discussion.

(6) Reviewer 3 suggested that the comparison between the full and main-effects-only model should involve a re-estimation of main effects in the latter case.

This is indeed what we did in our analysis. We have clarified the description in the results and methods sections to make this clear.

(7) Reviewer 3 asked about the applicability of the approach to data beyond those analyzed in the present study and requirements to use it.

Our approach could be used for any combinatorial DMS dataset in which the phenotypic data are categorical (or can be converted to categorical form). Complete sampling is not required: a virtue of reference-free analysis is that by averaging the estimated effects of states and combinations over all variants that contain them, reference-free analysis is highly robust to missing data (except at the highest possible order of epistasis, where only a single variant represents a high-order effect) as long as variant sampling is unbiased with respect to phenotype. All the required code are publicly available at the github link provided in this manuscript. We have also described a general form of reference-free analysis for continuous data and applied it to 20 protein datasets in a recent publication (Park et al. Biorxiv 2023.09.02.556057).

(8)Reviewer 3 suggested that the text could be shortened and made less dense.

We agree and have done a careful edit to streamline the narrative.

Response to Reviewers’ Non-Public Recommendations

(1) Reviewer 1 noted that specific epistatic effects might in some cases produce global nonlinearities in the genotype-phenotype relationship. They then asked how our results might change if we did not impose a nonlinear transformation as part of the genotype-phenotype model. The reviewer’s underlying concern was that the non-specific transformation might capture high-order specific epistatic effects and thus reducing their importance.

Because our data are categorical, we required a model that characterizes the effect of particular amino acid states and combinations on the probability that a variant is in a null, weak, or strong activation class. A logistic model is the classic approach to this kind of analysis. The model structure assumes that amino acid states and combinations have additive effects on the log-odds of being in one functional class versus the lower functional class(es); the only nonlinear transformation is that which arises mathematically when log-odds are transformed into probability through the logistic link function. Thinking through the reviewer’s comment, we have concluded that our model does not make any explicit transformation to account for nonlinearity in the relationship between the effects of specific sequence states/combinations and the measured phenotype (activation class). If additional global nonlinearities are present in the genotype-phenotype relationship – such as could be imposed by limited dynamic range in the production of the fluorescence phenotype or the assay used to measure it – it is possible that the sigmoid shape of the logistic link function may also accommodate these nonlinearities. We have noted this part in the revised manuscript.

(2) Reviewer 1 observed that our model seems to prefer sets of several pairwise interactions among states across sites rather than fewer high-order interactions among those same states.

This finding arises because the pattern of phenotypic variation across genotypes in our dataset is consistent with that which would be produced by pairwise interactions rather than by high-order interactions. In a reference-free framework, these patterns are distinct from each other: a group of second-order terms cannot fit the patterns produced by high-order epistasis, and high-order terms cannot fit the pattern produced by pairwise interactions. Similarly, main-effect terms cannot fit the pattern of phenotypes produced by a pairwise interaction, and a pairwise epistatic term cannot fit the pattern produced by main effects of states at two sites. For example, third-order terms are required when the genotypes possessing a particular triplet of states deviate from that expected given all the main and second-order effects of those states; this deviation cannot be explained by any combination of first- and second-order effects.

We explain this point in detail in our recent manuscript (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057) and we summarize it here. Consider the simple example of two sites with two possible states (genotypes 00, 01, 10, and 11). If there are no main effects and no pairwise effects, this architecture will generate the same phenotype for all four variants – the global average (or zero-order effect). If there are pairwise effects but no main effects, this architecture will generate a set of phenotypes on which the average phenotype of genotypes with a 0 at the first site (00 and 01) equals the global average – as does the average of those with 0 at the second site (00 and 10). The epistatic effect causes the individual genotypes to deviate from the global average. This pattern can be fit only by a pairwise epistatic term, not by first-order terms. Conversely, if there are main effects but no pairwise effects, then the average phenotype of genotypes 00 and 01 will deviate from the global average (by an amount equal to the first-order effect), as will the average of (00 and 10): the phenotype of each genotype will be equal to the sum of the relevant first-order effects for the state it contains. This pattern cannot be fit by second-order model terms. The same logic extends to higher orders: a cluster of second-order terms cannot explain variation generated by third-order epistasis, because third-order variation is by definition is the deviation from the best second-order model.

(3) Reviewer 1 suggested several places in the text where citations to prior work would be appropriate.

We appreciate these suggestions and have modified the manuscript to refer to most of these works.

(4) Reviewer 1 pointed to the paper of Gong et al eLife 2013 and asked whether it is known how robust the proteins in our study are to changes in conformation/stability compared to other proteins, and whether this might impact the likelihood of observing higher-order epistasis in this system.

The DBDs that we study here are very stable, and previous work shows that mutations affect DNA specificity primarily by modifying the DBD’s affinity rather than its stability (McKeown et al., Cell 2014). Additionally, Gong et al.’s findings pertain to a globally nonlinear relationship between stability and function, which arises from the Boltzmann relationship between the energy of folding and occupancy of the folded state. Because our data are categorical – based on rank-order of measured phenotype rather than fluorescence as a continuous phenotype – the kind of global nonlinearity observed in Gong’s study are not expected to produce spurious estimates of epistasis in our work. We have modified the discussion to discuss the point.

(5) Reviewer 1 asked a) why the epistatic models produce landscapes on which variants have fewer neighbors on average than main-effects only models and b) why the average distance from all ERE-specific nodes to all SRE-specific nodes is greater with epistasis (but the average distance from ERE to nearest SRE is lower with epistasis).

In the main effects-only landscape, the functional genotypes are relatively similar to each other, because each must contain several of the states that contribute the most to a positive genetic score. Moreover, ERE-specific nodes are similar to each other, and SRE-specific nodes are similar to each other, because each must contain one or more of a relatively small number of specificity-determining states. When epistasis is added to the genetic architecture, two things happen: 1) more genotypes become functional because there are more combinations that can exceed the threshold score to produce a functional activator and 2) these additional functional variants are more different from each other – in general, and within the classes of ERE- or SRE-specific variants – because there are now more diverse combinations of states that can yield either phenotype. As a result, a broader span of sequence space is occupied, but ERE- and SRE-specific variants are more interspersed with each other. This means that the average distance between all pairs of nodes is greater, and this applies to all ERE-SRE pairs, as well. However, the interspersing means that the closest single SRE to any particular ERE is closer than it was without epistasis. We have added this explanation to the main text.

(6) Reviewer 2 asked us to explain why average path length increases with pairwise epistasis as the strength of selection for specificity increases.

This behavior occurs because of the existence of a local peak in the pairwise model. Genotypes on this peak contained few connections to other genotypes, all of which were less SRE specific. Thus, with strong selection, i.e. high population size, the simulations became stuck on the local peak, cycling among the genotypes many times before leaving, resulting in a large increase in the mean step number. As shown in the rest of the figure, when the longest set of paths are removed, there are still differences in the average number of steps with and without epistasis. This issue is described in the methods section.

(7) Reviewers made several suggestions for clarity in the text and figures.

We have modified the paper to address all of these comments.

(8) Reviewer 3 stated that the code should be available.

The code is available at https://github.com/JoeThorntonLab/DBD.GeneticArchitecture.

Author Response

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

Reviewer #1 (Public Review):

Summary:

The authors were trying to understand the relationship between the development of large trunks and longirrostrine mandibles in bunodont proboscideans of Miocene, and how it reflects the variation in diet patterns.

Strengths:

The study is very well supported, written, and illustrated, with plenty of supplementary material. The findings are highly significant for the understanding of the diversification of bunodont proboscideans in Asia during Miocene, as well as explaining the cranial/jaw disparity of fossil lineages. This work elucidates the diversification of paleobiological aspects of fossil proboscideans and their evolutionary response to open environments in the Neogene using several methods. The authors included all Asian bunodont proboscideans with long mandibles and I suggest that they should use the expression "bunodont proboscideans" instead of gomphotheres.

Weaknesses:

I believe that the only weakness is the lack of discussion comparing their results with the development of gigantism and long limbs in proboscideans from the same epoch.

Thank you for your comprehensive review and positive feedback on our study regarding the co-evolution of feeding organs in bunodont proboscideans during the Miocene. We appreciate your suggestion, and have decided to use the term "bunodont elephantiforms" (for more explicit clarification, we use elephantiforms to exclude some early proboscideans, like Moeritherium, ect.) instead of "gomphotheres," and we will make this change in our revised manuscript. We also appreciate the potential weakness you mentioned regarding the lack of discussion comparing our results with the development of gigantism and long limbs in proboscideans from the same epoch. We agree with the reviewer’s suggestion, and we are aware that gigantism and long limbs are potential factors for trunk development. Gigantism resulted in the loss of flexibility in elephantiforms, and long limbs made it more challenging for them to reach the ground. A long trunk serves as compensation for these limitations. limb bones were rare to find in our material, especially those preserved in association with the skull.

Reviewer #2 (Public Review):

This study focuses on the eco-morphology, the feeding behaviors, and the co-evolution of feeding organs of longirostrine gomphotheres (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) which are characterised by their distinctive mandible and mandible tusk morphologies. They also have different evolutionary stages of food acquisition organs which may have co-evolve with extremely elongated mandibular symphysis and tusks. Although these three longirostrine gomphothere families were widely distributed in Northern China in the Early-Middle Miocene, the relative abundances and the distribution of these groups were different through time as a result of the climatic changes and ecosysytems.

These three groups have different feeding behaviors indicated by different mandibular symphysis and tusk morphologies. Additionally, they have different evolutionary stages of trunks which are reflected by the narial region morphology. To be able to construct the feeding behavior and the relation between the mandible and the trunk of early elephantiformes, the authors examined the crania and mandibles of these three groups from the Early and Middle Miocene of northern China from three different museums and also made different analyses.

The analyses made in the study are:

(1) Finite Element (FE) analysis: They conducted two kinds of tests: the distal forces test, and the twig-cutting test. With the distal forces test, advantageous and disadvantageous mechanical performances under distal vertical and horizontal external forces of each group are established. With the twig-cutting test, a cylindrical twig model of orthotropic elastoplasity was posed in three directions to the distal end of the mandibular task to calculate the sum of the equivalent plastic strain (SEPS). It is indicated that all three groups have different mandible specializations for cutting plants.

(2) Phylogenetic reconstruction: These groups have different narial region morphology, and in connection with this, have different stages of trunk evolution. The phylogenetic tree shows the degree of specialization of the narial morphology. And narial region evolutionary level is correlated with that of character-combine in relation to horizontal cutting. In the trilophodont longirostrine gomphotheres, co-evolution between the narial region and horizontal cutting behaviour is strongly suggested.

(3) Enamel isotopes analysis: The results of stable isotope analysis indicate an open environment with a diverse range of habitats and that the niches of these groups overlapped without obvious differentiation.

The analysis shows that different eco-adaptations have led to the diverse mandibular morphology and open-land grazing has driven the development of trunk-specific functions and loss of the long mandible. This conclusion has been achieved with evidence on palaecological reconstruction, the reconstruction of feeding behaviors, and the examination of mandibular and narial region morphology from the detailed analysis during the study.

All of the analyses are explained in detail in the supplementary files. The 3D models and movies in the supplementary files are detailed and understandable and explain the conclusion. The conclusions of the study are well supported by data.

We appreciate your detailed and insightful review of our study. Your summary accurately captures the essence of our research, and we are pleased to note that multiple research methods were used to demonstrate our conclusions. Your recognition of the evidence-based conclusions from paleoecological, feeding behavior reconstruction, and morphological analyses reinforces the validity of our findings. Once again, we appreciate your time and thoughtful reviews.

Reviewer #1 (Recommendations For The Authors):

Thank you very much for the invitation to review this amazing manuscript. It is very well written and supported, and I have only minor suggestions to improve the text:

(1) Some references are not in chronological sequence in the text, and this should be reviewed.

We greatly appreciate the positive comments of the reviewer. We revised the reference of the manuscript as the reviewer’s suggestion.

(2) I suggest the use of the expression "bunodont proboscideans" instead of Gomphotheres because there is no agreement if Amebelodontidae and Choerolophodontidae are within Gomphotheriidae, as well as some brevirrostrine bunodont proboscideans from South America. So I think it is ok to use "Gomphotheriidae", but not gomphotheres to refer to all bunodont proboscideans included in the study.

The reviewer is correct. Using “gomphotheres” to refer to these three groups is inappropriate. We have replaced “gomphotheres” with "bunodont elephantiforms" throughout the entire manuscript. Here, we use “elephantiforms”, not “proboscideans”, to avoid confusion with some early proboscidean members like Moeritherium, ect.

(3) I was expecting some discussion on the development of large trunks related to the gigantism in these bunodont proboscideans, regarding the huge skulls and the columnar limbs.

We appreciate this suggestion, and we are aware that gigantism is a potential factor for trunk development. It is difficult to compare the three groups (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) in terms of their weight and limb bone length, because in our material, limb bones were rarely found, especially those associated with cranial material. Nevertheless, at this stage, all elephantiforms had significantly enlarged cranial sizes and limb bone lengths compared to early members like Phiomia. Gigantism caused the loss of flexibility in elephantiforms, and even the long limbs made it more difficult for an elephantiform to reach the ground. A long trunk compensates for this evolutionary change. Exploring these aspects further is a part of our future work.

(4) The reference to Alejandro et al should be replaced by Kramarz et al (and the correct surname of the authors). The name and surname of this reference need to be corrected. The correct names are Kramarz, A., Garrido, A., Bond, M. 2019. Please correct this in the text too.

We thank the reviewer for catching this error. This reference has been corrected.

Reviewer #2 (Recommendations For The Authors):

I believe your paper will lead to other studies on other Proboscidean groups on the evolution of the mandible and trunk. There are some corrections in the text:

• In line 199 in the text in pdf, "Tassy, 1994" should be "Tassy, 1996".

• In line 241, "studied" should be "studies"

• In line 313, "," after the word "tool" should be "."

We appreciate the reviewer for pointing these errors out and have revised these based on the suggestions.

• In the References, you write "et al." in some references. You should write the names of all of the authors.

• In the References: "Lister AM. 2013" and "Shoshani&Tassy" are not referenced in the text.

• In the References: "Tassy P. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn." should be "Tassy P. 1994. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn. 112, 1-2, 101-117" and replaced before "Tassy P. 1996".

We appreciate the reviewer’s suggestions and have revised these references.

Author Response

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

Reviewer #1

The authors provided experimental data in response to my comments/suggestions in the revision. Overall, most points were appropriate and satisfactory, but some issues remain.

(1) It is not fully addressed how atypical survivors are generated independently of Rad52-mediated homologous recombination.

The newly provided data indicate that the formation of atypical telomeres is independent of the Rad52 homologous recombination pathway.

"The atypical telomeres clones exhibit non-uniform telomere pattern", but the TG-hybridized signals after XhoI digestion are clear and uniform.

"Atypical telomere" clones may carry circular chromosomes embedded with short TG repeats, rather than linear chromosomes. In other words, atypical telomeres may differ from telomeres, the ends of chromosomes. Is atypical telomere formation dependent on NHEJ? Given that "two chromosomes underwent intra-chromosomal fusions" (Line 248), are atypical telomere clones detected frequently in SY13 cells containing two chromosomes?

We thank the reviewer’s questions. Frankly, we have not been able to determine the chromosome structures in these so-called "atypical survivors". As we mentioned in the manuscript, there could be mixed telomere structures, e.g. TG tract amplification, intro-chromosome telomere fusion and inter-chromosome telomere fusion. Worse still, these 'atypical survivors' may not have maintained a stable genome, and their karyotype may have undergone stochastic changes during passages. To avoid misunderstanding, we change the term "atypical" to "uncharacterized" in the revised manuscript.

We have previously shown that deletion of YKU70 does not affect MMEJ-mediated intra-chromosome fusion in single-chromosome SY14 cdc13Δ cells (Wu et al., 2020). In SY12 cells, double knockout of TLC1 and YKU resulted in synthetic lethality, and we were unable to continue our investigation. The result of synthetic lethality of TLC1 and YKU70 double deletion was shown in the Figure 7B in the reviewed preprint version 1, and the result was not included in the reviewed preprint version 2 in accordance with the reviewer's instructions.

"Atypical” survivors could be detected in SY13 cells (Figure 1D), but the frequency of their formation in the SY13 strain appeared to be lower than in SY12. As one can imagine, SY13 contains two chromosomes and its survivors should have a higher frequency of intra-chromosome fusions.

(2) From their data, it is possible that X and Y elements influence homologous recombination, type 1 and type 2 (type X), at telomeres. In particular, the presence of X and Y elements appears to be important for promoting type 1 recombination. In other words, although not essential, subtelomeres have some function in maintaining telomeres. I suggest that the authors include author response image 4 in the text. They could revise their conclusion and the paper title accordingly.

According to this suggestion, we have included author response image 4 in the revised manuscript as Figure 2E, Figure 5D, Figure 6C and Figure 6E. Accordingly, we have changed the title as “Elimination of subtelomeric repeat sequences exerts little effect on telomere essential functions in Saccharomyces cerevisiae”.

(3) Minor points: The newly added data indicate that X survivors are generated in a type 2-dependent manner. The authors could discuss how Y elements were eroded while retaining X elements (line 225, Figure 2A).

Thank this reviewer’s suggestion. We have discussed it in the revised manuscript (p.13 line 244-245). When telomere was deprotected, chromosome end resection took place. Since SY12 only has one Y’-element, it is hard to search homology sequences to repair the Y’-element in XVI-L. When the X-element in XVI-L was exposed by further resection, it is easier to find homology sequences to repair. So, in Type X survivor the Y’-element was eroded while retaining X-element.

Reviewer #2

I would like to congratulate the authors for their work and the efforts they put in improving the manuscript. The major criticism I had previously, ie testing the genetic requirements for the survivor subtypes, has been met. Below are a few minor comments that don't necessarily require a response.

(1) I think the Author response image 6 could have been included in the manuscript. I understand that the authors don't want to overinterpret survivor subtype frequencies, but this figure would have suggested some implication of Rad51 in the emergence of survivors even in the absence of Y' elements. At this stage, however, it is up to the authors, and leaving this figure out is also fine in my opinion.

According to the suggestion, the author response image 6 has been presented as Figure 6—figure supplement 7.

(2) Chromosome circularization seems to rely on microhomologies. Previously, the authors proposed that SY14 circularization depended on SSA (Wu et al. 2020), but here, since circularization appears to be Rad52-independent, it is likely to be based on MMEJ rather than SSA (although there are contradictory results on Rad52's role in MMEJ in the literature).

Yes, we mentioned it in the revised manuscript.

(3) p. 28 lines 511-513: "The erosion sites and fusion sequences differed from those observed in SY12 tlc1Δ-C1 cells (Figure 2D), suggesting the stochastic nature of chromosomal circularization": I don't think they are necessarily stochastic, because the sequences beyond the telomeres are now modified, the available microhomologies have changed as well.

We agreed with your opinion. In different chromosomes, there tend to be some hotspots for chromosome fusion. For example, in Figure 6C and 6F the resection site in Chr1 and Chr2 was the same in SY12XYΔ+Y tlc1Δ-C1 and SY12XYΔ tlc1Δ-C1. So, we speculate that there are some hotspots for chromosome fusion, but which site the cell will choose in one round chromosome fusion event is stochastic.

(4) Typos and other errors:

• p. 3 line 52: "subtelomerice" and "varies" are mispelled.

• p. 5 line 78: "processes" should be "process".

• Supp files are mislabelled (the numbers do not correspond to file name).

• Supp file 2: how come SY12 has only one Y' element and SY13 has two?

• p. 10 line 175: "emerging" should be "emergence".

• p.15 line 276: "counter-selected" should be "being counter-selected" or "counterselection".

• p. 29 line 523: "the formation of them" should be "their formation".

• p. 37 line 653: "could have been an ideal tool": the sentence is grammatically incorrect. Writing "AND could have been an ideal tool" is enough to make it structurally correct.

Thanks for pointing these errors out. We have corrected them in the revised manuscript. For the question “how come SY12 has only one Y' element and SY13 has two?” we were not sure at this moment. We speculated that one of the Y’ might be lost during genetic engineering of the chromosomes by CRISPR–Cas9 system.

Reviewer #3

The authors included statistical analyses of the qPCR data (Fig 4B) as requested, but did not comment on the striking difference in expression of MPH3 and HSP32 in the SY12 strain compared to BY4742. An improvement of the manuscript is the inclusion of rad52 tlc1 strains in their analyses, demonstrating that the "atypical and circular survivors" arose independently of homologous recombination. In addition, by analyzing rad51 and rad50 mutant strain they could demonstrate that the "type X" survivors had similar molecular requirements to type II survivors. Overall, the revised submission improves the article.

We thank the reviewer’s comments and suggestions. The SY12 strain (with three chromosomes) exhibited lower expression levels of both MPH3 and HSP32 compared to the parental strain BY4742 (with 16 chromosomes). We speculated that with the reduced chromosome numbers, the silencing proteins appeared to no longer be titrated by other telomeres that have been deleted. We have added these comments in the revised manuscript.

Wu, Z.J., Liu, J.C., Man, X., Gu, X., Li, T.Y., Cai, C., He, M.H., Shao, Y., Lu, N., Xue, X., et al. (2020). Cdc13 is predominant over Stn1 and Ten1 in preventing chromosome end fusions. Elife 9.

Reviewer #2 (Public Review):

In the manuscript "Full-length direct RNA sequencing uncovers stress-granule dependent RNA decay upon cellular stress", Dar, Malla, and colleagues use direct RNA sequencing on nanopores to characterize the transcriptome after arsenite and oxidative stress. They observe a population of transcripts that are shortened during stress. The authors hypothesize that this shortening is mediated by the 5'-3' exonuclease XRN1, as XRN1 knockdown results in longer transcripts. Interestingly, the authors do not observe a polyA-tail shortening, which is typically thought to precede decapping and XRN1-mediated transcript decay. Finally, the authors use G3BP1 knockout cells to demonstrate that stress granule formation is required for the observed transcript shortening.

The manuscript contains intriguing findings of interest to the mRNA decay community. That said, it appears that the authors at times overinterpret the data they get from a handful of direct RNA sequencing experiments. To bolster some of the statements additional experiments might be desirable.

A selection of comments:

(1) Considering that the authors compare the effects of stress, stress granule formation, and XRN1 loss on transcriptome profiles, it would be desirable to use a single-cell system (and validated in a few more). Most of the direct RNAseq is performed in HeLa cells, but the experiments showing that stress granule formation is required come from U2OS cells, while short RNAseq data showing loss of coverage on mRNA 5'ends is reanalyzed from HEK293 cells. It may be plausible that the same pathways operate in all those cells, but it is not rigorously demonstrated.

(2) An interesting finding of the manuscript is that polyA tail shortening is not observed prior to transcript shortening. The authors would need to demonstrate that their approach is capable of detecting shortened polyA tails. Using polyA purified RNA to look at the status of polyA tail length may not be ideal (as avidity to oligodT beads may increase with polyA tail length and therefore the authors bias themselves to longer tails anyway). At the very least, the use of positive controls would be desirable; e.g. knockdown of CCR4/NOT.

(3) The authors use a strategy of ligating an adapter to 5' phosphorylated RNA (presumably the breakdown fragments) to be able to distinguish true mRNA fragments from artifacts of abortive nanopore sequencing. This is a fantastic approach to curating a clean dataset. Unfortunately, the authors don't appear to go through with discarding fragments that are not adapter-ligated (presumably to increase the depth of analysis; they do offer Figure 1e that shows similar changes in transcript length for fragments with adapter, compared to Figure 1d). It would be good to know how many reads in total had the adapter. Furthermore, it would be good to know what percentage of reads without adapters are products of abortive sequencing. What percentage of reads had 5'OH ends (could be answered by ligating a different adapter to kinase-treated transcripts). More read curation would also be desirable when building the metagene analysis - why do the authors include every 3'end of sequenced reads (their RNA purification scheme requires a polyA tail, so non-polyadenylated fragments are recovered in a non-quantitative manner and should be discarded).

(4) The authors should come to a clear conclusion about what "transcript shortening" means. Is it exonucleolytic shortening from the 5'end? They cannot say much about the 3'ends anyway (see above). Or are we talking about endonucleolytic cuts leaving 5'P that then can be attached by XRN1 (again, what is the ratio of 5'P and 5'OH fragments; also, what is the ratio of shortened to full-length RNA)?

(5) The authors should clearly explain how they think the transcript shortening comes about. They claim it does not need polyA shortening, but then do not explain where the XRN1 substrate comes from. Does their effect require decapping? Or endonucleolytic attacks?

(6) XRN1 KD results in lengthened transcripts. That is not surprising as XRN1 is an exonuclease - and XRN1 does not merely rescue arsenite stress-mediated transcript shortening, but results in a dramatic transcript lengthening.

Author Response

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

We thank the reviewers and the editors for their careful reading of our manuscript and for the detailed and constructive feedback on our work. Please find attached the revised version of the manuscript. We performed an extensive revision of the manuscript to address the issues raised by the referees. We provide new analyses (regarding the response consistency and the neural complexity), added supplementary figures and edits to figures and texts. Based on the reviewers’ comments, we introduced several major changes to the manuscript.

Most notably, we

• added a limitation statement to emphasize the speculative nature of our interpretation of the timing of word processing/associative binding

• emphasized the limitations of the control condition

• added analyses on the interaction between memory retrieval after 12h versus 36h

• clarified our definition of episodic memory

• added detailed analyses of the “Feeling of having heard” responses and the confidence ratings

We hope that the revised manuscript addresses the reviewers' comments to their satisfaction. We believe that the revised manuscript has been significantly improved owing to the feedback provided. Below you can find a point-by-point response to each reviewer comment in blue. We are looking forward that the revision will be published in the Journal eLife.

Reviewer #1 (Public Review):

The authors show that concurrently presenting foreign words and their translations during sleep leads to the ability to semantically categorize the foreign words above chance. Specifically, this procedure was successful when stimuli were delivered during slow oscillation troughs as opposed to peaks, which has been the focus of many recent investigations into the learning & memory functions of sleep. Finally, further analyses showed that larger and more prototypical slow oscillation troughs led to better categorization performance, which offers hints to others on how to improve or predict the efficacy of this intervention. The strength here is the novel behavioral finding and supporting physiological analyses, whereas the biggest weakness is the interpretation of the peak vs. trough effect.

R1.1. Major importance:

I believe the authors could attempt to address this question: What do the authors believe is the largest implication of this studies? How far can this technique be pushed, and how can it practically augment real-world learning?

We revised the discussion to put more emphasis on possible practical applications of this study (lines 645-656).

In our opinion, the strength of this paper is its contribution to the basic understanding of information processing during deep sleep, rather than its insights on how to augment realworld learning. Given the currently limited data on learning during sleep, we believe it would be premature to make strong claims about potential practical applications of sleep-learning. In addition, as pointed out in the discussion section, we do not know what adverse effects sleep-learning has on other sleep-related mechanisms such as memory consolidation.

R1.2. Lines 155-7: How do the authors argue that the words fit well within the half-waves when the sounds lasted 540 ms and didn't necessarily start right at the beginning of each half-wave? This is a major point that should be discussed, as part of the down-state sound continues into the up-state. Looking at Figure 3A, it is clear that stimulus presented in the slow oscillation trough ends at a time that is solidly into the upstate, and would not neurolinguists argue that a lot of sound processing occurs after the end of the sound? It's not a problem for their findings, which is about when is the best time to start such a stimulus, but it's a problem for the interpretation. Additionally, the authors could include some discussion on whether possibly presenting shorter sounds would help to resolve the ambiguities here.

The word pairs’ presentations lasted on average ~540 ms. Importantly, the word pairs’ onset was timed to occur 100 ms before the maximal amplitude of the targeted peaks/troughs.

Therefore, most of a word’s sound pattern appeared during the negative going half-wave (about 350ms of 540ms). Importantly, Brodbeck and colleagues (2022) have shown that phonemes are continuously analyzed and interpreted with delays of about 50-200 ms, peaking at 100ms delay. These results suggest that word processing started just following the negative maximum of a trough and finished during the next peak. Our interpretation (e.g. line 520+) suggests that low-level auditory processing reaches the auditory cortex before the positive going half-wave. During the positive going half-wave the higher-level semantic networks appear the extract the presented word's meaning and associate the two simultaneously presented words. We clarified the time course regarding slow-wave phases and sound presentation in the manuscript (lines 158-164). Moreover, we added the limitation that we cannot know for sure when and in which slow-wave phase words were processed (lines 645-656). Future studies might want to look at shorter lasting stimuli to narrow down the timing of the word processing steps in relation to the sleep slow waves.

R1.3. Medium importance:

Throughout the paper, another concern relates to the term 'closed-loop'. It appears this term has been largely misused in the literature, and I believe the more appropriate term here is 'real-time' (Bergmann, 2018, Frontiers in Psychology; Antony et al., 2022, Journal of Sleep Research). For instance, if there were some sort of algorithm that assessed whether each individual word was successfully processed by the brain during sleep and then the delivery of words was subsequently changed, that could be more accurately labelled as 'closed-loop'.

We acknowledge that the meaning of “closed-loop” in its narrowest sense is not fulfilled here. We believe that “slow oscillation phase-targeted, brain-state-dependent stimulation” is the most appropriate term to describe the applied procedure (BSDBS, Bergmann, 2018). We changed the wording in the manuscript to brain-state-dependent stimulation algorithm. Nevertheless, we would like to point out that the algorithm we developed and used (TOPOSO) is very similar to the algorithms often termed closed-loop algorithm in memory and sleep (e.g. Esfahani et al., 2023; Garcia-Molina et al., 2018; Ngo et al., 2013, for a comparison of TOPOSO to these techniques see Wunderlin et al., 2022 and for more information about TOPOSO see Ruch et al., 2022).

R1.4. Figure 5 and corresponding analyses: Note that the two conditions end up with different sounds with likely different auditory complexities. That is, one word vs. two words simultaneously likely differ on some low-level acoustic characteristics, which could explain the physiological differences. Either the authors should address this via auditory analyses or it should be added as a limitation.

This is correct, the two conditions differ on auditory complexities. Accordingly, we added this issue as another limitation of the study (line 651-653). We had decided for a single word control condition to ensure that no associative learning (between pseudowords) could take place in the control condition because this was the critical learning process in the experimental condition. We would like to point out that we observed significant differences in brain responses to the presentation of word-pairs (experimental condition) vs single pseudowords (control condition) in the Trough condition, but not the Peak condition. If indeed low-level acoustic characteristics explained the EEG differences occurring between the two conditions then one would expect these differences occurring in both the trough and the peak condition because earlier studies showed that low-level acoustic processing proceeds in both phases of slow waves (Andrillon et al., 2016; Batterink et al., 2016; Daltrozzo et al., 2012).

R1.5. Line 562-7 (and elsewhere in the paper): "episodic" learning is referenced here and many times throughout the paper. But episodic learning is not what was enhanced here. Please be mindful of this wording, as it can be confusing otherwise.

The reported unconscious learning of novel verbal associations during sleep may not match textbook definitions of episodic memory. However, the traditional definitions of episodic memory have long been criticised (e.g., Dew & Cabeza, 2011; Hannula et al., 2023; Henke, 2010; Reder et al., 2009; Shohamy & Turk-Browne, 2013).

We stand by our claim that sleep-learning was of episodic nature. Here we use a computational definition of episodic memory (Cohen & Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014; O’Reilly & Rudy, 2000) and not the traditional definition of episodic memory that ties episodic memory to wakefulness and conscious awareness (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire & Dede, 2015; Tulving, 2002). We revised the manuscript to clarify that and how our definition differs from traditional definitions. Please see reviewer comment R3.1 for a more extensive answer.

Reviewer #2 (Public Review):

In this project, Schmidig, Ruch and Henke examined whether word pairs that were presented during slow-wave sleep would leave a detectable memory trace 12 and 36 hours later. Such an effect was found, as participants showed a bias to categorize pseudowords according to a familiar word that they were paired with during slow-wave sleep. This behavior was not accompanied by any sign of conscious understanding of why the judgment was made, and so demonstrates that long-term memory can be formed even without conscious access to the presented content. Unconscious learning occurred when pairs were presented during troughs but not during peaks of slow-wave oscillations. Differences in brain responses to the two types of presentation schemes, and between word pairs that were later correctly- vs. incorrectly-judged, suggest a potential mechanism for how such deep-sleep learning can occur.

The results are very interesting, and they are based on solid methods and analyses. Results largely support the authors' conclusions, but I felt that there were a few points in which conclusions were not entirely convincing:

R2.1. As a control for the critical stimuli in this study, authors used a single pseudoword simultaneously played to both ears. This control condition (CC) differs from the experimental condition (EC) in a few dimensions, among them: amount of information provided, binaural coherence and word familiarity. These differences make it hard to conclude that the higher theta and spindle power observed for EC over CC trials indicate associative binding, as claimed in the paper. Alternative explanations can be made, for instance, that they reflect word recognition, as only EC contains familiar words.

We agree. In the revised version of the manuscript, we emphasise this as a limitation of our study (line 653-656). Moreover, we understand that the differences between stimuli of the control and the experimental condition must not rely only on the associative binding of two words. We cautioned our interpretation of the findings.

Interestingly, EC vs CC exhibits differences following trough- but not peak targeting (see R1.4). If indeed all the EC vs CC differences were unrelated to associative binding, we would expect the same EC vs CC differences when peaks were targeted. Hence, the selective EC vs CC differences in the trough condition suggest that the brain is more responsive to sound, information, word familiarity and word semantics during troughs, where we found successful learning, compared to peaks, where no learning occurred. Troughtargeted word pairs (EC) versus foreign words (CC) enhanced the theta power 336 at 500 ms following word onset and this theta enhancement correlated significantly with interindividual retrieval performance indicating that theta probably promoted associative learning during sleep. This correlation was insignificant for spindle power.

R2.2. The entire set of EC pairs were tested both following 12 hours and following 36 hours. Exposure to the pairs during test #1 can be expected to have an effect over memory one day later, during test #2, and so differences between the tests could be at least partially driven by the additional activation and rehearsal of the material during test #1. Therefore, it is hard to draw conclusions regarding automatic memory reorganization between 12 and 36 hours after unconscious learning. Specifically, a claim is made regarding a third wave of plasticity, but we cannot be certain that the improvement found in the 36 hour test would have happened without test #1.

We understand that the retrieval test at 12h may have had an impact on performance on the retrieval test at 36h. Practicing retrieval of newly formed memories is known to facilitate future retrieval of the same memories (e.g. Karpicke & Roediger, 2008). Hence, practicing the retrieval of sleep-formed memories during the retrieval test at 12h may have boosted performance at 36h.

However, recent literature suggests that retrieval practice is only beneficial when corrective feedback is provided (Belardi et al., 2021; Metcalfe, 2017). In our study, we only presented the sleep-played pseudowords at test and participants received no feedback regarding the accuracy of their responses. Thus, a proper conscious re-encoding could not take place. Nevertheless, the retrieval at 12h may have altered performance at 36h in other ways. For example, it could have tagged the reactivated sleep-formed memories for enhanced consolidation during the next night (Rabinovich Orlandi et al., 2020; Wilhelm et al., 2011).

We included a paragraph on the potential carry-over effects from retrieval at 12h on retrieval at 36h in the discussion section (line 489-496; line 657-659). Furthermore, we removed the arguments about the “third wave of plasticity”.

R2.3. Authors claim that perceptual and conceptual processing during sleep led to increased neural complexity in troughs. However, neural complexity was not found to differ between EC and CC, nor between remembered and forgotten pairs. It is therefore not clear to me why the increased complexity that was found in troughs should be attributed to perceptual and conceptual word processing, as CC contains meaningless vowels. Moreover, from the evidence presented in this work at least, I am not sure there is room to infer causation - that the increase in HFD is driven by the stimuli - as there is no control analysis looking at HFD during troughs that did not contain stimulation.

With the analysis of the HFD we would like to provide an additional perspective to the oscillation-based analysis. We checked whether the boundary condition of Peak and Trough targeting changes the overall complexity or information content in the EEG. Our goal was to assess the change in neural complexity (relative to a pre-stimulus baseline) following the successful vs unsuccessful encoding of word pairs during sleep.

We acknowledge that a causal interpretation about HFD is not warranted, and we revised the manuscript accordingly. It was unexpected that we could not find the same results in the contrast of EC vs CC or correct vs incorrect word pairs. We suggest that our signal-to noise ratio might have been too weak.

One could argue that the phase targeting alone (without stimulation) induces peak/trough differences in complexity. We cannot completely rule out this concern. But we tried to use the EEG that was not influenced by the ongoing slow-wave: the EEG 2000-500ms before the stimulus onset and 500-2000ms after the stimulus onset. Therefore, we excluded the 1s of the targeted slow-wave, hoping that most of the phase inherent complexity should have faded out (see Figure 2). We could not further extend the time window of analysis due to the minimal stimulus onset interval of 2s. Of course we cannot exclude that the targeted Trough impacted the following HFD. We clarified this in the manuscript (line 384-425).

Furthermore, we did find a difference of neural complexity between the pre-stimulus baseline and the post-stimulus complexity in the Peak condition but not in the Trough condition (we now added this contrast to the manuscript, line 416-419). Hence, the change in neural complexity is a reaction to the interaction of the specific slow-wave phase with the processing of the word pairs. Even though these results cannot provide unambiguous, causal links, we think they can figure as an important start for other studies to decipher neural complexity during slow wave sleep.

Reviewer #3 (Public Review):

The study aims at creating novel episodic memories during slow wave sleep, that can be transferred in the awake state. To do so, participants were simultaneously presented during sleep both foreign words and their arbitrary translations in their language (one word in each ear), or as a control condition only the foreign word alone, binaurally. Stimuli were presented either at the trough or the peak of the slow oscillation using a closed-loop stimulation algorithm. To test for the creation of a flexible association during sleep, participant were then presented at wake with the foreign words alone and had (1) to decide whether they had the feeling of having heard that word before, (2) to attribute this word to one out of three possible conceptual categories (to which translations word actually belong), and (3) to rate their confidence about their decision.

R3.1. The paper is well written, the protocol ingenious and the methods are robust. However, the results do not really add conceptually to a prior publication of this group showing the possibility to associate in slow wave sleep pairs of words denoting large or small object and non words, and then asking during ensuing wakefulness participant to categorise these non words to a "large" or "small" category. In both cases, the main finding is that this type of association can be formed during slow wave sleep if presented at the trough (versus the peak) of the slow oscillation. Crucially, whether these associations truly represent episodic memory formation during sleep, as claimed by the authors, is highly disputable as there is no control condition allowing to exclude the alternative, simpler hypothesis that mere perceptual associations between two elements (foreign word and translation) have been created and stored during sleep (which is already in itself an interesting finding). In this latter case, it would be only during the awake state when the foreign word is presented that its presentation would implicitly recall the associated translation, which in turn would "ignite" the associative/semantic association process eventually leading to the observed categorisation bias (i.e., foreign words tending to be put in the same conceptual category than their associated translation). In the absence of a dis-confirmation of this alternative and more economical hypothesis, and if we follow Ocam's razor assumption, the claim that there is episodic memory formation during sleep is speculative and unsupported, which is a serious limitation irrespective of the merits of the study. The title and interpretations should be toned down in this respect

Our study conceptually adds to and extends the findings by Züst et al. (a) by highlighting the precise time-window or brain state during which sleep-learning is possible (e.g. slow-wave trough targeting), (b) by demonstrating the feasibility of associative learning during night sleep, and (c) by uncovering the longevity of sleep-formed memories.

We acknowledge that the reported unconscious learning of novel verbal associations during sleep may not match textbook definitions of episodic memory. However, the traditional definitions of episodic memory have long been criticised (e.g, (Dew & Cabeza, 2011; Hannula et al., 2023; Henke, 2010; Reder et al., 2009; Shohamy & Turk-Browne, 2013). We stand by our claim that sleep-learning was of episodic nature. We use a computational definition of episodic memory (Cohen & Eichenbaum, 1993; Henke, 2010; O’Reilly et al., 2014; O’Reilly & Rudy, 2000), and not the traditional definition of episodic memory that ties episodic memory to wakefulness and conscious awareness (Gabrieli, 1998; Moscovitch, 2008; Schacter, 1998; Squire & Dede, 2015; Tulving, 2002). The core computational features of episodic memory are 1) rapid learning, 2) association formation, and 3) a compositional and flexible representation of the associations in long-term memory.

Therefore, we revised the manuscript to emphasize how our definition differs from traditional definitions (line 64).

For the current study, we designed a retrieval task that calls on the core computational features of episodic memory by assessing flexible retrieval of sleep-formed compositional word-word associations. Reviewer 3 suggests an alternative interpretation for the learning observed here: mere perceptual associations between foreign words and translations words are stored during sleep, and semantic associations are only inferred at retrieval testing during ensuing wakefulness. First, these processing steps would require the rapid soundsound associative encoding, long-term storage, and the flexible sound retrieval, which would still require hippocampal processing and computations in the episodic memory system. Second, this mechanism seems highly laborious and inefficient. The sound pattern of a word at 12 hours after learning triggers the reactivation of an associated sound pattern of another word. This sound pattern then elicits the activation of the translation words’ semantics leading to the selection of the correct superordinate semantic category at test.

Overall, we believe that our pairwise-associative learning paradigm triggered a rapid conceptual-associative encoding process mediated by the hippocampus that provided for flexible representations of foreign and translation words in episodic memory. This study adds to the existing literature by examining specific boundary conditions of sleep-learning and demonstrates the longevity (at least 36 hours) of sleep-learned associations.

Other remarks:

R3.2. Lines 43-45 : the assumption that the sleeping brain decides whether external events can be disregarded, requires awakening or should be stored for further consideration in the waking state is dubious, and the supporting references date from a time (the 60') during which hypnopedia was investigated in badly controlled sleep conditions (leaving open the doubt about the possibility that it occurred during micro awakenings)

We revised the manuscript to add timelier and better controlled studies that bolster the 60ties-born claim (line 40-51). Recently, it has been shown that the sleeping brain preferentially processes relevant information. For example the information conveyed by unfamiliar voices (Ameen et al., 2022), emotional content (Holeckova et al., 2006; Moyne et al., 2022), our own compared to others’ names (Blume et al., 2018).

R3.3. 1st paragraph, lines 48-53 , the authors should be more specific about what kind of new associations and at which level they can be stored during sleep according to recent reports, as a wide variety of associations (mostly elementary levels) are shown in the cited references. Limitations in information processing during sleep should also be acknowledged.

In the lines to which R3 refers, we cite an article (Ruch & Henke, 2020) in which two of the three authors of the current manuscript elaborate in detail what kind of associations can be stored during sleep. We revised these lines to more clearly present the current understanding of the potential and the limitations of sleep-learning (line 40-51). Although information processing during sleep is generally reduced (Andrillon et al., 2016), a variety of different kinds of associations can be stored, ranging from tone-odour to word-word association (Arzi et al., 2012, 2014; Koroma et al., 2022; Züst et al., 2019).

R3.4. The authors ran their main behavioural analyses on delayed retrieval at 36h rather than 12h with the argument that retrieval performance was numerically larger at 36 than 12h but the difference was non-significant (line 181-183), and that effects were essentially similar. Looking at Figure 2, is the trough effect really significant at 12h ? In any case, the fact that it is (numerically) higher at 36 than 12h might suggest that the association created at the first 12h retrieval (considering the alternative hypothesis proposed above) has been reinforced by subsequent sleep.

The Trough effect at 12h is not significant, as stated on line 185 (“Planned contrasts against chance level revealed that retrieval performance significantly exceeded chance at 36 hours only (P36hours = 0.036, P12hours = 0.094).”). It seems that our wording was not clear. Therefore, we refined the description of the behavioural analysis in the manuscript (lines 188-193).

In brief, we report an omnibus ANOVA with a significant main effect of targeting type (Trough vs Peak, main effect Peak versus Trough: F(1,28) = 5.237, p = 0.030, d = 0.865). Because Trough-targeting led to significantly better memory retention than Peak-targeting, we computed a second ANOVA, solely including participants with through-targeted word-pair encoding. The memory retention in the Trough condition is above chance (MTrough = 39.11%, SD = 10.76; FIntercept (1,14) = 5.660, p = 0.032) and does not significantly differ between the 12h and 36h retrieval (FEncoding-Test Delay (1,14) = 1.308, p = 0.272). However, the retrieval performance at 36h numerically exceeds the performance at 12h and the direct comparison against chance reveals that the 36h but not the 12h retrieval was significant (P36hours = 0.036, P12hours = 0.094). Hence, we found no evidence for above chance performance at the 12h retrieval and focused on the retrieval after 36h in the EEG analysis.

We agree with the reviewer that the subsequent sleep seems to have improved consolidation and subsequent retrieval. We assume that the reviewer suggests that participants merely formed perceptual associations during sleep and encoded episodic-like associations during testing at 12h (as pointed out in R 3.1). However, we believe that it is unlikely that the awake encoding of semantic associations during the 12h retrieval led to improved performance after 36h. We changed the discussion regarding the interaction between retrieval at 12h and 36h (line 505-512, also see R 2.2)

R3.5> In the discussion section lines 419-427, the argument is somehow circular in claiming episodic memory mechanisms based on functional neuroanatomical elements that are not tested here, and the supporting studies conducted during sleep were in a different setting (e.g. TMR)

Indeed, the TMR and animal studies are a different setting compared to the present study. We re-wrote this part and only focused on the findings of Züst and colleagues (2019), who examined hippocampal activity during the awake retrieval of sleep-formed memories (lines 472-482). Additionally, we would like to emphasise that our main reasoning is that the task requirements called upon the episodic memory system.

R3.6. Supplementary Material: in the EEG data the differentiation between correct and incorrect ulterior classifications when presented at the peak of the slow oscillation is only significant in association with 36h delayed retrieval but not at 12h, how do the authors explain this lack of effect at 12 hour ?

We assume that the reviewer refers to the TROUGH condition (word-pairs targeted at a slow-wave trough) and not as written to the peak condition. We argue that the retention performance at 12h is not significantly above chance (M12hours = 37.4%, P12hours = 0.094).

Hence, the distinction between “correctly” and “incorrectly” categorised word pairs was not informative for the EEG analysis during sleep. For whatever reason the 12h retrieval was not significantly above chance, the less successful memory recall and thus a less balanced trial count makes recall accuracy a worse delineator for separating EEG trials then the recall performance after 36 hours.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor importance:

Abstract: The opening framing is confusing here and in the introduction. Why frame the paper in the broadest terms about awakenings and threats from the environment when this is a paper about intersections between learning & memory and sleep? I do understand that there is an interesting point to be made about the counterintuitive behavioral findings with respect to sleep generally being perceived as a time when stimuli are blocked out, but this does not seem to me to be the broadest points or the way to start the paper. The authors should consider this but of course push back if they disagree.

We understand the reviewer’s criticism but believe that this has more to do with personal preferences than with the scientific value or validity of our work. We believe that it is our duty as researchers to present our study in a broader context because this may help readers from various fields to understand why the work is relevant. To some readers, evidence for learning during sleep may seem trivial, to others, it may seem impossible or a weird but useless conundrum. By pointing out potential evolutionary benefits of the ability to acquire new information during sleep, we help the broad readership of eLife understand the relevance of this work.

Lines 31-32: "Neural complexity" -> "neural measures of complexity" because it isn't clear what "neural complexity" means at this point in the abstract. Though, note my other point that I believe this analysis should be removed.

To our understanding, “neural complexity” is a frequently used term in the field and yields more than 4000 entries on google scholar. Whereas ‘neural measures of complexity’ only finds 3 hits on google scholar [September 2023]. In order to link our study with other studies on neural complexity, we would like to keep this terminology. As an example, two recent publications using “neural complexity” are Lee et al. (2020) and Frohlich et al. (2022).

Lines 42-43: The line of work on 'sentinel' modes would be good to cite here (e.g., Blume et al., 2017, Brain & Language).

We added the suggested citation to the manuscript (lines 52).

Lines 84-90: While I appreciate the authors desire to dig deep and try to piece this all together, this is far too speculative in my opinion. Please see my other points on the same topic.

In this paragraph, we point out why both peaks and troughs are worth exploring for their contributions to sensory processing and learning during sleep. Peaks and troughs are contributing mutually to sleep-learning. Our speculations should inspire further work aimed at pinning down the benefits of peaks and troughs for sleep-learning. We clarified the purpose and speculative nature of our arguments in the revised version of the manuscript.

Line 109: "outlasting" -> "lasting over" or "lasting >"

We changed the wording accordingly.

Line 111: I believe 'nonsense' is not the correct term here, and 'foreign' (again) would be preferred. Some may be offended to hear their foreign word regarded as 'nonsense'. However, please let me know if I have misunderstood.

We would like to use the linguistic term “pseudoword” (aligned with reviewer 2’s comment) and we revised the manuscript accordingly.

Figure 1A: "Enconding" -> "Encoding"

Thank you for pointing this out.

Lines 201-2: Were there interactions between confidence and correctness on the semantic categorization task? Were correct responses given with more confidence than incorrect ones? This would not necessarily be a problem for the authors' account, as there can of course be implicit influences on confidence (i.e., fluency).

As is stated in the results section, confidence ratings did not differ significantly between correct and incorrect assignments (Trough condition: F(1,14) = 2.36, p = 0.15); Peak condition: F(1,14) = 0.48, p = 0.50).

Line 236: "Nicknazar" -> "Niknazar"

Thank you for pointing this out.

Line 266: "profited" -> "benefited"

We changed the wording accordingly.

Lines 280-4: There seems some relevance here with Malerba et al. (2018) and her other papers to categorize slow oscillations.

Diving into the details on how to best categorise slow oscillations is beyond the scope of this manuscript. Here, we build on work from the field of microstate analyses and use two measures to describe and quantify the targeted brain states: the topography of the electric field (i.e., the correlation of the electric field with an established template or “microstate”), and the field strength (global field power, GFP). While the topography of a quasi-stable electric field reflects activity in a specific neural network, the strength (GFP) of a field most likely mirrors the degree of activation (or inactivity) in the specific network. Here, we find that consistent targeting of a specific network state yielding a strong frontal negativity benefitted learning during sleep. For a more detailed explanation of the slow-wave phase targeting see (Ruch et al., 2022).

Lines 343-6: Was it intentional to have 0.5 s (0.2-0.7 s) surrounding the analysis around 500 ms but only 0.4 s (0.8-1.2 s) surrounding the analysis around 1 s? Could the authors use the same size interval or justify having them be different?

We apologise for the misleading phrasing and we clarified this in the revised manuscript. We applied the same procedure for the comparison of later correctly vs incorrectly classified pseudowords as we did for the comparison between EC and CC. Hence, we analysed the entire window from 0s to 2.5s with a cluster-based permutation approach. Contrary to the EC vs CC contrast, no cluster remained significant for the comparison of the subsequent memory effect. By mistake we reported the wrong time window. In the revised manuscript, the paragraph is corrected (lines 364-369).

Line 356-entire HFD section: it is unclear what's gained by this analysis, as it could simply be another reflection of the state of the brain at the time of word presentation. In my opinion, the authors should remove this analysis and section, as it does not add clarity to other aspects of the paper.

(If the authors keep the section) Line 361-2 - "Moreover, high HFD values have been associated with cognitive processing (Lau et al., 2021; Parbat & Chakraborty, 2021)." This statement is vague. Could the authors elaborate?

Please see our answer to Reviewer 2 (2.3) for a more detailed explanation. In brief, we would like to keep the analysis with the broad time window of -2 to -0.5 and from 0.5 to 2 s.

Lines 403-4: How was it determined that these neural networks mediated both conscious/unconscious processes? Perhaps the authors meant to make a different point, but the way it reads to me is that there is evidence that some neural networks are conscious and others are not and both forms engage in similar functions.

We revised the manuscript to be more precise and clear: “The conscious and unconscious rapid encoding and flexible retrieval of novel relational memories was found to recruit the same or similar networks including the hippocampus(Henke et al., 2003; Schneider et al., 2021). This suggests that conscious and unconscious relational memories are processed by the same memory system.” (p. 22, top).

Lines 433-41: Performance didn't actually significantly increase from 12 to 36 hours, so this is all too speculative in my opinion.

We removed the speculative claim that performance may have increased from the retrieval at 12 hours to the retrieval at 36 hours.

Line 534: "assisted by enhanced" -> "coincident with". It's unclear whether theta reflects successful processing as having occurred or whether it directly affects or assists with it.

We have adjusted the wording to be more cautious, as suggested (line 588).

Line 572-4: Rothschild et al. (2016) is relevant here.

Unfortunately, we do not see the relevance of this article within the context of our work.

Line 577 paragraph: The authors may consider adding a note on the importance of ethical considerations surrounding this form of 'inception'.

We extended this part by adding ethical considerations to the discussion section (Stickgold et al., 2021, line 657).

Line 1366: It would be better if the authors could eventually make their data publicly available. This is obviously not required, but I encourage the authors to consider it if they have not considered it already.

In my opinion, the discussion is too long. I really appreciate the authors trying to figure out the set of precise times in which each level of neural processing might occur and how this intersects with their slow oscillation phase results. However, I found a lot of this too speculative, especially given that the sounds may bleed into parts of other phases of the slow oscillation. I do not believe this is a problem unique to these authors, as many investigators attempting to target certain phases in the target memory reactivation literature have faced the same problem, but I do believe the authors get ahead of the data here. In particular, there seems to be one paragraph in the discussion that is multiple pages long (p. 22-24). This paragraph I believe has too much detail and should be broken up regardless, as it is difficult for the reader to follow.

Considering the recent literature, we believe this interpretation best explains the data. As argued earlier, we believe that a speculative interpretation of the reported phenomena can provide substantial added value because it inspires future experimental work. We have improved the manuscript by clearly distinguishing between data and interpretation. We do declare the speculative nature of some offered interpretations. We hope that these speculations, which are testable hypotheses (!), will eventually be confirmed or refuted experimentally.

Reviewer #2 (Recommendations For The Authors):

I very much enjoyed the paper and think it describes important findings. I have a few suggestions for improvement, and minor comments that caught my eye during reading:

(1) I was missing an analysis of CC ERP, and its comparison to EC ERP.

We added this analysis to the manuscript (line 299-301). The comparison of CC ERP with EC ERP did not yield any significant cluster for either the peak (cluster-level Monte Carlo p=0.54) or the trough (cluster-level Monte Carlo p>0.37). We assume that the noise level was too high for the identification of differences between CC and EC ERP.

(2) Regarding my public review comment #2, some light can be shed on between-test effects, I believe, using an item-based analysis - looking at correlations between items' classifications in test #1 and test #2. The assumption seems to be that items that were correct in test #1 remained correct in test #2 while other new correct classifications were added, owing to the additional consolidation happening between the two tests. But that is an empirical question that can be easily tested. If no consistency in item classification is found, on the other hand, or if only consistency in correct classification is found, that would be interesting in itself. This item-based analysis can help tease away real memory from random correct classification. For instance, the subset of items that are consistently classified correctly could be regarded as non-fluke at higher confidence and used as the focus of subsequent-memory analysis instead of the ones that were correct only in test #2.

Thanks, we re-analysed the data accordingly. Participants were consistent at choosing a specific object category for an item at 12 hours and 36 hours (consistency rate = 47% same category, chance level is 1/3). Moreover, the consistency rate did not differ between the Trough and the Peak condition (MTrough = 47.2%, MPeak = 47.0%, P = 0.98). The better retrieval performance in the Trough compared to the Peak condition after 36 hours is due to: A) if participants were correct at 12h, they chose again the correct answer at 36h (Trough: 20% & Peak: 14%). B) Following an incorrect answer at 12h, participants switched to another object category at 36h (Trough: 72%, Peak: 67%). C) If participants switched the object category following an incorrect answer at 12h, they switched more often to the correct category at 36h in the trough versus the peak condition (Trough: in 56% & Peak: 53%). Hence, the data support the reviewer’s assumption: items that were correct after 12 hours remained correct after 36 hours, while other new correct classifications were generated at 36h owing to the additional consolidation happening between the two tests. We added this finding to the manuscript (line 191-200, Figure S6):

Author response image 1.

As suggested, we re-analysed the ERP with respect to the subsequent memory effect. This time we computed four conditions according to the reviewer’s argument about consistently correctly classified pseudowords, presented in the figure below: ERP of trials that were correctly classified at 36h (blue), ERP of trials that were incorrectly classified at 36h (light blue), ERP of trials that were correctly classified twice (brown) and ERP of trials that were not correctly classified twice (orange, all trials that are not in brown). Please note that the two blue lines are reported in the manuscript and include all trials. The brown and the orange line take the consistency into account and together include as well all trials.

Author response image 2.

By excluding even more trials from the group of correct retrieval responses, the noise level gets high. Therefore, the difference between the twice-correct and the not-twice-correct trials is not significant (cluster-level Monte Carlo p > 0.27). Because the ERP of twice-correct trials seems very similar to the ERP of the trials correctly classified at 36h at frontal electrodes, we assume that our ERP effect is not driven by a few extreme subjects. Similarly, not-twicecorrect trials (orange) have a stronger frontal trough than the trials incorrectly classified at 36h (light blue).

(3) In a similar vein, a subject-based analysis would be highly interesting. First and foremost, readers would benefit from seeing the lines that connect individual dots across the two tests in figures 2B and 2C. It is reasonable to expect that only a subset of participants were successful learners in this experiment. Finding them and analyzing their results separately could be revealing.

We added a Figure S1 to the supplementary material, providing the pairing between performance of the 12h and the 36h retrieval.

It is an interesting idea to look at successful learners alone. We computed the ERP of the subsequent memory effect for those participants, who had an above change retrieval accuracy at 36h. The result shows a similar effect as reported for all participants (frontal cluster ~0-0.3s). The p-value is only 0.08 because only 9 of 15 participants exhibited an above chance retrieval performance at 36 hours.

Author response image 3.

ERP effect of correct (blue) vs incorrect (light blue) pseudoword category assignment of participants with a retrieval performance above chance at 36h (SD as shades):

We prefer to not include this data in the manuscript, but are happy to provide it here.

(4) I wondered why the authors informed subjects of the task in advance (that they will be presented associations when they slept)? I imagine this may boost learning as compared to completely naïve subjects. Whether this is the reason or not, I think an explanation of why this was done is warranted, and a statement whether authors believe the manipulation would work otherwise. Also, the reader is left wondering why subjects were informed only about test #1 and not about test #2 (and when were they told about test #2).

Subjects were informed of all the tests upfront. We apologize for the inconsistency in the manuscript and revised the method part. The explanation of why participants were informed is twofold: a) Participants had to sleep with in-ear headphones. We wanted to explain to participants why these are necessary and why they should not remove them. b) We hoped that participants would be expecting unconsciously sounds played during sleep, would process these sounds efficiently and would remain deeply asleep (no arousals).

(5) FoHH is a binary yes/no question, and so may not have been sensitive enough to demonstrate small differences in familiarity. For comparison, the Perceptual Awareness Scale (Ramsøy & Overgaard, 2004) that is typically used in studies of unconscious processing is of a 4-point scale, and this allows to capture more nuanced effects such as partial consciousness and larger response biases. Regardless, it would be informative to have the FoHH numbers obtained in this study, and not just their comparison between conditions. Also, was familiarity of EC and CC pseudowords compared? One may wonder whether hearing the pseudowords clearly vs. in one ear alongside a familiar word would make the word slightly more familiar.

We apologize for having simplified this part too much in the manuscript. Indeed, the FoHH is comparable to the PAS. We used a 4-point scale, where participants rated their feeling of whether they have heard the pseudoword during previous sleep. In the revised manuscript, we report the complete results (line 203-223). The FoHH did not differ between any of the suggested contrasts. Thus, for both the peak and the trough condition, the FoHH did not differ between sleep-played vs new; correct EC trials vs new; correct vs incorrect EC trials; EC vs CC trials. To illustrate the results, a figure of the FoHH has been added to the supplement (Figure S4).

(6) Similarly, it would be good to report the numbers of the confidence ratings in the paper as well.

In the revised manuscript, we extended the description of the confidence rating results. We added the descriptive statistics (line 224-236) and included a corresponding figure in the supplement (Figure S5).

Minor/aesthetic comments:

We implemented all the following suggestions.

(1) I suggest using "pseudoword" or "nonsense word" instead of "foreign word", because "foreign word" typically means a real word from a different language. It is quite confusing when starting to read the paper.

After reconsidering, we think that pseudoword is the appropriate linguistic term and have revised the manuscript accordingly.

(2) Lines 1000-1001: "The required sample size of N = 30 was determined based on a previous sleep-learning study". I was missing a description of what study you are referring to.

(3) I am not sure I understood the claim nor the rationale made in lines 414-417. Is the claim that pairs did not form one integrated engram? How do we know that? And why would having one engram not enable extracting the meaning from a visual-auditory presentation of the cue? The sentence needs some rewording and/or unpacking.

(4) Were categories counterbalanced (i.e., did each subjects' EC contain 9 animal words, 9 tool words and 9 place words)?

(5) Asterisks indicating significant effects are missing from Figure 4 and S2.

(6) Fig1 legend: "Participants were played with pairs" is ungrammatical.

(7) Line 1093: no need for a comma.

(8) Line 1336: missing opening parenthesis

(9) Line 430: "observe" instead of "observed".

(10) Line 466: two dots instead of one..

Reviewer #3 (Recommendations For The Authors):

Methods: 2 separate ANOVAs are performed (lines 160-185), but would not it make more sense to combine both in one ? If kept separated then a correction for multiple comparisons might be needed (p/2 = 0.025)

We computed an omnibus ANOVA. In a next step, we examined the effect in the significant targeting condition by computing another ANOVA. For further explanations, see reviewer comment 3.4.

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Reaching these goals does require an enormous amount of flexibility by educators. It is easy to make a to-do list, it is much more difficult to complete that to-do list. I think it would be helpful to have direct student incentives so that they can see the immediate reward for their efforts instead of the abstract idea that being more educated may make their lives more successful as adults.

Author Response

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

Public Review

[...] A particular strength of the present study is the structural characterization of human PURA, which is a challenging target for structural biology approaches. The molecular dynamics simulations are state-of-the-art, allowing a statistically meaningful assessment of the differences between wild-type and mutant proteins. The functional consequences of PURA mutations at the cellular level are fascinating, particularly the differential compartmentalization of wild-type and mutant PURA variants into certain subcellular condensates.

Weaknesses that warrant rectification relate to (i) The interpretation of statistically non-significant effects seen in the molecular dynamic simulations.

We removed from the manuscript the sentence which indicated that we analyzed statistically non-significant effects. Therefore, the above statement has been resolved.

(ii) The statistical analysis of the differential compartmentalization of PURA variants into processing bodies vs. stress granules, and

We re-analyzed all cell-biological data and adjusted the statistical analysis of P-bodies and Stress-granule intensity analysis. The new, and improved statistics have replaced the original analyses in the corresponding figures (Figs. 1C and 2B).

(iii) Insufficient documentation of protein expression levels and knock-down efficiencies.

Quantification of protein expression levels by Western blotting is shown in Appendix Figure S1. Quantification of knock-down efficiencies by Western blot experiments (Appendix Figure S3).

Recommendations for the authors: Reviewer #1

Concerns and Suggested Changes

(a) I have only one concern about the computational part and that is about statements such as "There are also large differences in the residue surrounding the mutation spot (residues 90 to 100), where the K97E mutant also shows much greater fluctuation. However, these differences are not significant due to the large standard deviations." If the differences are not statistically significant, then I would suggest either removing such a statement or increasing the statistics.

We agree with the Reviewer’s comment. We removed this sentence from the text.

Recommendations for the authors: Reviewer #2

General Comments

This is a challenging structural target and the authors have made considerable efforts to determine the effect of several mutations on the structure and function. Many of the constructs, however, could not be expressed and/or purified in bacteria. However, it is not clear to what extent other expression systems (e.g. Drosophila or human) were considered and if this would have been beneficial.

We did not use other expression systems because the wild-type protein is well-behaved when expressed in E. coli. In case a mutant variant cannot be expressed or does not behave well in E. coli, this constitutes a clear indication that the respective mutation impairs the protein’s integrity. Thus, by using E. coli as a reference system for all the variants of PURA protein, we could assess the influence of the mutations on the structural integrity and solubility. Only for the variants that did not show impairment in E. coli expression, we continued to assess in more detail why they are nevertheless functionally impaired and cause PURA Syndrome.

Concerns and Suggested Changes

(a) The schematic in Figure 3A would have been helpful for interpreting the mutations discussed in Figures 1 and 2. I would suggest moving it earlier in the text.

We changed the figure according to the Reviewer’s suggestion.

(b) I believe the RNA used for binding studies in Figures 3C and D was (CGG)8. Are the two "free" RNA bands a monomer and a dimer (duplex?)?

Although we do not know for certain, it is indeed likely that the two free RNA bands represent either different secondary structures of the free RNA or a duplex of two molecules. Of note, PURA binds to both “free” RNA bands, indicating that it either does not discriminate between them or melts double-stranded RNA in these EMSAs.

There also seems to be considerable cooperativity in the binding, so I wonder if a shorter RNA oligonucleotide might facilitate the measurement of Kds.

The length of the used RNA was selected based on the estimated elongated size of the full-length PURA and the presence of 3 PUR repeats. Assuming that one PUR repeat interacts with about 6-7 bases (data from the co-structure of Drosophila PURA with DNA; PDB-ID: 5FGP) and that full-length PURA forms a dimer consisting of three PUR repeats, the full-length protein in its extended form should cover a nucleic-acid stretch of about 24 bases.

Also, it is not clear how the affinities were measured particularly for hsPURA III since free band is never fully bound at the highest protein concentration.

It was not our goal to measure Kds for the interaction of PURA variants with RNA. The EMSA experiments were conducted to detect relative differences in the interaction between PURA variants and RNA. To estimate the differences, we measured total intensity of the bound (shifted) and unbound RNA. The intensities of the bands observed on the scanned EMSA gels were quantified with FUJI ImageJ software. We calculated the percentage of the shifted RNA and normalized it. hsPURA III fragment shows much lower affinity therefore it does not fully shift RNA with the highest protein concentration when compared to the full-length PURA and to PURA I-II.

(c) Do the human PURA I+II and dmPURA I+ II crystallize in the same space group and have similar packing? Can the observed structural flexibility be due to crystal contacts?

hsPURA I+II and dmPURA I+II crystallize in different space groups with different crystal packing. In both cases, the asymmetric unit contains 4 independent molecules with the flexible part of the structure composed of the β4 and β8 (β ridge) exposed to solvent. In the case of the Drosophila structure, we do not observe any flexibility of both β-strands. In contrast, for the human PURA structure the β ridge exhibits lots of flexibility and it adopts different conformations in all 4 molecules of the asymmetric unit. We observe similar flexibility of the β4 and β8 (β ridge) in the structure of K97E mutant which contains 2 molecules in the asymmetric unit. We would like to add that we expect crystal contacts to rather stabilize than destabilize domains.

Similarly, can the conformations observed for the K97E mutant be partially explained by packing?

Regarding the sequence shift observed for the β5 and β6 strands in hsPURA I+II K97E variant: although the β5 strand with shifted amino acid sequence is involved in the contact with the symmetry-related molecule with another β5 strand we don’t consider this interaction as a source of the shift. To be sure that the shift is not forced by the crystallization, we had performed NMR measurement which confirmed that in solution there is a strong change in the β-stands comparing WT and K97E mutant. This is an unambiguous indication that the structural changes observed in the crystal structure are also happening in solution. In addition, the MD simulations provide additional confirmation of our interpretation that K97E destabilizes the corresponding PUR domain. Taken together, we provide proof from three different angles that the observed differences indeed affect the integrity and hence function of the protein.

(d) Perhaps, it is my misunderstanding, but I find the NMR data on the Arg sidechains for the K97E confusing. If they are visible for K97E and not WT, doesn't this indicate that there is an exchange between two conformations or more dynamics in the WT structure? This does not seem to be the opposite of the expectation if K97E is thought to have more conformational flexibility.

Due to a technical issue (peak contour level), arginine side chain resonances were not clearly visible in the WT spectrum. The figure 5F has been updated. Now, they do correspond to those seen in the mutant spectrum. However, to prevent any confusion or mis/overinterpretation, we removed the sentence regarding arginine side chain: "Intriguingly, arginine side chain resonances Nε-Hε were only visible in the K97E variant, while they were broadened out in the wild-type spectrum."

(e) The most speculative part of the paper is the interpretation of SG and PB localization of PURA in Fig 1 and 2. There is an important issue with the statistics that must be clarified because it would appear that statistical significance was determined using each SG or PB as an independent measurement. This is incorrect and significance should be measured by only using the means of three biological replicates. This is well described here. It is not clear at this time if the reported P values will be confirmed upon reanalysis, and this may require reinterpretation of the data.

We are grateful for this clarifying comment and agree that the statistical analysis of P-body and stress granule was misleading. Of note, while the figures depicted all the values independent of the biological repeats, the statistical analyses were done on the mean value of each replicate of each cell line and not all raw data points.

We prepared new Plots, only showing the mean value of each replicate, and also re-calculated P-values. The values have changed only slightly in this new analysis because we now also included the previously labeled outliers (red points) to better demonstrate that significance still exists even when considering them.

In the new analysis of stress-granule association, only the value of the K97E mutant lost its significance, indicating that its association to stress granules is not lost. Therefore, we adjusted the following sentences in the manuscript.

Results:

Original: "While quantification showed a reduced association of hsPURA K97E mutant with G3BP1-positive granules (Fig 1B), the two other mutants, I206F and F233del, showed the same co-localization to stress granules as the wild type control."

Corrected: "In all the patient-related mutations, no significant reduction in stress granule association was seen when compared to the wild type control (Fig 1C)."

Original: "The observation that only one of the patient-related mutations of hsPURA, K97E, showed reduced stress granule association indicates that this feature may not constitute a major hallmark of the PURA syndrome. It should be noted however that this interpretation must be considered with some caution as the experiments were performed in a PURA wild-type background."

Corrected: "As we did not observe significant changes in the association of patient-related mutations of hsPURA to stress granules, it is suggested that that this feature may not constitute a major hallmark of the PURA syndrome. It should be noted however that this interpretation must be considered with some caution as the experiments were performed in a PURA wild-type background."

(f) A western blot showing the level of overexpression of the PURA proteins should be shown in Figure 1 as well as the KD of endogenous PURA for Figure S2?

As requested, a Western blot showing the level of overexpression of the different PURA proteins has been added as Appendix Figure S1.

A Western blot of the siRNA-mediated knock-down experiments of PURA and their corresponding control has been added to Appendix Figure S3. Quantification of three biological repeats showed a significant reduction of PURA protein levels upon knock down.

(g) While I appreciate that rewriting is time-consuming, I would recommend considering restructuring the manuscript because I think that it would aid the overall clarity. I think the foundation of the work is the structural characterization and would suggest beginning the paper with this data and the biochemical characterization. The co-localization with SGs and PBs and how this may be relevant to disease is much more speculative and is therefore better to present later. While I appreciate that the structural interpretation of why some mutants localize to PBs differently is not entirely clear, I do think that this would provide some context for the discussion.

In the initial version of the manuscript we first presented the structural characterization of PURA and afterwards the co-localization with SGs and PBs. As this reviewer stated him-/herself in (e), we also noticed that the SG and PB interpretation is the most speculative part of this manuscript. We felt that having this at the end of the results section would weaken the manuscript. On the other hand, we consider that the structural interpretation of mutations is much stronger and has a greater impact for future research. After long discussion we decided to swap the order to leave the most important results for the end of the manuscript.

Recommendations for the authors: Reviewer #3

Concerns and Suggested Changes:

(a) For the characterization of G3BP1-positive stress granules in HeLa cells upon depletion of PURA, it remains unclear what is the efficiency of siRNA? The authors should provide a western blot to indicate how much the endogenous levels were reduced.

We completely agree with the stated concern and addressed it accordingly. We had performed this experiment prior to submission but for some unknown reason it was not included in the manuscript.

The Western blot of siRNA-mediated knock-down experiments of PURA and their corresponding control is now shown in Appendix Figure S3. Quantification of three biological repeats, showed a significant reduction of PURA protein levels upon knock down.

(b) How does knocking down PURA affect DCP1A-positive structures in HeLa cells? Would P bodies be formed even in the absence (or reduction) of total PURA?

Indeed, the stated question is very interesting. In fact, we have already shown in our recent publication (Molitor et al., 2023) that a knock down of PURA in HeLa and NHDF cells leads to a significant reduction of P-bodies. We actually referred to this finding on page 6:

"Since hsPURA was recently shown to be required for P-body formation in HeLa cells and fibroblasts (Molitor et al. 2023), PURA-dependent liquid phase separation could potentially also directly contribute to the formation of these granules."

On the same page, we also refer to the underlying molecular mechanism:

"However, when putting this observation in perspective with previous reports, it seems unlikely that P-body formation directly depends on phase separation by hsPURA, but rather on its recently reported function as gene regulator of the essential P-body core factors LSM14a and DDX6 (Molitor et al., 2023)."

Author Response

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

eLife assessment

These ingenious and thoughtful studies present important findings concerning how people represent and generalise abstract patterns of sensory data. The issue of generalisation is a core topic in neuroscience and psychology, relevant across a wide range of areas, and the findings will be of interest to researchers across areas in perception, learning, and cognitive science. The findings have the potential to provide compelling support for the outlined account, but there appear other possible explanations, too, that may affect the scope of the findings but could be considered in a revision.

Thank you for sending the feedback from the three peer reviewers regarding our paper. Please find below our detailed responses addressing the reviewers' comments. We have incorporated these suggestions into the paper and provided explanations for the modifications made.

We have specifically addressed the point of uncertainty highlighted in eLife's editorial assessment, which concerned alternative explanations for the reported effect. In response to Reviewer #1, we have clarified how Exp. 2c and Exp. 3c address the potential alternative explanation related to "attention to dimensions." Further, we present a supplementary analysis to account for differences in asymptotic learning, as noted by Reviewer #2. We have also clarified how our control experiments address effects associated with general cognitive engagement in the task. Lastly, we have further clarified the conceptual foundation of our paper, addressing concerns raised by Reviewers #2 and #3.

Reviewer #1 (Public Review):

Summary:

This manuscript reports a series of experiments examining category learning and subsequent generalization of stimulus representations across spatial and nonspatial domains. In Experiment 1, participants were first trained to make category judgments about sequences of stimuli presented either in nonspatial auditory or visual modalities (with feature values drawn from a two-dimensional feature manifold, e.g., pitch vs timbre), or in a spatial modality (with feature values defined by positions in physical space, e.g., Cartesian x and y coordinates). A subsequent test phase assessed category judgments for 'rotated' exemplars of these stimuli: i.e., versions in which the transition vectors are rotated in the same feature space used during training (near transfer) or in a different feature space belonging to the same domain (far transfer). Findings demonstrate clearly that representations developed for the spatial domain allow for representational generalization, whereas this pattern is not observed for the nonspatial domains that are tested. Subsequent experiments demonstrate that if participants are first pre-trained to map nonspatial auditory/visual features to spatial locations, then rotational generalization is facilitated even for these nonspatial domains. It is argued that these findings are consistent with the idea that spatial representations form a generalized substrate for cognition: that space can act as a scaffold for learning abstract nonspatial concepts.

Strengths:

I enjoyed reading this manuscript, which is extremely well-written and well-presented. The writing is clear and concise throughout, and the figures do a great job of highlighting the key concepts. The issue of generalization is a core topic in neuroscience and psychology, relevant across a wide range of areas, and the findings will be of interest to researchers across areas in perception and cognitive science. It's also excellent to see that the hypotheses, methods, and analyses were pre-registered.

The experiments that have been run are ingenious and thoughtful; I particularly liked the use of stimulus structures that allow for disentangling of one-dimensional and two-dimensional response patterns. The studies are also well-powered for detecting the effects of interest. The model-based statistical analyses are thorough and appropriate throughout (and it's good to see model recovery analysis too). The findings themselves are clear-cut: I have little doubt about the robustness and replicability of these data.

Weaknesses:

I have only one significant concern regarding this manuscript, which relates to the interpretation of the findings. The findings are taken to suggest that "space may serve as a 'scaffold', allowing people to visualize and manipulate nonspatial concepts" (p13). However, I think the data may be amenable to an alternative possibility. I wonder if it's possible that, for the visual and auditory stimuli, participants naturally tended to attend to one feature dimension and ignore the other - i.e., there may have been a (potentially idiosyncratic) difference in salience between the feature dimensions that led to participants learning the feature sequence in a one-dimensional way (akin to the 'overshadowing' effect in associative learning: e.g., see Mackintosh, 1976, "Overshadowing and stimulus intensity", Animal Learning and Behaviour). By contrast, we are very used to thinking about space as a multidimensional domain, in particular with regard to two-dimensional vertical and horizontal displacements. As a result, one would naturally expect to see more evidence of two-dimensional representation (allowing for rotational generalization) for spatial than nonspatial domains.

In this view, the impact of spatial pre-training and (particularly) mapping is simply to highlight to participants that the auditory/visual stimuli comprise two separable (and independent) dimensions. Once they understand this, during subsequent training, they can learn about sequences on both dimensions, which will allow for a 2D representation and hence rotational generalization - as observed in Experiments 2 and 3. This account also anticipates that mapping alone (as in Experiment 4) could be sufficient to promote a 2D strategy for auditory and visual domains.

This "attention to dimensions" account has some similarities to the "spatial scaffolding" idea put forward in the article, in arguing that experience of how auditory/visual feature manifolds can be translated into a spatial representation helps people to see those domains in a way that allows for rotational generalization. Where it differs is that it does not propose that space provides a scaffold for the development of the nonspatial representations, i.e., that people represent/learn the nonspatial information in a spatial format, and this is what allows them to manipulate nonspatial concepts. Instead, the "attention to dimensions" account anticipates that ANY manipulation that highlights to participants the separable-dimension nature of auditory/visual stimuli could facilitate 2D representation and hence rotational generalization. For example, explicit instruction on how the stimuli are constructed may be sufficient, or pre-training of some form with each dimension separately, before they are combined to form the 2D stimuli.

I'd be interested to hear the authors' thoughts on this account - whether they see it as an alternative to their own interpretation, and whether it can be ruled out on the basis of their existing data.

We thank the Reviewer for their comments. We agree with the Reviewer that the “attention to dimensions” hypothesis is an interesting alternative explanation. However, we believe that the results of our control experiments Exp. 2c and Exp. 3c are incompatible with this alternative explanation.

In Exp. 2c, participants are pre-trained in the visual modality and then tested in the auditory modality. In the multimodal association task, participants have to associate the auditory stimuli and the visual stimuli: on each trial, they hear a sound and then have to click on the corresponding visual stimulus. It is thus necessary to pay attention to both auditory dimensions and both visual dimensions to perform the task. To give an example, the task might involve mapping the fundamental frequency and the amplitude modulation of the auditory stimulus to the colour and the shape of the visual stimulus, respectively. If participants pay attention to only one dimension, this would lead to a maximum of 25% accuracy on average (because they would be at chance on the other dimension, with four possible options). We observed that 30/50 participants reached an accuracy > 50% in the multimodal association task in Exp. 2c. This means that we know for sure that at least 60% of the participants paid attention to both dimensions of the stimuli. Nevertheless, there was a clear difference between participants that received a visual pre-training (Exp. 2c) and those who received a spatial pre-training (Exp. 2a) (frequency of 1D vs 2D models between conditions, BF > 100 in near transfer and far transfer). In fact, only 3/50 participants were best fit by a 2D model when vision was the pre-training modality compared to 29/50 when space was the pre-training modality. Thus, the benefit of the spatial pre-training cannot be due solely to a shift in attention toward both dimensions.

This effect was replicated in Exp. 3c. Similarly, 33/48 participants reached an accuracy > 50% in the multimodal association task in Exp. 3c, meaning that we know for sure that at least 68% of the participants actually paid attention to both dimensions of the stimuli. Again, there was a clear difference between participants who received a visual pre-training (frequency of 1D vs 2D models between conditions, Exp. 3c) and those who received a spatial pre-training (Exp. 3a) (BF > 100 in near transfer and far transfer).

Thus, we believe that the alternative explanation raised by the Reviewer is not supported by our data. We have added a paragraph in the discussion:

“One alternative explanation of this effect could be that the spatial pre-training encourages participants to attend to both dimensions of the non-spatial stimuli. By contrast, pretraining in the visual or auditory domains (where multiple dimensions of a stimulus may be relevant less often naturally) encourages them to attend to a single dimension. However, data from our control experiments Exp. 2c and Exp. 3c, are incompatible with this explanation. Around ~65% of the participants show a level of performance in the multimodal association task (>50%) which could only be achieved if they were attending to both dimensions (performance attending to a single dimension would yield 25% and chance performance is at 6.25%). This suggests that participants are attending to both dimensions even in the visual and auditory mapping case.”

Reviewer #2 (Public Review):

Summary:

In this manuscript, L&S investigates the important general question of how humans achieve invariant behavior over stimuli belonging to one category given the widely varying input representation of those stimuli and more specifically, how they do that in arbitrary abstract domains. The authors start with the hypothesis that this is achieved by invariance transformations that observers use for interpreting different entries and furthermore, that these transformations in an arbitrary domain emerge with the help of the transformations (e.g. translation, rotation) within the spatial domain by using those as "scaffolding" during transformation learning. To provide the missing evidence for this hypothesis, L&S used behavioral category learning studies within and across the spatial, auditory, and visual domains, where rotated and translated 4-element token sequences had to be learned to categorize and then the learned transformation had to be applied in new feature dimensions within the given domain. Through single- and multiple-day supervised training and unsupervised tests, L&S demonstrated by standard computational analyses that in such setups, space and spatial transformations can, indeed, help with developing and using appropriate rotational mapping whereas the visual domain cannot fulfill such a scaffolding role.

Strengths:

The overall problem definition and the context of spatial mapping-driven solution to the problem is timely. The general design of testing the scaffolding effect across different domains is more advanced than any previous attempts clarifying the relevance of spatial coding to any other type of representational codes. Once the formulation of the general problem in a specific scientific framework is done, the following steps are clearly and logically defined and executed. The obtained results are well interpretable, and they could serve as a good stepping stone for deeper investigations. The analytical tools used for the interpretations are adequate. The paper is relatively clearly written.

Weaknesses:

Some additional effort to clarify the exact contribution of the paper, the link between analyses and the claims of the paper, and its link to previous proposals would be necessary to better assess the significance of the results and the true nature of the proposed mechanism of abstract generalization.

(1) Insufficient conceptual setup: The original theoretical proposal (the Tolman-Eichenbaum-Machine, Whittington et al., Cell 2020) that L&S relate their work to proposes that just as in the case of memory for spatial navigation, humans and animals create their flexible relational memory system of any abstract representation by a conjunction code that combines on the one hand, sensory representation and on the other hand, a general structural representation or relational transformation. The TEM also suggests that the structural representation could contain any graph-interpretable spatial relations, albeit in their demonstration 2D neighbor relations were used. The goal of L&S's paper is to provide behavioral evidence for this suggestion by showing that humans use representational codes that are invariant to relational transformations of non-spatial abstract stimuli and moreover, that humans obtain these invariances by developing invariance transformers with the help of available spatial transformers. To obtain such evidence, L&S use the rotational transformation. However, the actual procedure they use actually solved an alternative task: instead of interrogating how humans develop generalizations in abstract spaces, they demonstrated that if one defines rotation in an abstract feature space embedded in a visual or auditory modality that is similar to the 2D space (i.e. has two independent dimensions that are clearly segregable and continuous), humans cannot learn to apply rotation of 4-piece temporal sequences in those spaces while they can do it in 2D space, and with co-associating a one-to-one mapping between locations in those feature spaces with locations in the 2D space an appropriate shaping mapping training will lead to the successful application of rotation in the given task (and in some other feature spaces in the given domain). While this is an interesting and challenging demonstration, it does not shed light on how humans learn and generalize, only that humans CAN do learning and generalization in this, highly constrained scenario. This result is a demonstration of how a stepwise learning regiment can make use of one structure for mapping a complex input into a desired output. The results neither clarify how generalizations would develop in abstract spaces nor the question of whether this generalization uses transformations developed in the abstract space. The specific training procedure ensures success in the presented experiments but the availability and feasibility of an equivalent procedure in a natural setting is a crucial part of validating the original claim and that has not been done in the paper.

We thank the Reviewer for their detailed comments on our manuscript. We reply to the three main points in turn.

First, concerning the conceptual grounding of our work, we would point out that the TEM model (Whittington et al., 2020), however interesting, is not our theoretical starting point. Rather, as we hope the text and references make clear, we ground our work in theoretical work from the 1990/2000s proposing that space acts as a scaffold for navigating abstract spaces (such as Gärdenfors, 2000). We acknowledge that the TEM model and other experimental work on the implication of the hippocampus, the entorhinal cortex and the parietal cortex in relational transformations of nonspatial stimuli provide evidence for this general theory. However, our work is designed to test a more basic question: whether there is behavioural evidence that space scaffolds learning in the first place. To achieve this, we perform behavioural experiments with causal manipulation (spatial pre-training vs no spatial pre-training) have the potential to provide such direct evidence. This is why we claim that:

“This theory is backed up by proof-of-concept computational simulations [13], and by findings that brain regions thought to be critical for spatial cognition in mammals (such as the hippocampal-entorhinal complex and parietal cortex) exhibit neural codes that are invariant to relational transformations of nonspatial stimuli. However, whilst promising, this theory lacks direct empirical evidence. Here, we set out to provide a strong test of the idea that learning about physical space scaffolds conceptual generalisation.“

Second, we agree with the Reviewer that we do not provide an explicit model for how generalisation occurs, and how precisely space acts as a scaffold for building representations and/or applying the relevant transformations to non-spatial stimuli to solve our task. Rather, we investigate in our Exp. 2-4 which aspects of the training are necessary for rotational generalisation to happen (and conclude that a simple training with the multimodal association task is sufficient for ~20% participants). We now acknowledge in the discussion the fact that we do not provide an explicit model and leave that for future work:

“We acknowledge that our study does not provide a mechanistic model of spatial scaffolding but rather delineate which aspects of the training are necessary for generalisation to happen.”

Finally, we also agree with the Reviewer that our task is non-naturalistic. As is common in experimental research, one must sacrifice the naturalistic elements of the task in exchange for the control and the absence of prior knowledge of the participants. We have decided to mitigate as possible the prior knowledge of the participants to make sure that our task involved learning a completely new task and that the pre-training was really causing the better learning/generalisation. The effects we report are consistent across the experiments so we feel confident about them but we agree with the Reviewer that an external validation with more naturalistic stimuli/tasks would be a nice addition to this work. We have included a sentence in the discussion:

“All the effects observed in our experiments were consistent across near transfer conditions (rotation of patterns within the same feature space), and far transfer conditions (rotation of patterns within a different feature space, where features are drawn from the same modality). This shows the generality of spatial training for conceptual generalisation. We did not test transfer across modalities nor transfer in a more natural setting; we leave this for future studies.”

(2) Missing controls: The asymptotic performance in experiment 1 after training in the three tasks was quite different in the three tasks (intercepts 2.9, 1.9, 1.6 for spatial, visual, and auditory, respectively; p. 5. para. 1, Fig 2BFJ). It seems that the statement "However, our main question was how participants would generalise learning to novel, rotated exemplars of the same concept." assumes that learning and generalization are independent. Wouldn't it be possible, though, that the level of generalization depends on the level of acquiring a good representation of the "concept" and after obtaining an adequate level of this knowledge, generalization would kick in without scaffolding? If so, a missing control is to equate the levels of asymptotic learning and see whether there is a significant difference in generalization. A related issue is that we have no information on what kind of learning in the three different domains was performed, albeit we probably suspect that in space the 2D representation was dominant while in the auditory and visual domains not so much. Thus, a second missing piece of evidence is the model-fitting results of the ⦰ condition that would show which way the original sequences were encoded (similar to Fig 2 CGK and DHL). If the reason for lower performance is not individual stimulus difficulty but the natural tendency to encode the given stimulus type by a combo of random + 1D strategy that would clarify that the result of the cross-training is, indeed, transferring the 2D-mapping strategy.

We agree with the Reviewer that a good further control is to equate performance during training. Thus, we have run a complementary analysis where we select only the participants that reach > 90% accuracy in the last block of training in order to equate asymptotic performance after training in Exp. 1. The results (see Author response image 1) replicates the results that we report in the main text: there is a large difference between groups (relative likelihood of 1D vs. 2D models, all BF > 100 in favour of a difference between the auditory and the spatial modalities, between the visual and the spatial modalities, in both near and far transfer, “decisive” evidence). We prefer not to include this figure in the paper for clarity, and because we believe this result is expected given the fact that 0/50 and 0/50 of the participants in the auditory and visual condition used a 2D strategy – thus, selecting subgroups of these participants cannot change our conclusions.

Author response image 1.

Results of Exp. 1 when selecting participants that reached > 90% accuracy in the last block of training. Captions are the same as Figure 2 of the main text.

Second, the Reviewer suggested that we run the model fitting analysis only on the ⦰ condition (training) in Exp. 1 to reveal whether participants use a 1D or a 2D strategy already during training. Unfortunately, we cannot provide the model fits only in the ⦰ condition in Exp. 1 because all models make the same predictions for this condition (see Fig S4). However, note that this is done by design: participants were free to apply whatever strategy they want during training; we then used the generalisation phase with the rotated stimuli precisely to reveal this strategy. Further, we do believe that the strategy used by the participants during training and the strategy during transfer are the same, partly because – starting from block #4 – participants have no idea whether the current trial is a training trial or a transfer trial, as both trial types are randomly interleaved with no cue signalling the trial type. We have made this clear in the methods:

“They subsequently performed 105 trials (with trialwise feedback) and 105 transfer trials including rotated and far transfer quadruplets (without trialwise feedback) which were presented in mixed blocks of 30 trials. Training and transfer trials were randomly interleaved, and no clue indicated whether participants were currently on a training trial or a transfer trial before feedback (or absence of feedback in case of a transfer trial).”

Reviewer #3 (Public Review):

Summary:

Pesnot Lerousseau and Summerfield aimed to explore how humans generalize abstract patterns of sensory data (concepts), focusing on whether and how spatial representations may facilitate the generalization of abstract concepts (rotational invariance). Specifically, the authors investigated whether people can recognize rotated sequences of stimuli in both spatial and nonspatial domains and whether spatial pre-training and multi-modal mapping aid in this process.

Strengths:

The study innovatively examines a relatively underexplored but interesting area of cognitive science, the potential role of spatial scaffolding in generalizing sequences. The experimental design is clever and covers different modalities (auditory, visual, spatial), utilizing a two-dimensional feature manifold. The findings are backed by strong empirical data, good data analysis, and excellent transparency (including preregistration) adding weight to the proposition that spatial cognition can aid abstract concept generalization.

Weaknesses:

The examples used to motivate the study (such as "tree" = oak tree, family tree, taxonomic tree) may not effectively represent the phenomena being studied, possibly confusing linguistic labels with abstract concepts. This potential confusion may also extend to doubts about the real-life applicability of the generalizations observed in the study and raises questions about the nature of the underlying mechanism being proposed.

We thank the Reviewer for their comments. We agree that we could have explained ore clearly enough how these examples motivate our study. The similarity between “oak tree” and “family tree” is not just the verbal label. Rather, it is the arrangement of the parts (nodes and branches) in a nested hierarchy. Oak trees and family trees share the same relational structure. The reason that invariance is relevant here is that the similarity in relational structure is retained under rigid body transformations such as rotation or translation. For example, an upside-down tree can still be recognised as a tree, just as a family tree can be plotted with the oldest ancestors at either top or bottom. Similarly, in our study, the quadruplets are defined by the relations between stimuli: all quadruplets use the same basic stimuli, but the categories are defined by the relations between successive stimuli. In our task, generalising means recognising that relations between stimuli are the same despite changes in the surface properties (for example in far transfer). We have clarify that in the introduction:

“For example, the concept of a “tree” implies an entity whose structure is defined by a nested hierarchy, whether this is a physical object whose parts are arranged in space (such as an oak tree in a forest) or a more abstract data structure (such as a family tree or taxonomic tree). [...] Despite great changes in the surface properties of oak trees, family trees and taxonomic trees, humans perceive them as different instances of a more abstract concept defined by the same relational structure.”

Next, the study does not explore whether scaffolding effects could be observed with other well-learned domains, leaving open the question of whether spatial representations are uniquely effective or simply one instance of a familiar 2D space, again questioning the underlying mechanism.

We would like to mention that Reviewer #2 had a similar comment. We agree with both Reviewers that our task is non-naturalistic. As is common in experimental research, one must sacrifice the naturalistic elements of the task in exchange for the control and the absence of prior knowledge of the participants. We have decided to mitigate as possible the prior knowledge of the participants to make sure that our task involved learning a completely new task and that the pre-training was really causing the better learning/generalisation. The effects we report are consistent across the experiments so we feel confident about them but we agree with the Reviewer that an external validation with more naturalistic stimuli/tasks would be a nice addition to this work. We have included a sentence in the discussion:

“All the effects observed in our experiments were consistent across near transfer conditions (rotation of patterns within the same feature space), and far transfer conditions (rotation of patterns within a different feature space, where features are drawn from the same modality). This shows the generality of spatial training for conceptual generalisation. We did not test transfer across modalities nor transfer in a more natural setting; we leave this for future studies.”

Further doubt on the underlying mechanism is cast by the possibility that the observed correlation between mapping task performance and the adoption of a 2D strategy may reflect general cognitive engagement rather than the spatial nature of the task. Similarly, the surprising finding that a significant number of participants benefited from spatial scaffolding without seeing spatial modalities may further raise questions about the interpretation of the scaffolding effect, pointing towards potential alternative interpretations, such as shifts in attention during learning induced by pre-training without changing underlying abstract conceptual representations.

The Reviewer is concerned about the fact that the spatial pre-training could benefit the participants by increasing global cognitive engagement rather than providing a scaffold for learning invariances. It is correct that the participants in the control group in Exp. 2c have poorer performances on average than participants that benefit from the spatial pre-training in Exp. 2a and 2b. The better performances of the participants in Exp. 2a and 2b could be due to either the spatial nature of the pre-training (as we claim) or a difference in general cognitive engagement. .

However, if we look closely at the results of Exp. 3, we can see that the general cognitive engagement hypothesis is not well supported by the data. Indeed, the participants in the control condition (Exp. 3c) have relatively similar performances than the other groups during training. Rather, the difference is in the strategy they use, as revealed by the transfer condition. The majority of them are using a 1D strategy, contrary to the participants that benefited from a spatial pre-training (Exp 3a and 3b). We have included a sentence in the results:

“Further, the results show that participants who did not experience spatial pre-training were still engaged in the task, but were not using the same strategy as the participants who experienced spatial pre-training (1D rather than 2D). Thus, the benefit of the spatial pre-training is not simply to increase the cognitive engagement of the participants. Rather, spatial pre-training provides a scaffold to learn rotation-invariant representation of auditory and visual concepts even when rotation is never explicitly shown during pre-training.”

Finally, Reviewer #1 had a related concern about a potential alternative explanation that involved a shift in attention. We reproduce our response here: we agree with the Reviewer that the “attention to dimensions” hypothesis is an interesting (and potentially concerning) alternative explanation. However, we believe that the results of our control experiments Exp. 2c and Exp. 3c are not compatible with this alternative explanation.

Indeed, in Exp. 2c, participants are pre-trained in the visual modality and then tested in the auditory modality. In the multimodal association task, participants have to associate the auditory stimuli and the visual stimuli: on each trial, they hear a sound and then have to click on the corresponding visual stimulus. It is necessary to pay attention to both auditory dimensions and both visual dimensions to perform well in the task. To give an example, the task might involve mapping the fundamental frequency and the amplitude modulation of the auditory stimulus to the colour and the shape of the visual stimulus, respectively. If participants pay attention to only one dimension, this would lead to a maximum of 25% accuracy on average (because they would be at chance on the other dimension, with four possible options). We observed that 30/50 participants reached an accuracy > 50% in the multimodal association task in Exp. 2c. This means that we know for sure that at least 60% of the participants actually paid attention to both dimensions of the stimuli. Nevertheless, there was a clear difference between participants that received a visual pre-training (Exp. 2c) and those who received a spatial pre-training (Exp. 2a) (frequency of 1D vs 2D models between conditions, BF > 100 in near transfer and far transfer). In fact, only 3/50 participants were best fit by a 2D model when vision was the pre-training modality compared to 29/50 when space was the pre-training modality. Thus, the benefit of the spatial pre-training cannot be due solely to a shift in attention toward both dimensions.

This effect was replicated in Exp. 3c. Similarly, 33/48 participants reached an accuracy > 50% in the multimodal association task in Exp. 3c, meaning that we know for sure that at least 68% of the participants actually paid attention to both dimensions of the stimuli. Again, there was a clear difference between participants who received a visual pre-training (frequency of 1D vs 2D models between conditions, Exp. 3c) and those who received a spatial pre-training (Exp. 3a) (BF > 100 in near transfer and far transfer).

Thus, we believe that the alternative explanation raised by the Reviewer is not supported by our data. We have added a paragraph in the discussion:

“One alternative explanation of this effect could be that the spatial pre-training encourages participants to attend to both dimensions of the non-spatial stimuli. By contrast, pretraining in the visual or auditory domains (where multiple dimensions of a stimulus may be relevant less often naturally) encourages them to attend to a single dimension. However, data from our control experiments Exp. 2c and Exp. 3c, are incompatible with this explanation. Around ~65% of the participants show a level of performance in the multimodal association task (>50%) which could only be achieved if they were attending to both dimensions (performance attending to a single dimension would yield 25% and chance performance is at 6.25%). This suggests that participants are attending to both dimensions even in the visual and auditory mapping case.”

Conclusions:

The authors successfully demonstrate that spatial training can enhance the ability to generalize in nonspatial domains, particularly in recognizing rotated sequences. The results for the most part support their conclusions, showing that spatial representations can act as a scaffold for learning more abstract conceptual invariances. However, the study leaves room for further investigation into whether the observed effects are unique to spatial cognition or could be replicated with other forms of well-established knowledge, as well as further clarifications of the underlying mechanisms.

Impact:

The study's findings are likely to have a valuable impact on cognitive science, particularly in understanding how abstract concepts are learned and generalized. The methods and data can be useful for further research, especially in exploring the relationship between spatial cognition and abstract conceptualization. The insights could also be valuable for AI research, particularly in improving models that involve abstract pattern recognition and conceptual generalization.

In summary, the paper contributes valuable insights into the role of spatial cognition in learning abstract concepts, though it invites further research to explore the boundaries and specifics of this scaffolding effect.

Reviewer #1 (Recommendations For The Authors):

Minor issues / typos:

P6: I think the example of the "signed" mapping here should be "e.g., ABAB maps to one category and BABA maps to another", rather than "ABBA maps to another" (since ABBA would always map to another category, whether the mapping is signed or unsigned).

Done.

P11: "Next, we asked whether pre-training and mapping were systematically associated with 2Dness...". I'd recommend changing to: "Next, we asked whether accuracy during pre-training and mapping were systematically associated with 2Dness...", just to clarify what the analyzed variables are.

Done.

P13, paragraph 1: "only if the features were themselves are physical spatial locations" either "were" or "are" should be removed.

Done.

P13, paragraph 1: should be "neural representations of space form a critical substrate" (not "for").

Done.

Reviewer #2 (Recommendations For The Authors):

The authors use in multiple places in the manuscript the phrases "learn invariances" (Abstract), "formation of invariances" (p. 2, para. 1), etc. It might be just me, but this feels a bit like 'sloppy' wording: we do not learn or form invariances, rather we learn or form representations or transformations by which we can perform tasks that require invariance over particular features or transformation of the input such as the case of object recognition and size- translation- or lighting-invariance. We do not form size invariance, we have representations of objects and/or size transformations allowing the recognition of objects of different sizes. The authors might change this way of referring to the phenomenon.

We respectfully disagree with this comment. An invariance occurs when neurons make the same response under different stimulation patterns. The objects or features to which a neuron responds is shaped by its inputs. Those inputs are in turn determined by experience-dependent plasticity. This process is often called “representation learning”. We think that our language here is consistent with this status quo view in the field.

Reviewer #3 (Recommendations For The Authors):

• I understand that the objective of the present experiment is to study our ability to generalize abstract patterns of sensory data (concepts). In the introduction, the authors present examples like the concept of a "tree" (encompassing a family tree, an oak tree, and a taxonomic tree) and "ring" to illustrate the idea. However, I am sceptical as to whether these examples effectively represent the phenomena being studied. From my perspective, these different instances of "tree" do not seem to relate to the same abstract concept that is translated or rotated but rather appear to share only a linguistic label. For instance, the conceptual substance of a family tree is markedly different from that of an oak tree, lacking significant overlap in meaning or structure. Thus, to me, these examples do not demonstrate invariance to transformations such as rotations.

To elaborate further, typically, generalization involves recognizing the same object or concept through transformations. In the case of abstract concepts, this would imply a shared abstract representation rather than a mere linguistic category. While I understand the objective of the experiments and acknowledge their potential significance, I find myself wondering about the real-world applicability and relevance of such generalizations in everyday cognitive functioning. This, in turn, casts some doubt on the broader relevance of the study's results. A more fitting example, or an explanation that addresses my concerns about the suitability of the current examples, would be beneficial to further clarify the study's intent and scope.

Response in the public review.

• Relatedly, the manuscript could benefit from greater clarity in defining key concepts and elucidating the proposed mechanism behind the observed effects. Is it plausible that the changes observed are primarily due to shifts in attention induced by the spatial pre-training, rather than a change in the process of learning abstract conceptual invariances (i.e., modifications to the abstract representations themselves)? While the authors conclude that spatial pre-training acts as a scaffold for enhancing the learning of conceptual invariances, it raises the question: does this imply participants simply became more focused on spatial relationships during learning, or might this shift in attention represent a distinct strategy, and an alternative explanation? A more precise definition of these concepts and a clearer explanation of the authors' perspective on the mechanism underlying these effects would reduce any ambiguity in this regard.

Response in the public review.

• I am wondering whether the effectiveness of spatial representations in generalizing abstract concepts stems from their special nature or simply because they are a familiar 2D space for participants. It is well-established that memory benefits from linking items to familiar locations, a technique used in memory training (method of loci). This raises the question: Are we observing a similar effect here, where spatial dimensions are the only tested familiar 2D spaces, while the other 2 spaces are simply unfamiliar, as also suggested by the lower performance during training (Fig.2)? Would the results be replicable with another well-learned, robustly encoded domain, such as auditory dimensions for professional musicians, or is there something inherently unique about spatial representations that aids in bootstrapping abstract representations?

On the other side of the same coin, are spatial representations qualitatively different, or simply more efficient because they are learned more quickly and readily? This leads to the consideration that if visual pre-training and visual-to-auditory mapping were continued until a similar proficiency level as in spatial training is achieved, we might observe comparable performance in aiding generalization. Thus, the conclusion that spatial representations are a special scaffold for abstract concepts may not be exclusively due to their inherent spatial nature, but rather to the general characteristic of well-established representations. This hypothesis could be further explored by either identifying alternative 2D representations that are equally well-learned or by extending training in visual or auditory representations before proceeding with the mapping task. At the very least I believe this potential explanation should be explored in the discussion section.

Response in the public review.

I had some difficulty in following an important section of the introduction: "... whether participants can learn rotationally invariant concepts in nonspatial domains, i.e., those that are defined by sequences of visual and auditory features (rather than by locations in physical space, defined in Cartesian or polar coordinates) is not known." This was initially puzzling to me as the paragraph preceding it mentions: "There is already good evidence that nonspatial concepts are represented in a translation invariant format." While I now understand that the essential distinction here is between translation and rotation, this was not immediately apparent upon first reading. This crucial distinction, especially in the context of conceptual spaces, was not clearly established before this point in the manuscript. For better clarity, it would be beneficial to explicitly contrast and define translation versus rotation in this particular section and stress that the present study concerns rotations in abstract spaces.

Done.

• The multi-modal association is crucial for the study, however to my knowledge, it is not depicted or well explained in the main text or figures (Results section). In my opinion, the details of this task should be explained and illustrated before the details of the associated results are discussed.

We have included an illustration of a multimodal association trial in Fig. S3B.

Author response image 2.

• The observed correlation between the mapping task performance and the adoption of a 2D strategy is logical. However, this correlation might not exclusively indicate the proposed underlying mechanism of spatial scaffolding. Could it also be reflective of more general factors like overall performance, attention levels, or the effort exerted by participants? This alternative explanation suggests that the correlation might arise from broader cognitive engagement rather than specifically from the spatial nature of the task. Addressing this possibility could strengthen the argument for the unique role of spatial representations in learning abstract concepts, or at least this alternative interpretation should be mentioned.

Response in the public review.

• To me, the finding that ~30% of participants benefited from the spatial scaffolding effect for example in the auditory condition merely through exposure to the mapping (Fig 4D), without needing to see the quadruplets in the spatial modality, was somewhat surprising. This is particularly noteworthy considering that only ~60% of participants adopted the 2D strategy with exposure to rotated contingencies in Experiment 3 (Fig 3D). How do the authors interpret this outcome? It would be interesting to understand their perspective on why such a significant effect emerged from mere exposure to the mapping task.

• I appreciate the clarity Fig.1 provides in explaining a challenging experimental setup. Is it possible to provide example trials, including an illustration that shows which rotations produce the trail and an intuitive explanation that response maps onto the 1D vs 2D strategies respectively, to aid the reader in better understanding this core manipulation?

• I like that the authors provide transparency by depicting individual subject's data points in their results figures (e.g. Figs. 2 B, F, J). However, with an n=~50 per condition, it becomes difficult to intuit the distribution, especially for conditions with higher variance (e.g., Auditory). The figures might be more easily interpretable with alternative methods of displaying variances, such as violin plots per data point, conventional error shading using 95%CIs, etc.

• Why are the authors not reporting exact BFs in the results sections at least for the most important contrasts?

• While I understand why the authors report the frequencies for the best model fits, this may become difficult to interpret in some sections, given the large number of reported values. Alternatives or additional summary statistics supporting inference could be beneficial.

As the Reviewer states, there are a large number of figures that we can report in this study. We have chosen to keep this number at a minimum to be as clear as possible. To illustrate the distribution of individual data points, we have opted to display only the group's mean and standard error (the standard errors are included, but the substantial number of participants per condition provides precise estimates, resulting in error bars that can be smaller than the mean point). This decision stems from our concern that including additional details could lead to a cluttered representation with unnecessary complexity. Finally, we report what we believe to be the critical BFs for the comprehension of the reader in the main text, and choose a cutoff of 100 when BFs are high (corresponding to the label “decisive” evidence, some BFs are larger than 1012). All the exact BFs are in the supplementary for the interested readers.

Author Response

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

eLife assessment

This valuable work provides a near-complete description of the mechanosensory bristles on the Drosophila melanogaster head and the anatomy and projection patterns of the bristle mechanosensory neurons that innervate them. The data presented are solid. The study has generated numerous invaluable resources for the community that will be of interest to neuroscientists in the field of circuits and behaviour, particularly those interested in mechanosensation and behavioural sequence generation.

We express our gratitude to the Reviewers for their valuable suggestions, which significantly enhanced the manuscript. The revisions were undertaken, not with the expectation of acceptance, but rather driven by our sincere belief that these revisions would enhance the manuscript's impact for future readers.

Public Reviews:

Reviewer #1 (Public Review):

Sensory neurons of the mechanosensory bristles on the head of the fly project to the sub esophageal ganglion (SEZ). In this manuscript, the authors have built on a large body of previous work to comprehensively classify and quantify the head bristles. They broadly identify the nerves that various bristles use to project to the SEZ and describe their region-specific innervation in the SEZ. They use dye-fills, clonal labelling, and electron microscopic reconstructions to describe in detail the phenomenon of somatotopy - conserved peripheral representations within the central brain - within the innervation of these neurons. In the process they develop novel tools to access subsets of these neurons. They use these to demostrate that groups of bristles in different parts of the head control different aspects of the grooming sequence.

Reviewer #2 (Public Review):

The authors combine genetic tools, dye fills and connectome analysis techniques to generate a "first-of-its-kind", near complete, synaptic resolution map of the head bristle neurons of Drosophila. While some of the BMN anatomy was already known based on previous work by the authors and other researchers, this is the first time a near complete map has been created for the head BMNs at electron microscopy resolution.

Strengths:

(1) The authors cleverly use techniques that allow moving back and forth between periphery (head bristle location) and brain, as well as moving between light microscopy and electron microscopy data. This allows them to first characterize the pathways taken by different head BMNs to project to the brain and also characterize anatomical differences among individual neurons at the level of morphology and connectivity.

(2) The work is very comprehensive and results in a near complete map of all I’m head BMNs.

(3) Authors also complement this anatomical characterization with a first-level functional analysis using optogenetic activation of BMNs that results in expected directed grooming behavior.

Weaknesses:

(1) The clustering analysis is compelling but cluster numbers seem to be arbitrarily chosen instead of by using some informed metrics.

We made revisions to the manuscript that address this concern. Please see our response to “recommendations for authors” for a description of these revisions.

(2) It could help provide context if authors revealed some of the important downstream pathways that could explain optogenetics behavioral phenotypes and previously shown hierarchical organization of grooming sequences.

We made revisions to the manuscript that address this recommendation. Please see our response to “recommendations for authors” for a description of these revisions.

(3) In contrast to the rigorous quantitative analysis of the anatomical data, the behavioral data is analyzed using much more subjective methods. While I do not think it is necessary to perform a rigorous analysis of behaviors in this anatomy focused manuscript, the conclusions based on behavioral analysis should be treated as speculative in the current form e.g. calling "nodding + backward walking" as an avoidance response is not justified as it currently stands. Strong optogenetic activation could lead to sudden postural changes that due to purely biomechanical constraints could lead to a couple of backward steps as seen in the example videos. Moreover since the quantification is manual, it is not clear what the analyst interprets as backward walking or nodding. Interpretation is also concerning because controls show backward walking (although in fewer instances based on subjective quantification).

While unbiased machine vision-based methods would nicely complement the present work, this type of analysis is not yet working to distinguish between different head grooming movements. Therefore, we are currently limited to manual annotation for our behavioral analysis. That said, we do not believe that our manual annotation is subjective. The grooming movements that we examine in this work are distinguishable from each other through frame-by-frame manual annotation of video at 30 fps. Our annotation of the grooming and backward motions performed by flies are based on previous publications that established a controlled vocabulary defining each movement (Hampel et al., 2020a, 2017, 2015; Seeds et al., 2014). In this work, we added head nodding to this controlled vocabulary that is described in the Materials and methods. We have added additional text to the third paragraph of the Material and methods section entitled “Behavioral analysis procedures” that we hope better describes our behavioral analysis. This description now reads:

Head nodding was annotated when the fly tilted its head downward by any amount until it returned its head back in its original position. This movement often occurred in repeated cycles. Therefore, the “start” was scored at the onset of the first forward movement and the “stop” when the head returned to its original position on the last nod.

We do not make any firm conclusions about the head movements (nodding) and backwards motions. We refer to nodding as a descriptive term that would allow the reader to better understand what the behavior looks like. We make no firm conclusions about any behavioral functional role that either the nodding or the backward motions might have, with the exception of nodding in the context of grooming. We only suggest that the behaviors appear to be avoidance responses. Furthermore, backward walking was not mentioned. Instead we refer to backward motions. We are only reporting our annotations of these movements that do occur, and are significantly different from controls. We speculate that these could be avoidance responses based on support from the literature. Future studies will be required to understand whether these movements serve real behavioral roles.

Summary:

The authors end up generating a near-complete map of head BMNs that will serve as a long-standing resource to the Drosophila research community. This will directly shape future experiments aimed at modeling or functionally analyzing the head grooming circuit to understand how somatotopy guides behaviors.

Reviewer #3 (Public Review):

Eichler et al. set out to map the locations of the mechanosensory bristles on the fly head, examine the axonal morphology of the bristle mechanosensory neurons (BMNs) that innervate them, and match these to electron microscopy reconstructions of the same BMNs in a previously published EM volume of the female adult fly brain. They used BMN synaptic connectivity information to create clusters of BMNs that they show occupy different regions of the subesophageal zone brain region and use optogenetic activation of subsets of BMNs to support the claim that the morphological projections and connectivity of defined groups of BMNs are consistent with the parallel model for behavioral sequence generation.

The authors have beautifully cataloged the mechanosensory bristles and the projection paths and patterns of the corresponding BMN axons in the brain using detailed and painstaking methods. The result is a neuroanatomy resource that will be an important community resource. To match BMNs reconstructed in an electron microscopy volume of the adult fly brain, the authors matched clustered reconstructed BMNs with light-level BMN classes using a variety of methods, but evidence for matching is only summarized and not demonstrated in a way that allows the reader to evaluate the strength of the evidence. The authors then switch from morphology-based categorization to non-BMN connectivity as a clustering method, which they claim demonstrates that BMNs form a somatotopic map in the brain. This map is not easily appreciated, and although contralateral projections in some populations are clear, the distinct projection zones that are mentioned by the authors are not readily apparent. Because of the extensive morphological overlap between connectivity-based clusters, it is not clear that small projection differences at the projection level are what determines the post-synaptic connectivity of a given BMN cluster or their functional role during behavior. The claim the somatotopic organization of BMN projections is preserved among their postsynaptic partners to form parallel sensory pathways is not supported by the result that different connectivity clusters still have high cosine similarity in a number of cases (i.e. Clusters 1 and 3, or Clusters 1 and 2). Finally, the authors use tools that were generated during the light-level characterization of BMN projections to show that specifically activating BMNs that innervate different areas of the head triggers different grooming behaviors. In one case, activation of a single population of sensory bristles (lnOm) triggers two different behaviors, both eye and dorsal head grooming. This result does not seem consistent with the parallel model, which suggests that these behaviors should be mutually exclusive and rely on parallel downstream circuitry.

We made revisions to the manuscript that address this recommendation. Please see our response to “recommendations for authors” for a description of these revisions.

This work will have a positive impact on the field by contributing a complete accounting of the mechanosensory bristles of the fruit fly head, describing the brain projection patterns of the BMNs that innervate them, and linking them to BMN sensory projections in an electron microscopy volume of the adult fly brain. It will also have a positive impact on the field by providing genetic tools to help functionally subdivide the contributions of different BMN populations to circuit computations and behavior. This contribution will pave the way for further mechanistic study of central circuits that subserve grooming circuits.

Recommendations for the authors:

All three reviewers appreciated the work presented in this manuscript. There were also a few overlapping concerns that were raised that are summarised below, should the authors wish to address them:

Somatotopy: We recommend that the authors describe the extent of prior knowledge in more detail to highlight their contribution better.

We made revisions that better highlight the extent of prior knowledge about somatotopy. We describe how previous studies showed bristle mechanosensory neurons in insects are somatotopically organized, but these studies were not comprehensive descriptions of complete somatotopic maps for the head or body. To our knowledge, our study provides the first comprehensive and synaptic resolution somatotopic map of a head for any animal. This sets the stage for the complete definition of the interface between somatotopically-organized mechanosensory neurons and postsynaptic circuits, which has broad implications for future studies on aimed grooming, and mechanosensation in general. Below we itemize revisions to the Introduction, Discussion, and Figures to provide a clearer statement of the significance of our study as it relates to somatotopy.

(1) Newly added Figure 1 – figure supplement 1 more explicitly grounds the study in somatotopy, providing a working model of the organization of the circuit pathways that produce the grooming sequence. This model features somatotopy as shown in Figure 1 – figure supplement 1C.

(2) Figure 1 – figure supplement 1 is incorporated into the Introduction in the second, third, and fourth paragraphs, the first paragraph of the Results section titled “Somatotopically-organized parallel BMN pathways”, and the second and third paragraphs of the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence”.

(3) We added text to the end of the fourth paragraph of the Introduction that now reads: “In this model, parallel-projecting mechanosensory neurons that respond to stimuli at specific locations on the head or body could connect with somatotopically-organized parallel circuits that elicit grooming of those locations (Figure 1 – figure supplement 1A-C). The previous discovery of a mechanosensory-connected circuit that elicits aimed grooming of the antennae provides evidence of this organization (Hampel 2015). However, the extent to which distinct circuits elicit grooming of other locations is unknown, in part, because the somatotopic projections of the mechanosensory neurons have not been comprehensively defined for the head or body.”

(4) There is a Discussion section that further explains the extent of prior knowledge and our contributions on somatotopy that is titled “A synaptic resolution somatotopic map of the head BMNs”. Additionally, the previous version of this section had a paragraph on the broader implications of our work as it relates to somatotopy across species. In light of the reviewer comments, we decided to make this paragraph into its own Discussion section to better highlight the broader significance of our work. This section is titled “First synaptic resolution somatotopic map of the head”.

The somatotopy isn't overtly obvious - perhaps they could try mapping presynaptic sites and provide landmarks to improve visualisation.

We made the following revisions to better highlight the head BMN somatotopy. One point of confusion from the previous manuscript version stemmed from us not explicitly defining the somatotopic organization that we observed. There seemed to be confusion that we were defining the head somatotopy based only on the small projection differences among BMNs from neighboring head locations. While we believe that these small differences indeed correspond to somatotopy, we failed to highlight that there are overt differences in the brain projections of BMNs from distant locations on the head. For example, Figure 5B (right panel) shows the distinct projections between the LabNv (brown) and AntNv (blue) BMNs that innervate bristles on the ventral and dorsal head, respectively. Thus, BMN types innervating neighboring bristles show overlapping projections with small projection differences, whereas those innervating distant bristles show non overlapping projections into distinct zones.

Our analysis of postsynaptic connectivity similarity also shows somatotopic organization among the BMN postsynaptic partners, as BMN types innervating the same or neighboring bristle populations show high connectivity similarity (Figure 8, old Figure 7). Below we highlight major revisions to the text and Figures that hopefully better reveal the head somatotopy.

(1) In the last paragraph of the Introduction we added text that explicitly frames the experiments in terms of somatotopic organization: “This reveals somatotopic organization, where BMNs innervating neighboring bristles project to the same zones in the CNS while those innervating distant bristles project to distinct zones. Analysis of the BMN postsynaptic connectome reveals that neighboring BMNs show higher connectivity similarity than distant BMNs, providing evidence of somatotopically organized postsynaptic circuit pathways.”

(2) We mention an example of overt somatotopy from Figure 5 in the Results section titled “EM-based reconstruction of the head BMN projections in a full adult brain”. The text reads “For example, BMNs from the Eye- and LabNv have distinct ventral and anterior projections, respectively. This shows how the BMNs are somatotopically organized, as their distinct projections correspond to different bristle locations on the head (Figure 5B,C).”

(3) In new Figure 8 (part of old Figure 7), we modified panels that correspond to the cosine similarity analysis of postsynaptic connectivity. The major revision was to plot the cosine similarity clusters onto the head bristles so that the bristles are now colored based on their clusters (C). This shows how neighboring BMNs cluster together, and therefore show similar postsynaptic connectivity. We believe that this provides a nice visualization of somatotopic organization in BMN postsynaptic connectivity. We also added the clustering dendrogram as recommended by Reviewer #2 (Figure 8A).

(4) In new Figure 8, we added new panels (D-F) that summarize our anatomical and connectomic analysis showing different somatotopic features of the head BMNs. Different BMN types innervate bristles at neighboring and distant proximities (D). BMNs that innervate neighboring bristles project into overlapping zones (E, example of reconstructed BM-Fr and -Ant neurons with non-overlapping BM-MaPa neurons) and show postsynaptic connectivity similarity (F, example connectivity map of three BM types on cosine similarity data).

(5) To accompany the new Figure 8D-F panels, we added a paragraph to summarize the different somatotopic features of the head BMNs that were identified based on our anatomical and connectomic analysis. This is the last paragraph in the Results section titled “Somatotopically-organized parallel BMN pathways”:

Our results reveal head bristle proximity-based organization among the BMN projections and their postsynaptic partners to form parallel mechanosensory pathways. BMNs innervating neighboring bristles project into overlapping zones in the SEZ, whereas those innervating distant bristles project to distinct zones (example of BM-Fr, -Ant, and -MaPa neurons shown in Figure 8D,E). Cosine similarity analysis of BMN postsynaptic connectivity revealed that BMNs innervating the same bristle populations (same types) have the highest connectivity similarity. Figure 8F shows example parallel connections for BM-Fr, -Ant, and -MaPa neurons (vertical arrows), where the edge width indicates the number of synapses from each BMN type to their major postsynaptic partners. Additionally, BMNs innervating neighboring bristle populations showed postsynaptic connectivity similarity, while BMNs innervating distant bristles show little or none. For example, BM-Fr and -Ant neurons have connections to common postsynaptic partners, whereas BM-MaPa neurons show only weak connections with the main postsynaptic partners of BM-Fr or -Ant neurons (Figure 8F, connections under 5% of total BMN output omitted). These results suggest that BMN somatotopy could have different possible levels of head spatial resolution, from specific bristle populations (e.g. Ant bristles), to general head areas (e.g. dorsal head bristles).

We also refer to Figure 8D-F to illustrate the different somatotopic features in the Discussion. These references can be found in the following Discussion sections titled “A synaptic resolution somatotopic map of the head BMNs (fourth paragraph)”, and “Parallel circuit architecture underlying the grooming sequence (second paragraph)”.

(6) In addition to improving the Figures, we provide additional tools that enable readers to explore the BMN somatotopy in a more interactive way. That is, we provide 5 different FlyWire.ai links in the manuscript Results section that enable 3D visualization of the different reconstructed BMNs (e.g. FlyWire.ai link 1).

Note: In working on old Figure 7 to address this Reviewer suggestion, we also reordered panels A-E. We believe that this was a more logical ordering than in the previous draft. These panels are now the only data shown in Figure 7, as the cosine similarity analysis is now in Figure 8. We hope that splitting these panels into two Figures will improve manuscript readability.

Light EM Mapping: A better description of methods by which this mapping was done would be helpful. Perhaps the authors could provide a few example parallel representations of the EM and light images in the main figure would help the reader better appreciate the strength of their approach.

We have done as the Reviewers suggested and added panels to Figure 6 that show examples of the LM and EM image matching (Figure 6A,B). We added two examples that used different methods for labeling the LM imaged BMNs, including MCFO labeling of an individual BM-InOc neuron and driver line labeling of a major portion of BM-InOm neurons using InOmBMN-LexA. These panels are referred to in the first paragraph of the Results section titled “Matching the reconstructed head BMNs with their bristles”. Note that examples for all LM/EM matched BMN types are shown in Figure 6 – figure supplement 2.

We had provided Figure 6 – figure supplement 2 in the reviewed manuscript that shows all the above requested “parallel representations of the EM and light images”. However, the Reviewer critiques made us realize that the purpose of this figure supplement was not clearly indicated. Therefore, we have revised Figure 6 – figure supplement 2 and its legend to make its purpose clearer. First, we changed the legend title to better highlight its purpose. The legend is now titled: “Matching EM reconstructed BMN projections with light microscopy (LM) imaged BMNs that innervate specific bristles”. Second, we added label designations to the figure panel rows that highlight the LM and EM comparisons. That is, the rows for light microscopy images of BMNs are indicated with LM and the rows for EM reconstructed BMN images are labeled with EM. Reviewer #3 had indicated that it was not clear what labeling methods were used to visualize the LM imaged BM-InOm neurons in Figure 6 – figure supplement 2N. Therefore, we added text to the figure and the legend to better highlight the different methods used. Panels A and B were also cropped to accommodate the above mentioned revisions.

The manuscript also provides an extensive Materials and methods section that describes the different lines of evidence that were used to assign the reconstructed BMNs as specific types. We changed the title to better highlight the purpose of this methods section to “Matching EM reconstructed BMN projections with light microscopy imaged BMNs that innervate specific bristles”. The evidence used to support the assignment of the different BMN types is also summarized in Figure 6 – figure supplement 3.

Parallel circuit model: The authors motivate their study with this. We're recommending that they define expectations of such circuitry, its alternatives (including implications for downstream pathways), and behavior before they present their results. We're also recommending that they interpret their behavioural results in the context of these circuits.

Our primary motivation for doing the experiments described in this manuscript was to help define the neural circuit architecture underlying the parallel model that drives the Drosophila grooming sequence. This manuscript provides a comprehensive assessment of the first layer of this circuit architecture. A byproduct of this work is a contribution that offers immediate utility and significance to the Drosophila connectomics community. Namely, the description of the majority of mechanosensory neurons on the head, with their annotation in the recently released whole brain connectome dataset (FlyWire.ai). In writing this manuscript, we tried to balance both of these things, which was difficult to write. We very much appreciate the Reviewers' comments that have highlighted points of confusion in our original draft. We hope that the revised draft is now clearer and more logically presented. We have made revisions to the text and provided a new figure supplement (Figure 1 - figure supplement 1) and new panels in Figure 8. Below we highlight the major revisions.

(1) The Introduction was revised to more explicitly ground the study in the parallel model, while also removing details that were not pertinent to the experiments presented in the manuscript.

The first paragraph introduces different features of the parallel model. To better focus the reader on the parts of the model that were being assessed in the manuscript, we removed the following sentences: “Performance order is established by an activity gradient among parallel circuits where earlier actions have the highest activity and later actions have the lowest. A winner-take-all network selects the action with the highest activity and suppresses the others. The selected action is performed and then terminated to allow a new round of competition and selection of the next action.” Note that these sentences are included in the third and fourth paragraphs of the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence”.

The first paragraph of the Introduction now introduces a bigger picture view of the model that emphasizes the two main features: 1) a parallel circuit architecture that ensures all mutually exclusive actions to be performed in sequence are simultaneously readied and competing for output, and 2) hierarchical suppression among the parallel circuits, where earlier actions suppress later actions.

(2) Newly added Figure 1 – figure supplement 1 provides a working model of grooming (Reviewer # 1 suggestion). We now more strongly emphasize that the study aimed to define the parallel neural circuit architecture underlying the grooming sequence, focusing on the mechanosensory layer of this architecture. In particular, we refer to the new Figure 1 – figure supplement 1 that has been added to better convey the hypothesized grooming neural circuit architecture. Figure 1 – figure supplement 1 is incorporated into the Introduction (paragraphs two, three, and four), Results section titled “Somatotopically-organized parallel BMN pathways (first paragraph)”, and last Discussion section titled “Parallel circuit architecture underlying the grooming sequence (second and third paragraphs)”.

(3) New panels in Figure 8 update the model of parallel circuit organization as it relates to somatotopy (D-F). These panels show the parallel circuits hypothesized by the model, but also indicate convergence, with different possible levels of head resolution for these circuits. We describe above where these panels are referenced in the text.

(4) We added a new paragraph in the last Discussion section titled “Parallel circuit architecture underlying the grooming sequence” that better incorporates the results from this manuscript into the working model of grooming. This paragraph is shown below.

Here we define the parallel architecture of BMN types that elicit the head grooming sequence that starts with the eyes and proceeds to other locations, such as the antennae and ventral head. The different BMN types are hypothesized to connect with parallel circuits that elicit grooming of specific locations (described above and shown in Figure 1 – figure supplement 1A,C). Indeed, we identify distinct projections and connectivity among BMNs innervating distant bristles on the head, providing evidence supporting this parallel architecture (Figure 8D-F). However, we also find partially overlapping projections and connectivity among BMNs innervating neighboring bristles. Further, optogenetic activation of BMNs at specific head locations elicits grooming of both those locations and neighboring locations (Figure 9). These findings raise questions about the resolution of the parallel architecture underlying grooming. Are BMN types connected with distinct postsynaptic circuits that elicit aimed grooming of their corresponding bristle populations (e.g. Ant bristles)? Or are neighboring BMN types that innervate bristles in particular head areas connected with circuits that elicit grooming of those areas (e.g. dorsal or ventral head)? Future studies of the BMN postsynaptic circuits will be required to define the resolution of the parallel pathways that elicit aimed grooming.

Aside from this summary of major concerns, the detailed recommendations are attached below.

Reviewer #1 (Recommendations For The Authors):

I appreciate the quality and exhaustive body of work presented in this manuscript. I have a few comments that the authors may want to consider:

(1) The authors motivate this study by posing that it would allow them to uncover whether the complex grooming behaviour of flies followed a parallel model of circuit function. It would have been nice to have been introduced to what the alternative model might be and what each would mean for organisation of the circuit architecture. Some guiding schematics would go a long way in illustrating this point. Modifying the discussion along these lines would also be helpful.

We made several revisions to the manuscript that address this recommendation. Among these revisions, we added Figure 1 – figure supplement 1 that includes a working model for grooming. Please see above for a description of these revisions.

(2) The authors mention the body of work that has mapped head bristles and described somatotopy. It would be useful to discuss in more detail what these studies have shown and highlight where the gaps are that their study fills.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

(3) The dye-fills and reconstructions that are single colour could use a boundary to demarcate the SEZ. This would help in orienting the reader.

We agree with Reviewer #1 that Figure 4 and its supplements could use some indicator that would orient the reader with respect to the dye filled or stochastically labeled neurons. The images are of the entire SEZ in the ventral brain, and in the case of some panels, the background staining enables visualization of the brain (e.g. Figure 4H,M,N. To help orient the reader in this region, we added a dotted line to indicate the approximate SEZ midline. This also enables the reader to more clearly see which of the BMN types cross the midline.

Midline visual guides were added for Figure 4, Figure 4 – figure supplement 2, Figure 4 – figure supplement 3, Figure 4 – figure supplement 4, Figure 4 – figure supplement 5, Figure 4 – figure supplement 6, Figure 4 – figure supplement 7, Figure 4 – figure supplement 8, Figure 6 – figure supplement 2.

(4) The comparison between the EM and the fills/clones are not obvious. And particularly because they are not directly determined, it would be nice to have the EM reconstruction alongside the dye-fills. This would work very nicely in the supplementary figure with the multiple fills of the same bristles. I think this would really drive home the point.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

(5) Are there unnoticed black error-bars floating around in many of the gray-scale images?

The black bars were masking white scale bars in the images. We have removed the black bars and remade the images without scale bars. This was done for the following Figures: Figure 4, Figure 4 – figure supplement 2, Figure 4 – figure supplement 3, Figure 4 – figure supplement 4, Figure 4 – figure supplement 5, Figure 4 – figure supplement 6, Figure 4 – figure supplement 7, Figure 4 – figure supplement 8, Figure 6 – figure supplement 2.

Reviewer #2 (Recommendations For The Authors):

(1) The only point in the paper I found myself going back and forth between methods/supp and text was when authors discuss about the clustering. I think it would help the reader if a few sentences about cosine clustering used for connectivity based clustering were included in the main text. Also, for NBLAST hierarchical clustering, it would help if some informed metrics could be used for defining cluster numbers (e.g. Braun et al, 2010 PLOS ONE shows how Ward linkage cost could be used for hierarchical clustering).

Depending on where the cut height is placed on the dendrogram for cosine similarity of BMNs, different features of the BMN type postsynaptic connectivity are captured. As the number of clusters is increased (lower cut height), clustering is mainly among BMNs of the same type, showing that these BMNs have the highest connectivity similarity. As the number of clusters is reduced (higher cut height), BMNs innervating neighboring bristles on the head are clustered, revealing three general clusters corresponding to the dorsal, ventral, and posterior head. This reveals somatotopy based clustering among same and neighboring BMN types. The cut height shown in Figure 8 and Figure 8 – figure supplement 2 was chosen because it highlighted both of these features.

The NBLAST clustering shows similar results to the connectivity based clustering with respect to neighboring and distant BMN types. As the number of clusters increases BMNs of the same type are clustered, and these types can be further subdivided into morphologically distinct subtypes. As the number of clusters is reduced, the clustering captures neighboring BMNs. Thus, neighboring BMN types showed high morphology similarity (and proximity) with each other, and low similarity with distant BMN types.

Please see our responses to a Reviewer #3 critique below for further description of the clustering results.

On the same lines it would help if the clustering dendrograms were included in the main figure.

We thank Reviewer #2 for this comment. We have added the dendrogram to Figure 8A, a change that we feel makes this Figure much easier to understand.

(2) It could help provide intuition if the authors revealed some of the downstream targets and their implication in explaining the behavioral phenotypes.

While this will be the subject of at least two forthcoming manuscripts, we have added text to the present manuscript that provides insight into BMN postsynaptic targets. Our previous work (Hampel et al. 2015) described a mechanosensory connected neural circuit that elicits grooming of the antennae. While this previous study demonstrated that the Johnston’s organ mechanosensory neurons are synaptically and functionally connected with this circuit, our preliminary analysis indicates that it is also connected with BM-Ant neurons. We hypothesize that there are additional such circuits that are responsible for eliciting grooming of other head locations.

To better highlight potential downstream targets in the manuscript, we now mention the antennal circuit in the Introduction. This text reads: In this model, parallel-projecting mechanosensory neurons that respond to stimuli at specific locations on the head or body could connect with somatotopically-organized parallel circuits that elicit grooming of those locations (Figure 1 – figure supplement 1A-C). The previous discovery of a mechanosensory-connected circuit that elicits aimed grooming of the antennae provides evidence of this organization (Hampel 2015). However, the extent to which distinct circuits elicit grooming of other locations is unknown, in part, because the somatotopic projections of the mechanosensory neurons have not been comprehensively defined for the head or body.

There is also text in the Discussion that addresses this Reviewer comment. It describes the antennal circuit and mentions the possibility that other similar circuits may exist. This can be found in the third paragraph of the section titled “Circuits that elicit aimed grooming of specific head locations”.

(3) Authors find that opto activation of BMNs leads to grooming of targeted as well as neighboring areas. Is there any sequence observed here? i.e. first clean targeted area and then clean neighboring area? I wonder if the answer to this is something as simple as common post-synaptic targets which is essentially reducing the resolution of the BMN sensory map. Some more speculation on this interesting result could be helpful.

We appreciate and agree with this point from Reviewer #2, and have tried to better emphasize the possible implications for grooming that the overlapping projections and connectivity among BMNs innervating neighboring bristles may have. This is now better addressed in the Results and Discussion sections. Below we highlight where this is addressed:

(1) In the second paragraph of the Results section titled “Activation of subsets of head BMNs elicits aimed grooming of specific locations” we added text that suggests the possibility that grooming of the stimulated and neighboring locations could be due to the overlapping projections and connectivity. This text reads: This suggested that head BMNs elicit aimed grooming of their corresponding bristle locations, but also neighboring locations. This result is consistent with our anatomical and connectomic data indicating that BMNs innervating neighboring bristles show overlapping projections and postsynaptic connectivity similarity (see Discussion).

(2) In the fourth paragraph of the Discussion section titled “A synaptic resolution somatotopic map of the head BMNs”, we added a sentence to the end of the fourth paragraph that alludes to further discussion of this topic. This sentence reads: This overlap may have implications for aimed grooming behavior. For example, neighboring BMNs could connect with common neural circuits to elicit grooming of overlapping locations (discussed more below).

(3) In the fourth paragraph of the Discussion section titled “Circuits that elicit aimed grooming of specific head locations” there is a paragraph that mentions the possibility of mechanosensory convergence onto common postsynaptic circuits to promote grooming of the stimulated area, along with neighboring areas. This paragraph is below.

We find that activation of specific BMN types elicits both aimed grooming of their corresponding bristle locations and neighboring locations. This suggests overlap in the locations that are groomed with the activation of different BMN types. Such overlap provides a means of cleaning the area surrounding the stimulus location. Interestingly, our NBLAST and cosine similarity analysis indicates that neighboring BMNs project into overlapping zones in the SEZ and show common postsynaptic connectivity. Thus, we hypothesize that neighboring BMNs connect with common neural circuits (e.g. antennal grooming circuit) to elicit overlapping aimed grooming of common head locations.

(4) In the new second paragraph of the Discussion section titled “Parallel circuit architecture underlying the grooming sequence” we further discuss the issue of the BMN “sensory map. This paragraph is below.

Here we define the parallel architecture of BMN types that elicit the head grooming sequence that starts with the eyes and proceeds to other locations, such as the antennae and ventral head. The different BMN types are hypothesized to connect with parallel circuits that elicit grooming of specific locations (described above and shown in Figure 1 – figure supplement 1A,C). Indeed, we identify distinct projections and connectivity among BMNs innervating distant bristles on the head, providing evidence supporting this parallel architecture (Figure 8D-F). However, we also find partially overlapping projections and connectivity among BMNs innervating neighboring bristles. Further, optogenetic activation of BMNs at specific head locations elicits grooming of both those locations and neighboring locations (Figure 9). These findings raise questions about the resolution of the parallel architecture underlying grooming. Are BMN types connected with distinct postsynaptic circuits that elicit aimed grooming of their corresponding bristle populations (e.g. Ant bristles)? Or are neighboring BMN types that innervate bristles in particular head areas connected with circuits that elicit grooming of those areas (e.g. dorsal or ventral head)? Future studies of the BMN postsynaptic circuits will be required to define the resolution of the parallel pathways that elicit aimed grooming.

(4) If authors were to include a summary table that shows all known attributes about BMN type as columns that could be very useful as a resource to the community. Table columns could include attributes like "bristle name", "nerve tract", "FlyWire IDs of all segments corresponding to the bristle class". "split-Gal4 line or known enhancer" , etc.

We provided a table that includes much of this information after the manuscript had already gone out for review. We regret that this was not available. This is now provided as Supplementary file 3. This table provides the following information for each reconstructed BMN: BMN name, bristle type, nerve, flywire ID, flywire coordinates, NBLAST cluster (cut height 1), NBLAST cluster (cut height 5), and cosine cluster (cut height 4.5). Note that the driver line enhancers for targeting specific BMN types are shown in Figure 3I.

Specific Points:

Figure 4C-V:

• I find it a bit difficult to distinguish ipsi- from contra-lateral projections. Maybe indicate the midline as a thin, stippled line?

We thank the Reviewer #2 for this suggestion. We have now added lines in the panels in Figure 4C-V to indicate the approximate location of the midline. We also added lines to the Figure 4 – figure supplements as described above.

I think this Fig reference is wrong "the red-light stimulus also elicited backward motions with control flies (Figure 6B,C, control, black trace, Video 5)." should be Fig 8B,C

We have fixed this error.

Reviewer #3 (Recommendations For The Authors):

Introduction:

Motivating this study in terms of understanding the neural mechanisms that execute the parallel model seems to overstate what you will achieve with the current study. If you want to motivate it this way, I suggest focusing on the grooming sequence of the head along (eyes, antennae, proboscis).

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions. Please note that many of the revisions focus on the head grooming sequence. We also made minor revisions to the Introduction that further emphasize the focus on head grooming.

Results:

Figure 1. Please indicate that this is a male fly in either the figure title or in the figure itself.

We added a male symbol to Figure 1A.

Figure 3. Panel J is referenced in the main body text and in the figure caption, but there is no Fig 3J.

Panel J is shown in the upper right corner of Figure 3. We realize that the placement of this panel is not ideal, but this was the only place that we could fit it. Additionally, the panel works nicely at that location to better enable comparison with panel C. We have revised the text in the Figure 3 legend to better highlight the location of this Figure panel: “Shown in the upper right corner of the figure are the aligned expression patterns of InOmBMN-LexA (red), dBMN-spGAL4 (green), and TasteBMN-spGAL4 (brown).”

We also added text to a sentence in the results section entitled “Head BMNs project into discrete zones in the ventral brain” that indicates the panel location. This text reads: To further visualize the spatial relationships between these projections, we computationally aligned the expression patterns of the different driver lines into the same brain space (Figure 3J, upper right corner).

Matching the BMNs to EM reconstructions: why cut the dendrogram at H=5? Would be better to determine cluster number using an unbiased method.

To match the morphologically distinct EM reconstructed BMNs to their specific bristles, we relied on different lines of evidence, including NBLAST results (discussed more below), dye fill/stochastic labeling/driver line labeling matches, published morphology, nerve projection, bristle number, proximity to other BMNs, and postsynaptic connectivity (summarized in Figure 6 – figure supplement 3). The following Materials and methods section provides a detailed description of the evidence used to assign each BMN type in “Matching EM reconstructed BMN projections with light microscopy imaged BMNs that innervate specific bristles”. In many cases, BMN type could be assigned with confidence solely based on morphological comparisons with our light level data (e.g. dye fills), in conjunction with bristle counts to indicate an expected number of BMNs showing similar morphology. Thus, the LM/EM matches and NBLAST clustering were largely complementary.

The EM reconstructed BMNs were matched as particular BMN types, in part based on examination of the NBLAST data at different cut heights. NBLAST clustering of the BMNs revealed general trends at higher and lower cut heights (Figure 6 – figure supplement 1A, Supplementary file 3). The lowest cut heights included mostly BMNs of the same type innervating the same bristle populations, and smaller clusters that subdivided into morphologically distinct subtypes (see Supplementary file 3 for clusters produced at cut height 1). This revealed that BMNs of the same type tended to show the highest morphological similarity with each other, but they also showed intratype morphological diversity. Higher cut heights produced clusters of BMNs innervating neighboring bristles populations (e.g. ventral head BMNs), showing high morphological similarity among neighboring BMN types.

We selected the cut height 5 shown in Figure 6 – figure supplement 1A,B because it captures examples of both same and neighboring type clustering. For example, it captures a cluster of mostly BM-Taste neurons (Cluster 16), and neighboring BMN types, including those from the dorsal head (Cluster 14) or ventral head (Cluster 15).

Based on reviewer comments, we realized that the way we wrote the BMN matching section in the Results indicated more reliance on the NBLAST clustering than what was actually necessary, distorting the way we actually matched the BMNs. Therefore, we softend the first couple of sentences to place less emphasis on the importance of the NBLAST. We also indicated that the readers can find the resulting clusters at different cut heights, referring to Figure 6 – figure supplement 1A and Supplementary file 3. The first two sentences of the first paragraph in the Results section titled “Matching the reconstructed head BMNs with their bristles” now read:

The reconstructed BMN projections were next matched with their specific bristle populations. The projections were clustered based on morphological similarity using the NBLAST algorithm (example clustering at cut height 5 shown in Figure 6 – figure supplement 1A,B, Supplementary file 3, FlyWire.ai link 2) (Costa et al., 2016). Clusters could be assigned as BMN types based on their similarity to light microscopy images of BMNs known to innervate specific bristles.

The number of reconstructed BMNs is remarkably similar to what is expected based on bristle counts for each group except for lnOm. Why do you think there is such a large discrepancy there?

We believe that there is a discrepancy between the number of reconstructed BM-InOm neurons and the number expected based on InOm bristle counts because these bristle counts were based on few flies and these numbers appear to be variable. We did not further investigate the numbers of InOm bristles in this manuscript because we only needed an estimate of their numbers, given that there is over an order of magnitude difference in the eye bristles versus any other head bristle population. Therefore, we could relatively easily conclude that the head BMNs were related to the InOm bristles, based on their sheer numbers and their morphology.

Figure 6 - figure supplement 2N, please describe these panels better. Main text says the upper image is from lnOmBMN-LexA, but the figure legend doesn't agree.

We have added text to the figure legend that now makes the contents of panel 2N clear to the reader. Further, we now indicate in the figure legend for each panel, the method used to obtain the labeled neurons (i.e. fill, MCFO, driver), to avoid similar confusion for the other panels.

Figure 6 - figure supplement 4D. How frequently is there a mismatch between the number of BMNs for a given type across hemispheres?

Although the full reconstruction of the BMNs on both sides of the brain was beyond the scope of this work, the BMNs on both sides have since been reconstructed and annotated (Schlegal et al. 2023). We plan to provide more analysis of BMNs on both sides of the brain in a forthcoming manuscript. However, the BMN numbers tend to show agreement on both sides of the brain. The table below shows a comparison between the two sides:

Author response table 1.

Figures 6 and 7. It would be helpful to include a reference brain in all panels that show cluster morphology. Without landmarks there is nothing to anchor the eye to allow the reader to see the described differences in BMN projection zones and patterns.

While we apologize for not making this specific change, we have made revisions to other parts of the manuscript to better highlight the somatotopic organization among the BMNs (revisions described above). Please note that we now provide FlyWire.ai publicly available links that enable readers to view the BMN projections in 3D. They can also toggle a brain mesh on and off to provide spatial reference.

"BMN somatotopic map": It would be helpful to show or describe in more detail what the unique branch morphology for each zone is. It is quite difficult to appreciate, as the groups also have a lot of overlap. Would the unique regions that the BMN groups innervate be easier to see if you plotted presynaptic sites by group? I am left unsure about whether there is a somatotopic map here.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions. Please note that we did not examine the fine branch morphological differences between BMN types having overlapping projections. Showing these differences would require more extensive anatomical analysis that is beyond the scope of this work. For showing definitive somatotopy, we focused on the overt differences between BMNs innervating bristles at distant locations on the head.

Overall the strict adherence to the parallel model impacts the interpretation of the data. It would be helpful for the authors to discuss which aspects of the current study are consistent with the parallel model and which results are not consistent.

We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

Discussion:

"Circuits that elicit aimed grooming of specific head locations": In the previous paragraph you mention "BMN types innervating neighboring bristle populations have overlapping projections into zones that correspond roughly to the dorsal, ventral, and posterior head. The overlap is likely functionally significant, as cosine similarity analysis revealed that neighboring head BMN types have common postsynaptic partners. However, overlap between neighboring BMN types is only partial, as they show differing projections and postsynaptic connectivity." Then in this paragraph, you say, "How do the parallel-projecting head BMNs interface with postsynaptic neural circuits to elicit aimed grooming of specific head locations? Different evidence supports the hypothesis that the BMNs connect with parallel circuits that each elicit a different aimed grooming movement (Seeds et al., 2014)." The overlapping postsynaptic BMN connectivity seems in conflict with the claim that the circuits are parallel.

We apologize for this confusion. We now better describe this apparent discrepancy between our results and the parallel model of grooming behavior. We made several revisions to the manuscript that address this recommendation. Please see above for a description of these revisions.

We have made additional changes to the manuscript:

(1) We added Supplementary file 2 that includes links for downloading the image stacks used to generate panels in Figure 1, Figure 2, Figure 3, Figure 4, and figure supplements for these figures. These image stacks are stored in the Brain Image Library (BIL). Rows in the spreadsheet correspond to each image stack. Columns provide information about each stack including: figure panels that each image stack contributed to, image stack title, DOI for each stack (link provides metadata for each stack and file download link), image stack file name, genotype of imaged fly, and information about image stack. References to this file have been made at different locations throughout the text and Figure legends. We also added a section on the BIL data in the Materials and methods entitled “Light microscopy image stack storage and availability”. Old Supplementary file 2 has been renamed Supplementary file 3.

(2) We added a new reference for FlyWire.ai (Dorkenwald et al. 2023) that was posted as a preprint during the revision of this manuscript.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

This is a strong manuscript about the existence of proteins coded by intracellular parasites (here Coxiella) that have evolved to parasitise the lipid transport machinery of their hosts. This is a first in that the parasite protein acts at a distance from the parasite itself, manipulating two of the host organelles - and not acting at their site of contact with PVs. There is considerable research into one protein and its effect when expressed by itself.

Despite all the advances there are a couple of areas where the manuscript can be improved, and a few extra fairly straightforward experiments added about the amphipathic helix. Even though these are unlikely change the overall message, they would make the story more complete.

Major points

More details are required about the amphipathic helix. Check that the AH does target LDs by expression of the AH alone in a GFP chimera +/- oleate and then mutagenesis. Also show the AH in a helical wheel projection (eg by Heliquest) and say if it aligns with similar AHs in homologs (see my point below)

Fig 1B: In infected cells, do the affected LDs tend to be close to the PVs?

Also in Fig 1B: highlight small KDEL+ve ER rings around LDs here. Study whether LDs have these in infected cells without the confounder (?artefact) of EPF1 over-expression

Fig 2A the ER looks quite different here from Fig 1B, even at t0. Grossly the strands are spaced wider apart. In detail there are no rings around LDs. Can the authors explain this? Which morphology is common, especially in cells early in infection without co-expressed protein?

Fig 6 & Line 237: "As the N-terminal region of CbEPF1 is undefined": I suggest that the authors could do more here. At minimum change model to highlight the strong probability that the N term is a globular domain that functions at the LD ER interface. (What are three other unidentified LD proteins? I suggest omitting them).

Although the Alphafold prediction for EPF1 is low confidence only, in a few minutes of BLAST searching I found the homolog A0A1J8NR10_9COXI (also FFAT+ve) which has a moderately confident structural prediction for its N terminus. This model has a quite large internal hydrophobic cavity, indicating lipid transfer capability and function similar to known LTPs. This means that action as a "tether" possibly results from experiments with viral promoters (see minor point on terminology).

Minor:

Fig 2B: add more arrowheads/arrows to fit legend (says they are both multiple)

FFAT selectivity for MOSPD2: say if this fits the di Mattia or (as appears likely) it extends the known differences between VAPA/B and MOSPD2. Also say if VAPA is expected to behave as VAPB

Explain how "Mutations in the CbEPF1 FFAT motif(s) did not influence CbEPF1-GFP localization to either the host ER (Supplementary Fig. 1)". In F3mt this shows that EPF1 has a way to target ER other than FFAT/VAP. Discuss if that is via AH insertion in ER.

Also, the (admittedly low) level of ER targeting is possibly slightly reduced by F3mt, as shown by greater GFP in the nucleus in the single cells shown. If this is a feature of the whole field of cells, it implies that the FFATs normally work with the AHs to target EPF1 to the ER.

"clustering" LDs w F3mt: could this indicate dimer formation by CbEPF1? Note: to me it appears wrong to describe fig 4A as showing ER exclusion. LD proximities to each other dominate. It's not 100% clear that LDs cluster as their proximities are not universal: "LD-LD interactions" may be (very) weak.

Fig 5: can levels of EPF1 here be compared to those in cells undergoing natural infection(approximate comparison by qPCR better than nothing if no antibodies are available)? Fig 5a: would it be possible to increase the number of cells counted to attempt to make the reduced number of LD in F3mt significant?

Minor

Line 226: no sequence homology: misses the point- there is the common feature of an AH

Issue to be discussed, as probably too difficult to experiment on: when EPF1 is on the ER does it engage vap only weakly (implying a means to mask its motifs), since if the interaction is strong vap is then unable to bind other partners?

Line 245: "MOSPD2, a sole VAP that is known to localize on LD surfaces" (worth citing Zouiouich again here). Do the cells/tissues infected by Coxiella express MOSPD2?

Line 259/260: this "suggestion" about cholesterol should be toned down. It is a speculation that could be tested in future, but the data here do not suggest it.

'Tether' this word implies more than just bridging but also a role in the physical formation of the contact. Since EPF1 most likely has an LTP domain, it seems linguistically confusing to refer to it as a tether, especially since the experiments that physically later LD-ER contact involve probable over-expression.

Discuss whether it is 100% certain that EPF1 is in the host cytosol or whether some experiment(s) at a future date (proteomic/western blotting) will be needed to make that conclusion 100% secure.

Referees cross-commenting

COMMENT 1

I realise that both reviewer 1 and reviewer 3 have considered this MS carefully, but I think that their reviews could be improved in some respects. I will add two comments, one for each of the other reviews.

Reviewer 1. The review poses multiple questions to the authors suggesting that answering these questions experimentally would strengthen the paper. Some of the points seem to misunderstand what is the accepted standard for membrane cell biological research into membrane contact sites. While it might be that the authors can rebut these points, I think it is preferable to use Cross Commenting as an opportunity to address these issues beforehand.

Major Comment 1: CbEPF1 and ER-LD contact

Looking at endogenous proteins: I wondered about the same point, but I concluded that this is not likely to be possible in the scope of this submission. If it were possible then I guess the authors would have attempted it. Looking on Google Scholar I could find no example of an endogenous Coxiella proteins being tagged in the bacterial genome. So the only way to find the portion is via an antibody. Assuming the authors do not have one, I do not think we should ask for one at this stage in the publication process.

Electron microscopy: the reviewer is incorrect to say that this is necessary. It may be the gold standard, but it is a huge amount of extra work. Furthermore it is not at all necessary when the protein in question localises clearly to the interface between organelles identified by confocal microscopy.

Can a specific CbEPF1 domain be identified? Here a Amphipathic Helix has been identified, but the lack of dissection of that region by the authors explains this question by the reviewer, which is also shared by Reviewer 3. I agree with the implication that more should be done to dissect that.

Major Comment 2: CbEPF1 FFAT motifs and VAP binding

Are the two FFAT motifs redundant or synergistic? I would say that the authors have addressed that to a reasonable extent

CbEPF1 binding specificity towards a VAP/MOSPD2 Ditto

Major Comment 3: LD clustering

Since this is an effect of mutated protein only, I think that the 3 questions posed at the end here need only be addressed in Discussion.

Major Comment 4: CbEPF1-mediated increase in LD number and size

less LD upon expression of F1mt or F2mt, compared to WT: this seems wrong. The numbers are the same. The comment about IF images are unjustified as they have been quantified and do show a difference. I agree that the biological relevance is unclear, and that this might be addressed. That would require making a mutant Coxiella strain. While that would make a big different to this work, my feeling is that this is well over a year's work.I would be guided by the authors on that and I would not suggest it as required for this MS.

De novo LD production at the ER is unlikely: This statement is ill-considered as the FFAT motifs ARE required (Fig 5). Furthermore, in all systems ever reported de novo LD production takes place at the ER, so any alternative would be quite extraordinary.

Altogether, strengthening this aspect of the study: In my view, this area does not need more work and it would not be constructive to ask for more.

Major Comment 5: Functional relevance

assessing the phenotype of a Coxiella CbEPF1 mutant I agree that this would be good, but it mightn't be feasible within the confines of this one paper. In the various projects that have made transposon mutants of Coxiella, has a strain been made that affects EPF1? If not, then the authors should state this and discuss it as work for the future. The reviewers cannot expect any experiments!

Is VAP required for Coxiella intracellular growth/vacuole maturation? On the surface this suggestion seems to offer an experimental route to understanding EPF1. However, VAP binds to >100 cellular proteins, many relating to lipids traffic and a considerable number of these already lcoalised to lipids droplets (ORP2, MIGA2, VPS13A/C). It is therefore unlikely that such an experiment would be interpretable, and I recommend that this request be reconsidered.

Are LD formation induced upon infection? Are ER-LD contact increased upon infection? These are very reasonable ideas and the results would be interesting additions to this paper.

COMMENT 2 I have given one set of comments already. Here are my comments for Reviewer 3.

The review makes a few assumptions that I question. While it might be that the authors can rebut these assumptions, I think it is preferable to use Cross Commenting as an opportunity to address these issues beforehand.

Major Point 1: What is surprising is that the BFP-KDEL signal is also localizing to the LD surface: "Surprising" is misguided, as it seems to deny the probability that there is a class of proteins that sit at organelle interfaces binding to both partners simultaneously. Maybe the reviewer means "significant" here, in which case I would agree.

The authors must perform LD isolation the reviewer is incorrect to say that this must be done. It is a huge amount of extra work. Furthermore it is not at all necessary when the protein in question localises clearly to the structures, and its may not even work as the protein may need a reasonably high general concentration to avoid gradual dissociation (wit any re-association) during organelle purification.

what features of the protein enable its LD binding? Here an Amphipathic Helix has been identified, but the lack of dissection of that region by the authors explains this question by the reviewer, which is also shared by Reviewer 1. I agree with the implication that more should be done to dissect that.

Major Point 2: Quantitative image analysis:

Mander's Colocalization analyses with Costes correction are required No. The images in Figure 4 speak for themselves.

Please show the LD phenotype of untransfected, and CbEPF1-GFP transfected cells also This s a good idea.

provide a means to quantify the clustering of LDs Unnecessary. Not all findings need to be quantified.

Major Point 3:

Data depends largely on overexpression of the protein in uninfected cells. I agree

What is the localization of the protein in infected cells? I wondered about the same point, but I concluded that this is not likely to be possible in the scope of this submission. If it were possible then I guess the authors would have attempted it. Looking on Google Scholar I could find no example of an endogenous Coxiella proteins being tagged in the bacterial genome. So the only way to find the portion is via an antibody. Assuming the authors do not have one, I do not think we should ask for one at this stage in the publication process.

What happens to ER-LD contacts upon infection with C. burnetii? This is a very valid question, and answering it would not only strengthen the manuscript but should be achievable in 1-3 months.

Significance

This work takes a reasonably big step towards uncovering how parasites have mimicked the molecular machinery of contact sites, here in the context of ER-LD interactions and tantalizingly suggestive of lipid transfer at that contact site (although hard to get strong evidence for that at this stage). This provides yet more evidence for the conservation and overall importance to cells of lipid transfer at contact sites, as well as reminding us of the ability of parasites to attack every aspect of cell function.

Reviewer #1 (Public Review):

Summary:

The motivating questions are an accurate reflection of the current state of knowledge surrounding striatal pathway function. The comparisons of pathway function across striatal subregion, activation & inhibition, and task context are laudable and extremely important for advancing the subfield. Had these manipulations, to the largest extent possible been performed in single animals (e.g. activate dSPNs of DMS or DLS in the same mouse across the 3 tasks), this would have significantly strengthened the impact and conclusions that could be drawn by making this set of studies even more so internally consistent and directly comparable. While this is no longer possible, a conceptually related and fantastic contribution to the subfield (and likely beyond in terms of Opto manipulations of brain areas) would be to directly demonstrate that within their studies their DMS pathway manipulations do not impact nearby DLS activity (and vice versa). This is a significant and non-essential request. More feasibly and reasonably, it would be fantastic and strengthen the conclusions here to more fully detail their opsin expression patterns in DMS vs DLS groups and perhaps attempt to relate individual opsin profiles and fiberoptic targeting with behavioral outcomes across tests.

Strengths:

A comprehensive and paired comparison of inhibition and activation of striatal pathways across subregions and tasks is a very important and meaningful step towards reconciling contradictory results on striatal pathway function that are observed across labs (who typically focus on one subregion, one task setting, and often do not directly report comparisons of activation and inhibition).

Weaknesses:

Figure 1A - the example DMS vs DLS opsin expression and fiber targeting are not terribly convincing that the manipulations will be specific to each subregion (the example in Figure 2A is a little better but I have a similar concern still). The specificity of these manipulations is key to interpretation and conclusions and I strongly feel they should be strengthened here. The best evidence would be direct neural recordings (light in DMS, no effect in DLS, and vice versa), but this is a tall ask and not expected. The next best option, which is readily feasible, is to show not only fiberoptic targeting summaries (as in Figure 1A, Figure 2A) but also a summary of opsin spread for all animals (especially given the two examples appear to have significant spread across DMS and DLS). It would be of great benefit to the field to have these in the Allen Common Coordinate Framework. It would also be fine and useful to utilize the authors' current classical histological atlas alignment methods (e.g. Paxinos pdf). These histological summary figures would also benefit from being larger and more visible (perhaps as separate supplemental figures associated with the main figures).

Related to the above, it is a concern that the classic view is supported or not because of individual variations in virus/fiber targeting to striatal subregions which likely have greater granularity than the traditional dorsal medial vs lateral (e.g. Hunnicutt et al 2016, Foster et al 2021, Hintiryan et al 2016). Although there may not be enough animals or variation in targeting in the present study to find meaningful relationships, it would strengthen the paper and be a great benefit to the field to know whether for key findings if the strength of behavioral effects correlated with anterior/posterior or medial/lateral or dorsal/ventral fiberoptic coordinates (or the volume of opsin expression profiles).

Conceptually, a clear new idea or integrative interpretation of prior work (nor even the large body of results within this work) comes to the fore, save for the already appreciated fact that the classic view of opposing pathways is sometimes supported and sometimes not. Two tangible suggestions that I believe would facilitate the influence of this study - (1) can the authors more thoughtfully bridge the logical steps in their results sections and the prior context around them (some topic sentences jump right into results, e.g. line 195: "The inhibition experiment showed), and (2) in discussion, rather than emphasizing when/where the classic view is supported and not, more content on precisely why would be helpful. Some questions more specifically, if DMS/DLS pathway activation/inhibition is *mostly* oppositely appetitive/aversive, what does that mean in the context of spontaneous or reward-guided locomotion? Self-initiated pathway activation/inhibition is in part learned (with very intriguing differences across pathways in the expression across learning) - how should we think about striatal pathway function with regards to learning, spontaneous/innate behaviors, vs over-trained behaviors? When the classic view fails in the dorsal striatum - why? And is a complimentary "model" an actual alternative concept, a distinct mode of circuit function, or just a negative result on the classic view?

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Reply to the reviewers

We thank the reviewers for their valuable comments, which definitely make our story stronger.

2. Description of the planned revisions

Reviewer 1

Comments:

No data are shown from the genome-wide screening approach, including the common regulators of KRAS and HRAS. Information about how imaging data were processed and analysed is missing. A final table of 8 selected factors with phosphatase activity is presented without providing further insight about the selection criteria and other factors.

This information will be included in the revised manuscript. In the subsequent characterization via image-based quantification of GFP-KRAS membrane localization, a Manders´ coefficient was calculated. A respective chapter in the methods section on how this was done is missing.

This information will be provided in the revised manuscript. I would be happy to see the following analyses to strengthen the dataset:

• Reconstitution experiments and further validation to show that it is dependent on the enzymatic activity of MTMRs.

MTMR3 knockdown (KD) cells will be rescued with wildtype (WT) MTMR3 or the phosphatase mutant MTMR3 (C413S, PMID: 11676921). MTMR4 KD cells will be rescued with WT MTMR4 or the phosphatase mutant MTMR4 (C407S, PMID: 20736309). In these cells, the PM localization of KRAS and PtdSer will be examined by confocal and electron microscopy. - Additive effect upon depletion of multiple MTMRs? Are they functionally co-operative?

MTMR3 and 4 KD cells will be rescued with WT MTMR4 and 3, respectively, and the PM localization of KRAS and PtdSer will be examined by confocal and electron microscopy. - Signalling analysis is very limited (Fig. 5). Do the authors detect any defects in K-RAS driven downstream signaling in these cells upon depletion of MTMRs.

Human pancreatic ductal adenocarcinoma (PDAC) cell lines that harbor oncogenic mutant KRAS and their growth is KRAS signaling-dependent (MiaPaCa2 and AsPC1), and PDAC cell line harboring WT KRAS and their growth is KRAS signaling-independent (BxPC3) will be infected with lentivirus expressing shRNAs against MTMR 2, 3, 4 or 7. Their growth (proliferation assay) and KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) will be measure. Reviewer 2

Major comments

The unbiased siRNA screen used to identify proteins that impact KRAS membrane localization was a very nice approach to identify MTMR proteins. Although there is a clear phenotype of KRAS mislocalization associated with knockdown of the various MTMR proteins, the data provided does not prove a causational role for the MTMR proteins in maintaining PtdSer content, nor KRAS localization, at the PM. The current data does not provide a mechanism by which MTMR proteins are influencing this process, but rather speculates using existing literature that it is the loss in MTMR 3-phosphotase activity that leads to decreased PtdSer in the membrane. There is a series of conversions and exchanges that act upon PI3P (the substrate of MTMR proteins) and PI to generate PtdSer in the PM; thus, it is a dynamic process that is influenced by a variety of different proteins and transporters [3, 4, 5, 6]. To prove their single-protein-driven hypothesis, the authors should clone and express a mutant MTMR protein construct that contains an inactive phosphatase catalytic domain, to prove that it is indeed MTMR's generation of PI (which is further converted into PI4P) in the membrane that is responsible for maintaining PtdSer content and KRAS localization. Without this, there is not enough evidence to support this claim.

MTMR3 knockdown (KD) cells will be rescued with wildtype (WT) MTMR3 or the phosphatase mutant MTMR3 (C413S, PMID: 11676921). MTMR4 KD cells will be rescued with WT MTMR4 or the phosphatase mutant MTMR4 (C407S, PMID: 20736309). In these cells, the PM localization of KRAS and PtdSer will be examined by confocal and electron microscopy. In addition, the authors speculate that ORP5 is a critical intermediate in this process, and that the loss in PI4P/ORP5 at the PM following MTMR knockdown is responsible for the decrease in PtdSer at the PM. The authors should knockdown ORP5 in MTMR-wildtype cells, since it is downstream of their proposed mechanism, and see whether this leads to comparable reductions in PtdSer levels and KRAS mislocalization at the PM. This would confirm ORP5 as having a major role in this setting and would support the initial mechanistic hypothesis. These experiments are imperative to forming an appropriate conclusion, especially since some of their current data contradicts their mechanistic hypothesis: the authors identify a decrease in whole cell PtdSer content, not just PM PtdSer content, when MTMR proteins are knocked down. Based on this result, one would predict that a secondary or supporting mechanism must exist that contributes to a reduction in whole cell PtdSer content, which likely contributes to its loss at the PM as well. The authors describe in line 360 how "previous work has shown that PM PI4P depletion indirectly blocks PtdSer synthase 1 and 2 activities," to explain this reduction in total cell levels of PtdSer. The authors should look at PtdSer synthase 1 and 2 activities in the presence of MTMR knockdown, as the loss in PtdSer at the PM may rely more heavily on synthase activity than ORP-dependent transfer of PtdSer.

Investigating the PM localization of KRAS and PtdSer after silencing ORP5 in MTMR WT mammalian cell lines has been published (PMID: 31451509 and 34903667). In these studies, silencing ORP5 1) reduces the levels of PtdSer and KRAS from the plasma membrane (PM), 2) reduces KRAS signal output, 3) blocks the growth of KRAS-dependent PDAC in vitro and in vivo. These studies have been appropriately cited in our manuscript in lines 82 and 277. Although the c. Elegans model that was used to investigate downstream let-60 (RAS ortholog) activity through a multi-vulva phenotype is quite intriguing, it is more critical to assess downstream RAS pathway activation, especially in the human colorectal adenocarcinoma or the human mammary gland ductal carcinoma cell lines. Not only would this line of questioning provide a higher significance and increase the clinical applicability of these findings, but it is also crucial to support the author's claim that MTMR knockdown can influence mutant KRAS activity. Although small changes in KRAS localization to the PM can have significant effects on downstream signaling, these effects need to be measured and confirmed in this setting. The authors should perform western blots to assess the activation of both the PI3K and MAPK pathway in the MTMR knockdown cell lines.

Human pancreatic ductal adenocarcinoma (PDAC) cell lines that harbor oncogenic mutant KRAS and their growth is KRAS signaling-dependent (MiaPaCa2 and AsPC1), and PDAC cell line harboring WT KRAS and their growth is KRAS signaling-independent (BxPC3) will be infected with lentivirus expressing shRNAs against MTMR 2, 3, 4 or 7. Their growth (proliferation assay) and KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) will be measure. In addition to this, it might be important to know whether there are any changes in the levels of the KRAS protein itself, as recycling/transport pathways may be impacted by its lack of recruitment to the plasma membrane.

Total KRAS protein expression will be measured in MTMR KD cell lines. Finally, the authors show that proliferation is inhibited by MTMR knockdown as a readout of RAS activity. The authors should also assess the levels of cell death, as the inhibition of mutant KRAS in cancer cells would likely lead to cell death. The authors do not describe why reducing any one of the MTMR proteins alone is sufficient to deplete the PM of PtdSer. This sort of discussion is important for understanding compensatory or regulatory mechanisms in place between the MTMR proteins, as this may influence PtdSer levels at the PM. For example, it has been shown that MTMR2 can stabilize MTMR13 on membranes. Do the levels, stability, or localization of the other MTMR proteins change when one specific MTMR is knocked down? Is this why we see an effect on PtdSer in any one of the knockdowns? The authors should at the very least provide western blots for each of the MTMR proteins discussed in the presence of each individual MTMR knockdown.

MTMR3 knockdown (KD) cells will be rescued with WT MTMR3 or the phosphatase mutant MTMR3 (C413S, PMID: 11676921). MTMR4 KD cells will be rescued with WT MTMR4 or the phosphatase mutant MTMR4 (C407S, PMID: 20736309). In these cells, the PM localization of KRAS and PtdSer will be examined by confocal and electron microscopy. In addition, we will measure endogenous MTMR 2/3/4/7 proteins levels in the presence of each individual MTMR KD by immunoblotting. In addition to the above experiments, the MTMR hairpins should be expressed in a secondary or tertiary cell line to prove that these events are not specific to the current model used. Since their current human mammary gland ductal carcinoma cell line overexpresses a mutant KRAS-GFP construct, perhaps doing similar experiments in a cancer cell line that already expresses an endogenous mutant KRAS might provide a better model.

Human pancreatic ductal adenocarcinoma (PDAC) cell lines that harbor oncogenic mutant KRAS and their growth is KRAS signaling-dependent (MiaPaCa2 and AsPC1), and PDAC cell line harboring WT KRAS and their growth is KRAS signaling-independent (BxPC3) will be infected with lentivirus expressing shRNAs against MTMR 2, 3, 4 or 7. Their growth (proliferation assay) and KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) will be measure. Although this protein would not include a GFP-tag, other ways of visualizing its localization at the PM (such as immunofluorescent staining) could be used to confirm its localization there.

The anti-KRAS antibody for IF has not been reported to my knowledge. In addition, the effects on downstream RAS signaling could be measured through western blot of PI3K and MAPK pathways.

Human pancreatic ductal adenocarcinoma (PDAC) cell lines that harbor oncogenic mutant KRAS and their growth is KRAS signaling-dependent (MiaPaCa2 and AsPC1), and PDAC cell line harboring WT KRAS and their growth is KRAS signaling-independent (BxPC3) will be infected with lentivirus expressing shRNAs against MTMR 2, 3, 4 or 7. Their growth (proliferation assay) and KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) will be measure. Supplemental Figure 4 is incorrectly referred to in the text as Supplemental Figure 3 (line 257-258). The text reads, "Confocal microscopy further demonstrates that HRASG12V cellular localization is not disrupted after silencing MTMR 2/3/4/7 (Fig. S3)" but Figure S3 is an EM image of PM basal sheets from T47D cells expressing GFP-KRASG12V. Supplemental Figure 4 shows that mutant HRAS is unaffected by the various MTMR knockdowns.

They will be labeled correctly in the revised manuscript. Since the authors show decreased proliferation in mutant KRAS cells following MTMR knockdown, the authors should also investigate any changes to proliferation rates in mutant HRAS cell lines following MTMR knockdown. This data is necessary to prove that MTMR-driven changes in downstream RAS signaling are specific to mutant KRAS and not mutant HRAS.

Cell proliferation assay will be performed using MTMR 2/3/4/7-silenced T47D cell lines stably expressing oncogenic mutant HRAS (HRASG12V) to address this questions. It may also be important for the authors to also show any effects on wildtype RAS localization to the PM when MTMR-2,-3,-4, and -7 are knocked down, to show whether this is a oncoprotein-specific event.

Cells expressing the truncated mutant KRAS, which contains the minimal membrane anchor and does not have G-domain will be infected with lentivirus expressing shRNA against MTMR 2/3/4/7, and their localization will be examined. The representative images chosen for Figure 4 diminish the reliability of the data, as it is difficult to see a visible change in the PI3P probe between the control and MTMR knockdown cells in these images. Since the authors rely on the Mander's coefficient and the number of gold particles throughout much of the paper, having the same conclusion quantitatively but not qualitatively for these assays is confusing. Perhaps the authors should elaborate on whether MTMR knockdown has a stronger effect on PtSer and KRAS PM presence than PI3P PM presence.

We will include the discussion in the revised manuscript. They should also describe their method for identifying early endosomes, since they switch back and forth between describing the content of the PM and of early endosomes, such as in Figure 1 and Figure 4.

We will include the information in the revised manuscript. Minor comments:

An additional experiment that may add another layer of clinical applicability would be the use of an MTMR inhibitor in this cell line, to see whether similar effects can be achieved pharmacologically [7]. This would provoke other researchers to investigate MTMR inhibitors in vitro and in vivo to assess the effect on mutant KRAS cancers.

• This is an important point, but while vanadate, a general phospho-tyrosine phosphatase (PTP) inhibitor, has been reported to inhibit myotubulin, a family member of MTMR (PMID: 8995372 and 1943774), there are no commercially available MTMR-specific inhibitors. Using vanadate to inhibit MTMR proteins will produce non-specific effects by blocking other PTPs. The inclusion of cell lines that express KRAS proteins of different mutational statuses would be extremely interesting, as KRAS' orientation within the plasma membrane has been shown to be altered by these mutations. This fact should potentially be considered when choosing a secondary or tertiary cell line to do additional experiments in, but it is not necessary for the authors to elaborate on how MTMR proteins may impact different KRAS mutants for the scope of this project.

For the aforementioned experiments using human KRAS-dependent and -independent PDAC cell lines, we will use MiaPaCa2 (KRASG12C) and AsPC1 (KRASG12D). Reviewer #3

*Major comments: *

One of the two main manuscript claims indicates that KRAS12V "function" is impaired upon MTMR knockdown. While this is an obvious phenotype expected by mislocalizing KRAS from the inner PM it is not sufficiently demonstrated in the current version of the manuscript. Western blots of at least MAPK and PI3K signalling following MTMR knockdown in KRAS-dependent cell lines should be included. In addition to the T47D cells used in the manuscript, it would be ideal to include a KRAS-mutant cell line from tumour types where KRAS mutations are more frequent that in breast.

• Human pancreatic ductal adenocarcinoma (PDAC) cell lines that harbor oncogenic mutant KRAS and their growth is KRAS signaling-dependent (MiaPaCa2 and AsPC1), and PDAC cell line harboring WT KRAS and their growth is KRAS signaling-independent (BxPC3) will be infected with lentivirus expressing shRNAs against MTMR 2, 3, 4 or 7. Their growth (proliferation assay) and KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) will be measure. Since the MTMR dependent phenotypes are mutant-KRAS specific it would be interesting to study the resulting phenotypes in HRAS-mutant cell line.

Cell proliferation assay will be performed using MTMR 2/3/4/7-silenced T47D cell lines stably expressing oncogenic mutant HRAS (HRASG12V) to address these questions.

**Referee cross-commenting**

After reading the reviews of my colleagues I think there is a clear agreement on the need to further substantiate that KRAS membrane mis-localization is indeed affecting oncogenic output. The use of other KRAS addicted and non-addicted models would further enhance this analysis.

Likewise, the other two reviewers request experimental evidences to validate the role of MTMR enzymatic activity in the process. This is a pertinent request that I failed to put forward. Suggestions include the use of reconstitution experiments catalytically dead mutants. Also, the use of MTMR small molecule inhibitors is proposed. If those exist with sufficient specificity this would indeed be appropriate to perform.

Experiments addressing these comments have been described above.

3. Description of the revisions that have already been incorporated in the transferred manuscript

N/A

• *

4. Description of analyses that authors prefer not to carry out

*Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. *

Reviewer 2

R2 suggests to investigate PtdSer synthase 1 and 2 activities in presence of MTMR knockdown, as the loss in PtdSer at the PM may rely more heavily on synthase activity than ORP-dependent transfer of PtdSer.

Although it is intriguing to examine the effect of MTMR loss on the activities of PtdSer synthase 1 and 2, our lab does not have resources/techniques to carry out the experiment. * *

The results of this paper rely heavily on one experimental technique, which is calculating a Mander's coefficient and counting the co-localization of the probe of interest with the CellMask stain of the plasma membrane. How this coefficient is derived is explained in appropriate detail in the methods section of this manuscript; however, a secondary route of identifying these changes in membrane constituents would greatly enhance the paper's conclusions. This would eliminate any doubt surrounding the accuracy of the technique, since so much of the data relies on one experimental output.

In addition to Manders' coefficient for examining the colocalization of KRAS and LactC2 (the PtdSer probe) to propose KRAS/PS redistribution to endomembranes after MTMR loss. To complement this, we also performed quantitative EM to demonstrate the PM depletion of KRAS and PtdSer from the inner PM leaflet. We believe these two techniques would appropriate to investigate KRAS/PtdSer PM depletion and cellular re-distribution. * *

Reviewer 3

To further support the conclusions, oncogenic signalling should be studied in the C.elegans model by immunofluorescence of immunohistochemistry. Furthermore, although not strictly required to support the author's claims, it would be interesting to elucidate whether the inhibition of the multivulva phenotype upon MTMR knockdown in vivo results as a consequence of cell death.

Our collaborator for C. elegans study does not have resources to carry out the proposed IF and IHC experiment. Instead, we will measure KRAS signaling (e.g. phosphorylated ERK and Akt by immunoblot) and the growth of KRAS-dependent PDAC after MTMR loss. These experiments would be more clinically and physiologically relevant.

I think it's important to note that task analysis is ONLY used when learners require "detailed specification." Task analysis is important IF we assume that the ID's job is to tell the learner exactly what needs to be done and how. Task analysis is essential when using a behavioristic learning model. However, it may not be as applicable to more constructivist learning models.

Author Response

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

eLife assessment

This important study identifies the mitotic localization mechanism for Aurora B and INCENP (parts of the chromosomal passenger complex, CPC) in Trypanosoma brucei. The mechanism is different from that in the more commonly studied opisthokonts and there is solid support from RNAi and imaging experiments, targeted mutations, immunoprecipitations with crosslinking/mass spec, and AlphaFold interaction predictions. The results could be strengthened by biochemically testing proposed direct interactions and demonstrating that the targeting protein KIN-A is a motor. The findings will be of interest to parasitology researchers as well as cell biologists working on mitosis and cell division, and those interested in the evolution of the CPC.

We thank the editor and the reviewers for their thorough and positive assessment of our work and the constructive feedback to further improve our manuscript. Please find below our responses to the reviewers’ comments. Please note that the conserved glycine residue in the Switch II helix in KIN-A was mistakenly labelled as G209 in the original manuscript. We now corrected it to G210 in the revised manuscript.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The CPC plays multiple essential roles in mitosis such as kinetochore-microtubule attachment regulation, kinetochore assembly, spindle assembly checkpoint activation, anaphase spindle stabilization, cytokinesis, and nuclear envelope formation, as it dynamically changes its mitotic localization: it is enriched at inner centromeres from prophase to metaphase but it is relocalized at the spindle midzone in anaphase. The business end of the CPC is Aurora B and its allosteric activation module IN-box, which is located at the C-terminal part of INCENP. In most well-studied eukaryotic species, Aurora B activity is locally controlled by the localization module of the CPC, Survivin, Borealin, and the N-terminal portion of INCENP. Survivin and Borealin, which bind the N terminus of INCENP, recognize histone residues that are specifically phosphorylated in mitosis, while anaphase spindle midzone localization is supported by the direct microtubule-binding capacity of the SAH (single alpha helix) domain of INCENP and other microtubule-binding proteins that specifically interact with INCENP during anaphase, which are under the regulation of CDK activity. One of these examples includes the kinesin-like protein MKLP2 in vertebrates.

Trypanosoma is an evolutionarily interesting species to study mitosis since its kinetochore and centromere proteins do not show any similarity to other major branches of eukaryotes, while orthologs of Aurora B and INCENP have been identified. Combining molecular genetics, imaging, biochemistry, cross-linking IP-MS (IP-CLMS), and structural modeling, this manuscript reveals that two orphan kinesin-like proteins KIN-A and KIN-B act as localization modules of the CPC in Trypanosoma brucei. The IP-CLMS, AlphaFold2 structural predictions, and domain deletion analysis support the idea that (1) KIN-A and KIN-B form a heterodimer via their coiled-coil domain, (2) Two alpha helices of INCENP interact with the coiled-coil of the KIN-A-KIN-B heterodimer, (3) the conserved KIN-A C-terminal CD1 interacts with the heterodimeric KKT9-KKT11 complex, which is a submodule of the KKT7-KKT8 kinetochore complex unique to Trypanosoma, (4) KIN-A and KIN-B coiled-coil domains and the KKT7-KKT8 complex are required for CPC localization at the centromere, (5) CD1 and CD2 domains of KIN-A support its centromere localization. The authors further show that the ATPase activity of KIN-A is critical for spindle midzone enrichment of the CPC. The imaging data of the KIN-A rigor mutant suggest that dynamic KIN-A-microtubule interaction is required for metaphase alignment of the kinetochores and proliferation. Overall, the study reveals novel pathways of CPC localization regulation via KIN-A and KIN-B by multiple complementary approaches.

Strengths:

The major conclusion is collectively supported by multiple approaches, combining site-specific genome engineering, epistasis analysis of cellular localization, AlphaFold2 structure prediction of protein complexes, IP-CLMS, and biochemical reconstitution (the complex of KKT8, KKT9, KKT11, and KKT12).

We thank the reviewer for her/his positive assessment of our manuscript.

Weaknesses:

• The predictions of direct interactions (e.g. INCENP with KIN-A/KIN-B, or KIN-A with KKT9-KKT11) have not yet been confirmed experimentally, e.g. by domain mutagenesis and interaction studies.

Thank you for this point. It is true that we do not have evidence for direct interactions between KIN-A with KKT9-KKT11. However, the interaction between INCENP with KIN-A/KIN-B is strongly supported by our cross-linking IP-MS of native complexes. Furthermore, we show that deletion of the INCENPCPC1 N-terminus predicted to interact with KIN-A:KIN-B abolishes kinetochore localization.

• The criteria used to judge a failure of localization are not clearly explained (e.g., Figure 5F, G).

As suggested by the reviewer in recommendation #14, we have now included example images for each category (‘kinetochores’, ‘kinetochores + spindle’, ‘spindle’) along with a schematic illustration in Fig. 5F.

• It remains to be shown that KIN-A has motor activity.

We thank the reviewer for this important comment. Indeed, motor activity remains to demonstrated using an in vitro system, which is beyond the scope of this study. What we show here is that the motor domain of KIN-A effectively co-sediments with microtubules and that spindle localization of KIN-A is abolished upon deletion of the motor domain. Moreover, mutation of a conserved Glycine residue in the Switch II region (G210) to Alanine (‘rigor mutation’, (Rice et al., 1999)), renders KIN-A incapable of translocating to the central spindle, suggesting that its ATPase activity is required for this process. To clarify this point in the manuscript, we have replaced all instances, where we refer to ‘motor activity’ of KIN-A with ‘ATPase activity’ when referring to experiments performed using the KIN-A rigor mutant. In addition, we have included a Multiple Sequence Alignment (MSA) of KIN-A and KIN-B from different kinetoplastids with human Kinesin-1, human Mklp2 and yeast Klp9 in Figure 6A and S6A, showing the conservation of key motifs required for ATP coordination and tubulin interaction. In the corresponding paragraph in the main text, we describe these data as follows:

‘We therefore speculated that anaphase translocation of the kinetoplastid CPC to the central spindle may involve the kinesin motor domain of KIN-A. KIN-B is unlikely to be a functional kinesin based on the absence of several well-conserved residues and motifs within the motor domain, which are fully present in KIN-A (Li et al., 2008). These include the P-loop, switch I and switch II motifs, which form the nucleotide binding cleft, and many conserved residues within the α4-L12 elements, which interact with tubulin (Fig. S6A) (Endow et al., 2010). Consistent with this, the motor domain of KIN-B, contrary to KIN-A, failed to localize to the mitotic spindle when expressed ectopically (Fig. S2E) and did not co-sediment with microtubules in our in vitro assay (Fig. S6B).’

• The authors imply that KIN-A, but not KIN-B, interacts with microtubules based on microtubule pelleting assay (Fig. S6), but the substantial insoluble fractions of 6HIS-KINA and 6HIS-KIN-B make it difficult to conclusively interpret the data. It is possible that these two proteins are not stable unless they form a heterodimer.

This is indeed a possibility. We are currently aiming at purifying full-length recombinant KIN-A and KIN-B (along with the other CPC components), which will allow us to perform in vitro interaction studies and to investigate biochemical properties of this complex (including the role of the motor domains of KIN-A and KIN-B) within the framework of an in-depth follow-up study. To address the point above, we have added the following text in the legend corresponding to Fig. S6:

‘Microtubule co-sedimentation assay with 6HIS-KIN-A2-309 (left) and 6HIS-KIN-B2-316 (right). S and P correspond to supernatant and pellet fractions, respectively. Note that both constructs to some extent sedimented even in the absence of microtubules. Hence, lack of microtubule binding for KIN-B may be due to the unstable non-functional protein used in this study.’

• For broader context, some prior findings should be introduced, e.g. on the importance of the microtubule-binding capacity of the INCENP SAH domain and its regulation by mitotic phosphorylation (PMID 8408220, 26175154, 26166576, 28314740, 28314741, 21727193), since KIN-A and KIN-B may substitute for the function of the SAH domain.

We have modified the introduction to include the following text and references mentioned by the reviewer: ‘The localization module comprises Borealin, Survivin and the N-terminus of INCENP, which are connected to one another via a three-helical bundle (Jeyaprakash et al., 2007, 2011; Klein et al., 2006). The two modules are linked by the central region of INCENP, composed of an intrinsically disordered domain and a single alpha helical (SAH) domain. INCENP harbours microtubule-binding domains within the N-terminus and the central SAH domain, which play key roles for CPC localization and function (Samejima et al., 2015; Kang et al., 2001; Noujaim et al., 2014; Cormier et al., 2013; Wheatley et al., 2001; Nakajima et al., 2011; Fink et al., 2017; Wheelock et al., 2017; van der Horst et al., 2015; Mackay et al., 1993).’

Reviewer #2 (Public Review):

How the chromosomal passenger complex (CPC) and its subunit Aurora B kinase regulate kinetochore-microtubule attachment, and how the CPC relocates from kinetochores to the spindle midzone as a cell transitions from metaphase to anaphase are questions of great interest. In this study, Ballmer and Akiyoshi take a deep dive into the CPC in T. brucei, a kinetoplastid parasite with a kinetochore composition that varies greatly from other organisms.

Using a combination of approaches, most importantly in silico protein predictions using alphafold multimer and light microscopy in dividing T. brucei, the authors convincingly present and analyse the composition of the T. brucei CPC. This includes the identification of KIN-A and KIN-B, proteins of the kinesin family, as targeting subunits of the CPC. This is a clear advancement over earlier work, for example by Li and colleagues in 2008. The involvement of KIN-A and KIN-B is of particular interest, as it provides a clue for the (re)localization of the CPC during the cell cycle. The evolutionary perspective makes the paper potentially interesting for a wide audience of cell biologists, a point that the authors bring across properly in the title, the abstract, and their discussion.

The evolutionary twist of the paper would be strengthened 'experimentally' by predictions of the structure of the CPC beyond T. brucei. Depending on how far the authors can extend their in-silico analysis, it would be of interest to discuss a) available/predicted CPC structures in well-studied organisms and b) structural predictions in other euglenozoa. What are the general structural properties of the CPC (e.g. flexible linkers, overall dimensions, structural differences when subunits are missing etc.)? How common is the involvement of kinesin-like proteins? In line with this, it would be good to display the figure currently shown as S1D (or similar) as a main panel.

We thank the reviewer for her/his encouraging assessment of our manuscript and the appreciation on the extent of the evolutionary relevance of our work. As suggested, we have moved the phylogenetic tree previously shown in Fig. S1D to the main Fig. 1F. Our AF2 analysis of CPC proteins and (sub)complexes from other kinetoplastids failed to predict reliable interactions among CPC proteins except for that between Aurora B and the IN box. It therefore remains unclear whether CPC structures are conserved among kinetoplastids. Because components of CPC remain unknown in other euglenozoa (other than Aurora B and INCENP), we cannot perform structural predictions of CPC in diplonemids or euglenids.

It remains unclear how common the involvement of kinesin-like proteins with the CPC is in other eukaryotes, partly because we could not identify an obvious homolog of KIN-A/KIN-B outside of kinetoplastids. Addressing this question would require experimental approaches in various eukaryotes (e.g. immunoprecipitation and mass spectrometry of Aurora B) as we carried out in this manuscript using Trypanosoma brucei.

Reviewer #3 (Public Review):

Summary:

The protein kinase, Aurora B, is a critical regulator of mitosis and cytokinesis in eukaryotes, exhibiting a dynamic localisation. As part of the Chromosomal Passenger Complex (CPC), along with the Aurora B activator, INCENP, and the CPC localisation module comprised of Borealin and Survivin, Aurora B travels from the kinetochores at metaphase to the spindle midzone at anaphase, which ensures its substrates are phosphorylated in a time- and space-dependent manner. In the kinetoplastid parasite, T. brucei, the Aurora B orthologue (AUK1), along with an INCENP orthologue known as CPC1, and a kinetoplastid-specific protein CPC2, also displays a dynamic localisation, moving from the kinetochores at metaphase to the spindle midzone at anaphase, to the anterior end of the newly synthesised flagellum attachment zone (FAZ) at cytokinesis. However, the trypanosome CPC lacks orthologues of Borealin and Survivin, and T. brucei kinetochores also have a unique composition, being comprised of dozens of kinetoplastid-specific proteins (KKTs). Of particular importance for this study are KKT7 and the KKT8 complex (comprising KKT8, KKT9, KKT11, and KKT12). Here, Ballmer and Akiyoshi seek to understand how the CPC assembles and is targeted to its different locations during the cell cycle in T. brucei.

Strengths & Weaknesses:

Using immunoprecipitation and mass-spectrometry approaches, Ballmer and Akiyoshi show that AUK1, CPC1, and CPC2 associate with two orphan kinesins, KIN-A and KIN-B, and with the use of endogenously expressed fluorescent fusion proteins, demonstrate for the first time that KIN-A and KIN-B display a dynamic localisation pattern similar to other components of the CPC. Most of these data provide convincing evidence for KIN-A and KIN-B being bona fide CPC proteins, although the evidence that KIN-A and KIN-B translocate to the anterior end of the new FAZ at cytokinesis is weak - the KIN-A/B signals are very faint and difficult to see, and cell outlines/brightfield images are not presented to allow the reader to determine the cellular location of these faint signals (Fig S1B).

We thank the reviewer for their thorough assessment of our manuscript and the insightful feedback to further improve our study. To address the point above, we have acquired new microscopy data for Fig. S1B and S1C, which now includes phase contrast images, and have chosen representative cells in late anaphase and telophase. We hope that the signal of Aurora BAUK1, KIN-A and KIN-B at the anterior end of the new FAZ can be now distinguished more clearly.

They then demonstrate, by using RNAi to deplete individual components, that the CPC proteins have hierarchical interdependencies for their localisation to the kinetochores at metaphase. These experiments appear to have been well performed, although only images of cell nuclei were shown (Fig 2A), meaning that the reader cannot properly assess whether CPC components have localised elsewhere in the cell, or if their abundance changes in response to depletion of another CPC protein.

We chose to show close-ups of the nucleus to highlight the different localization patterns of CPC proteins under the different RNAi conditions. In none of these conditions did we observe mis-localization of CPC subunits to the cytoplasm. To clarify this point, we added the following sentence in the legend for Figure 2A:

‘A) Representative fluorescence micrographs showing the localization of YFP-tagged Aurora BAUK1, INCENPCPC1, KIN-A and KIN-B in 2K1N cells upon RNAi-mediated knockdown of indicated CPC subunits. Note that nuclear close-ups are shown here. CPC proteins were not detected in the cytoplasm. RNAi was induced with 1 μg/mL doxycycline for 24 h (KIN-B RNAi) or 16 h (all others). Cell lines: BAP3092, BAP2552, BAP2557, BAP3093, BAP2906, BAP2900, BAP2904, BAP3094, BAP2899, BAP2893, BAP2897, BAP3095, BAP3096, BAP2560, BAP2564, BAP3097. Scale bars, 2 μm.’

Ballmer and Akiyoshi then go on to determine the kinetochore localisation domains of KIN-A and KIN-B. Using ectopically expressed GFP-tagged truncations, they show that coiled-coil domains within KIN-A and KIN-B, as well as a disordered C-terminal tail present only in KIN-A, but not the N-terminal motor domains of KIN-A or KIN-B, are required for kinetochore localisation. These data are strengthened by immunoprecipitating CPC complexes and crosslinking them prior to mass spectrometry analysis (IP-CLMS), a state-of-the-art approach, to determine the contacts between the CPC components. Structural predictions of the CPC structure are also made using AlphaFold2, suggesting that coiled coils form between KIN-A and KIN-B, and that KIN-A/B interact with the N termini of CPC1 and CPC2. Experimental results show that CPC1 and CPC2 are unable to localise to kinetochores if they lack their N-terminal domains consistent with these predictions. Altogether these data provide convincing evidence of the protein domains required for CPC kinetochore localisation and CPC protein interactions. However, the authors also conclude that KIN-B plays a minor role in localising the CPC to kinetochores compared to KIN-A. This conclusion is not particularly compelling as it stems from the observation that ectopically expressed GFP-NLS-KIN-A (full length or coiled-coil domain + tail) is also present at kinetochores during anaphase unlike endogenously expressed YFP-KIN-A. Not only is this localisation probably an artifact of the ectopic expression, but the KIN-B coiled-coil domain localises to kinetochores from S to metaphase and Fig S2G appears to show a portion of the expressed KIN-B coiled-coil domain colocalising with KKT2 at anaphase. It is unclear why KIN-B has been discounted here.

As the reviewer points out, a small fraction of GFP-NLS-KIN-B317-624 is indeed detectable at kinetochores in anaphase, although most of the protein shows diffuse nuclear staining. There are various explanations for this phenomenon: It is conceivable that the KIN-B motor domain may contribute to microtubule binding and translocation of the CPC from kinetochores onto the spindle in anaphase. In our experiments, ectopically expressed KIN-B317-624 likely outcompetes a fraction of endogenous KIN-B for binding to KIN-A, which could interfere with this translocation process, leaving a population of CPC ‘stranded’ at kinetochores in anaphase. Another possibility, hinted at by the reviewer, is that the C-terminus of KIN-B interacts with receptors at the kinetochore/centromere. Although we do not discount this possibility, we nevertheless decided to focus on KIN-A in this study, because the anaphase kinetochore retention phenotype for both full-length GFP-NLS-KIN-A and -KIN-A309-862 is much stronger than for KIN-B317-624. Two additional reasons were that (i) KIN-A is highly conserved within kinetoplastids, whereas KIN-B orthologs are missing in some kinetoplastids, and (ii) no convincing interactions between KIN-B and kinetochore proteins were predicted by AF2.

To address the reviewer’s point, we decided to include KIN-B in the title of this manuscript, which now reads: ‘Dynamic localization of the chromosomal passenger complex is controlled by the orphan kinesins KIN-A and KIN-B in the kinetoplastid parasite Trypanosoma brucei’.

Moreover, we modified the corresponding paragraph in the results section as follows:

‘Intriguingly, unlike endogenously YFP-tagged KIN-A, ectopically expressed GFP fusions of both full-length KIN-A and KIN-A310-862 clearly localized at kinetochores even in anaphase (Figs. 2, F and H). Weak anaphase kinetochore signal was also detectable for KIN-B317-624 (Fig. S2F). GFP fusions of the central coiled-coil domain or the C-terminal disordered tail of KIN-A did not localize to kinetochores (data not shown). These results show that kinetochore localization of the CPC is mediated by KIN-A and KIN-B and requires both the central coiled-coil domain as well as the C-terminal disordered tail of KIN-A.’

Next, using a mixture of RNAi depletion and LacI-LacO recruitment experiments, the authors show that kinetochore proteins KKT7 and KKT9 are required for AUK1 to localise to kinetochores (other KKT8 complex components were not tested here) and that all components of the KKT8 complex are required for KIN-A kinetochore localisation. Further, both KKT7 and KKT8 were able to recruit AUK1 to an ectopic locus in the S phase, and KKT7 recruited KKT8 complex proteins, which the authors suggest indicates it is upstream of KKT8. However, while these experiments have been performed well, the reciprocal experiment to show that KKT8 complex proteins cannot recruit KKT7, which could have confirmed this hierarchy, does not appear to have been performed. Further, since the LacI fusion proteins used in these experiments were ectopically expressed, they were retained (artificially) at kinetochores into anaphase; KKT8 and KIN-A were both able to recruit AUK1 to LacO foci in anaphase, while KKT7 was not. The authors conclude that this suggests the KKT8 complex is the main kinetochore receptor of the CPC - while very plausible, this conclusion is based on a likely artifact of ectopic expression, and for that reason, should be interpreted with a degree of caution.

We previously showed that RNAi-mediated depletion of KKT7 disrupts kinetochore localization of KKT8 complex members, whereas kinetochore localization of KKT7 is unaffected by disruption of the KKT8 complex (Ishii and Akiyoshi, 2020). Moreover, in contrast to the KKT8 complex, KKT7 remains at kinetochores in anaphase (Akiyoshi and Gull, 2014). These data show that KKT7 is upstream of the KKT8 complex. In this context, the LacI-LacO tethering approach can be very useful to probe whether two proteins (or domains of proteins) could interact in vivo either directly or indirectly. However, a recruitment hierarchy cannot be inferred from such experiments because the data just shows whether X can recruit Y to an ectopic locus (but not whether X is upstream of Y or vice versa). Regarding the retention of Aurora BAUK1 at kinetochores in anaphase upon ectopic expression of GFP-KKT8-LacI, we agree with the reviewer that these data need to be carefully interpreted. Nevertheless, the notion that the KKT7-KKT8 complex recruits the CPC to kinetochores is also strongly supported by IP-MS, RNAi experiments, and AF2 predictions. For clarification and to address the reviewer’s point, we re-formulated the corresponding paragraph in the main text:

‘We previously showed that KKT7 lies upstream of the KKT8 complex (Ishii and Akiyoshi, 2020). Indeed, GFP-KKT72-261-LacI recruited tdTomato-KKT8, -KKT9 and -KKT12 (Fig. S4E). Expression of both GFP-KKT72-261-LacI and GFP-KKT8-LacI resulted in robust recruitment of tdTomato-Aurora BAUK1 to LacO foci in S phase (Figs. 4, E and F). Intriguingly, we also noticed that, unlike endogenous KKT8 (which is not present in anaphase), ectopically expressed GFP-KKT8-LacI remained at kinetochores during anaphase (Fig. 4F). This resulted in a fraction of tdTomato-Aurora BAUK1 being trapped at kinetochores during anaphase instead of migrating to the central spindle (Fig. 4F). We observed a comparable situation upon ectopic expression of GFP-KIN-A, which is retained on anaphase kinetochores together with tdTomato-KKT8 (Fig. S4F). In contrast, Aurora BAUK1 was not recruited to LacO foci marked by GFP- KKT72-261-LacI in anaphase (Fig. 4E).’

Further IP-CLMS experiments, in combination with recombinant protein pull-down assays and structural predictions, suggested that within the KKT8 complex, there are two subcomplexes of KKT8:KKT12 and KKT9:KKT11, and that KKT7 interacts with KKT9:KKT11 to recruit the remainder of the KKT8 complex. The authors also assess the interdependencies between KKT8 complex components for localisation and expression, showing that all four subunits are required for the assembly of a stable KKT8 complex and present AlphaFold2 structural modelling data to support the two subcomplex models. In general, these data are of high quality and convincing with a few exceptions. The recombinant pulldown assay (Fig. 4H) is not particularly convincing as the 3rd eluate gel appears to show a band at the size of KKT11 (despite the labelling indicating no KKT11 was present in the input) but no pulldown of KKT9, which was present in the input according to the figure legend (although this may be mislabeled since not consistent with the text). The text also states that 6HIS-KKT8 was insoluble in the absence of KKT12, but this is not possible to assess from the data presented.

We thank the reviewer for pointing out an error in the text: ‘Removal of both KKT9 and KKT11 did not impact formation of the KKT8:KKT12 subcomplex’ should read ‘Removal of either KKT9 or KKT11 did not impact formation of the KKT8:KKT12 subcomplex’. Regarding the very faint band perceived to be KKT11 in the 3rd eluate: This band runs slightly lower than KKT11 and likely represents a bacterial contaminant (which we have seen also in other preps in the past). We have made a note of this in the corresponding legend (new Fig. 4I). Moreover, we provide the estimated molecular weights for each subunit, as suggested by the reviewer in recommendation #14 (see below):

‘(I) Indicated combinations of 6HIS-tagged KKT8 (~46 kDa), KKT9 (~39 kDa), KKT11 (~29 kDa) and KKT12 (~23 kDa) were co-expressed in E. coli, followed by metal affinity chromatography and SDS-PAGE. The asterisk indicates a common contaminant.’

The corresponding paragraph in the results section now reads:

To validate these findings, we co-expressed combinations of 6HIS-KKT8, KKT9, KKT11 and KKT12 in E. coli and performed metal affinity chromatography (Fig. 4I). 6HIS-KKT8 efficiently pulled down KKT9, KKT11 and KKT12, as shown previously (Ishii and Akiyoshi, 2020). In the absence of KKT9, 6HIS-KKT8 still pulled down KKT11 and KKT12. Removal of either KKT9 or KKT11 did not impact formation of the KKT8:KKT12 subcomplex. In contrast, 6HIS-KKT8 could not be recovered without KKT12, indicating that KKT12 is required for formation of the full KKT8 complex. These results support the idea that the KKT8 complex consists of KKT8:KKT12 and KKT9:KKT11 subcomplexes.’

It is also surprising that data showing the effects of KKT8, KKT9, and KKT12 depletion on KKT11 localisation and abundance are not presented alongside the reciprocal experiments in Fig S4G-J.

YFP-KKT11 is delocalized upon depletion of KKT8 and KKT9 (see below). Unfortunately, we were unsuccessful in our attempts at deriving the corresponding KKT12 RNAi cell line, rendering this set of data incomplete. Because these data are not of critical importance for this study, we decided not to invest more time in attempting further transfections.

Author response image 1.

The authors also convincingly show that AlphaFold2 predictions of interactions between KKT9:KKT11 and a conserved domain (CD1) in the C-terminal tail of KIN-A are likely correct, with CD1 and a second conserved domain, CD2, identified through sequence analysis, acting synergistically to promote KIN-A kinetochore localisation at metaphase, but not being required for KIN-A to move to the central spindle at anaphase. They then hypothesise that the kinesin motor domain of KIN-A (but not KIN-B which is predicted to be inactive based on non-conservation of residues key for activity) determines its central spindle localisation at anaphase through binding to microtubules. In support of this hypothesis, the authors show that KIN-A, but not KIN-B can bind microtubules in vitro and in vivo. However, ectopically expressed GFP-NLS fusions of full-length KIN-A or KIN-A motor domain did not localise to the central spindle at anaphase. The authors suggest this is due to the GPF fusion disrupting the ATPase activity of the motor domain, but they provide no evidence that this is the case. Instead, they replace endogenous KIN-A with a predicted ATPase-defective mutant (G209A), showing that while this still localises to kinetochores, the kinetochores were frequently misaligned at metaphase, and that it no longer concentrates at the central spindle (with concomitant mis-localisation of AUK1), causing cells to accumulate at anaphase. From these data, the authors conclude that KIN-A ATPase activity is required for chromosome congression to the metaphase plate and its central spindle localisation at anaphase. While potentially very interesting, these data are incomplete in the absence of any experimental data to show that KIN-A possesses ATPase activity or that this activity is abrogated by the G209A mutation, and the conclusions of this section are rather speculative.

Thank you for this important comment, which relates to a similar point raised by Reviewer 1 (see above). Indeed, ATPase and motor activity of KIN-A remain to demonstrated biochemically using recombinant proteins, which is beyond the scope of this study. We generated MSAs of KIN-A and KIN-B from different kinetoplastids with human Kinesin-1, human Mklp2 and yeast Klp9, which are now presented in Figure 6A and S6A. These clearly show that key motifs required for ATP or tubulin binding in other kinesins are highly conserved in KIN-A (but not KIN-B). This includes the conserved glycine residue in the Switch II helix (G234 in human Kinesin-1, G210 in T. brucei KIN-A), which forms a hydrogen bond with the γ-phosphate of ATP, and upon mutation has been shown to impair ATPase activity and trap the motor head in a strong microtubule (‘rigor’) state (Rice et al., 1999; Sablin et al., 1996). The prominent rigor phenotype of KIN-AG210A is consistent with KIN-A having ATPase activity. In addition to the data in Fig. 6A and S6A, we made following changes to the main text:

‘We therefore speculated that anaphase translocation of the kinetoplastid CPC to the central spindle may involve the kinesin motor domain of KIN-A. KIN-B is unlikely to be a functional kinesin based on the absence of several well-conserved residues and motifs within the motor domain, which are fully present in KIN-A (Li et al., 2008). These include the P-loop, switch I and switch II motifs, which form the nucleotide binding cleft, and many conserved residues within the α4-L12 elements, which interact with tubulin (Fig. S6A) (Endow et al., 2010). Consistent with this, the motor domain of KIN-B, contrary to KIN-A, failed to localize to the mitotic spindle when expressed ectopically (Fig. S2E) and did not co-sediment with microtubules in our in vitro assay (Fig. S6B).

Ectopically expressed GFP-KIN-A and -KIN-A2-309 partially localized to the mitotic spindle but failed to concentrate at the midzone during anaphase (Figs. 2, F and G), suggesting that N-terminal tagging of the KIN-A motor domain may interfere with its function. To address whether the ATPase activity of KIN-A is required for central spindle localization of the CPC, we replaced one allele of KIN-A with a C-terminally YFP-tagged G210A ATP hydrolysis-defective rigor mutant (Fig. 6A) (Rice et al., 1999) and used an RNAi construct directed against the 3’UTR of KIN-A to deplete the untagged allele. The rigor mutation did not affect recruitment of KIN-A to kinetochores (Figs. S6, C and D). However, KIN-AG210A-YFP marked kinetochores were misaligned in ~50% of cells arrested in metaphase, suggesting that ATPase activity of KIN-A promotes chromosome congression to the metaphase plate (Figs. S6, E-H).’

Impact:

Overall, this work uses a wide range of cutting-edge molecular and structural predictive tools to provide a significant amount of new and detailed molecular data that shed light on the composition of the unusual trypanosome CPC and how it is assembled and targeted to different cellular locations during cell division. Given the fundamental nature of this research, it will be of interest to many parasitology researchers as well as cell biologists more generally, especially those working on aspects of mitosis and cell division, and those interested in the evolution of the CPC.

We thank the reviewer for his/her feedback and thoughtful and thorough assessment of our study.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Why did the authors omit KIN-B from the title?

We decided to add KIN-B in the title. Please see our response to Reviewer #3 (public review).

(2) Abstract, line 28, "Furthermore, the kinesin motor activity of KIN-A promotes chromosome alignment in prometaphase and CPC translocation to the central spindle upon anaphase onset." This must be revised - see public review.

We changed this section of the abstract as follows:

‘Furthermore, the ATPase activity of KIN-A promotes chromosome alignment in prometaphase and CPC translocation to the central spindle upon anaphase onset. Thus, KIN-A constitutes a unique ‘two-in-one’ CPC localization module in complex with KIN-B, which directs the CPC to kinetochores (from S phase until metaphase) via its C-terminal tail, and to the central spindle (in anaphase) via its N-terminal kinesin motor domain.’

(3) Line 87-90. The findings by Li et al., 2008 (KIN-A and KIN-B interacting with Aurora B and epistasis analysis) should be introduced more comprehensively in the Introduction section.

We added the following sentence in the introduction:

‘In addition, two orphan kinesins, KIN-A and KIN-B, have been proposed to transiently associate with Aurora BAUK1 during mitosis (Li et al., 2008; Li, 2012).’

(4) Figure 1B. The way the Trypanosoma cell cycle is defined should be briefly explained in the main text, rather than just referring to the figure.

The ‘KN’ annotation of the trypanosome cell cycle is explained in the Figure 1 legend. We now also added a brief description in the main text:

‘We next assessed the localization dynamics of fluorescently tagged KIN-A and KIN-B over the course of the cell cycle (Figs. 1, B-E). T. brucei possesses two DNA-containing organelles, the nucleus (‘N’) and the kinetoplast (‘K’). The kinetoplast is an organelle found uniquely in kinetoplastids, which contains the mitochondrial DNA and replicates and segregates prior to nuclear division. The ‘KN’ configuration serves as a good cell cycle marker (Woodward and Gull, 1990; Siegel et al., 2008).’

(5) Line 118. Throughout the paper, it is not clear why GFP-NLS fusion was used instead of GFP fusion. Please justify the fusion of NLS.

NLS refers to a short ‘nuclear localization signal’ (TGRGHKRSREQ) (Marchetti et al., 2000), which ensures that the ectopically expressed construct is imported into the nucleus. When we previously expressed truncations of KKT2 and KKT3 kinetochore proteins, many fragments did not go into the nucleus presumably due to the lack of an NLS, which prevented us from determining which domains are responsible for their kinetochore localization. We have since then consistently used this short NLS sequence in our inducible GFP fusions in the past without any complications. We added a sentence in the Materials & Methods section under Trypanosome culture: ‘All constructs for ectopic expression of GFP fusion proteins include a short nuclear localization signal (NLS) (Marchetti et al., 2000).’ To avoid unnecessary confusion, we removed ‘NLS’ from the main text and figures.

(6) Line 121, "Unexpectedly". It is not clear why this was unexpected.

To clarify this point, we modified this paragraph in the results section:

‘To our surprise, KIN-A-YFP and GFP-KIN-B exhibited a CPC-like localization pattern identical to that of Aurora BAUK1: Both kinesins localized to kinetochores from S phase to metaphase, and then translocated to the central spindle in anaphase (Figs. 1, C-E). Moreover, like Aurora BAUK1, a population of KIN-A and KIN-B localized at the new FAZ tip from late anaphase onwards (Figs. S1, B and C). This was unexpected, because KIN-A and KIN-B were previously reported to localize to the spindle but not to kinetochores or the new FAZ tip (Li et al., 2008). These data suggest that KIN-A and KIN-B are bona fide CPC proteins in trypanosomes, associating with AuroraAUK1, INCENPCPC1 and CPC2 throughout the cell cycle.’

(7) Line 127-129. Defining homologs and orthologs is tricky - there are many homologs and paralogs of kinesin-like proteins. The method to define the presence or absence of KIN-A/KIN-B homologs should be described in the Materials and Methods section.

Due to the difficulty in defining true orthologs for kinesin-like proteins, we took a conservative approach: reciprocal best BLAST hits. We first searched KIN-A homologs using BLAST in the TriTryp database or using hmmsearch using manually prepared hmm profiles. When the top hit in a given organism found T. brucei KIN-A in a reciprocal BLAST search in T. brucei proteome, we considered the hit as a true ortholog. We modified the Materials and Methods section as below.

‘Searches for homologous proteins were done using BLAST in the TriTryp database (Aslett et al., 2010) or using hmmsearch using manually prepared hmm profiles (HMMER version 3.0; Eddy, 1998). The top hit was considered as a true ortholog only if the reciprocal BLAST search returned the query protein in T. brucei.’

(8) Line 156. For non-experts of Trypanosoma cell biology, it is not clear how the nucleolar localization is defined.

The nucleolus in T. brucei is discernible as a DAPI-dim region in the nucleus.

(9) Fig.2G and Fig.S2F. These data imply that the coiled-coil and C-terminal tail domains of KIN-A/KIN-B are important for anaphase spindle midzone enrichment. However, it is odd that this was not mentioned. This reviewer recommends that the authors quantify the midzone localization data of these constructs and discuss the role of the coiled-coil domains.

One possibility is that KIN-A and KIN-B need to form a complex (via their coiled-coil domains) to localize to the spindle midzone. Another likely possibility, which is discussed in the manuscript, is that N-terminal tagging of KIN-A impairs motor activity. This is supported by the fact that the central spindle localization is also disrupted in full-length GFP-KIN-A. We decided not to provide a quantification for these data due to low sample sizes for some of the constructs (e.g. expression not observed in all cells).

(10) Line 288-289, "pLDDT scores improved significantly for KIN-A CD1 in complex with KKT9:KKT11 (>80) compared to KIN-A CD1 alone (~20) (Figs. S3, A and B)." I can see that pLDDT score is about 20 at KIN-A CD1 from Figs S3A, but the basis of pLDDT > 80 upon inclusion go KKT9:KKT11 is missing.

We added the pLDDT and PAE plots for the AF2 prediction of KIN-A700-800 in complex with KKT9:KKT11 in Fig. S5B.

(11) Fig. 5A. Since there is no supporting biochemical data for KIN-A-KKT9-KKT11 interaction, it is important to assess the stability of AlphaFold-based structural predictions of the KIN-A-KKT9-KKT11 interaction. Are there significant differences among the top 5 prediction results, and do these interactions remain stable after the "simulated annealing" process used in the AlphaFold predictions? Are predicted CD1-interacting regions/amino residues in KKT9 and KKT11 evolutionarily conserved?

See above. The interaction was predicted in all 5 predictions as shown in Fig. S5B. Conservation of the CD1-interacting regions in KKT9 and KKT11 are shown below:

Author response image 2.

KKT9 (residues ~53 – 80 predicted to interact with KIN-A in T. brucei)

Author response image 3.

KKT11 (residues 61-85 predicted to interact with KIN-A in T. brucei)

(12) Line 300, Fig. S5D and E, "failed to localize at kinetochores". From this resolution of the microscopy images, it is not clear if these proteins fail to localize at kinetochores as the KKT and KIN-A310-716 signals overlap. Perhaps, "failed to enrich at kinetochores" is a more appropriate statement.

We changed this sentence according to the reviewer’s suggestion.

(13) Line 309 and Fig 5D and F, "predominantly localized to the mitotic spindle". From this image shown in Fig 5D, it is not clear if KIN-A∆CD1-YFP and Aurora B are predominantly localized to the spindle or if they are still localized to centromeres that are misaligned on the spindle. Without microtubule staining, it is also not clear how microtubules are distributed in these cells. Please clarify how the presence or absence of kinetochore/spindle localization was defined.

As shown in Fig. S5E and S5F, deletion of CD1 clearly impairs kinetochore localization of KIN-A (kinetochores marked by tdTomato-KKT2). Moreover, misalignment of kinetochores, as observed upon expression of the KIN-AG210A rigor mutant, would result in an increase in 2K1N cells and proliferation defects, which is not the case for the KIN-A∆CD1 mutant (Fig. 5H, Fig. S5I). KIN-A∆CD1-YFP appears to localize diffusely along the entire length of the mitotic spindle, whereas we still observe kinetochore-like foci in the rigor mutant. Unfortunately, we do not have suitable antibodies that would allow us to distinguish spindle microtubules from the vast subpellicular microtubule array present in T. brucei and hence need to rely on tagging spindle-associated proteins such as MAP103.

(14) Fig. 5F, G, S5F. Along the same lines, it would be helpful to show example images for each category - "kinetochores", "kinetochores + spindle", and "spindle".

As suggested by the reviewer, we have now included example images for each category (‘kinetochores’, ‘kinetochores + spindle’, ‘spindle’) along with a schematic illustration in Fig. 5F.

(15) Line 332 and Fig. S6A. The experiment may be repeated in the presence of ATP or nonhydrolyzable ATP analogs.

We thank the reviewer for the suggestion. We envisage such experiments for an in-depth follow-up study.

(16) Line 342, "motor activity of KIN-A". Until KIN-A is shown to have motor activity, the result based on the rigor mutant does not show that the motor activity of KIN-A promotes chromosome congression. The result suggests that the ATPase activity of KIN-A is important.

We changed that sentence as suggested by the reviewer.

(17) Line 419 -. The authors base their discussion on the speculation that KIN-A is a plus-end directed motor. Please justify this speculation.

Indeed, the notion that KIN-A is a plus-end directed motor remains a hypothesis, which is based on sequence alignments with other plus-end directed motors and the observation that the KIN-A motor domain is involved in translocation of the CPC to the central spindle in anaphase. We have modified the corresponding section in the discussion as follows:

‘It remains to be investigated whether KIN-A truly functions as a plus-end directed motor. The role of the KIN-B in this context is equally unclear. Since KIN-B does not possess a functional kinesin motor domain, we deem it unlikely that the KIN-A:KIN-B heterodimer moves hand-over-hand along microtubules as do conventional (kinesin-1 family) kinesins. Rather, the KIN-A motor domain may function as a single-headed unit and drive processive plus-end directed motion using a mechanism similar to the kinesin-3 family kinesin KIF1A (Okada and Hirokawa, 1999).’

(18) Line 422-423, "plus-end directed motion using a mechanism similar to kinesin-3 family kinesins (such as KIF1A)." Please cite a reference supporting this statement.

See above. We cited a paper by (Okada and Hirokawa, 1999).

Reviewer #2 (Recommendations For The Authors):

Please provide a quantification of data shown in Figure 2F-H and described in lines 151-166.

We decided not to provide a quantification for these data due to low sample sizes for some of the constructs (e.g. expression not observed in all cells).

It appears as if the paper more or less follows a chronological order of the experiments that were performed before AF multimer enabled the insightful and compelling structural analysis. That is a matter of style, but in some cases, the writing could be updated, shortened, or re-arranged into a more logical order. Concrete examples:

(i) Line 144: "we did not include CPC2 for further analysis in this study" Although CPC2 features at a prominent and interesting position in the predicted structures of the kinetoplastid CPC, shown in later main figures.

We attempted RNAi-mediated depletion of CPC2 using two different shRNA constructs. However, we cannot exclude the possibility that the knockdown of CPC2 was less efficient compared with the other CPC subunits. For this reason, we decided to remove all the data on CPC2 from Fig. S2.

(ii) The work with the KIN-A motor domain only and KIN-A ∆motor domain (Fig 2) begs the question about a more subtle mutation to interfere with the motor domain. Which is ultimately presented in Fig 6. I think that the final paragraph and Figure 6 follow naturally after Figure 2.

We appreciate the suggestion. However, we would like to keep Figure 6 there.

(iii) The high-confidence structural predictions in Fig 3 and Fig 4 are insightful. The XL-MS descriptions that precede them are not so helpful (Fig 3A and 4G and in the text). To emphasize their status as experimental support for the predicted structures, which is very important, it would be good to discuss the XL-MS after presenting the models.

As suggested, we have re-arranged the text and/or figures such that the AF2 predictions are discussed first and the CLMS data are brought in afterwards.

Figure 1A prominently features an arbitrary color code and a lot of protein IDs without a legend. That is not a very convincing start. Figure S1 is more informative, containing annotated protein names and results of the KIN-A and KIN-B IPs. Please improve Figure 1A, for example by presenting a modified version of Figure S1. In all these types of figures, please list both protein names and gene IDs.

We agree with the reviewer that the IP-MS data in Fig. S1 is more informative and hence decided to swap the heatmaps in Fig. 1A and Fig. S1A. We further annotated the heatmap corresponding to the Aurora BAUK1 IP-MS (now presented in Fig. S1) as suggested by the reviewer.

The visualization of the structural predictions is not consistent among figures:

(i) The structure in Fig 4I is important and could be displayed larger. The pLDDT scores, and especially those of the non-displayed models, do not add much information and should not be a main panel. If the authors want to display the pLDDT scores, I recommend a panel (main or supplement) of the structure colored for local prediction confidences, as in Fig 5A.

(ii) In Figure 5A itself, it is hard to follow the chains in general, and KIN-A in particular, since the structure is pLDDT-coloured. Please present an additional panel colored by chain (consistent with Fig 4I, as mentioned above).

(iii) The summarizing diagram, currently displayed as Fig 4J, should be placed after Fig 5A and take the discovered KIN-A - KKT9-11 connection into account. Ideally, it also covers the suspected importance of the motor domain and serves as a summarising diagram.

We thank the reviewer for the constructive comments. For each structure prediction, we now present two images side by side; one coloured by chain and one colored by pLDDT. We recently re-ran AF2 for the full CPC and also for the KKT7N-KKT8 complex, and got improved predictions. Hence some of the models in Fig. 3/S3 and Fig. 4/S4 have been updated accordingly. For the CLMS plots, we also decided to colour the cross-links according to whether the 30 angstrom distance constraints were fulfilled or not in the AF2 prediction. We also increased the size of the structures shown in Fig. 4. Furthermore, we decided to remove the summarizing diagram from Fig. 4 and instead made a new main Fig. 7, which shows a more detailed schematic, which also takes into account the proposed function of the KIN-A motor domain, as suggested by the reviewer, and other points addressed in the Discussion.

The methods section for the structural predictions lacks essential information. Predictions can only be reproduced if the version of AF2 multimer v2.x is specified and key parameters are mentioned.

As suggested, we have added the details in the Materials and Methods section as follows.

‘Structural predictions of KIN-A/KIN-B, KIN-A310-862/KIN-B317-624, CPC1/CPC2/KIN-A300-599/KIN-B 317-624, and KIN-A700-800/KKT9/KKT11 were performed using ColabFold version 1.3.0 (AlphaFold-Multimer version 2), while those of AUK1/CPC1/CPC2/KIN-A1-599/KIN-B, KKT71-261/KKT9/KKT11/KKT8/KKT12, KKT9/KKT11/KKT8/KKT12, and KKT71-261/KKT9/KKT11 were performed using ColabFold version 1.5.3 (AlphaFold-Multimer version 2.3.1) using default settings, accessed via https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.3.0/AlphaFold2.ipynb and https://colab.research.google.com/github/sokrypton/ColabFold/blob/v1.5.3/AlphaFold2.ipynb.’

Line 121, please explain the "Unexpectedly" by including a reference to the work from Li and colleagues. A statement with some details would be useful, as the difference between both studies appears to be crucial for the novelty of this paper. Alternatively, refer to this being covered in the discussion.

To clarify this point, we modified this paragraph in the results section:

‘To our surprise, KIN-A-YFP and GFP-KIN-B exhibited a CPC-like localization pattern identical to that of Aurora BAUK1: Both kinesins localized to kinetochores from S phase to metaphase, and then translocated to the central spindle in anaphase (Figs. 1, C-E). Moreover, like Aurora BAUK1, a population of KIN-A and KIN-B localized at the new FAZ tip from late anaphase onwards (Figs. S1, B and C). This was unexpected, because KIN-A and KIN-B were previously reported to localize to the spindle but not to kinetochores or the new FAZ tip (Li et al., 2008). These data suggest that KIN-A and KIN-B are bona fide CPC proteins in trypanosomes, associating with AuroraAUK1, INCENPCPC1 and CPC2 throughout the cell cycle.’

Line 285 refers to "conserved" regions in the C-terminal part of KIN-A, referring to Figure 5. Please expand the MSA in Figure 5B to get an idea about the conservation/variation outside CD1 and CD2.

We now present the full MSA for KIN-A proteins in kinetoplastids in Fig. S5A.

Please specify what is meant by Line 367-369 for someone who is not familiar with the work by Komaki et al. 2022. Either clarify in the text or clarify in the text with data to support it.

We updated the corresponding section in the discussion as follows:

‘Komaki et al. recently identified two functionally redundant CPC proteins in Arabidopsis, Borealin Related Interactor 1 and 2 (BORI1 and 2), which engage in a triple helix bundle with INCENP and Borealin using a conserved helical domain but employ an FHA domain instead of a BIR domain to read H3T3ph (Komaki et al., 2022).’

Data presented in Figure S6A, the microtubule co-sedimentation assay, is not convincing since a substantial amount of KIN-A/B is pelleted in the absence of microtubules. Did the authors spin the proteins in BRB80 before the assay to continue with soluble material and reduce sedimentation in the absence of microtubules? If the authors want to keep the wording in lines 331-332, the MT-binding properties of KIN-A and KIN-B need to be investigated in more detail, for example with a titration and a quantification thereof. Otherwise, they should change the text and replace "confirms" with "is consistent with". In any case, the legend needs to be expanded to include more information.

To address the point above, we have added the following text in the legend corresponding to Fig. S6:

‘Microtubule co-sedimentation assay with 6HIS-KIN-A2-309 (left) and 6HIS-KIN-B2-316 (right). S and P correspond to supernatant and pellet fractions, respectively. Note that both constructs to some extent sedimented even in the absence of microtubules. Hence, lack of microtubule binding for KIN-B may be due to the unstable non-functional protein used in this study.’

We have also updated the main text in the results section:

‘We therefore speculated that anaphase translocation of the kinetoplastid CPC to the central spindle may involve the kinesin motor domain of KIN-A. KIN-B is unlikely to be a functional kinesin based on the absence of several well-conserved residues and motifs within the motor domain, which are fully present in KIN-A (Li et al., 2008). These include the P-loop, switch I and switch II motifs, which form the nucleotide binding cleft, and many conserved residues within the α4-L12 elements, which interact with tubulin (Fig. S6A) (Endow et al., 2010). Consistent with this, the motor domain of KIN-B, contrary to KIN-A, failed to localize to the mitotic spindle when expressed ectopically (Fig. S2E) and did not co-sediment with microtubules in our in vitro assay (Fig. S6B).’

Details:

The readability of the pAE plots could be improved by arranging sequences according to their position in the structure. For example in Fig4I, KKT8 could precede KKT12. If it is easy to update this, the authors might want to do so.

We re-ran the AF2 predictions for the KKT7N – KKT8 complex in Fig. 4/S4 and changed the order according to the reviewer’s suggestion (KKT9:KKT11:KKT8:KKT12).

The same paper is referred to as Je Van Hooff et al. 2017 and as Van Hooff et al. 2017

Thank you for pointing this out. We have corrected the citation.

Reviewer #3 (Recommendations For The Authors):

(1) Please state at the end of the introduction/start of the results section that this work was performed in procyclic trypanosomes. Given that the cell cycles of procyclic and bloodstream forms differ, this is important.

We added this information at the end of the introduction:

‘Here, by combining biochemical, structural and cell biological approaches in procyclic form T. brucei, we show that the trypanosome CPC is a pentameric complex comprising Aurora BAUK1, INCENPCPC1, CPC2 and the two orphan kinesins KIN-A and KIN-B.’

(2) Please define NLS at first use (line 118), and for clarity, explain the rationale for using GFP with an NLS.

NLS refers to a short ‘nuclear localization signal’ (TGRGHKRSREQ) (Marchetti et al., 2000), which ensures that the ectopically expressed construct is imported into the nucleus. When we previously expressed truncations of KKT2 and KKT3 kinetochore proteins, many fragments did not go into the nucleus presumably due to the lack of an NLS, which prevented us from determining which domains are responsible for their kinetochore localization. We have since then consistently used this short NLS sequence in our inducible GFP fusions in the past without any complications. We added a sentence in the Materials & Methods section under Trypanosome culture: ‘All constructs for ectopic expression of GFP fusion proteins include a short nuclear localization signal (NLS) (Marchetti et al., 2000).’ To avoid unnecessary confusion, we removed ‘NLS’ from the main text and figures.

(3) Lines 148-150 - it would strengthen this claim if KIN-A/B protein levels were assessed by Western blot.

We now present a Western blot in Fig. S2C, showing that bulk KIN-B levels are clearly reduced upon KIN-A RNAi. The same is true also to some extent for KIN-A levels upon KIN-B RNAi, although this is less obvious, possibly due to the lower efficiency of KIN-B compared to KIN-A RNAi as judged by fluorescence microscopy (quantified in Fig. 2D and 2E).

(4) Line 253 - the text mentions the removal of both KKT9 and KKT11, which is not consistent with the figure (Fig 4H) - do you mean the removal of either KKT9 or KKT11?

Yes, we thank the reviewer for pointing out this mistake in the text, which has now been corrected.

(5) Line 337 - please include a reference for the G209A ATPase-defective rigor mutant - has this been shown to result in KIN-A being inactive previously?

Please see above our answer in public review.

(6) It is not always obvious when fluorescent fusion proteins are being expressed endogenously or ectopically, or when they are being expressed in an RNAi background or not without tracing the cell lines in Table S1 - please ensure this is clearly stated throughout the manuscript.

We now made sure that this is clearly stated in the main text as well as in the figure legends.

(7) Line 410 - 'KIN-A C-terminal tail is stuffed full of conserved CDK1CRK3 sites' - what does 'stuffed full' really mean (this is rather imprecise) and what are the consensus sites - are these CDK1 consensus sites that are assumed to be conserved for CRK3? I'm not aware of consensus sites for CRK3 having been determined, but if they have, this should be referenced.

We have modified the corresponding section in the discussion as follows:

‘In support of this, the KIN-A C-terminal tail harbours many putative CRK3 sites (10 sites matching the minimal S/T-P consensus motif for CDKs) and is also heavily phosphorylated by Aurora BAUK1 in vitro (Ballmer et al. 2024). Finally, we speculate that the interaction of KIN-A motor domain with microtubules, coupled to the force generating ATP hydrolysis and possibly plus-end directed motion, eventually outcompetes the weakened interactions of the CPC with the kinetochore and facilitates the extraction of the CPC from chromosomes onto spindle microtubules during anaphase. Indeed, deletion of the KIN-A motor domain or impairment of its motor function through N-terminal GFP tagging causes the CPC to be trapped at kinetochores in anaphase. Central spindle localization is additionally dependent on the ATPase activity of the KIN-A motor domain as illustrated by the KIN-A rigor mutant.’

(8) Lines 412-416: this proposal is written rather definitively - given no motor activity has been demonstrated for KIN-A, please make clear that this is still just a theory.

See above.

(9) Fig 1: KKT2 is not highlighted in Fig 1A - given this has been used for colocalization in Fig 1C-E, was it recovered, and if not, why not? Fig 1B-E: the S phase/1K1N terminology is somewhat misleading. Not all S phase cells will have elongated kinetoplasts - usually an asterisk is used to signify replicated DNA, not kinetoplast shape. If it is to be used here for elongation, then for consistency, N should be used for G2/mitotic cells.

Fig. 1A (now Fig. S1A) only shows the tip 30 hits. KKT2 was indeed recovered with Aurora BAUK1 (see Table S2) and is often used as a kinetochore marker in trypanosomes by our lab and others since the signal of fluorescently tagged KKT2 is relatively bright and KKT2 localizes to centromeres throughout the cell cycle.

(10) A general comment for all image figures is that these do not have accompanying brightfield images and it is therefore difficult to know where the cell body is, or sometimes which nuclei and kinetoplasts belong to which cell where DNA from more than one cell is within the image. It would be beneficial if brightfield images could be added, or alternatively, the cell outlines were traced onto DAPI or merged images. Also, brightfield images would allow the stage of cytokinesis (pre-furrowing/furrowing/abscission) in anaphase cells to be determined.

Since this study primarily addresses the recruitment mechanism of the CPC to kinetochores and to the central spindle from S phase to metaphase and in anaphase, respectively, and CPC proteins are not observed outside of the nucleus during these cell cycle stages, we did not present brightfield images in the figures. However, this point is particularly valid for discerning the localization of KIN-A and KIN-B to the new FAZ tip from late anaphase onwards. Hence, we acquired new microscopy data for Fig. S1B and S1C, which now includes phase contrast images, and have chosen representative cells in late anaphase and telophase. We hope that the signal of Aurora BAUK1, KIN-A and KIN-B at the anterior end of the new FAZ can be now distinguished more clearly.

(11) Fig 2A: legend should state that the micrographs show the localisation of the proteins within the nucleus as whole cells are not shown. 2C: can INCENP not be split into 2 lines - the 'IN' looks like 1N at first glance, which is confusing.

We have applied the suggested change in Fig. 2.

(12) Fig 3 (and other AF2 figures): Could the lines for satisfied & not satisfied in the key be thicker so they more closely resemble the lines in the figure and are less likely to be confused with the disordered regions of the CPC components?

We have now made those lines thicker.

(13) Why were different E value thresholds used in Fig 3 and Fig 4?

The CLMS data in Fig. 3 and Fig. 4 now both use the same E value threshold of E-3 (previously E-4 was used in Fig. 4). To determine a sensible significance threshold, we included some yeast protein sequences (‘false positives’) in the database used in pLink2 for identification of crosslinked peptides. Note that we recently also re-ran AF2 for the full CPC and for the KKT7N-KKT8 complex and got improved predictions. Hence some of the models in Fig. 3/S3 and Fig. 4/S4 have been updated accordingly. For the CLMS plots, we also decided to colour the cross-links according to whether the 30 angstrom distance constraints were fulfilled or not in the AF2 prediction.

(14) Fig 4H legend - please give the expected sizes of these recombinant proteins & check the 3rd elution panel (see public review comments).

See above response in public review.

(15) Fig 4I - please explain what the colours of the PAE plot and the values in the key signify, as well as how the Scored Residue values are arrived at. Please also define the pIDDT in the legend.

We have cited DeepMind’s 2021 methods paper, in which the outputs of AlphaFold are explained in detail. We also added a short description of the pLDDT and PAE scores and the corresponding colour coding in the legends of Fig. 3 and Fig. 4, respectively.

From figure 3 legend:

‘(B) Cartoon representation showing two orientations of the trypanosome CPC, coloured by protein on the left (Aurora BAUK1: crimson, INCENPCPC1: green, CPC2: cyan, KIN-A: magenta, and KIN-B: yellow) or according to their pLDDT values on the right, assembled from AlphaFold2 predictions shown in Figure S3. The pLDDT score is a per-residue estimate of the confidence in the AlphaFold prediction on a scale from 0 – 100. pLDDT > 70 (blue, cyan) indicates a reasonable accuracy of the model, while pLDDT < 50 (red) indicates a low accuracy and often reflects disordered regions of the protein (Jumper et al., 2021). BS3 crosslinks in (B) were mapped onto the model using PyXlinkViewer (blue = distance constraints satisfied, red = distance constraints violated, Cα-Cα Euclidean distance threshold = 30 Å) (Schiffrin et al., 2020).’

From Figure 4 legend:

‘(G) AlphaFold2 model of the KKT7 – KKT8 complex, coloured by protein (KKT71-261: green, KKT8: blue, KKT12: pink, KKT9: cyan and KKT11: orange) (left) and by pLDDT (center). BS3 crosslinks in (H) were mapped onto the model using PyXlinkViewer (Schiffrin et al., 2020) (blue = distance constraints satisfied, red = distance constraints violated, Cα-Cα Euclidean distance threshold = 30 Å). Right: Predicted Aligned Error (PAE) plot of model shown on the left (rank_2). The colour indicates AlphaFold’s expected position error (blue = low, red = high) at the residue on the x axis if the predicted and true structures were aligned on the residue on the y axis (Jumper et al., 2021).’

(16) Fig 6 legend - Line 730 should say (F) not (C).

Thank you for pointing out this typo.

(17) Fig S1A - a key is missing for the colours. Fig S1B/C - cell outlines or a brightfield image are really needed here - see earlier comment. Fig S1D - there doesn't seem to be a method for how this tree was generated.

See above response in public review regarding Fig. S1A and S1B/C. The tree in Fig. S1D is based on (Butenko et al., 2020).

(18) Fig S2: A: how was protein knockdown validated (especially for CPC2 where there was little obvious phenotype)? Fig S2B: the y-axis should read proportion of cells, not percentage. Fig S2E - NLS should be labelled.

Thank you for pointing out the mistake in the labelling.

(19) Fig S3: PAE plots should be labelled with protein names, not A-E. Similarly, the pIDDT plots should be labelled as in Fig 4I.

We have corrected the labelling in Fig. S3.

(20) Fig S5A-D - cell cycle stage labels are missing from images.

Thank you for pointing out the missing cell cycle stage labels.

Addition by editor:

In line 126 the statement that KIN-A and KIN-B "associate with Aurora-AUK1, INCENP-CPC1 and CPC2 throughout the cell cycle" seems too strong. There is no direct evidence for this. Please re-phrase as "likely associate" or "suggest... that ... may...".

We have modified that sentence according to the editor’s suggestion.

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Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Sang et al. proposed a pair of IR60b-expressing pharyngeal neurons in Drosophila use IR25a, IR76b, and IR60b channels to detect high Na+ and limit its consumption. Some of the key findings that support this thesis are: 1) animals that lacked any one of these channels - or with their IR60b-expressing neurons selectively silenced - showed much reduced rejection of high Na+, but restored rejection when these channels were reintroduced back in the IR60b neurons; 2) animals with TRPV artificially expressed in their IR60b neurons rejected capsaicin-laced food whereas WT did not; 3) IR60b-expressing neurons exhibited increased Ca2+ influx in response to high Na+ and such response went away when animals lacked any of the three channels.

Strengths:

The experiments were thorough and well designed. The results are compelling and support the main claim. The development and the use of the DrosoX two-choice assay put forward for a more quantitative and automatic/unbiased assessment for ingestion volume and preference.

Weaknesses:

There are a few inconsistencies with respect the the exact role by which IR60b neurons limit high salt consumption and the contribution of external (labellar) high-salt sensors in regulating high salt consumption. These weaknesses do not significantly impact the main conclusion, however.

Reviewer #2 (Public Review):

Summary:

In this paper, Sang et al. set out to identify gustatory receptors involved in salt taste sensation in Drosophila melanogaster. In a two-choice assay screen of 30 Ir mutants, they identified that Ir60b is required for avoidance of high salt. In addition, they demonstrate that activation of Ir60b neurons is sufficient for gustatory avoidance using either optogenetics or TRPV1 to specifically activate Ir60b neurons. Then, using tip recordings of labellar gustatory sensory neurons and proboscis extension response behavioral assays in Ir60b mutants, the authors demonstrate that Ir60b is dispensable for labellar taste neuron responses to high salt and the suppression of proboscis extension by high salt. Since external gustatory receptor neurons (GRNs) are not implicated, they look at Poxn mutants, which lack external chemosensory sensilla but have intact pharyngeal GRNs. High salt avoidance was reduced in Poxn mutants but was still greater than Ir60b mutants, suggesting that pharyngeal gustatory sensory neurons alone are sufficient for high salt avoidance. The authors use a new behavioral assay to demonstrate that Ir60b mutants ingest a higher volume of sucrose mixed with high salt than control flies do, suggesting that the action of Ir60b is to limit high salt ingestion. Finally, they identify that Ir60b functions within a single pair of gustatory sensory neurons in the pharynx, and that these neurons respond to high salt but not bitter tastants.

Strengths:

A great strength of this paper is that it rigorously corroborates previously published studies that have implicated specific Irs in salt taste sensation. It further introduces a new role for Ir60b in limiting high salt ingestion, demonstrating that Ir60b is necessary and sufficient for high salt avoidance and convincingly tracing the action of Ir60b to a particular subset of gustatory receptor neurons. Overall, the authors have achieved their aim by identifying a new gustatory receptor involved in limiting high salt ingestion. They use rigorous genetic, imaging, and behavioral studies to achieve this aim, often confirming a given conclusion with multiple experimental approaches. They have further done a great service to the field by replicating published studies and corroborating the roles of a number of other Irs in salt taste sensation. An aspect of this study that merits further investigation is how the same gustatory receptor neurons and Ir in the pharynx can be responsible for regulating the ingestion of both appetitive (sugar) and aversive tastants (high salt).

A previous report published in eLife from John Carlson’s lab (Joseph et al, 2017) showed that the Ir60b GRN in the pharynx responds to sucrose resulting in sucrose repulsion. Thus, stimulation of this pharyngeal GRN results in gustatory avoidance only, not both attraction and avoidance. (lines 205-207)

Weaknesses:

There are several weaknesses that, if addressed, could greatly improve this work.

(1) The authors combine the results and discussion but provide a very limited interpretation of their results. More discussion of the results would help to highlight what this paper contributes, how the authors interpret their results, and areas for future study.

We agree and have now separated the Results and Discussion, and in so doing have greatly expanded discussion of the results.

(2) The authors rename previously studied populations of labellar GRNs to arbitrary letters, which makes it difficult to understand the experiments and results in some places. These GRN populations would be better referred to according to the gustatory receptors they are known to express.

One of the corresponding authors (Craig Montell) introduced this alternative GRN nomenclature in a review in 2021: Montell, C. (Drosophila sensory receptors—a set of molecular Swiss Army Knives. Genetics 217, 1-34) (Montell, 2021). We are not fans of referring to different classes of GRNs based on the receptors that they express since it is not obvious which receptors to use. For example, the GRNs that respond to bitter compounds all express multiple GR co-receptors. The same is true for the GRNs that respond to sugars. The former system of referring to GRNs simply as sugar, bitter, salt and water GRNs is also not ideal since the repertoire of chemicals that stimulates each class is complex. For example, the Class A GRNs (formerly sugar GRNs) are also activated by low Na+, glycerol, fatty acids, and acetic acid, while the B GRNs (former bitter GRNs) are also stimulated by high Na+, acids, polyamines, and tryptophan. In addition, there are five classes of GRNs. At first mention of the Class A—E GRNs, we mention the most commonly used former nomenclature of sugar, bitter, salt and water GRNs. In addition, for added clarify, we now also include a mention of one of the receptors that mark each class. (lines 51-59)

(3) The conclusion that GRNs responsible for high salt aversion may be inhibited by those that function in low salt attraction is not well substantiated. This conclusion seems to come from the fact that overexpression of Ir60b in salt attraction and salt aversion sensory neurons still leads to salt aversion, but there need not be any interaction between these two types of sensory neurons if they act oppositely on downstream circuits.

We did not make this claim.

(4) The authors rely heavily on a new Droso-X behavioral apparatus that is not sufficiently described here or in the previous paper the authors cite. This greatly limits the reader's ability to interpret the results.

We expanded the description of the apparatus in the Droso-X assay section of the Materials and Methods. (lines 588-631)

Reviewer #3 (Public Review):

Summary:

Sang et al. successfully demonstrate that a set of single sensory neurons in the pharynx of Drosophila promotes avoidance of food with high salt concentrations, complementing previous findings on Ir7c neurons with an additional internal sensing mechanism. The experiments are well-conducted and presented, convincingly supporting their important findings and extending the understanding of internal sensing mechanisms. However, a few suggestions could enhance the clarity of the work.

Strengths:

The authors convincingly demonstrate the avoidance phenotype using different behavioral assays, thus comprehensively analyzing different aspects of the behavior. The experiments are straightforward and well-contextualized within existing literature.

Weaknesses:

Discussion

While the authors effectively relate their findings to existing literature, expanding the discussion on the surprising role of Ir60b neurons in both sucrose and salt rejection would add depth. Additionally, considering Yang et al. 2021's (https://doi.org/10.1016/j.celrep.2021.109983) result that Ir60b neurons activate feeding-promoting IN1 neurons, the authors should discuss how this aligns with their own findings.

Yang et al. demonstrated that the activation of Ir60b neurons can trigger the activation of IN1 neurons akin to pharyngeal multimodal (PM) neurons, potentially leading to enhanced feeding (Yang et al, 2021). However, our research reveals a specific pattern of activation for Ir60b neurons. Instead of being generalists, they are specialized for certain sugars, such as sucrose and high salt. Consequently, while Ir60b GRNs activate IN1 neurons, we contend that there are other neurons in the brain responsible for inhibiting feeding. (lines 412-417)

Lines 187: The discussion primarily focuses on taste sensillae outside the labellum, neglecting peg-type sensillae on the inner surface. Clarification on whether these pegs contribute to the described behaviors and if the Poxn mutants described also affect the pegs would strengthen the discussion.

We added the following to the Discussion section. “We also found that the requirement for Ir60b appears to be different when performing binary liquid capillary assay (DrosoX), versus solid food binary feeding assays. When we employed the DrosoX assay to test mutants that were missing salt aversive GRNs in labellar bristles but still retained functional Ir60b GRNs, the flies behaved the same as wild-type flies (e.g. Figure 3J and 3L). However, using solid food binary assays, Poxn mutants, which are missing labellar taste bristles but retain Ir60b GRNs (LeDue et al, 2015), displayed repulsion to high salt food that was intermediate between control flies and the Ir60b mutant (Figure 2J). Poxn mutants retain taste pegs (LeDue et al., 2015), and these hairless taste organs become exposed to food only when the labial palps open. We suggest that there are high-salt sensitive GRNs associated with taste pegs, which are accessed when the labellum contacts a solid substrate, but not when flies drink from the capillaries used in DrosoX assays. This explanation would also account for the findings that the Ir60b mutant is indifferent to 300 mM NaCl in the DrosoX assay (Figure 3B), but prefers 1 mM sucrose alone over 300 mM NaCl and 5 mM sucrose in the solid food binary assay (Figure 1B).”. (lines 430-444)

In line 261 the authors state: "We attempted to induce salt activation in the I-type sensilla by ectopically expressing Ir60b, similar to what was observed with Ir56b 8; however, this did not generate a salt receptor (Figures S6A)"

An obvious explanation would be that these neurons are missing the identified necessary co-receptors Ir76b and Ir25a. The authors should discuss here if the Gr33a neurons they target also express these co-receptors, if yes this would strengthen their conclusion that an additional receptor might be missing.

We clarified this point in the Discussion section as follows, “An open question is the subunit composition of the pharyngeal high Na+ receptor, and whether the sucrose/glucose and Na+ receptors in the Ir60b GRN are the same or distinct. Our results indicate that the high salt sensor in the Ir60b GRN includes IR25a, IR60b and IR76b since all three IRs are required in the pharynx for sensing high levels of NaCl. I-type sensilla do not elicit a high salt response, and we were unable to induce salt activation in I-type sensilla by ectopically expressing Ir60b, under control of the Gr33a-GAL4. This indicates that IR25a, IR60b and IR76b are insufficient for sensing high Na+. The inability to confer a salt response by ectopic expression of Ir60b was not due to absence of Ir25a and Ir76b in Gr33a GRNs since Gr33a and Gr66a are co-expressed (Moon et al, 2009), and Gr66a GRNs express Ir25a and Ir76b (Li et al, 2023). Thus, the high salt receptor in Ir60b GRNs appears to require an additional subunit. Given that Na+ and sugars are structurally unrelated, we suggest that the Na+ and sucrose/glucose receptors do not include the identical set of subunits, or that that they activate a common receptor through disparate sites”. (lines 464-477)

Methods

The description of the Droso-X assay seems to be missing some details. Currently, it is not obvious how the two-choice is established. Only one capillary is mentioned, I assume there were two used? Also, the meaning of the variables used in the equation (DrosoX and DrosoXD) are not explained.

We expanded the description of the apparatus in the Droso-X assay section of the Materials and Methods. (lines 588-631)

The description of the ex-vivo calcium imaging prep. is unclear in several points:

(1) It is lacking information on how the stimulus was applied (was it manually washed in? If so how was it removed?).

We expanded the description of the apparatus in the ex vivo calcium imaging section of the Materials and Methods. (lines 682-716)

(2) The authors write: "A mild swallow deep well was prepared for sample fixation." I assume they might have wanted to describe a "shallow well"?

We deleted the word “deep.”.(line 691)

(3) "...followed by excising a small portion of the labellum in the extended proboscis region to facilitate tastant access to pharyngeal organs." It is not clear to me how one would excise a small portion of the labellum, the labellum depicts the most distal part of the proboscis that carries the sensillae and pegs. Did the authors mean to say that they cut a part of the proboscis?

Yes. We changed the sentence to “…followed by excising a small portion of the extended proboscis to facilitate tastant access to the pharyngeal organs.”.(lines 693)-695

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

In this manuscript, Sang et al. proposed a pair of IR60b-expressing pharyngeal neurons in Drosophila use IR25a, IR76b, and IR60b channels to detect high Na+ and limit its consumption. Some of the key findings that support this thesis are: 1) animals that lacked any one of these channels - or with their IR60b-expressing neurons selectively silenced - showed much reduced rejection of high Na+, but restored rejection when these channels were reintroduced back in the IR60b neurons; 2) animals with TRPV artificially expressed in their IR60b neurons rejected capsaicin-laced food whereas WT did not; 3) IR60b-expressing neurons exhibited increased Ca2+ influx in response to high Na+ and such response went away when animals lacked any of the three channels. In general, I find the collective evidence presented by the authors convincing. But I feel the MS can benefit from having a discussion session and a few simple experiments. Below I listed some inconsistencies I hope the authors can address or at least discuss.

We have now added a Discussion section, and expanded the discussion.

(1) The role of IR60b neurons on suppressing PER appeared inconsistent. On the one hand, optogenetic activation of these neurons suppressed PER (Fig 1D), on the other hand, IR60b mutants were as competent to suppress PER in response to high salt as WT (Fig 2G). Are pharyngeal neurons expected to modulate PER? It might be worth including a retinal-free or genotype control to ascertain the PER suppression exhibited by IR60b>CsChrimson is genuine.

Please note that Figure 2G is now Figure 2H.

Our interpretation is that activation of aversive GRNs by high salt either in labellar bristles or in the pharynx is sufficient to inhibit repulsion to high salt. Consistent with this conclusion, optogenetic activation of Ir60b GRNs, which are specific to the pharynx, is sufficient to reduce the PER to sucrose containing food (Figure 1D). However, mutation of Ir60b has no impact on the PER to sucrose plus high (300 mM) NaCl since the high-salt activated GRNs in labellar bristles are not impaired by the Ir60b mutation. In contrast, Ir25a and Ir76b are required in both labellar bristles and in the pharynx to reject high salt. As a consequence, mutation of either Ir25a or Ir76b impairs the repulsion to high salt. Thus, there is no inconsistency between the optogenetics and PER results. We clarified this point in the Discussion section. In terms of controls for IR60b>CsChrimson, we show that UAS-CsChrimson alone or UAS-CsChrimson in combination with the Gr5a driver has no impact on the PER (Figure 1D). In addition, we now include a retinal free control (Figure 1D). These findings provide the key genetic controls and are described in the Results section. (lines 167-170)

(2) The role of labellar high-salt sensors in regulating salt intake appeared inconsistent. On the one hand, they appeared to have a role in limiting high salt consumption because poxn mutants were significantly more receptive to high salt than WT (Fig. 2J). On the other hand, selectively restoring IR76b or IR25a in only the IR60b neurons in these mutants - thus leaving the labellar salt sensors still defective - reverted the flies to behave like WT when given a choice between sucrose vs. sucrose+high salt (Fig 3J, L).

We now offer an explanation for these seemingly conflicting results in the Discussion section. When we employed the DrosoX assay with mutants with functional Ir60b GRNs, but were missing salt aversive GRNs in labellar bristles, the flies behaved the same as control flies (e.g. Figure 3J and L). However, using solid food binary assays, Poxn mutants, which are missing labellar taste bristles but retain Ir60b GRNs (LeDue et al., 2015), display aversion high salt food intermediate between control and Ir60b mutant flies (Figure 2J). Poxn mutants retain taste pegs (LeDue et al., 2015), which are exposed to food substrates only when the labial palps open. We suggest that the taste pegs harbor high salt sensitive GRNs, and they may be exposed to solid substrates, but not to the liquid in capillary tubes used in the DrosoX assays. This explanation would also account for the findings that the Ir60b mutant is indifferent to 300 mM NaCl in the DrosoX assay (Figure 3B), but prefers 1 mM sucrose alone over 300 mM NaCl and 5 mM sucrose in the solid food binary assay (Figure 1B). (lines 433-444)

(3) The behavior sensitivity of IR60b mutant to high salt again appeared somewhat inconsistent when assessed in the two different choice assays. IR60b mutant flies were indifferent to 300 mM NaCl when assayed with DrosoX (Fig 3A, B) but were clearly still sensitive to 300 mM NaCl when assayed with "regular" assay - they showed much reduced preference for 5 mM sucrose over 1 mM sucrose when the 5 mM sucrose was adulterated with 300 mM NaCl (Fig 1B).

The explanation provided above may also account for the findings that the Ir60b mutant is indifferent to 300 mM NaCl in the DrosoX assay (Figure 3B), but not when selecting between 300 mM NaCl and 5 mM sucrose versus 1 mM sucrose in the solid food binary assay (Figure 1B). Alternatively, the different behavioral responses might be due to the variation in sucrose concentrations in each of these two assays, which employed 5 mM sucrose in the solid food binary assay, as opposed to 100 mM sucrose in the DrosoX assay. This disparity in attractive valence between these two concentrations of sucrose might consequently impact feeding amount and preference. This point is now also included in the Discussion section. (lines 441-449)

(4) Given the IR60b neurons exhibited clear IR60b/IR25a/IR76b-dependent sucrose sensitivity, too, I am curious how the various mutant animals behave when given a choice between 100 mM sorbitol vs. 100 mM sorbitol + 300 mM NaCl, a food choice assay not complicated by the presence of sucrose. Similarly, I am curious if the Ca2+ response of IR60 neurons differs significantly when presented with 100 mM sucrose vs. when presented with 100 mM sucrose + 300 mM NaCl. In principle, the magnitude for the latter should be significantly larger than the former as animals appeared to be capable of discriminating these two choices solely relying on their IR60b neurons.

To investigate the aversion induced by high salt in the absence of a highly attractive sugar, such as sucrose, we combined 300 mM salt with 100 mM sorbitol, which is a tasteless but nutritive sugar (Burke & Waddell, 2011; Fujita & Tanimura, 2011). Using two-way choice assays, we found that the Ir25a, Ir60b, and Ir76b mutants exhibited substantial reductions in high salt avoidance (Figure 3—figure supplement 2A). In addition, we performed DrosoX assays using 100 mM sorbitol alone, or sorbitol mixed with 300 mM NaCl. Sorbitol alone provoked less feeding than sucrose since it is a tasteless sugar (Figure 3—figure supplement 2B and C). Nevertheless, addition of high salt to the sorbitol reduced food consumption (Figure 3—figure supplement 2B and C). (lines 300-308)

We also conducted a comparative analysis of the Ca2+ responses within the Ir60b GRN, examining its reaction to various stimuli, including 100 mM sucrose alone, 300 mM NaCl alone, and a combination of 100 mM sucrose and 300 mM NaCl. We found that the Ca2+ responses were significantly higher when we exposed the Ir60b GRN to 300 mM NaCl alone, compared with the response to 100 mM sucrose alone (Figure 4—figure supplement 1D). However, the GCaMP6f responses was not higher when we presented 100 mM sucrose with 300 mM NaCl, compared with the response to 300 mM NaCl alone (Figure 4—figure supplement 1D). (lines 360-367)

Minor issues

(1) The labels of sucrose concentration on Figure 2D were flipped.

This has been corrected.

(2) The phrasing of the sentence that begins in line 196 (i.e., "This suggests the internal sensor ...") is not as optimal.

We changed the sentence to, “We found that the aversive behavior to high salt was reduced in the Poxn mutants relative to the control (Figure 2J), consistent with previous studies demonstrating roles for GRNs in labellar bristles in high salt avoidance (Jaeger et al, 2018; McDowell et al, 2022; Zhang et al, 2013).”. (lines 217-219)

(3) In Line 231, I am not sure why the authors think ectopic expressing IR60b in labellar neurons would allow them to become activated by Na+. It seems highly unlikely to me, especially given IR60b also plays a role in sensing sugar.

We added the following paragraph to the Discussion addressing this point, “An open question is the subunit composition of the pharyngeal high Na+ receptor, and whether the sucrose/glucose and Na+ receptors in the Ir60b GRN are the same or distinct. Our results indicate that the high salt sensor in the Ir60b GRN includes IR25a, IR60b and IR76b since all three IRs are required in the pharynx for sensing high levels of NaCl. I-type sensilla do not elicit a high salt response, and we were unable to induce salt activation in I-type sensilla by ectopically expressing Ir60b, under control of the Gr33a-GAL4. This indicates that IR25a, IR60b and IR76b are insufficient for sensing high Na+. The inability to confer a salt response by ectopic expression of Ir60b was not due to absence of Ir25a and Ir76b in Gr33a GRNs since Gr33a and Gr66a are co-expressed (Moon et al., 2009), and Gr66a GRNs express Ir25a and Ir76b (Li et al., 2023). Thus, the high salt receptor in Ir60b GRNs appears to require an additional subunit. Given that Na+ and sugars are structurally unrelated, we suggest that the Na+ and sucrose/glucose receptors do not include the identical set of subunits, or that that they activate a common receptor through disparate sites.”. (lines 464-477)

Reviewer #2 (Recommendations For The Authors):

Line 41, acutely excessive salt ingestion can lead to death, not just health issues

We now state that, “consumption of excessive salt can contribute to various health issues in mammals, including hypertension, osteoporosis, gastrointestinal cancer, autoimmune diseases, and can lead to death.”. (lines 41-43)

Line 46, delete the comma after flies

Done. (line 47)

Lines 51-56: This description is unnecessarily confusing and does not cite proper sources. Renaming these GRNs arbitrarily can only create confusion, plus this description lacks nuance. If E GRNs are Ir94e positive, this description is out of date. Furthermore, If D GRNs are ppk23 and Gr66a positive then they will respond to both bitter and high salt.

Papers to consult: https://elifesciences.org/articles/37167 10.1016/j.cell.2023.04.038

We have now added citations. We prefer the A—E nomenclature, which was introduced in a 2021 Genetics review by one of the authors of this manuscript (Montell) (Montell, 2021) since naming different classes of GRNs on the basis of markers or as sweet, bitter, salt and water GRNs is misleading and an oversimplification. We cite the Genetics 2021 review, and for added clarity include both types of former names (markers and sweet, bitter, salt and water). Class D GRNs are not marked by Gr66a. The eLife reference cited above provided the initial rationale for stating that Class E GRNs are marked by Ir94e and activated by low salt. According to the Taisz et al reference (Cell 2023), the Class E GRNs, which are marked by Ir94e, are also activated by pheromones, which we now mention (Taisz et al, 2023). (lines 51-59)

Line 62, E GRNs are not required for low salt behaviors

We do not state that E GRNs are required for low salt behaviors, only that they sense low Na+ levels. (line 58)

Line 70-81 - Great deal of emphasis on labellar GRNs but then no mention of how pharyngeal GRNs fit into categories A-E

We devote the following paragraph to pharyngeal GRNs. We do not mention how they fit in with the A—E categories because it is not clear.

“In addition to the labellum and taste bristles on other external structures, such as the tarsi, fruit flies are endowed with hairless sensilla on the surface of the labellum (taste pegs), and three internal taste organs lining the pharynx, the labral sense organ (LSO), the ventral cibarial sense organ (VCSO), and the dorsal cibarial sense organ (DCSO), which also function in the decision to keep feeding or reject a food (Chen & Dahanukar, 2017, 2020; LeDue et al., 2015; Nayak & Singh, 1983; Stocker, 1994). A pair of GRNs in the LSO express a member of the gustatory receptor family, Gr2a, and knockdown of Gr2a in these GRNs impairs the avoidance to slightly aversive levels of Na+ (Kim et al, 2017). Pharyngeal GRNs also promote the aversion to bitter tastants, Cu2+, L-canavanine, and bacterial lipopolysaccharides (Choi et al, 2016; Joseph et al., 2017; Soldano et al, 2016; Xiao et al, 2022). Other pharyngeal GRNs are stimulated by sugars and contribute to sugar consumption (Chen & Dahanukar, 2017; Chen et al, 2021; LeDue et al., 2015). Remarkably, a pharyngeal GRN in each of the two LSOs functions in the rejection rather the acceptance of sucrose (Joseph et al., 2017).”. (lines 74-89)

Line 89, aversive --> aversion

We changed this part.

Line 90, gain of aversion capsaicin avoidance suggests they are sufficient for avoidance, not essential for avoidance.

We changed “essential” to “sufficient.”. (line 100)

Line 104, what are you recording from here? Labellar or pharyngeal GRNs

We added “S-type and L-type sensilla” to the sentence. (line 119)

Line 107, How are A GRNS marked with tdTomato? It is important to mention how you are defining A GRNs.

We modified the sentence as follows: “Using Ir56b-GAL4 to drive UAS-mCD8::GFP, we also confirmed that the reporter was restricted to a subset of Class A GRNs, which were marked with LexAop-tdTomato expressed under the control of the Gr64f-LexA (Figure 1—figure supplement 1D—F).”. (lines 120-123)

Line 124, should read "concentrated as sea water."

We made the change. (line 142)

Line 125, I am not sure what is meant by "alarm neurons"

We changed “additional pain or alarm neurons” to “nociceptive neurons.”. (line 144)

Line 141, Are you definitely A GRNs as only labellar GRNs, i.e. the Gr5a-GAL4 pattern with labellar plus few pharyngeal GRNs? Or are the defining it as Gr64f-GAL4 (i.e. labellar plus many pharyngeal GRNs)

We refer to the Class A—E GRNs as labellar GRNs. Therefore, in this instance, we removed the reference to A GRNs and B GRNs, and simply mention the drivers that we used (Gr5a-GAL4 and Gr66a-GAL4) to express UAS-CsChrimson. The modified sentence is, “As controls we drove UAS-CsChrimson under control of either the Gr5a-GAL4 or the Gr66a-GAL4.”. (lines 51-59, 160-161)

Line 180, labellar hairs--> labellar taste bristles

We made the change. (line 204)

Line 190, possess only --> only possess

We made the change. (line 216)

Line 202, Should this read increased?

Yes. We changed “reduced” to “increased.”. (line 225)

Line 206, The information provided here and in reference 47 was not sufficient for me to understand how the Droso-X system works and whether it has been validated. Better diagrams and much more description is required for the reader to understand this system and assess its validity

We now explain that the DrosoX “system consists of a set of five separately housed flies, each of which is exposed to two capillary tubes with different liquid food options. One capillary contained 100 mM sucrose and the other contained 100 mM sucrose mixed with 300 mM NaCl. The volume of food consumed from each capillary is then monitored automatically over the course of 6 hours and recorded on a computer.”. (lines 238-243)

Line 218-219, It would be helpful to expand on this to explain how the previous paper detected no difference. Is this because the contact time with the food is the same but the rate of ingestion is slower?

Yes. This is correct. We now clarify this point by stating that, “In a prior study, it was observed that the repulsion to high salt exhibited by the Ir60b mutant was indistinguishable from wild-type (Joseph et al., 2017). Specifically, the flies were presented with drop of liquid (sucrose plus salt) at the end of a probe, and the Ir60b mutant flies fed on the food for the same period of time as control flies (Joseph et al., 2017). However, this assay did not discern whether or not the volume of the high salt-containing food consumed by the Ir60b mutant flies was reduced relative to control flies. Therefore, to assess the volume of food ingested, we used the DrosoX system, which we recently developed (Figure 3—figure supplement 1A) (Sang et al, 2021). This system consists of a set of five separately housed flies, each of which is exposed to two capillary tubes with different liquid food options. One capillary contained 100 mM sucrose and the other contained 100 mM sucrose mixed with 300 mM NaCl. The volume of food consumed from each capillary was then monitored automatically over the course of 6 hours and recorded on a computer. We found that control flies consuming approximately four times more of the 100 mM sucrose than the sucrose mixed with 300 mM NaCl (Figure 3A). In contrast, the Ir25a, Ir60b, and Ir76b mutants consumed approximately two-fold less of the sucrose plus salt (Figure 3A). Consequently, they ingested similar amounts of the two food options (Figure 3B; ingestion index). Thus, while the Ir60b mutant and control flies spend similar amounts of time in contact with high salt-containing food when it is the only option (Joseph et al., 2017), the mutant consumes considerably less of the high salt food when presented with a sucrose option without salt.”. (lines 226-251)

Lines 231-235, Is this evidence for this, that Ir60b expression in the Ir25a or Ir76b pattern will induce high salt responses in the labellum? You should elaborate on this to clearly state what you mean rather than implying it. I do not think that overexpression of one Ir is enough evidence for this sweeping conclusion.

We agree. We eliminated this point. (lines 227-232)

Lines 261-263, Please elaborate here, how did you target the I-type sensilla and where are these neurons? So they already express Ir76b and Ir25a?

We now explain in the Results that, “We attempted to induce salt activation in the I-type sensilla by ectopically expressing Ir60b, under control of the Gr33a-GAL4. Gr33a is co-expressed with Gr66a (Moon et al., 2009), which has been shown to be co-expressed Ir25a and Ir76b (Li et al., 2023). When we performed tip recordings from I7 and I10 sensilla, we did not observe a significant increase in action potentials in response to 300 mM NaCl (Figure 4—figure supplement 1A), indicating that ectopic expression of Ir60b in combination with Ir25a and Ir76b is not sufficient to generate a high salt receptor.”. (lines 324-330)

Lines 300-303, The discussion needs to be greatly expanded. What is the proposed mechanism by which the same neurons/receptors can inhibit sucrose and high salt feeding? What is the author's interpretation of what this study adds to our understanding of taste aversion?

We have now added a Discussion section and greatly expanded the discussion.

Reviewer #3 (Recommendations For The Authors):

In line 73 there is a typo in "esophagus"

We changed this part.

In line 331, the use of a mixture of sucrose and "saponin" seems to be a mistake; "NaCl" is likely intended.

We made the correction. (lines 546 and 640)

On several occasions, the authors refer to the pharynx as a taste organ (for example 1st sentence of the abstract). I am not sure this is correct, the actual pharyngeal taste organs are the LSO, DSCO, and VSCO which are located in the pharynx.

We made the corrections. (lines 24, 90, 92, 93, and 356)

In line 155 the authors refer to Ir25a and Ir76b as "broadly tuned". I think it is not correct to refer to co-receptors this way, I'd suggest to just call them co-receptors.

We made the correction. (lines 177-178)

In line 182, stating "Gr2a is also expressed in the proboscis" is unclear. Clarify whether it refers to sensillae, pharyngeal taste organs, etc.

We clarified it refers to pharyngeal taste organs. (lines 206-207)

Line 253: "These finding imply that all three Irs are coexpressed in the pharynx." "The pharynx" is very unspecific, did the authors mean to say "the same neuron"?

We now clarify by saying “in the Ir60b GRN in the pharynx.”. (line 317)

Figures & Legends

I found it confusing that the same color scale is being reused for different panels with different meanings repeatedly and in inconsistent ways. For example in Figure 2, red and blue are being used for Ir25a² mutants, while blue is also being used for Gr64f-Gal4 and S type sensilla. It is also not easily visible nor mentioned in the caption which of the 3 color scales presented belong to which panels.

We modified the colors in the figures so that they are used in a consistent way. We now also define the colors in the legends.

In Figure 2 F-I, indicating the stimulus sequence in each panel would enhance clarity. The color scale in Figure 3 could benefit from explicit explanations of different shades in the caption for easier interpretation.

For example: "The ingestion of (a, dark color) 100 mM sucrose alone and (b, light color) in combination with 300 mM"

We made the suggested modification.

In Figure 4a the authors highlight that Ir76b and Ir25a label 2 neurons in the LSO. Did the imaging in 4c also capture the second cell, and if so did it respond to their stimulation?

No, the focal plane differs, and the signal in Figure 4C is considerably weaker compared to the immunohistochemistry shown in Figure 4A. Notably, the other neuron did not exhibit a response to NaCl.

In Figure 4f a legend for the color scale is missing, or the color might not be necessary at all. Also, the asterisks seem to be shifted to the right.

We fixed the shifted asterisks and eliminated the color.

Figure 4i is mislabeled 4f

We made the correction.

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Reply to the reviewers

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

__Summary __

Geng et al. explore the molecular mechanisms underlying the role of KIF1C in RNA transport, focusing on how it interacts with RNA. KIF1C is shown to form dynamic puncta when overexpressed in COS-7 cells that do not appear to colocalise with organelle markers. An IDR in the tail of the kinesin is necessary and sufficient for the formation of these structures and FRAP experiments show that they can exchange their contents with proteins in the cytosol and that their formation can can be reversibly modulated by hypotonic shock, consistent with LLPS. In vitro, the IDR and flanking regions can undergo phase separation at physiologically relevant concentrations and salt conditions. In cells, KIF1C puncta enrich for RNAs and support their transport, and depletion of RNA modulates KIF1C LLPS properties. A model is proposed whereby KIF1C mediated RNA transport to the cell periphery promotes the formation of a protein-RNA condensate that may act to fine tune local RNA activity.

__Major comments __

In general, the claims made here are well-supported by the data. However, I think that some exploration of the extent of LLPS at different KIF1C expression levels in cells is important but missing. The authors carefully estimate the endogenous concentration of KIF1C in COS-7 cells (at around 25 nm), but is isn't clear how this compares to that observed in transient transfection experiments. Although this is partly addressed in the vitro assays, I am still left with some questions over the extent of this phenomenon in a cellular context. Can the authors provide some experimental evidence to support the proposition that LLPS occurs (perhaps in a more localised fashion?, as Fig.9) at lower KIF1C expression levels? One way to address this might be a GFP-knock-in (although how feasible this is may depend on the genomic context), alternatively, the authors could generate cell lines that express KIF1C-GFP from a very weak promoter, demonstrate LLPS using their established assays, show that this is comparable to endogenous expression.

Response: We thank the reviewer for this suggestion. We have carried out additional experiments to explore the extent of KIF1C LLPS at endogenous levels. We used antibody against KIF1C to stain WT and KIF1C knockout (KO) cells. Although the antibody shows a high background of non-specific signal in the cytoplasm and nucleoplasm of both WT and KO cells, we were able to observe small puncta of KIF1C at the periphery of WT but not KO cells (new Figure 8). This finding supports our hypothesis that endogenous KIF1C undergoes LLPS upon reaching a high local concentration at the periphery of cells. Two lines of evidence support that these puncta of endogenous KIF1F protein are RNA-containing biomolecular condensates formed by LLPS (new Figure 8). First, these small puncta of endogenous KIF1C incorporate RAB13 mRNA, suggesting that they are RNA granules. Second, the puncta do not form in cells stably expressing KIF1C DIDR at near-endogenous levels.

Minor comments

Lines 107-109 and Figure 1B on localisation of other kinesin-3s. The authors state that they localise to certain organelle but don't show co-staining for those organelles.

Response: The localization of other kinesin-3s to certain organelles has been shown in the cited literature. In response to the reviewer's request, we now verify these findings by staining cells expressing the other kinesin-3s for specific organelles (new Figure S1 A).

Lines 172-183 and Figure 3. Evidence is provided through FRAP experiments that KIF1C puncta exchange with the cytosolic pool. However, the extent of recovery appears to saturate at Response: We agree that the data suggest the existence of an immobile pool of KIF1C within the condensates. We have added this information to the main text (lines 178-182). We note that these findings are consistent with recent studies demonstrating membrane-less organelles with at least partially solid-like properties, including nucleoli and stress granules as well as microtubule associated proteins (see references, reviewed in Van Treeck & Parker 2019).

Line 238 - Fig. S5C is cited as data on endogenous concentration of KIF1C - this should be Fig. S6C.

Response: Thank you. We have corrected this (now Fig S8 C).

Line 331-332 - I did not fully follow the logic here the RNAse A injection experiment supports the idea that KIF1C interaction with RNA is sequence selective. Could the authors expand on this.

Response: We thank the reviewer for this comment. We have rewritten the text (lines 235-238, 246-248).

__Reviewer #1 (Significance (Required)): __

This study introduces a new and exciting concept to motor protein biology: that some cytoskeletal motors and motor-cargo complexes can undergo phase separation, and that this is important for their function. The experiments are logical, progressive, and form a clear and compelling case. The main limitation is that demonstration of LLPS in cells is limited to over-expressed protein. Some exploration/demonstration of LLPS properties of KIF1C in cells at near to endogenous expression levels would enhance the study.

The work should be of interest to a broad range of readers, from the cytoskeletal motor community, those interested in mRNA regulation, as well as scientists studying phase separation more generally.

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

This paper investigates mRNA transport by the kinesin Kif1C and tests the hypothesis that liquid condensation of the disordered C terminal region is important for mRNA recruitment. It is based on prior work from other labs showing that Kif1C recruits and transports a set of mRNAs to the periphery of cells. The mechanism of the KifC1-mRNA interaction was not investigated in the prior work, so the proposal that a liquid condensate is involved is novel. It is also topical, since there is intense current interest in transport and regulation of mRNAs by condensate-mediated mechanisms. The most useful part of this paper to the field may be the identification of IDR2 as required for mRNA binding in Fig 7.

__Major comments __

A major concern is reliance on expression of tagged KifC1 in Cos cells in several figures. The expression level in these experimental probably far exceeds normal, though this comparison is not reported. It is possibly justified to use over-expression to reveal a condensate mechanism, but it is concerning and the authors needs to strongly qualify their conclusions. One way to moderate this concern would be to examine condensation as a function of expression level.

Response: We thank the reviewer for this suggestion. We have carried out additional experiments to explore the extent of KIF1C LLPS at endogenous levels. We used antibody against KIF1C to stain WT and KIF1C knockout (KO) cells. Although the antibody shows a high background of non-specific signal in the cytoplasm and nucleoplasm of both WT and KO cells, we were able to observe small puncta of KIF1C at the periphery of WT but not KO cells (new Figure 8). This finding supports our hypothesis that endogenous KIF1C undergoes LLPS upon reaching a high local concentration at the periphery of cells. Two lines of evidence support that these puncta of endogenous KIF1F protein are RNA-containing biomolecular condensates formed by LLPS (new Figure 8). First, these small puncta of endogenous KIF1C incorporate RAB13 mRNA, suggesting that they are RNA granules. Second, the puncta do not form in cells stably expressing KIF1C DIDR at near-endogenous levels.

Another significant concern is that the biochemical reconstitution figure tests protein alone, not protein + RNA. Disordered RNA binding proteins usually phase separate better in the presence of RNA. The best reconstitution papers evaluate specificity of RNA recruitment to condensates. Specificity testing in a reconstituted system may not be required for a first paper, but testing the effect of some kind of RNA seems important.

Response: The purified CC4+IDR and IDR constructs form condensates at low mM concentrations and in the absence of RNA or crowding agents, thus we did not test whether they would phase separate better in the presence of RNA. In response to the reviewer's comments, we now evaluate the specificity of RNA recruitment to the KIF1C condensates. We utilized the purified CC4+IDR protein and added the same GU-rich and polyA RNAs used in cells (now Fig 4 B) at different concentrations. Interestingly, there is selective incorporation of GU-rich oligos in condensates at low RNA concentrations, incorporation of both RNAs into condensates at medium concentrations, and an inhibition of condensate formation at high RNA concentrations (new Fig 7 E,F).

A final concern is that the specificity of mRNA recruitment to Kif1C puncta in cells is not critically evaluated. Among endogenous mRNAs, only one (Rab13) is tested. The paper would be stronger with a second positive mRNA and a negative control mRNA.

Response: We have now tested whether the specificity of mRNA recruitment to KIF1C puncta applies to additional mRNAs. We carried out single-molecule FISH (smFISH) experiments for two additional mRNAs. Based on the literature showing KIF1C-dependent localization of specific RNAs, we chose NET1 as a second positive mRNA and CAM1 as a negative control mRNA (Pichon et al., 2021). We first show that NET1 mRNA is mislocalized in KIF1C KO cells whereas CAM1 mRNA is not (new Fig S7 C,D). We then rescued the KO cells with FL or DIDR constructs and show that the FL protein rescues NET1 mRNA localization to the cell periphery whereas the DIDR construct does not (new Fig S7 E,F).

__Reviewer #2 (Significance (Required)): __

The mechanism of the KifC1-mRNA interaction was not investigated in the prior work, so the proposal that a liquid condensate is involved in novel. It is also topical, since there is intense current interest in transport and regulation of mRNAs by condensate-mediated mechanisms. The most useful part of this paper to the field may be the identification of IDR2 as required for mRNA binding.

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

KIF1C is a member of the kinesin-3 family, which is responsible for fast organelle transport in cells. The cargos of KIFIC are diverse, such as Golgi apparatus, Rab6 vesicles, exon junction complex (EJC), integrins, and RNA. Mutations in the KIF1C coding sequence leads to neurodegenerative diseases, such as hereditary spastic paraparesis (HSP). In addition, as an RNA transporter, KIF1C transports various types of mRNAs (e. g., APC-dependent mRNAs, KIF1C's own mRNA) along the microtubules and clusters them to cytoplasmic protrusions to fulfill certain biological functions.

In the current manuscript, Gen et.al., investigated the intracellular behaviors of the kinesin-3 member KIF1C. The study revealed that the KIF1C can form dynamic condensates both in cells and in vitro via an unstructured domain within the tail of the motor. KIF1C was found to also interact with synthesized RNA and other RNA granules in cells. In addition, the authors also show the KIFIC participates intracellular transport of endogenous mRNA, Rab13mRNA, identified a 47aa fragment in the KIF1C's IDR is critical for the KIF1C- Rab13mRNA interaction. Finally, as well as other prion-like proteins, the PPLS of KIF1C is buffered by the non-specific RNA pool in the cytoplasm.

In summary, this is an interesting work in the field, and reveals novel results about the mechanisms of motor protein transport that will be broadly interesting. The assays are generally well performed, and the results and discussion are well described, but some descriptions in the article should be more rigorous and objective. The article is very long, and I think it would benefit from streamlining and reducing the number of figures to make it more accessible for non-specialists in the field.

Here are some concerns:

__Major: __

Fig. 1A shows the domain organization of all kinesin-3 members, but Figure 1B only represents KIF1Bβ, KIF13B and KIF16B as controls. Generally, the KIF1Bα has the highest sequence similarity with KIF1C in kinesin-3 family (very high sequence similarity before aa 992 in KIF1C, which locates in IDR, probably contains IDR2a from Fig. S10A). In addition, both KIF1C and KIF1Bα contain a PLD from the prediction in this paper (Figure S2C). Although the authors show the phenotype of KIF1Bα in the Fig. S9, it might be better to put some descriptions up front, as readers may consider why the authors did not use KIF1Bα as a control. Actually, I kept thinking about this concern before I got to the discussion.

Response: We thank the reviewer for this suggestion. We have moved the descriptions of KIF1Ba phenotypes to earlier in the manuscript. We show that KIF1Ba forms puncta in cells but unlike KIF1C, the KIF1Ba puncta do not colocalize with known RNA granules P-bodies or stress granules (now in Fig S5 B,C). We show that, unlike KIF1C, the KIF1Ba puncta do not incorporate GU-rich or polyA RNA (now in Fig S6 B).

It would be better if the authors can combine the Fig. 2B and 2C, since the article did not mention Fig. 2B at all. In addition, Fig. S3 does not help this article too much. Probably it would be better if the authors could take the ΔIDR-mNG data from the Fig. S3 and put into the Fig. 2. as a negative control, especially for Fig. 2D an 2F. As for whether the phenotype of the ΔIDR-mNG construct is "similar to a constitutively active KIF1C construct containing only the motor domain (amino acids 1-348) (Fig. S3 C)", I do not think it is important here, since in this part, the authors are aiming to confirm the IDR is critical for KIF1C phase separation.

Response: We have combined Figures 2B and 2C as suggested. We prefer to leave Figure S3 intact since, as the reviewer mentioned, the article is already long and these data are not critical for the story.

The description "the condensate properties can be modulated by adjacent coiled-coil segments" in the abstract and the sentence "However, the coiled-coil segments in the stalk domain appear to facilitate puncta formation as the addition of increasing amounts of coiled coil resulted in increased KIF1C enrichment in puncta as compared to the IDR alone" in the article are not accurate, since there is no direct evidence in this manuscript that shows that. In Fig. 2D, as well as Fig. 6A and Fig. S7, it is manifest at a glance there are lots of IDR-mNG localized in nucleus, which decreases the concentration of this construct in cytoplasm which in turn may lower its capability to form puncta. This is important, as the results in Fig.4 show that the concentration of protein directly affects the formation of phase separated puncta in cells. From my view, the words "modulate", "tune" ... usually describe active processes, and these words may be confusing unless there are enough evidence support direct regulation. But the data presented in this article suggests to us that it is likely a passive process, such as the coiled coil region preventing the CC4-IDR construct from entering the nucleus (Fig. 2D, Fig. 6A and Fig. S7). Moreover, CC4 does affect the critical concentration of IDR in vitro (Fig. 5E), but that could be attributed to the coiled coil domain increasing its solubility. I like the word "influence" used in a subtitle in the discussion portion.

Response: We have removed this from the text.

In addition, the in vitro study of this paper in Fig. 5 did not show any significant difference of the puncta formation between IDR-mNG and CC4 - IDR-mNG (Diameter: 0.43 {plus minus} 0.22 μm (mean {plus minus} STD) for IDR-mNG vs 0.48 {plus minus} 0.27 μm (mean {plus minus} STD) for CC4-IDR-mNG. Roundness: No value was show in the article). So, a stricter assay or a more accurate description is required here to avoid any misleading to the readers.

Response: We now include p values showing that the differences in diameter and roundness are statistically significant (data moved to Fig S8 B).

The description for Fig. 5 "At 2 uM protein concentration and 100 mM NaCl, the KIF1C(IDR) droplets were smaller [diameter 0.43 {plus minus} 0.22 μm (mean {plus minus} STD)] than KIF1C(CC4+IDR) droplets [0.48 {plus minus} 0.27 μm (mean {plus minus} STD)] (Fig. 5 C)" does not appear accurate as well, since there is no significant difference between the value 0.43 {plus minus} 0.22 μm and the value 0.48 {plus minus} 0.27 μm, so it should not be descripted as "smaller". In addition, the article mentioned that "The KIF1C(IDR) puncta were also less round than those of KIF1C(CC4+IDR) (Fig. 5 C)", but there is no corresponding value from the quantification show the KIF1C(IDR) is less round.

Response: We now include p values showing that the differences in diameter and roundness are statistically significant (data moved to Fig S8 B).

The description in sentence "We thus tested whether ... LLPS is mutually exclusive (Fig. S5 A)" may not be accurate. Results in Fig. S5 only show there is no direct interaction between KIF1C and CLIP-170 or these two proteins do not colocalize. The words "mutually exclusive" means two proteins competent each other in the same location from my understanding.

Response: We have replaced the words "mutually exclusive" with "no colocalization" (line 204).

In addition, is it necessary to put Fig. S5 into this article? Since from my side, it does not help too much for the whole story. In cells, kinesin motors are autoinhibited in the cytoplasm. For this KIF1C, most of motors appear autoinhibited as well, even when the authors removed the IDR based on Fig. S3C (ΔIDR-mNG vs. MD-mNG). In this case, it is hard to investigate the potential interaction between the KIF1C (or its ΔIDR mutant) with the microtubules or with the tubulin due to the autoinhibition of constructs used in Fig. S5. It would be better to use other active versions of KIF1C, such as ΔP (Soppina et. al., PNAS, 2014) or other mutants (Ren et. al., PNAS, 2018; Wang et. al., Nat. Commun., 2022) if the authors want to show this part in the article.

Response: We agree that this data is not essential for the story, however, it may be of interest and benefit to others in the field studying LLPS of microtubule-associated proteins and we prefer to leave Figure S5 (now Figure S4) in the supplementary information.

The conclusion "This result suggests that the IDR- driven LLPS of KIF1C does not depend on mRNA incorporation, but is strongly affected by it" may not be accurate, there is no direct evidence that shows that mRNA, at least Rab13mRNA incorporation strongly affects the IDR- driven LLPS of KIF1C. Perhaps a knock out of Rab13mRNA would alter the formation of condensates, which would support a direct effect on LLPS.

Response: We have changed the text (line 306).

In addition, the sentence "These results also show that the LLPS is resistant to truncations of large portions of IDR" may not accurate, from my view, except IDR2a, the rest of the IDR may not participate or contribute too much to the formation of puncta, but that doesn't mean LLPS is resistant to the truncation of these portions in IDR, these are different logics. The quantification from Fig. 7E also show there is no significant difference between the ST and truncations except ΔIDR2 and ΔIDR2a in statistics, such as ST (21.8 {plus minus} 12.0 puncta per cell, 2.06 {plus minus} 0.83 μm diameter), ΔPLD (20.1 {plus minus} 13.7 puncta per cell, 1.62 {plus minus} 0.94 μm diameter), ΔIDR1 (23.1 {plus minus} 14.3 puncta per cell, 2.13 {plus minus} 1.04 μm diameter), ΔIDR3 (18.4 {plus minus} 8.1 puncta per cell, 1.71 {plus minus} 0.98 μm diameter).

Response: We have changed the text (line 307).

I am not sure I agree with the author's interpretation of their FRAP data in Fig. 3. It appears to me that there is a large immobile population of molecules, as the bleached areas recover less than 50% of their initial intensity. However, the authors conclude that there is rapid exchange of molecules in the puncta. The authors need to further analyze and discuss both the exchange rate of the population of molecules that exchange, but also the fraction of apparently immobile molecules that do not recover in their experiments. These data appear to suggest that a large percentage of the molecules in the KIF1C puncta in fact do not exchange with the cytoplasm and undermine their argument for a liquid-like phase of the puncta.

Response: We agree that the data suggest the existence of an immobile pool of KIF1C within the condensates. We have added this information to the main text (lines 178-182). We note that these findings are consistent with recent studies demonstrating membrane-less organelles with at least partially solid-like properties, including nucleoli and stress granules as well as microtubule associated proteins (see references, reviewed in Van Treeck & Parker 2019).

__Minor: __

As mentioned above, Fig. 2 F needs a negative control, since the values of FL and IDR are lower than other constructs, maybe use the Δ IDR-mNG protein is better. In addition, from my view, the lower value of IDR construct does not represent this construct has lower capability to form puncta, but more likely because most of this protein localizes in nucleus, thus dramatically lowering the cytoplasmic concentration.

Response: We have changed the text as suggested (lines 152-154).

Fig. 6A probably need a negative control as well, maybe use the same construct ΔIDR in Fig. S7 is better.

Response: We have now included KIF1Ba as a negative control (Fig S6 B).

Although I guess the reason for using hTERT-RPE1 cells in Rab13mRNA rescue assay (Fig. 6D-G) probably is easier to get KIF1C knock out cells (if I am correct), it would be better if there is a brief introduction for the reason to use hTERT-RPE1 here, since all previous assay in the article used COS-7 cells.

Response: You are correct and we have added text introducing the use of hTERT-RPE1 cells (line 269).

Is there any specific reason to use the construct ST in Fig. 7? Since in Fig. 6, the authors used FL-length KIFIC, if the authors want to avoid any effects caused by motor domain, the construct CC4-IRD also could be a simpler candidate.

Response: No specific reason other than to be consistent as most experiments that we carried out in cells used the ST construct (e.g. FRAP assay in Fig 3, hypotonic assay in Fig 3, RNaseA injection in Fig 4, RNA incorporation in Fig 4). (Note that Fig 7 is now Fig 6).

This article is a great case for motor-cargo interaction, since the RNA binding site of KIF1C is within its tail domain. This left me curious about if the interaction between the KIF1C and the membrane-less RNA granule is sufficient to release the KIF1C motor from autoinhibition? I guess the binding of RNA is not enough to release the KIF1C from autoinhibition. From Fig. S3C and Fig. 6D, seems the motor still in autoinhibition, even remove the Rab13mRNA binding region.

Response: We believe the question of whether the RNA binding relieves autoinhibition of KIF1C is beyond the scope of this manuscript and we plan to address this in the future with recombinant full-length KIF1C and RAB13 mRNAs.

There are some grammar mistakes, e.g., There should be a "is" between "IDR" and "critical" in the title "A subregion of the KIF1C IDR critical for enrichment of Rab13mRNA in condensates".

Response: Thank you. We have corrected this (line 289).

There should be a definition for the full names of the abbreviate "RBD" mentioned in the article although the readers may guess that is an RNA binding domain, if possible, it would be better but not necessary if the authors could show the residues or the region in IDR.

Response: RBD is defined at the beginning to the section "KIF1C condensates display properties of RNA granules" (line 219) but in response to the reviewer's comment, we now include this definition a second time in the Discussion section (line 420).

In the results (line 126), the authors refer to the KIF1C IDR without first defining this region in the introduction. I would re-word this sentence for clarity by first defining what an IDR is and how it's assessed in the current study.

Response: The IDR is defined at the end of the Introduction (lines 94-95).

What is the significance of the roundness measurement in Fig. 5? This should be described for the reader.

Response: Roundness refers to the shape of the droplet and this is now included in the text (line 323, data moved to Fig S8 B).

The authors state several times that this is the first kinesin shown to undergo LLPS. However, is this true? What about the recent work showing that the yeast Tea2 kinesin undergoes LLPS with other +TIP components (Maan et al. NCB 2023).

Response: We thank the reviewer for this comment. The recent work from the Dogterom lab (Maan et al., 2023) demonstrates that the end binding (EB) protein Mal3 forms condensates alone and with the kinesin-7 family member Tea2 and its cargo Tip1 for enrichment at microtubule plus ends. The authors show images of Mal 3 droplets and the requirement of the IDR domain and the crowding agent polyethylene glycol for droplet formation. The authors state that "Tea2 and Tip1 formed condensates under similar crowding conditions and concentrations on their own (Extended Data Fig. 5)." However, Extended Data Fig 5 reports on the fluorescence intensity of Mal3-EGFP colocalizing with Tea2 or Tip1. No images of Tea2-only droplets are shown and no information is provided on the Tea2 and/or PEG concentrations required for droplet formation or the liquid nature of Tea2 droplets. Thus, we do not feel comfortable stating that Tea2 on its own undergoes LLPS. We do reference the Maan et al., 2023 work in the Discussion listing microtubule-associated proteins shown to undergo LLPS (line 403) and when comparing the mM concentrations of KIF1C required for LLPS to the mM concentrations of these other microtubule-associated proteins (line 417).

The authors don't discuss KIF5A, but their analysis reveals it also contains a low complexity region that may undergo LLPS (Fig. S2D). This would fit with recent reports that KIF5A tends to oligomerize more than other KIF5 isoforms, and that mutations in KIF5A that impact the tail domain may lead to aberrant oligomerization. I feel that it would be useful to the field for the authors to discuss these results in light of their own.

Response: We thank the reviewer for this suggestion. Although it is intriguing that KIF5A is predicted to contain an IDR, there is, however, no data to suggest that KIF5A undergoes LLPS. Rather, the current literature suggests that KIF5A undergoes higher-order oligomerization and accumulation at the cell periphery, especially for the isoform lacking exon 27 (Nakano et al., 2022, Baron et al., 2022, Pant et al., 2023, Soustelle et al., 2023). It thus does not seem prudent for us to speculate on whether or not KIF5A undergoes LLPS.

__Reviewer #3 (Significance (Required)): __

The study is novel and interesting and will be impactful for the cytoskeletal and RNA biology communities. The experiments are of high quality and controls are appropriate. The finding that motor proteins can participate in LLPS will be of high interest for a variety of fields and provides a very interesting advance over current knowledge in the field.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #3

Evidence, reproducibility and clarity

KIF1C is a member of the kinesin-3 family, which is responsible for fast organelle transport in cells. The cargos of KIFIC are diverse, such as Golgi apparatus, Rab6 vesicles, exon junction complex (EJC), integrins, and RNA. Mutations in the KIF1C coding sequence leads to neurodegenerative diseases, such as hereditary spastic paraparesis (HSP). In addition, as an RNA transporter, KIF1C transports various types of mRNAs (e. g., APC-dependent mRNAs, KIF1C's own mRNA) along the microtubules and clusters them to cytoplasmic protrusions to fulfill certain biological functions.

In the current manuscript, Gen et.al., investigated the intracellular behaviors of the kinesin-3 member KIF1C. The study revealed that the KIF1C can form dynamic condensates both in cells and in vitro via an unstructured domain within the tail of the motor. KIF1C was found to also interact with synthesized RNA and other RNA granules in cells. In addition, the authors also show the KIFIC participates intracellular transport of endogenous mRNA, Rab13mRNA, identified a 47aa fragment in the KIF1C's IDR is critical for the KIF1C- Rab13mRNA interaction. Finally, as well as other prion-like proteins, the PPLS of KIF1C is buffered by the non-specific RNA pool in the cytoplasm.

In summary, this is an interesting work in the field, and reveals novel results about the mechanisms of motor protein transport that will be broadly interesting. The assays are generally well performed, and the results and discussion are well described, but some descriptions in the article should be more rigorous and objective. The article is very long, and I think it would benefit from streamlining and reducing the number of figures to make it more accessible for non-specialists in the field.

Here are some concerns:

Major:

1. Fig. 1A shows the domain organization of all kinesin-3 members, but Figure 1B only represents KIF1Bβ, KIF13B and KIF16B as controls. Generally, the KIF1Bα has the highest sequence similarity with KIF1C in kinesin-3 family (very high sequence similarity before aa 992 in KIF1C, which locates in IDR, probably contains IDR2a from Fig. S10A). In addition, both KIF1C and KIF1Bα contain a PLD from the prediction in this paper (Figure S2C). Although the authors show the phenotype of KIF1Bα in the Fig. S9, it might be better to put some descriptions up front, as readers may consider why the authors did not use KIF1Bα as a control. Actually, I kept thinking about this concern before I got to the discussion.
2. It would be better if the authors can combine the Fig. 2B and 2C, since the article did not mention Fig. 2B at all. In addition, Fig. S3 does not help this article too much. Probably it would be better if the authors could take the ΔIDR-mNG data from the Fig. S3 and put into the Fig. 2. as a negative control, especially for Fig. 2D an 2F. As for whether the phenotype of the ΔIDR-mNG construct is "similar to a constitutively active KIF1C construct containing only the motor domain (amino acids 1-348) (Fig. S3 C)", I do not think it is important here, since in this part, the authors are aiming to confirm the IDR is critical for KIF1C phase separation.
3. The description "the condensate properties can be modulated by adjacent coiled-coil segments" in the abstract and the sentence "However, the coiled-coil segments in the stalk domain appear to facilitate puncta formation as the addition of increasing amounts of coiled coil resulted in increased KIF1C enrichment in puncta as compared to the IDR alone" in the article are not accurate, since there is no direct evidence in this manuscript that shows that. In Fig. 2D, as well as Fig. 6A and Fig. S7, it is manifest at a glance there are lots of IDR-mNG localized in nucleus, which decreases the concentration of this construct in cytoplasm which in turn may lower its capability to form puncta. This is important, as the results in Fig.4 show that the concentration of protein directly affects the formation of phase separated puncta in cells. From my view, the words "modulate", "tune" ... usually describe active processes, and these words may be confusing unless there are enough evidence support direct regulation. But the data presented in this article suggests to us that it is likely a passive process, such as the coiled coil region preventing the CC4-IDR construct from entering the nucleus (Fig. 2D, Fig. 6A and Fig. S7). Moreover, CC4 does affect the critical concentration of IDR in vitro (Fig. 5E), but that could be attributed to the coiled coil domain increasing its solubility. I like the word "influence" used in a subtitle in the discussion portion.

In addition, the in vitro study of this paper in Fig. 5 did not show any significant difference of the puncta formation between IDR-mNG and CC4 - IDR-mNG (Diameter: 0.43 {plus minus} 0.22 μm (mean {plus minus} STD) for IDR-mNG vs 0.48 {plus minus} 0.27 μm (mean {plus minus} STD) for CC4-IDR-mNG. Roundness: No value was show in the article). So, a stricter assay or a more accurate description is required here to avoid any misleading to the readers.

The description for Fig. 5 "At 2 uM protein concentration and 100 mM NaCl, the KIF1C(IDR) droplets were smaller [diameter 0.43 {plus minus} 0.22 μm (mean {plus minus} STD)] than KIF1C(CC4+IDR) droplets [0.48 {plus minus} 0.27 μm (mean {plus minus} STD)] (Fig. 5 C)" does not appear accurate as well, since there is no significant difference between the value 0.43 {plus minus} 0.22 μm and the value 0.48 {plus minus} 0.27 μm, so it should not be descripted as "smaller". In addition, the article mentioned that "The KIF1C(IDR) puncta were also less round than those of KIF1C(CC4+IDR) (Fig. 5 C)", but there is no corresponding value from the quantification show the KIF1C(IDR) is less round. 4. The description in sentence "We thus tested whether ... LLPS is mutually exclusive (Fig. S5 A)" may not be accurate. Results in Fig. S5 only show there is no direct interaction between KIF1C and CLIP-170 or these two proteins do not colocalize. The words "mutually exclusive" means two proteins competent each other in the same location from my understanding.

In addition, is it necessary to put Fig. S5 into this article? Since from my side, it does not help too much for the whole story. In cells, kinesin motors are autoinhibited in the cytoplasm. For this KIF1C, most of motors appear autoinhibited as well, even when the authors removed the IDR based on Fig. S3C (ΔIDR-mNG vs. MD-mNG). In this case, it is hard to investigate the potential interaction between the KIF1C (or its ΔIDR mutant) with the microtubules or with the tubulin due to the autoinhibition of constructs used in Fig. S5. It would be better to use other active versions of KIF1C, such as ΔP (Soppina et. al., PNAS, 2014) or other mutants (Ren et. al., PNAS, 2018; Wang et. al., Nat. Commun., 2022) if the authors want to show this part in the article. 5. The conclusion "This result suggests that the IDR- driven LLPS of KIF1C does not depend on mRNA incorporation, but is strongly affected by it" may not be accurate, there is no direct evidence that shows that mRNA, at least Rab13mRNA incorporation strongly affects the IDR- driven LLPS of KIF1C. Perhaps a knock out of Rab13mRNA would alter the formation of condensates, which would support a direct effect on LLPS.

In addition, the sentence "These results also show that the LLPS is resistant to truncations of large portions of IDR" may not accurate, from my view, except IDR2a, the rest of the IDR may not participate or contribute too much to the formation of puncta, but that doesn't mean LLPS is resistant to the truncation of these portions in IDR, these are different logics. The quantification from Fig. 7E also show there is no significant difference between the ST and truncations except ΔIDR2 and ΔIDR2a in statistics, such as ST (21.8 {plus minus} 12.0 puncta per cell, 2.06 {plus minus} 0.83 μm diameter), ΔPLD (20.1 {plus minus} 13.7 puncta per cell, 1.62 {plus minus} 0.94 μm diameter), ΔIDR1 (23.1 {plus minus} 14.3 puncta per cell, 2.13 {plus minus} 1.04 μm diameter), ΔIDR3 (18.4 {plus minus} 8.1 puncta per cell, 1.71 {plus minus} 0.98 μm diameter). 6. I am not sure I agree with the author's interpretation of their FRAP data in Fig. 3. It appears to me that there is a large immobile population of molecules, as the bleached areas recover less than 50% of their initial intensity. However, the authors conclude that there is rapid exchange of molecules in the puncta. The authors need to further analyze and discuss both the exchange rate of the population of molecules that exchange, but also the fraction of apparently immobile molecules that do not recover in their experiments. These data appear to suggest that a large percentage of the molecules in the KIF1C puncta in fact do not exchange with the cytoplasm and undermine their argument for a liquid-like phase of the puncta.

Minor:

1. As mentioned above, Fig. 2 F needs a negative control, since the values of FL and IDR are lower than other constructs, maybe use the Δ IDR-mNG protein is better. In addition, from my view, the lower value of IDR construct does not represent this construct has lower capability to form puncta, but more likely because most of this protein localizes in nucleus, thus dramatically lowering the cytoplasmic concentration.
2. Fig. 6A probably need a negative control as well, maybe use the same construct ΔIDR in Fig. S7 is better.
3. Although I guess the reason for using hTERT-RPE1 cells in Rab13mRNA rescue assay (Fig. 6D-G) probably is easier to get KIF1C knock out cells (if I am correct), it would be better if there is a brief introduction for the reason to use hTERT-RPE1 here, since all previous assay in the article used COS-7 cells.
4. Is there any specific reason to use the construct ST in Fig. 7? Since in Fig. 6, the authors used FL-length KIFIC, if the authors want to avoid any effects caused by motor domain, the construct CC4-IRD also could be a simpler candidate.
5. This article is a great case for motor-cargo interaction, since the RNA binding site of KIF1C is within its tail domain. This left me curious about if the interaction between the KIF1C and the membrane-less RNA granule is sufficient to release the KIF1C motor from autoinhibition? I guess the binding of RNA is not enough to release the KIF1C from autoinhibition. From Fig. S3C and Fig. 6D, seems the motor still in autoinhibition, even remove the Rab13mRNA binding region.
6. There are some grammar mistakes, e.g., There should be a "is" between "IDR" and "critical" in the title "A subregion of the KIF1C IDR critical for enrichment of Rab13mRNA in condensates".
7. There should be a definition for the full names of the abbreviate "RBD" mentioned in the article although the readers may guess that is an RNA binding domain, if possible, it would be better but not necessary if the authors could show the residues or the region in IDR.
8. In the results (line 126), the authors refer to the KIF1C IDR without first defining this region in the introduction. I would re-word this sentence for clarity by first defining what an IDR is and how it's assessed in the current study.
9. What is the significance of the roundness measurement in Fig. 5? This should be described for the reader.
10. The authors state several times that this is the first kinesin shown to undergo LLPS. However, is this true? What about the recent work showing that the yeast Tea2 kinesin undergoes LLPS with other +TIP components (Maan et al. NCB 2023).
11. The authors don't discuss KIF5A, but their analysis reveals it also contains a low complexity region that may undergo LLPS (Fig. S2D). This would fit with recent reports that KIF5A tends to oligomerize more than other KIF5 isoforms, and that mutations in KIF5A that impact the tail domain may lead to aberrant oligomerization. I feel that it would be useful to the field for the authors to discuss these results in light of their own.

Significance

The study is novel and interesting and will be impactful for the cytoskeletal and RNA biology communities. The experiments are of high quality and controls are appropriate. The finding that motor proteins can participate in LLPS will be of high interest for a variety of fields and provides a very interesting advance over current knowledge in the field.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this manuscript by DeHaro-Arbona et al., the authors wish to understand how a signaling pathway (Notch) is dynamically decoded to elicit a specific transcriptional output. In particular, they investigate the kinetic properties of Notch-responsive nuclear complexes (the DNA binding factor CSL and its co-activator Mastermind (mam) along with several candidate interacting partners). Their experimental model is the polytene chromosome of the Drosophila salivary gland, in which the naturally inactive Notch can be artificially induced through the expression of a constitutively active form of Notch.

The authors develop a series of CRISPR and transgenic lines enabling the live imaging of these complexes at a specific locus and in various backgrounds (genetic perturbations/drug treatments). This quantitative live imaging data suggests that Notch nuclear complexes form hubs, and the authors characterize their binding dynamics. Interestingly, they elegantly demonstrate that the content of these hubs and their kinetic properties can evolve, even within Notch ON cells. Hence, they propose the existence of distinct hubs, distinguishing an open (CSL), engaged (CSK-Mam), or active (CSL-Mam-Med-PolII) configuration in Notch ON cells and an inactive hub (in Notch OFF having previously been exposed to Notch) state, that would explain the surprising transcriptional memory that the authors observe hours after Notch withdrawal.

We thank the reviewer for this constructive summary of our work

Reviewer #2 (Public Review):

The manuscript from deHaro-Arbona et al, entitled "Dynamic modes of Notch transcription hubs conferring memory and stochastic activation revealed by live imaging the co-activator Mastermind", uses single molecule microscopy imaging in live tissues to understand the dynamics and molecular determinants of transcription factor recruitment to the E(spl)-C locus in Drosophila salivary gland cells under Notch-ON and -OFF conditions. Previous studies have identified the major players that are involved in transcription regulation in the Notch pathway, as well as the importance of general transcriptional coregulators, such as CBP/P300 and the Mediator CDK module, but the detailed steps and dynamics involved in these processes are poorly defined. The authors present a wealth of single molecule data that provides significant insights into Notch pathway activation, including:

(1) Activation complexes, containing CSL and Mam, have slower dynamics than the repressor complexes, containing CSL and Hairless.

(2) Contribution of CSL, NICD, and Mam IDRs to recruitment.

(3) CSL-Mam slow-diffusing complexes are recruited and form a hub of high protein concentrations around the target locus in Notch-ON conditions.

(4) Mam recruitment is not dependent on transcription initiation or RNA production.

(5) CBP/P300 or its associated HAT activity is not required for Mam recruitment.

(6) Mediator CDK module and CDK8 activity are required for Mam recruitment, and vice-versa, but not CSL recruitment.

(7) Mam is not required for chromatin accessibility but is dependent on CSL and NICD.

(8) CSL recruitment and increased chromatin accessibility persist after NICD removal and loss of Mam, which confers a memory state that enables rapid re-activation in response to subsequent Notch activation.

(9) Differences in the proportions of nuclei with both Pol II and with Mam enrichment, which results in transcription being probabilistic/stochastic. These data demonstrate that the presence of Mamcomplexes is not sufficient to drive all the steps required for transcription in every Notch-ON nucleus.

(10) The switch from more stochastic to robust transcription initiation was elicited when ecdysone was added.

Overall, the manuscript is well written, concise, and clear, and makes significant contributions to the Notch field, which are also important for a general understanding of transcription factor regulation and behavior in the nucleus. I recommend that the authors address my relatively minor criticisms detailed below.

We thank the reviewer for their thorough and constructive summary of our work. We are glad that they overall found it insightful and interesting. Below we have addressed the points they have raised.

Page 7, bottom. The authors speculate, "It is possible therefore that, once recruited, Mam can be retained at target loci independently of CSL by interactions with other factors so that it resides for longer." Is it possible that another interpretation of that data is that Mam is a limiting factor?

As indicated our comment is a speculation and is based on the observations summarized in the paragraph. We are not entirely sure what the reviewer is proposing as an alternate model. However, if it relates to the relative concentrations of the different factors, this would not account for the differences in trajectory durations. And for most aspects of our analysis, K[off] has the most profound influence on the results. Furthermore, differences persist even when CSL levels are considerably reduced (as in conditions with Hairless RNAi).

Page 9. The authors write, "A very low level of enrichment was evident for... for the CSL Cterminus..". The recruitment of CSL ct IDR does not appear to be statistically significant or there is no apparent difference (Figure S2C), suggesting the CSL ct IDR does not play a role in enrichment.

We agree with the comments of the reviewer and have adjusted the text on page 9 accordingly.

Page 9. The authors write, "Notably, MamnIDR::GFP fusion was present in droplets, suggesting it can self-associate when present in a high local concentration (Figure S2B)." Is this result only valid for Mam nIDR or does full-length Mam also localize into droplets, as has been previously observed for full-length mammalian Maml1 in transfected cells?

We agree that the observed foci of MamL1 that have been detected in mammalian cells are interesting. We have not tried to replicate those data because the large size of Mam has made it challenging to produce a full-length form in over-expression. We note however that another portion of Mam, MamIDR, does not make droplets when over-expressed despite it containing a large section of the disordered region of the Drosophila Mam. We have now included a comment about the mammalian data in the text (page 9) to put our findings in context.

Previous studies in mammalian cells suggest that Maml1 is a high-confidence target for phosphorylation by CDK8, see Poss et al 2016 Cell Reports https://doi.org/10.1016/j.celrep.2016.03.030. By sequence comparison, does fly Mam have similar potential phosphorylation sites, and might these be critical for Mam/CDK module recruitment?

We thank the reviewer for highlighting this point. Indeed, we were very excited when we learnt that MamL1 was found to be a high confidence CDK8 target and we looked hard in the Mam sequence for potential phosphorylation sites. Sadly, there is very little conservation between the fly and the mammalian proteins beyond the helical region that contacts CSL and NICD. Furthermore, there are no identifiable putative CDK8 phosphorylation sites based on conventional motifs. It therefore remains to be established whether or not Mam is a direct target of the CDK8 kinase activity. We have added an explanatory comment in the text (page 11).

Page 11: The authors write, "The differences in the effects on Mam and CSL imply that the CDK module is specifically involved in retaining Mam in the hub, and that in its absence other CSL complexes "win-out", either because the altered conditions favour them and/or because they are the more abundant." Are the "other" complexes the authors are referring to Hairless-containing complexes? With the reagents the authors have in hand couldn't this be explicitly shown for CSLcomplexes rather than speculated upon?

The reviewer is correct that CSL complexes containing Hairless are good candidates to be recruited in these conditions. We have compared the levels of Hairless at E(spl)-C following treatments with Senexin and have not detected a difference. However, it appears that the high proportion of unbound Hairless makes it difficult to detect/quantify the enrichment at E(spl)-C. We have therefore taken a different strategy, which is to measure the recruitment of a mutant form of CSL that is compromised for Hairless binding. Recruitment of the mutant CSL is detected in Notch-ON conditions, but is significantly reduced/absent following Senexin treatment. These data favour the model proposed by the reviewer that in the absence of CDK8 activity, the CSL-Hairless complexes win out. These new data have been added in new Supplementary Figure S3F and S3G (and see text page 11)

Page 12/13: The authors write, "Based on these results we propose that, after Notch activity decays, the locus remains accessible because when Mam-containing complexes are lost they are replaced by other CSL complexes (e.g. co-repressor complexes)." Again, why not actually test this hypothesis rather than speculate? The dynamics of Hairless complexes following the removal of Notch would be very interesting and build upon previously published results from the Bray lab.

We thank the reviewer for this comment and we agree it’s possible that the proportion of Hairless complexes increases after Notch withdrawal. However, for the reasons outlined above, it is difficult to quantify changes in Hairless, (and our preliminary experiment did not reveal any large-scale effect) and because of the complexity of the genetics we cannot straightforwardly extend the experiment to analyze the behaviour of the mutant CSL as above. Therefore, at present, we cannot say whether the loss of Mam is compensated by an increase in Hairless. We hope in future to investigate the characteristics of the memory in more depth.

Page 13: The authors write, "As Notch removal leads to a loss of Mam, but not CSL, from the hub, it should recapitulate the effects of MamDN." While the data in Figure 5B seem to support this hypothesis, it's not clear to me that the loss of Mam and MamDN should phenocopy each other, bc in the case of MamDN, NICD would still be present.

We apologise that this sentence was a bit misleading. We have now rewritten it to improve accuracy (page 13) “As Notch removal leads to a loss of Mam, but not CSL, from the hub, we hypothesised it would recapitulate the effects of MamDN on chromatin accessibility and transcription of targets.”

The temporal dynamics for Mam recruitment using the temperature- and optogenetic-paradigms are quite different. For example, in the optogenetic time course experiments, the preactivated cells are in the dark for 4 hours, while in the temperature-controlled experiments, there is still considerable enrichment of Mam at 4 hours. For the preactivated optogenetic experiments, how sure are the authors that Mam is completely gone from the locus, and alternatively, can the optogenetic experimental results be replicated in the temperature-controlled assays? My concern is whether the putative "memory" observation is just due to incomplete Mam removal from the previous activation event.

We appreciate the concerns of the reviewer. However, we are confident that the 4-hour optogenetic inactivation is much more effective than the equivalent time for temperature shifts. The temperature sensitive experiment involves a longer decay, because not only the protein but also the mRNA has to decay to fully remove NICD activity. The optogenetic experiments, involve only protein decay and so are more acute. Furthermore, we have tested (and we show in Figure 5H) that Mam is fully depleted after 4 hours “Off” in the optogenetic experiments.

In order to further strengthen the evidence in favour of the memory hub, we have extended the time-frame further to show that CSL is retained at the locus even after 24 hours “Notch OFF” in both the temperature and the optogenetic paradigm. We have also measured the effects on transcription after a 24hr OFF period using the optogenetic paradigm and seen that robust transcription is initiated in cells that have experienced a previous activation (preactivated) compared to those that have not (naïve). These new data have been added to new Figure 5 C-F and strongly support the memory model.

Reviewer #3 (Public Review):

Summary:

DeHaro-Arbona and colleagues investigate the in vivo dynamics of Notch-dependent transcriptional activation with a focus on the role of the Mastermind (MAM) transcriptional co-activator. They use GFP and HALO-tagged versions of the CSL DNA-binding protein and MAM to visualize the complex, and Int/ParB to visualize the site of Notch-dependent E(Spl)-C transcription. They make several conclusions. First, MAM accumulates at E(Spl)-C when Notch signaling is active, just like CSL. Second, MAM recruits the CDK module of Mediator but does not initiate chromatin accessibility. Third, after signaling is turned off, MAM leaves the site quickly but CSL and chromatin accessibility are retained. Fourth, RNA pol II recruitment, Mediator recruitment, and active transcription were similar and stochastic. Fifth, ecdysone enhances the probability of transcriptional initiation.

Strengths:

The conclusions are well supported by multiple lines of extensive data that are carefully executed and controlled. A major strength is the strategic combination of Drosophila genetics, imaging, and quantitative analyses to conduct compelling and easily interpretable experiments. A second major strength is the focus on MAM to gain insights into the dynamics of transcriptional activation specifically.

We thank the reviewer for their positive comments about the strengths of our work.

Weaknesses:

Weaknesses are minor. There were no p-values reported for data presented in Figure S1D and no indication of how variable measurements were. In addition, the discussion of stochasticity was not integrated optimally with relevant literature.

We thank the reviewer for noting these points. The statistical tests have now been included for Figure S1D (now Figure S1F). We have amplified the discussion about stochasticity, to include more reference to the literature and to make clear also the distinction with transcription bursting (page 19, 20).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The authors have an elegant series of manipulations that provide strong evidence for their hypotheses and conclusions. Their exploitation of a unique biological system amenable to imaging in the larval salivary gland is well-considered and well-performed. Most of the conclusions are supported by the data. I only have the concerns below.

(1) One of the main findings is the composition of Notch nuclear complexes and their interactions within a 'hub'. Yet most of the data showing hubs focus on labeling one protein component (+the locus or transcription), but multi-color imaging is rarely used to show how CSL-Mam, Mam-Med... protein signals coalescence to form a hub. Given the powerful tool developed, it would be important to show these multi-state hubs. Related to this, if the authors expect that hubs are formed independently of transcription or Notch pathway activation, do the authors see clustering at other non-specific loci in the nucleus? If not, can the authors comment on why they think that is the case? If so, do they demonstrate consistent residence time profiles with the tracked E(spl) locus?

We apologise that it was not evident from the data shown that the proteins co-localize. First we stress that all the experiments are multicolor and most rely on very powerful methods to measure co-recruitment at a chromosomal locus- something that is very rarely achieved by others studying hubs. Second, we have in all cases confirmed that the proteins do colocalize. We have modified the diagram of our analysis pipeline to make more clear that this relies on multi-colour imaging, and adjusted all the figure labels to indicate the position of E(spl)-C. We have also added panels to new supplementary Figure S1C with examples of the co-localization between CSL and Mam and a plot confirming their levels of recruitment are correlated across multiple nuclei.

We would like to clarify that our data show that the hubs do require Notch activation for their establishment. Other regions of enrichment are detected in Notch-ON conditions, but these are less prominent and, with no independent method for identifying them, can’t be compared between nuclei. In SPT experiments, other clusters with consistent residence are detected as reported in our recent paper which expanded on the SPT data (Baloul et al, 2023). We also detect co-localizations and “hubs” in other tissues, but those analyses are ongoing and beyond the scope of this paper.

(2) The authors convincingly show that Notch hub complexes exhibit a memory. While the data showing rapid hub reformation upon Notch withdrawal are solid and convincing (Figure 5, in particular, F), the claim that this memory fosters rapid transcriptional reactivation is less clear. Yet in order to invoke transcriptional memory, it's necessary to solidify this transcriptional response angle. The authors should consider quantifying the changes in transcription activity (at the TS and not in the cytoplasm as currently shown), as well as the timing of transcriptional reactivation (with the MS2 system or smFISH). Manipulating the duration of the activation and dark recovery periods could help to draw a better correlation between the timing of hub reformation and that of transcriptional response and would also help determine how persistent this phenomenon is.

We thank the reviewer for these suggestions. We have carried out several new experiments to probe further the persistence of memory and to show the effects on transcription when Notch is inactivated/reactivated. First, we have extended the time period for Notch inactivation by temperature control and show that the CSL hub persists even at 24 hours and that no transcription from the target E(spl)m3 is detected –neither at the transcription start-site nor in the cytoplasm. Second, we have extended the Notch OFF time period to 24 hours using the optogenetic approach and show that transcription is robustly reinitiated in preactivated nuclei when Notch is re-activated with 30 mins light treatment while little if any E(spl)m3 transcription is detected in naïve nuclei with the same treatment. These new data are included in new Figure 5 C-F and see page 13-14. Both these new experiments substantiate the model that the nuclei retain transcriptional memory.

(3) The manuscript ends with the finding that the presence of a Mam hub does not always correlate with transcription. They conclude that transcription is initially stochastic. The authors find this surprising and even state that this could not be observed without their in vivo live imaging approaches. I don't understand why this result is surprising or unexpected, as we now know that transcription is generally a stochastic process and that most (if not all) loci are transcribed in a bursting manner. The fact that E(spl)-C locus is bursty is already obvious from the smFISH data. The fact that active nascent transcription does not correlate with local TF hubs was already observed in early Drosophila embryos (with Zelda hubs and two MS2 reporters, hb-MS2, sna-MS2). If, in spite of the inherent stochasticity of transcription (bursting), the data are surprising for other reasons, the authors should explain it better.

We apologise that we had not made clear the reasons why the results were unexpected. We have substantially rewritten this section, and the discussion section, to clarify. We have also moderated the language used to better reflect the overall context of our results. We briefly summarise here. As the reviewer correctly states, it is well known that transcription is inherently bursty. Indeed the MS2 transcription profiles in “ON” nuclei are bursty, which likely reflects the switching of the promoter. However, in other contexts where we have monitored transcription although it is bursty it has nevertheless been initiated synchronously in response to Notch in all nuclei in a manner that was fully penetrant. What we observe in our current conditions, is that some nuclei never initiate transcription over the time-course of our experiments (2-3 hours), and those that are ON rarely switch off. This implies that there is another rate-limiting step. Supplying a second signal can modulate this so that it occurs with much higher frequency/penetrance. We consider this to be a second tier of regulation above the fundamental transcriptional bursting.

The fact that Mam is recruited in all nuclei, whether or not they are actively transcribing was surprising because recruitment of the activation complex has been considered as the limiting step. This is somewhat different from Zelda, which is thought to be permissive and needed at an early step to prime genes for later activation rather than to be the last step needed to fire transcription. We note also that we are not monitoring the position of the hub with respect to the promoter, as in the Zelda experiments (Zelda hubs may still persist, but they are not overlapping with the nascent RNA), we are monitoring the presence or absence of Mam hub in proximity to a genomic region.

Minor suggestions:

(1) The genotypes of the samples should be indicated in the figure legends.

We thank the reviewer for this suggestion. We have provided a table (new Table S3) where all of the genetic combinations are provided in detail for each figure. We considered that this approach would be preferable because it would be quite cumbersome to have the genotypes in each legend as they would become very long and repetitive.

(2) While the schematic Fig1A explains how the locus is detected, the presence of ParS/ParB is never indicated in subsequent panels and Figure. I assume that all panels depicting enrichment profiles, use a given radius from the ParS/ParB dot to determine the zero of the x-axis (grey zone). This should be clearly stated in all panels/figure legends concerned.

We apologies if this was not made explicit. Yes, all panels depicting enrichment profiles, use immunofluorescence signal from ParA/ParB recruitment to determine the zero of the x-axis. We have now marked this more clearly In all figures (grey bar, grey shading or labelled 0). All images where the locus is indicated by an arrowhead, by a coloured bar above the intensity plots or by grey shading in the graphs have been captured with dual colour and the signal from ParA/B recruitment used to define its location. This is now clearly stated in the analysis methods and in the legend. We have also modified the diagram in new supplementary Figure S1B, showing our analysis pipeline, to make that more explicit.

(3) FRAP/SPT experiments: the author should provide more details. How many traces? Are traces showing bleaching removed?

P7: does the statement ' The residences are likely an underestimation because bleaching and other technical limitations also affect track durations' imply that traces showing bleaching have not been removed from the analysis?

The authors could justify the choice of the model for fitting FRAP/Spt experiments and be cautious about their interpretation. For example, interpreting a kinetic behavior as a DNA-specific binding event can be accurate, only if backed up with measurements with a mutant version of the DNA binding domain.

We apologise if some of this information was not evident. The number of trajectories is provided in new Figure S1F, which indicates the number of trajectories analyzed for each condition in Figure 1.

We have now added also the numbers of trajectories analyzed for the ring experiments.

The comments on page 7 about bleaching refer to the technical limitations of the SPT approach. However, as bleached particles cannot be distinguished from those that leave the plane of imaging, they have not been filtered or removed. We have not sought to make claims about absolute residence times for that reason. Rather the point is to make a comparison between the different molecules. As the same fluorescent ligand and imaging conditions are used in all the experiments, all the samples are equivalently affected by bleaching. We subdivide trajectories according to their properties and infer that those which are essentially stationary are bound to chromatin, as is common practice in the field. We note that we have previously shown that a DNA binding mutant of CSL does not produce a hub at E(spl)-C in Notch-ON conditions and has a markedly more rapid recovery in FRAP experiments (Gomez-Lamarca et al, 2018) consistent with the slow recovery being related to DNA binding. This point has been added to the text (page 8).

(4) The authors should quantify their RNAi efficiency for Hairless-RNAi, Med13-RNAi, white-RNAi, yellow-RNAi, CBP-RNAi, and CDK8-RNAi.

We thank the reviewer for this comment. We have made sure that we are using well validated RNAis in all our experiments and have included the references in Table S2 where they have been used. We have now evaluated the knock-down in the precise conditions used in our experiments by quantitative RT-PCR and added those data, which show efficient knock-down is occurring, to new Supplementary Figure S1D and Figure S3J. We note also that the RNAi experiments are complemented by experiments inhibiting the complexes with specific drugs and that these yield similar results.

(5) Figure 3 A: could the author show that transcription is indeed inhibited upon triptolide treatment with smFISH (with for example m3 probes)? Why not use alpha-amanitin?

We thank the reviewer for this suggestion. We had omitted the smFISH data from this experiment in error. These data have now been added to new Supplementary Figure S3A and clearly show that transcription is inhibited following 1 hour exposure to triptolide. Triptolide is a very fast acting and very efficient inhibitor of transcription that acts at a very early step in transcription initiation. In our experience it is much more efficient than alpha-amanitin and is now the inhibitor of choice in many transcription studies.

(6) Figure 4 typo: panel B should be D and vice versa. Accessibility panels are referred to as Figure 4D, D' in the text but presented as panel B in the Figure.

We thank the reviewer for noting this mistake, it is now changed in the main text.

(7) The authors must add their optogenetic manipulation protocol to their methods section.

The method is described in detail in a recently published paper that reports its design and use. We have now also added a section explaining the paradigm in the methods (Page 31) as requested.

(8) Figure 3G needs a Y-axis label.

Our apologies, this has now been added.

(9) The authors should note why there was a change of control in Figure 3D compared to 3E and G (yellow RNAi vs white RNAi).

This is a pragmatic choice that relates to the chromosomal site of the RNAis being tested. Controls were chosen according to the chromosome that carries the UAS-RNAi: for the second chromosome this was yellow RNAi and for the third white RNAi. This is explained in the methods.

(10) Figure 1 would benefit from a diagram describing the genomic structure of the E(spl) locus and the relative position of the labelled locus within it.

We thank the reviewer for this suggestion and have added a diagram to Supplementary Figure S1A .

Reviewer #2 (Recommendations For The Authors):

Minor criticisms and typos:

Pet peeve: in some of the figure panels they are labeled Notch ON or OFF, but in others they are not, albeit that info is included in the figure legend. For the ease of the reader/reviewer, would it be possible to label all relevant figure panels either Notch ON or OFF for clarity?

We thank the reviewer for this suggestion and have modified the figures accordingly.

Page 7, top. "In comparison to their average distribution across the nucleus, both CSL and Mam trajectories were significantly enriched in a region of approximately 0.5 μm around the target locus in Notch-ON conditions, reflecting robust Notch dependant recruitment to this gene complex." Are the authors referring to Figure 1D here?

Thank you, this figure call-out has been added in the text.

Page 9. "...reported to interact with p300 and other factors (Figure S2B)." I believe the authors mean Figure S2C and not S2B.

Thank you, this has been corrected in the text.

Page 9. There is no Figure S2D.

Apologies, this was referring to Figure S1D, and is now corrected in the text.

Page 11: "...were at very reduced levels in nuclei co-expressing MamDN (Figure 4B).." Should be Figure 4CD.

Thank you, this has been corrected in the text.

Page 12: "...which was maintained in the presence of MamDN (Figure 4D, D')." Should be Figure 4B.

Thank you, this has been corrected in the text.

Reviewer #3 (Recommendations For The Authors):

In the Results section on Hub, the paragraph starting with "Third, we reasoned . ." the callout to Figure S2D should be Fig S1D.

Thank you, this has been corrected in the text

Figures: The font size in the Figures is so small that most words and numbers cannot be read on a printout. One has to go to the electronic version and increase the size to read it. This reviewer found that inconvenient and often annoying.

We apologise for this oversight, the font size has now been adjusted on all the graphs etc.

Figure legends: the legends are terse and in some cases leave explanations to the imagination (e.g. "px" in Figure 2E). It would be useful to go through them and make sure those who are not a Drosophila Notch person and not a transcription biochemist can make sense of them.

Our apologies for the lack of clarity in the legends. We have gone over them to make them more accessible and less succinct.

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Reply to the reviewers

Manuscript number: RC-2023-01938R

Corresponding author(s): Ilan, Davis

1. General Statements

We thank all four reviewers for their helpful and constructive comments. We have gone through each and every comment and proposed how we would address each point raised by the reviewers. We are confident our proposed revisions are feasible within a reasonable and expected time frame. Some of the comments regarding minor typo/aesthetics and extra references have already been addressed in the transferred manuscript. The changes are highlighted in yellow in the transferred manuscript.

2. Description of the planned revisions

Reviewer #1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Major points:

1. The presented work itself (Figures 1-4) does not need significant adjustments prior to publication, in my view, with only a few points to address. However, the work in Figure 5- doesn't really support the claims the authors make on its own, and would require some additional experiments or at the very least discussion of the caveats to its current form.

We thank the reviewer for these comments and will follow the reviewer’s suggestion by discussing the caveats regarding the interpretation of Figure 5. We will also add to the discussion to suggest future research approaches beyond the scope of this manuscript that would address the functional importance of localised mRNA translation. We will briefly mention in the discussion methods such as the quantification of the mRNA foci and the disruption of the mRNA localisation signals to disrupt localised translation and the use of techniques such as Sun-Tag (Tanenbaum et al, 2014) and FLARIM (Richer et al, 2021) to visualise local translation directly.

Tanenbaum et al, 2014 DOI: 10.1016/j.cell.2014.09.039

Richer et al, 2021 DOI: 10.1101/2021.08.13.456301

* __ Localized glia transcripts, are they "glial/CNS/PNS" significant or are they similar to other known datasets of protrusion transcriptomes? The authors compared their 4801 "total" localized to a local transcriptome dataset from the Chekulaeva lab finding that a significant fraction are localized in both. As the authors note, this is in good agreement with a recent paper from the Talifarro lab showing conservation of localization of mRNAs across different cell types. What the authors haven't done here, is further test this by looking at other non-neuronal projection transcriptomic datasets (for example Mardakheh Developmental Cell 2015, among others). If the predicted glia-localized processes are similar to non-neuronal processes transcriptomes, this would further strengthen this claim and rule out some level of CNS/PNS derived linage driving the similarities between glia and neuronal localized transcripts. __*

This is a good point and we thank the review for pointing out this interesting cancer data set. We will do as the reviewer suggests and intersect our data with Mardakheh Dev Cell 2015 to test the further generality of localisation in neurons and glia, in other cell types. Specifically, we plan to intersect both glial (this study) and neuronal (von Kuegelgen & Chekulaeva, 2020) dataset with protrusive breast cancer cells (Mardakeh et al, 2015).

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von Kuegelgen & Chekulaeva, 2020 DOI: 10.1002/wrna.1590

Mardakeh et al, 2015 DOI: 10.1016/j.devcel.2015.10.005

* __ The presentation/discussion around Figure 3 is a bit weaker than other parts of the manuscript, and it doesn't really contribute to the story in its current form. Notably there is no discussion about the significance of glia in neurological disorders until the very end of the manuscript (page 21), meaning when its first brought up.. it just sits there as a one off side point. The authors might consider strengthening/tightening up the discussion here, if they really want to keep it as a solo main figure rather than integrating it somewhere else/putting it into supplemental. In my view, Figures 2 & 3 should be merged into something a bit more streamlined. __*

This is a good point. We plan to strengthen the presentation of Figure 3 and discussion of the significance of glia in neurological disorders by adding a description of the Figure in the Results section and highlighting the significance of glia in nervous system disorders in the Discussion section.

* __ Why aren't there more examples of different mRNAs in Figure 4? Seems a waste to kick them all to supplemental. __*

We agree that it could be helpful to show different expression patterns in the main figure. To address this point we will add Pdi (Fig. S4D), which shows mRNA expression in both the glia and the surrounding muscle cell. This pattern is in contrast to Gs2, which is highly specific to glial cells. We will also note that although pdi mRNA is present in both the glia and muscle, Pdi protein is only abundant in the glia, suggesting that translation of pdi mRNA to protein is regulated in a cell-specific manner.

The plasticity experiments, while creative, I think need to be approached far more cautiously in their interpretation. Given that the siRNAs will completely deplete these mRNAs- it really needs to be stressed any/all of the effects seen could just be the result of "defective" or "altered" states in this glial population- which has spill over effects on plasticity in at the NMJ. Without directly visualizing if these mRNAs are locally translated in these processes and assessing if their translation is modulated by their plasticity paradigm, all these experiments can say is that these RNAs are needed in glia to modulate ghost bouton formation in axons. This represents the weakest part of this manuscript, and the part that I feel does not actually backup the claims currently being made. Without any experiments to A. quantify how much of these transcripts are localized vs in the cell body of these glia, B. visualize/quantify the translation of these mRNAs during baseline and during plasticity; the authors cannot use these data to claim that localized mRNAs are required for synaptic plasticity.

We are grateful to the reviewer for pointing out that we were not precise enough in defining our interpretation of the structural plasticity assay. We did not intend to claim that our results show that local translation of these transcripts is necessary for plasticity, only that these transcripts are localized and are required in the glia for plasticity in the adjacent neuron (in which the transcript levels are not disrupted in the experiment). Definitively proving that these transcripts are required locally and translated in response to synaptic activity would require genetic/chemical perturbations and imaging assays that would require a year or more to complete, so are beyond the scope of this manuscript. To address this point, we will clarify that the results do not show that localized transcripts are required, only that the transcripts are required somewhere specifically in the glial cell (without affecting the neuron level), and we can indeed show in an independent experiment that there are localized transcripts.

Reviewer #2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Major points:

1. * __ The authors analyse the 1700 shortlisted genes for Gene Ontology and associations with austism spectrum disorder, leading to interesting results. However, it is not clear to what extent the enrichments they observe are driven by their presumptive localization or if the associations are driven to a significant extent by the presence of these genes in the selected cell types in the Fly Cell Atlas. One way to address this would be to perform the GO and SFARI analysis on genes that are expressed in the same cells in the Fly Cell Atlas but were not shortlisted from the mammalian cell datasets - the results could then be compared to those obtained with the 1700 localized transcripts. __* This is a fair point raised by the reviewer as genes involved in neurological disease such as Autism Spectrum Disorder may be enriched in CNS/PNS cell types. We will follow the reviewer’s suggestion to perform GO and SFARI gene enrichment analysis in genes that were not shortlisted for presumptive glial localisation.

Although the authors attempt to justify its inclusion, I'm not convinced why it was important to use the whole cell transcriptome of perisynaptic Schwann cells as part of the selection process for localizing transcripts. Including this dataset may reduce the power of the pipeline by including mRNAs that are not localized to protrusions. How many of the shortlisted 1700 genes, and how many of the 11 glial localized mRNAs in Table 5, would be lost if the whole cell transcriptome were excluded. More generally, what is the distribution of the 11 validated localizing transcripts in each dataset in Table 4? This information might be valuable for determining which dataset(s), if any, has the best predictive power in this context.

We thank the reviewer for raising this point, which we will address with further analysis and adding to the discussion. We propose to address the criticism by running our analysis pipeline without the inclusion of the dataset using Perisynaptic Schwann Cells (PSCs) and then intersect with the PSCs-expressed genes, since their functional similarity with polarised Drosophila glial cells is highly relevant. We also agree with the reviewer that it would be a useful control for us to assess the ‘predictive power’ of each glial dataset by calculating their contribution to the shortlisted 1,700 glial localised transcripts and to the 11 experimentally validated transcripts via in situ hybridisation. To address this point, we plan to add this information in the revised manuscript.

* __ Did the authors check if any of the RNAi constructs are reducing levels of the target mRNA or protein? Doing so would strengthen the confidence in these important results significantly. In any case, the authors should also mention the caveat of potential off-target effects of RNAi. __*

We thank the reviewer for their useful comment and agree that the extent to which the RNAi expression reduces the levels of mRNA is not specifically known. We will add a FISH experiment on lac, pdi and gs2 RNAi showing very strong reduction in mRNA levels. We will also add an explanation of the caveats of the use of the RNAi system to the discussion.

Methods: what is the justification for assuming that if the RNAi cross caused embryonic or larval lethality then the 'next most suitable' RNAi line is reporting on a phenotype specific to the gene. If the authors want to claim the effect is associated with different degrees of knockdown they should show this experimentally. An alternative explanation is that the line used for phenotypic analysis in glia is associated with an off-target effect.

We thank the reviewer for this comment. We agree that off target effects cannot in principle be completely ruled out without considerable additional experimental analysis beyond the scope of this manuscript. To address the criticism we will remove the expression data of the lines that cause lethality and revise the discussion to explain that the level of knockdown in each line is unknown, and would require further experimental exploration.

Minor points:

1. It would be helpful to have in the Introduction (rather than the Results, as is currently the case) an operational definition of mRNA localization in the context of the study. And is it known whether or not localization in protrusions is the norm in mammalian glia or the Drosophila larval glia? I ask because it may be that almost all mRNAs diffuse into the protrusion, so this is not a selective process. One interesting approach to test this idea might be to test if the 1700 shortlisted transcripts have a significant underrepresentation of 'housekeeping' functions. We thank the reviewer for this excellent suggestion. To address the comment, we will move our explanation of the operational definition of mRNA localization to the Introduction. We will also perform enrichment analysis of housekeeping genes within 1,700 shortlisted transcripts compared to the transcriptome background, as the reviewer suggested.

Reviewer #3 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Major points:

1. The authors have pooled data from different studies across different type of glial cells performed from in vitro to in vivo. While pooling datasets may reveal common transcripts enriched in processes, this may not be the best approach considering these are completely different types of glial cells with distinct function in neuronal physiology. We thank the reviewer for highlighting the need for us to further justify why we pooled datasets. We will revise the manuscript to better emphasise that the overarching goal of our study was to try to discern a common set of localised transcripts shared between the cells. The problem with analysing and comparing individual data sets is that much of the variation may be due to differences in the methods used and amount of material, rather than differences in the type of cells used. We will revise the discussion to make this point and plan to explain that our approach corresponds well with a previous publication pooling localised mRNA datasets in neurons (von Kugelgen & Chekulaeva 2021).

von Kuegelgen & Chekulaeva, 2020 DOI: 10.1002/wrna.1590

It is important to note the limitations of the study. For example, DeSeq2 is biased for highly expressed transcripts. How robust was the prediction for low abundance transcripts?

The presented 1,700 transcripts were shortlisted based on their presence and expression level (TPM) in glial protrusions rather than their relative enrichment. Nevertheless, the reviewer makes a valid criticism of our use of DESeq2, where we compared enriched transcripts in glial and neuronal protrusions in Figure 1D. To address this point we will discuss this caveat in the relevant section.

The issue raised regarding low abundance transcript prediction raises an important question: does the likelihood of localisation to cell extremities correlate with mRNA abundance? We have already partially addressed this point, since our analysis of the fraction of localised transcripts per expression level quantiles shows only limited correlation. To address this comment, we will add these results in the revised manuscript as a supplementary figure.

The authors identify 1,700 transcripts that they classify as "predicted to be present" in the projections of the Drosophila PNS glia. This was based on the comparison to all the mammalian glial transcripts. Since the authors have access to a transcriptomic study from Perisynaptic Schwann cells (PSCs), the nonmyelinating glia associated with the NMJ isolated from mice; it would be more convincing to then validate the extent of overlap between Drosophila peripheral glial with the mammalian PSCs. This may reveal conserved features of localized transcripts in the PNS, particularly associated with the NMJ function.

Thank you for the valuable suggestion. A similar point was also raised by __[Reviewer #2 - Major point 2] __to re-run our pipeline excluding the PSCs dataset and intersect with the PSC transcriptome post-hoc. Please see the above section for our detailed response.

Fig 2: What is the extent of overlap between the translating fractions versus the localized fraction? It will be informative to perform the functional annotation of the translating glial transcripts as identified from Fig 1D.

This is an interesting question. To address this point, we plan to: (i) compare transcripts that are translated vs. localised in glial protrusions, and (ii) perform functional annotation enrichment analysis on the translated fraction of genes.

"We conclude predicted group of 1,700 are highly likely to be peripherally localized in Drosophila cytoplasmic glial projections". To validate their predictions, the authors test some of these candidates in only one glial cell type. It might be worthy to extend this for other differentially expressed genes localized in another glial type as well.

The presented in vivo analyses made use of the repo-GAL4 driver, which is active in all glial subtypes, including subperineurial, perineurial and wrapping glia that make distal projection to the larval neuromuscular junction. We agree that subtype-specific analysis would be highly informative, but we believe this is outside the scope of the current work where we aimed to identify conserved localised transcriptomes across all glial subtypes. Nevertheless, to address the comment, we plan to further clarify our use of pan-glial repo-GAL4 driver in the Results and Method section of the revised manuscript.

Figure 5: The authors perform KD of candidate transcripts to test the effect on synapse formation. However, these are KD with RNAi that spans across the entire cell. To make the claim about the importance of "target" RNA localization in glia stronger, ideally, they should disrupt the enrichment specifically in the glial protusions and test the impact on bouton formation. Do these three RNAs have any putative localization elements?

We agree with the review, that we would ideally test the effect of disruption of mRNA localization (and therefore localised translation). However, we feel these experiments are beyond the scope of this current study, as they will require a long road of defining localisation signals that are small enough to disrupt without affecting other functions. To address this comment we will revise the Discussion section to mention those difficulties explicitly, and clarify the limitations of the approach used in our study for greater transparency.

Reviewer #4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Major points:

1. The authors use FISH to validate the glial expression of their target genes, though these experiments are not quantified, and no controls are shown. The authors should provide a supplemental figure with "no probe" controls, and/or validate the specificity of the probe via glial knockdown of the target gene (see point 2). Furthermore, these data should be quantified (e.g. number of puncta colocalized with NMJ glia membrans). Thank you for requesting further information regarding the YFP smFISH probes. We have validated the specificity and sensitivity of the YFP probe in our recent publication (Titlow et al, 2023, Figure 1 and S1). Specifically, we demonstrated the lack of YFP probe signal from wild-type untagged biosamples and showed colocalization of YFP spots with additional probes targeting the endogenous exon of the transcript. Nevertheless, we will address this comment by adding control image panels of smFISH in wild-type (OrR) neuromuscular junction preparations.

Titlow et al, 2023 DOI: 10.1083/jcb.202205129

For the most part, the authors only use one RNAi line for their functional studies, and they only show data for one line, even if multiple were used. To rule out potential false negatives, the authors should leverage their FISH probes to show the efficacy of their knockdowns in glia. This would serve the dual purpose of validating the new probes (see point 1).

Thank you for the suggestion. This point was also raised by [Reviewer #2 - Major point 3]. Please see above for our detailed response.

In Figure 5 E, given the severe reduction in size in the stimulated Pdi KD animals, the authors should show images of the unstimulated nerve as well. Do the nerve terminals actually shrink in size in these animals following stimulation, rather than expand? The NMJ looks substantially smaller than a normal L3 NMJ, though their quantification of neurite size in F suggests they're normal until stimulation.

We share the same interpretation of the data with the reviewer that the neurite area is reduced post-potassium stimulation in pdi knockdown animals. We will follow the reviewer’s suggestion and add an image showing unstimulated neuromuscular junctions.

Minor points:

The authors claim that there is an enrichment of ASD-related genes in their final list of ~1400 genes that are enriched in glial processes. It is well-appreciated that synaptically-localized mRNAs are generally linked to ASDs. Can the authors comment on whether the transcripts localized to glial processes are even more linked to ASDs and neurological disorders than transcripts known to be localized to neuronal processes?

This is an interesting point. To address the comment, we will add a comparison of the degree of enrichment of ASD-related genes in neurite vs. glial protrusions in the revised manuscript.

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3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1

1. The use of blue/green or blue/green/magenta is difficult to resolve in some places. Swapping blue for cyan would greatly aid in visualizing their data.
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This comment is much appreciated. We have swapped blue for cyan in Figures 4 and S4. We have also changed Figure S1 to increase contrast and visibility as per reviewer’s comment.

Make the colouring/formatting of the tables more consistent, its distracting when its constantly changing (also there is no need for a blue background.. just use a basic white table).

This comment is much appreciated. We have applied a consistent colour palette to the Tables without background colourings and made the formatting uniform.

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Reviewer #2

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Introduction: 'Asymmetric mRNA localization is likely to be as important in glia, as it is in neurons,...'. Remove commas

Thank you for pointing this mistake out. We have made the corresponding edits.

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Reviewer #3

RNA localization in oligodendrocytes has been well studied and characterized. The authors should cite and discuss those papers (PMID: 18442491; PMID: 9281585).

We thank the reviewer for this useful suggestion. We have added these references to the paper.

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Reviewer #4

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• In Figure 5D, the authors should include a label to indicate that these images are from an unstimulated condition. We thank the reviewer for pointing this out. We have added the label as requested.

The authors are missing a number of key citations for studies that have explored the functional significance of mRNA trafficking in glia, and those that have validated activity-dependent translation:

- ____https://pubmed.ncbi.nlm.nih.gov/18490510____/

-____https://pubmed.ncbi.nlm.nih.gov/7691830____/

-____https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001053

-____https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450274____/

-_https://pubmed.ncbi.nlm.nih.gov/36261025_*/

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We thank the reviewer for the comment. We have added these references to the text.

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4. Description of analyses that authors prefer not to carry out

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Reply to the reviewers

1. General Statements [optional]

We thank the Reviewers for their helpful and constructive comments. In response to these suggestions we have performed new experiments and amended the manuscript, as we describe in our detailed response below.

2. Point-by-point description of the revisions

Reviewer #1

1. The Reviewer notes that while our analysis of centrosome size was comprehensive, we provided no analysis of centrosomal MTs, pointing out that while centrosome size declines as the embryos enter mitosis, the ability of centrosomes to organise MTs might not. This is a good point, and we now provide an analysis of centrosomal-MT behaviour (Figure 2). We find that there is a dramatic decline in centrosomal MT fluorescence at NEB, although the pattern of centrosomal MT recruitment prior to NEB is surprisingly complex.

The Reviewer questions how PCM client proteins can be recruited in different ways by the same Cdk/Cyclin oscillator. We apologise for not explaining this properly. It is widely accepted that Cdk/Cyclins drive cell cycle progression, in part, by phosphorylating different substrates at different activity thresholds (e.g. Coudreuse and Nurse, Nature, 2010; Swaffer et al., Cell, 2016). Moreover, it is also clear that Cdk/Cyclins can phosphorylate the same protein at different sites at different activity thresholds (e.g. Koivomagi et al., Nature, 2011; Asafa et al., Curr. Biol., 2022; Ord et al., Nat. Struct. Mol. Biol., 2019). Thus, we hypothesise that rising Cdk/Cyclin cell cycle oscillator (CCO) activity phosphorylates multiple proteins at different times and/or at different sites to generate the complicated kinetics of centrosome growth. We now explain this point more clearly throughout the manuscript.

The Reviewer is puzzled as to how we conclude that Cdk/Cyclins phosphorylate Spd-2 and Cnn at all the potential Cdk/Cyclin phosphorylation sites we mutate in our study. The Reviewer is right that we cannot make this conclusion, and we did not intend to make this claim. As we now clarify (p11, para.1), although it is unclear if Cdk/Cyclins phosphorylate Spd-2 or Cnn on all, some, or none of these sites, if either protein can be phosphorylated by Cdk/Cyclins, then these mutants should not be able to be phosphorylated in this way—allowing us to address the potential significance of any such phosphorylation. We now also note that several of these sites have been shown to be phosphorylated in embryos in Mass Spectroscopy screens (Figure S6).

The Reviewer highlights differences in how Spd-2 and Cnn help recruit γ-tubulin to centrosomes (Figure 6). They ask for a more detailed description, and are puzzled as to how this is compatible with direct regulation by a single oscillator. We now explain our thinking on this important point in much more detail. It appears that Spd-2 helps recruit γ-tubulin throughout S-phase, while Cnn has a more prominent role in late S-phase (Figure 6). This is consistent with our overall hypothesis of CCO regulation, as we postulate that low-level CCO activity promotes the Spd-2/γ-tubulin interaction in early S-phase, while higher CCO activity promotes the Cnn/γ-tubulin interaction in late-S-phase, potentially explaining the increase in the rate of γ-tubulin (but not γ-TuRC) recruitment we observe at this point (see minor comment #1, below, for an explanation of the various γ-tubulin complexes in flies). This is consistent with recent literature showing that CCO activity promotes γ-tubulin (but not γ-TuRC) recruitment by Cnn/SPD-5 in worms and flies (Ohta et al., 2021; Tovey et al., 2021).

The Reviewer was not convinced by our model (Figure 8, now Figure 9), raising two major concerns. First, they were unsure how a single oscillator could generate different patterns of protein recruitment. We addressed this in point #2 and #4, above, where we explain how different thresholds of CCO activity trigger different events, so there is no expectation that we should observe steady changes in recruitment over time as CCO activity rises. Second, they questioned how modest levels of Cdk/Cyclin activity can promote recruitment, while high levels of activity can inhibit recruitment. In point #1, above, we cite several examples where such positive and negative regulation by different Cdk/Cyclin activity levels have been described. We also now explain throughout the manuscript why this hypothesis provides a plausible explanation for our results: with moderate CCO activity promoting Spd-2-dependent PCM-client recruitment in early S-phase; higher CCO activity promoting a decrease in Spd-2 recruitment in mid-late-S-phase (so centrosomal Spd-2 levels decline); and even higher levels of CCO activity leading to a decrease in the interactions between the client proteins and the Spd-2/Cnn scaffold as the embryos enter mitosis (so the client proteins are rapidly released from the centrosome).