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

Cell polarization in dividing cells, including stem cells, is typically coupled such that polarity can inform the architecture, orientation, and/or asymmetry during cell division. In Drosophila neural stem cells (neuroblasts/SOP), Par polarity is coupled to the cell cycle, but the nature of this coupling remains unclear. In this work, Loyer and colleagues report on impacts of CDK1 inhibition on Bazooka/Par3 localization and basal fate determinant localization. They provide evidence for a novel phosphorylation site that appears unique to asymmetrically dividing cells and may be involved in regulation of asymmetric division. Finally, they show that CDK1 can, at least in principle, phosphorylate human Par3 in vitro.

Overall, the major claims of the abstract appear supported by the experimental work; however, we think the title overstates the overall conclusions that can be drawn from the work.

Major comments:

1. The major claim of the paper is the role of specific phosphorylation of S180 in asymmetrically dividing cells in polarization and sensory organ formation, which relies heavily on interpretation of S180A/D phosphomutants. The experiments are carefully performed and quantified, and are consistent with the conclusions drawn. However, we wondered if it possible that the phenotypes are not linked to phosphorylation (the authors acknowledge this in the Discussion)? In other words could the A/D mutants simply be weak Baz mutants? This could this potentially explain the extra-SOP phenotype if Baz function is generally altered, especially given that it is difficult to rationalise a role for SOP-specific phosphorylation in the processes that specify SOP cells in the precursor epithelial cells. The authors speculate that these early precursors may exhibit also phosphorylation, but this isn't examined. Chasing this down seems key to support the core titular claim of the paper.
2. Implicit in the core message of the paper is the elucidation of CDK1 regulation of polarity and specifically Baz. However, the connection between CDK1 and S180 (and Baz regulation overall) is relatively tenuous in this work. First, the S180A mutant does not phenocopy CDK1 inhibition with respect to basal determinant phenotypes, though obviously CDK1 may be more pleiotropic. Second, whether the CDK1 inhibition phenotype is linked to any effect on Baz/PAR behaviour is not really explored. Third, they do not test whether S180 phosphorylation is CDK1-dependent.
3. The method for quantifying domain signal only references prior work and should be described in this work. From our search of the cited reference, it appears to be peak signal intensity at a user specified point on the cortex. While this does not undermine the core findings as presented, it may not capture additional features that may be informative (domain size, fluorescence distribution, total signal etc.). For example domain coalescence would imply smaller, brighter domains, but similar total protein amounts, which appears to be the case from images, but isn't quantified per se.
4. The phosphospecific antibody signal is relatively weak, leading to relatively low signal to noise, which could compromise the ability to detect phospho-S180 in non-asymmetrically dividing cells or generally in cells in which Baz is not polarised and thus signal would be diffused around the cell rather than concentrated. Similar caveats could also apply to the lack of signal in interphase cells, where Baz may be less enriched at the cortex and not polarized. We are inclined to believe the authors conclusions, particularly given their examination of multiple cell types and tissues. However, it is a potential caveat as it may be most visible in polarised cells where it is asymmetrically enriched.
5. Examination of in vitro phosphorylation of human Par3D (Figure 6) seems out of place and does not add much. It is human, not Bazooka. They reveal 30 sites, 18 of which in both replicates, but most are not obvious CDK sites and the S180 equivalent site is missing. None of these sites is validated in vivo, at least in this work.

Minor comments:

• Figure 1: Uses metaphase arrested cells, presumably colcemid, but colcemid is only noted in Figure 2.
• Figure 2A: Scale bar is truncated
• Figure 2A: Example images of control neuroblasts could be useful to readers.
• Figure 2G' vs H': Because G' has two panels and H' has only one, we often confused the PKC and Mira box plots when comparing to Numb. Perhaps Mira could be in a separate sub panel or be more closely juxtaposed with Numb?
• Whereas both Numb/Mira were examined in CDK1(as), only Mira is reported for the S180A/D experiments. Is there a Numb phenotype as well?
• The discussion of the notch / Baz phenotypes (Figure 5) is rather complicated and a bit difficult to follow.
• Figure 5A: captions should indicate that RFP RNAi is depleting Baz.
• Box plots are used, but not described. i.e. outliers seem to be marked, but criteria unclear. Mean vs median, etc.

Some grammatical mistakes:

• Title: neuroblast (no 's'),
• Page 1: Cell fate difference(s?) in the resulting daughter cells
• Page 4: (As) CDK1 inhibition with 10 μM 1-NA-PP1 prevents neuroblasts from cycling and causes metaphase- arrested neuroblasts to slip out of mitosis. (Reword)
• Page 6: increased levels of basal fate(no 's') determinants

Significance

The links between cell cycle and cell polarity are clearly important and remain poorly understood. Hence, the work addresses key conceptual/mechanistic questions relevant to our fundamental understanding of stem cell biology and regulation of polarity and asymmetric cell division. In our opinion, there are clearly some interesting observations in the manuscript, the experiments are performed carefully, and the data are generally well described. That said, overall, the work seems somewhat premature.

1. The direct impact of CDK1 on Baz behaviour remains somewhat unclear. The authors do a good job of limiting the concentration of inhibitor to decouple effects of cell cycle progression from CDK1 levels per se, but this does potentially impact the strength of the phenotypes they can detect and hence the observed phenotypes are relatively minor. Note that driving cells out of mitosis with stronger CDK1 inhibition clearly impacts Baz localization, so the 'real' effect of CDK1 inhibition on Baz could be stronger than reported here. It is also unclear whether the phenotypes observed are directly linked to CDK1 regulation of PAR polarity or an indirect effect of cell cycle control of other processes. The authors' suggestion that it could be related to defects in cortical actin organization, which is known to be cell cycle controlled, seems most likely, but neither this or other models are explored further.
2. Using phosphospecific antibodies, they report on a novel putative CDK1 phosphorylation site, but aside from looking like a consensus CDK1 site, whether this site is CDK1 dependent is not examined. Notably, the corresponding phosphomutants have modest effects and don't obviously account for the CDK1 inhibition phenotype, leaving it somewhat unclear whether it is under cell cycle regulation.
3. The observation that S180 phosphorylation appears unique to asymmetrically dividing cells is very curious, but this observation is not followed up extensively. Again phenotypes of phosphomutants are quite modest, and while one can propose models to rationalise the phenotypes observed, these models are not fully explored.
4. The findings that human Par3D can be phosphorylated by CDK1 in vitro do not add much paritcularly as they obtain a very large number of putative sites raising questions of specificity, the sites are not validated, and an S180 equivalent site was not identified.

In summary, the individual findings of this work are interesting and generally solid. Each could be followed up to provide mechanistic insight into cell cycle- or cell type-dependent regulation of Par polarity. However, in their current state, the results seem more like a loosely connected set of observations.

Expertise: Cell polarity and asymmetric cell division

Author Response

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

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further improve the manuscript. Following the suggestions of the reviewers we have run a number of new simulations including mutations of the PIP binding residues and with an elastic network allowing more mobility of the linker. Together these excellent ideas have allowed us to strengthen the conclusions of the study. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Public Review):

Summary:

Here, the authors were attempting to use molecular simulation or probe the nature of how lipids, especially PIP lipids, bind to a medically-important ion channel. In particular, they look at how this binding impact the function of the channel.

Strengths:

The study is very well written and composed. The techniques are used appropriately, with plenty of sampling and analysis. The findings are compelling and provide clear insights into the biology of the system.

Weaknesses:

A few of the analyses are hard to understand/follow, and rely on "in house" scripts. This is particularly the case for the lipid binding events, which can be difficult to compute accurately. Additionally, a lack of experimental validation, or coupling to existing experimental data, limits the study.

Our analysis scripts have now been made publicly accessible as a Jupyter notebook on Github https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project

It is my view that the authors have achieved their aims, and their findings are compelling and believable. Their findings should have impacts on how researchers understand the functioning of the Nav1.4 channel, as well as on the study of other ion channels and how they interact with membrane lipids.

Reviewer #2 (Public Review):

Summary:

Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete and lacks the expected confirmatory studies which are relevant to such proposals.

Strengths:

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation.

Weaknesses:

However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Following the reviewers suggestion we have conducted new simulations demonstrating that there are many fewer protein-PIP interactions after mutating the positive residues as shown in the new Supplementary Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However, as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewer’s suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7, PIP binding to VSDI-III can impair activation of the channel.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

Done as described above

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it.

Done as described above

Minor points:

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP bound the channel is expected to recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Public Review):

Summary:

This work uses multiscale molecular dynamics simulations to demonstrate molecular mechanism(s) for phosphatidylinositol regulation of voltage gated sodium channel (Nav1.4) gating. Recent experimental work by Gada et al. JGP 2023 showed altered Nav1.4 gating when Nav1.4 current was recorded with simultaneous application of PI(4,5)P2 dephosphorylate. Here the authors revealed probable molecular mechanism that can explain PI(4,5)P2 modulation of Nav1.4 gating. They found PIP lipids interacting with the gating charges - potentially making it harder to move the voltage sensor domain and altering the channels voltage sensitivity. They also found a stable PIP binding site that reaches the D_IV S4-S5 linker, reducing the mobility of the linker and potentially competing with the C-terminal domain.

Strengths:

Using multiscale simulations with course-grained simulations to capture lipid-protein interactions and the overall protein lipid fingerprint and then all-atom simulations to verify atomistic details for specific lipidprotein interactions is extremely appropriate for the question at hand. Overall, the types of simulation and their length are suitable for the questions the authors pose and a thorough set of analysis was done which illustrates the observed PIP-protein interactions.

Weaknesses:

Although the set of current simulations and analysis supports the conclusions drawn nicely, there are some limitations imposed by the authors on the course-grained simulations. If those were not imposed, it would have allowed for an even richer set and more thorough exploration of the protein-lipid interactions. The Martini 2 force field indeed cannot change secondary structure but if run with a properly tuned elastic network instead of backbone restraints, the change in protein configuration can be sampled and/or some adaptation of the protein to the specific protein environment can be observed. Additionally, with the 4to1 heavy atoms to a bead mapping some detailed chemical specificity is averaged out but parameters for different PIP family members do exist - including specific PIP(4,5)P2 vs PIP(3,4)P2, and could have been explored.

We thank the reviewer for their excellent suggestions and have run new simulations with an elastic network instead of backbone restraints which have generated new insights. Indeed, as shown in the new panel Fig 4E, the new data allows us to demonstrate that the presence of PIP in the proposed binding site stabilises binding of the DIII-DIV linker to the inactivation receptor site, strengthening the conclusions of the paper.

We thank the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 line 16 to reflect this. We have not run additional CG simulations with each of these parameters but use the all-atom simulations to examine the interactions of phosphates at specific positions.

In our atomistic simulations, we backmapped both PI(4,5)P2 and PI(4)P in the binding site to study their specific interactions. We chose to focus on PI(4,5)P2 given its physiological significance. However, we agree that differences in binding with PI(3,4)P2 would be interesting and warrants future investigation. We also note that the newer Martini3 forcefield would be useful in further work to differentiate between PIP subspecies interactions.

Detailed Comments

We thank the reviewers for their thorough reading and helpful comments which has allowed us to further strengthen the manuscript. Below, we provide point-by-point responses to their suggestions.

Reviewer #1 (Recommendations For The Authors):

I don't have many suggestions for the manuscript, just a few text edits. Of course, experimental analysis would bolster the claims made in the text, but I don't believe that this is necessary, given the quality of the data.

I understand the focus on the PIP lipids, but it's a shame that the high binding likelihood of glycosphingolipid isn't considered or analysed in any way. This is an especially interesting lipid from the point-of-view of raftlike membrane domains. Given the potential role of raft-like domains in sodium channel function, I feel this would be worth a paragraph or two in the discussion.

We thank the reviewer for bringing our attention to this interesting point. Glycolipids accumulate around Nav1.4 in our complex membrane simulations, however, given reports that carbohydrates tend to interact too strongly in the Martini2.2 forcefield (Grünewald et al. 2022, Schmalhorst et al. 2017) and there are no specific residues on Nav1.4 that interact preferentially with glycolipid species, we chose not to focus on this. However, we have noted that interactions with other lipids deserve further attention in our revised discussion.

The analyses have been run using Martini 2. I don't suggest the authors repeat using the Martini 3 force field, but some mention of this in the discussion would be good.

We have added the following statement to the discussion: “Our coarse grain simulations were carried out using the Martini2.2 forcefield, for which lipid parameters for many plasma membrane lipids have been developed. We expect that future investigations of lipid-protein interactions will benefit from use of the newer, refined Martini 3 forcefield (Souza et al. 2021) as parameters become available for more lipid types.

This might just be an oversight, but no mention is made of an elastic network applied to the backbone beads.

Lack of a network has been known to cause the protein to collapse, so if this is missing, I'd like to see an RMSD to show that the protein dynamics are not compromised.

While no elastic network was used in our original CG simulations, weak protein backbone restraints (10 kJ mol-1 nm-2) used in our simulations allowed us to maintain the structure while allowing some protein movement. However, following the suggestion of reviewer 3, we conducted additional simulations with an elastic instead of backbone restraints as described in the results on page 9 line 30-37 (and in Fig 4E) of the revised manuscript.

Minor

•In Fig 3B, are these lipids binding to the channel at the same time? And therefore do the authors see cooperativity?

The Fig 3B caption has been amended in the revised manuscript to read “Representative snapshots from the five longest binding events from different replicates, showing the three different PIP species (PIP1 in blue, PIP2 in purple and PIP3 in pink) binding to VSD-IV and the DIII-IV linker.” We cannot comment on PIP cooperativity based on these simulations shown in Fig 3, due to the artificially high concentrations used here; however, in model complex membrane simulations we see co-binding of PIPs at the binding site. This is likely due to PIP’s ability to accumulate together and the high density of positively charged residues in the region, attracting and supporting multiple PIP bindings.

•What charges were used for the atomistic PIP lipids? Does this match the CG lipids?

We used the CHARMM-GUI PIP parameters for the atomistic simulations. SAPI24 (PIP2) has a headgroup charge of –4e which is one less negative charge than the CG PIP2; whereas SAPI14 (PIP1) has a charge of –3e which is the same as the CG PIP1. We have explicitly included this charge information in the updated Methods of the manuscript (on page 15-16).

•Line 259-260: "we performed embedded three structures"

Corrected in the revised manuscript.

•Line 272: "us" should be "µs"

Corrected in the revised manuscript.

•Line 434: kJ/mol should probably also have 'nm-2' included

Corrected in the revised manuscript.

•What charge state titratable residues were set to, and were pKa analyses done to decide this?

Charge states were assigned to default values at neutral pH. We appreciate that future studies could examine this more carefully using constant pH simulations or similar.

•It's stated that anisotropic scaling is used the AT sims - is this correct? If so, is there a reason this was chosen over semi-isotropic scaling?

Anisotropic scaling was used for the atomistic simulations allowing all box dimensions to change independently.

•I would recommend in-house analysis scripts are made available on GitHub or similar, just so the details can be seen.

Per the reviewer’s request, the Jupyter notebooks used for analysis has been made available on GitHub (https://github.com/etaoster/etaoster.github.io/tree/main/nav_pip_project ).<br /> -One coarse grained notebook:

• Lipid DE

• Contact occupancy + outlier plots

• Binding duration plots

• Minimum distance plots

• Number of ARG/LYS plots

• PIP Occupancy, binding duration, gating charge residues

• One atomistic notebook:

• RMSD, RMSF and distance between IFM and its binding pocket (using MDAnalysis)

• Atomistic PIP headgroup interaction analyses and plots (using ProLIF)

As a final note, I am NOT saying this needs to be done for the current study, but I recommend the authors try the PyLipID package (https://github.com/wlsong/PyLipID) if they haven't yet, as it might be useful for similar projects they run in the future (i.e. for binding site identification, accurate binding kinetics calculations, lipid pose generation etc.).

We thank the reviewer for this suggestion and will keep this in mind for future projects.

Reviewer #2 (Recommendations For The Authors):

Lin Y., Tao E., et al. used multiscale MD simulations to show that PI(4,5)P2 binds stably to an inactivated state of Nav channels at a conserved site within the DIV S4-S5 linker, which couples the voltage sensing domain (VSD) to the pore. The authors hypothesized that PI(4,5)P2 prolongs inactivation by binding to the same site where the C-terminal tail is proposed to bind during recovery from inactivation. They convincingly showed that PI(4,5)P2 reduces the mobility of both the DIV S4-S5 linker and the DIII-IV linker, thus slowing the conformational changes required for the channel to recover to the resting state. They also conducted MD simulations to show that phosphoinositides bind to VSD gating charges in the resting state of Nav channels. These interactions may anchor VDS at the resting state and impede its activation. Their results provide a mechanism by which phosphoinositides alter the voltage dependence of activation and the recovery rate from inactivation, an important step for developing novel therapies to treat Nav-related diseases. However, the study is incomplete lacks the expected confirmatory studies which are relevant to such proposals.

The authors identified a novel binding between phosphoinositides and the VSD of Nav and showed that the strength of this interaction is state-dependent. Based on their work, the affinity of PIPs to the inactivated state is higher than the resting state. This work will help pave the way for designing novel therapeutics that may help relieve pain or treat diseases like arrhythmia, which may result from a leftward shift of the channel's activation. However, the study lacks the expected confirmatory studies which are relevant to such proposals. For example, one would expect that the authors would mutate the positive residues that they claim to make interactions with phosphoinositides to show that there are much fewer interactions once they make these mutations. Another point is that the authors found that the main interaction site of PIPs with Nav1.4 is the VSD-DIV and DIII-DIV linker, an interaction that is expected to delay fast inactivation if it happens at the resting state. The authors should make a resting state model of the Nav1.4 channel to explain the recent experimental data showing that PIP2 delays the activation of Nav1.4, with almost no effect on the voltage dependence of fast inactivation.

Major concern:

(1) Lack of confirmatory experiments, e.g., mutating the positive residues that show a high affinity towards PIPs to a neutral and negative residue and assessing the effect of mutagenesis on binding.

(2) Nav1.4 is the only channel that has been studied in terms of the effect of PIPs on it, therefore the authors should build a resting state model of Nav1.4 and study the effect of PIPs on it. Minor points:

Following the reviewer’s suggestion we have conducted new simulations demonstrating that there are notably fewer protein-PIP interactions after performing charge neutralizing and charge reversal mutations to the positive residues as shown in the new Fig S6.

The reviewer mentions that if PIPs interact with the VSD-DIV and DIII-DIV linker in the resting state that it could delay fast inactivation. However as described in the original manuscript and depicted in the schematic (Fig 7) the C-terminal domain impeded PIP binding at the position in the resting state (but not the inactivated state), meaning that PIP does not bind in the resting state to delay fast inactivation. We have clarified this statement in the text on page 14 lines 1-2.

Following the reviewers suggestion we have examined PIP binding to a model of the resting state of Nav1.4 (in addition to the resting state of Nav1.7 described in the original manuscript) as described on page 12 lines 28-30 (and in Fig S12). Similar to what we saw for Nav1.7 PIP binding to VSDI-III can impair activation of the channel.

There are a lot of wrong statements in many areas, e.g., "These diseases 335 are associated with accelerated rates of channel recovery from inactivation, consistent with our observations that an interaction between PI(4,5)P2 and the residue corresponding to R1469 in other Nav 337 subtypes could be important for prolonging the fast-inactivated state." Prolonging the fast inactivated state would actually reduce recovery from inactivation and not accelerate it.

We disagree with this statement from the reviewer which may have come from a misreading of the mentioned sentence. Our statement in the original manuscript is consistent with the the original experiments that show that the presence of PIP prolongs the time spent in the fast inactivated state. Mutations at the PIP binding site are likely to reduce PIP binding, and with less PIP present the channel will recover from inactivation more quickly. We have reworded this sentence for clarity on page 13 line 27-30.

Reviewer #3 (Recommendations For The Authors):

As mentioned in the public review, overall, I am impressed with the manuscript and do think the conclusions are supported. There are, however, quite a few mistakes, mostly minor (listed below). Additionally, I do have a few questions and several extensions that could be done and I mention a few but fully realize many of those could be outside of the scope of the current manuscript.

We greatly appreciate the time taken by Reviewer 3 to carefully review our manuscript and provide detailed comments. We believe their suggestions have helped to improve our manuscript.

First comments are in general about the PIP subtype.

• In the paper you claim:

L196, "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding"

L367, "it does not distinguish between phosphate positions within each charge state (e.g. PI(3,4)P2 vs PI(4,5)P2)."

This is not true the PIP2 most commonly used in Martini 2 is from dx.doi.org/10.1021/ct3009655 and is a PI(3,4)P2 subtype. Also other extensions and alternative parameters exist for PIPs in Martini 2 e.g. http://cgmartini.nl/index.php/tools2/other-tools - Martini lipid .itp generator has all three main variants of both PIP1 and PIP2.

As described in the response to the public review we are grateful for the reviewer for pointing out that there do exist parameters for different PIP sub-species and have corrected our statement on page 14 to reflect this, and clarified the parameters chosen in the methods section (page 16 line 2-3). We have not run additional CG simulations with each of these parameters in the current work but use the all-atom simulations to examine the interactions of phosphates at specific positions.

• One detail that is missing in the manuscript is some mention of the charge state of the PIPs e.g. Fig.1D does not specify and Fig.4D PIP2 looks like -2 on position 5 and -1 on position 4. Which I think fits the used SAPI24, please specify. Also, what if you use SAPI25 with the flipped charges would that significantly alter the results?

The charge state of PIP2 is -2e on the 5’ phosphate and -1e on the 4’ phosphate, using the SAPI24 CHARMM lipid parameters. We have ensured that this charge information is stated clearly in the revised manuscript in the methods section on page 16 (line 21). We considered looking at SAPI25, however we expected that it would behave quite similarly, given that the PIP headgroup can adopt slightly different poses and orientations within the binding site across replicates and does fluctuate over simulations (Fig S8). We have noted this in the revised discussion on page 14 line 15-17.

• I was very intrigued and puzzled by the lower binding of PIP3 vs PIP2 in the Martini simulations. Could it be that PIP3 has a harder time fully entering the binding site, or maybe just sampling? i.e. and its lower number of binding events is a sampling issue.

We agree with the reviewer that PIP3 is less able to access the binding site than PIP2, likely because of its larger size. This might also be why we see PIP1 binding at the location via a more buried route (since it has the smallest headgroup size). However, PIP1 does not have enough negative charge to keep it in the binding site. It seems to be a Goldilocks-like situation where PIP2 has the optimal size and charge to allow access and stable binding at the site. We also see that when PIP3 enters the binding site it leaves before the end of the simulations. While it is hard to prove statistical significance given the number of binding and dissociation events even with the high and equal concentrations of all three PIP species in the enriched PIP membrane CG simulations, the data strongly suggests preferential binding of PIP2 over PIP3.

Also the same L196 sentence as above "However, this loss of resolution prevents distinction between phosphate positions on the inositol group and does not permit analysis of protein conformational changes induced by PIP binding". The later part is also wrong, there are no conformational changes due to the restraints on the protein backbone, from methods "backbone beads were weakly restrained to their starting coordinates using a force constant of 10 kJ mol−1nm−2". Martini in general might have a hard time with some conformational changes and definitely cannot sample changes in secondary structure, but conformational changes can, and have on many occasions, been successfully sampled (even full ion channel opening and closing).

On a similar note, in L179 you mention "owing to the flexibility of the linker." Hose does this fit with simulation with position restraints on all backbone atoms?

We applied fairly weak restraints to the backbone only – therefore we still observe some flexibility in the highly flexible loop portion of the linker, where sidechains are able to flip between membrane-facing and cytosol-facing orientations.

However, after reading the comments from the reviewer we have run additional simulations with an elastic network rather than backbone restraints on the DIII-DIV linker which have given further insight. As seen in Fig 4E and described in the results paragraph on page 9 line 30-37 of the revised manuscript, we can see that the presence of PIP does stabilise the linker in its receptor site. To accentuate this effect, we also ran simulation of the ‘IQM’ mutant known to have a less stable fast inactivated state due to weaker binding to the receptor. Without backbone restraints we can see partial dissociation of the DIII-DIV linker from the receptor that is partially rescued by the presence of PIP.

I know the paper focuses on PIPs, also very nicely in Fig.2B and Fig. S1-2 the lipid enrichment is shown for other lipids, but why show all lipid classes except cholesterol? And, for the left-hand panels in Fig. S1-2 those really should be leaflet specific - as both the membrane and protein are asymmetric.

The depletion/enrichment of Cholesterol is shown in Fig 2B and as are the Lipid Z-Density maps and contact occupancy structures a (in row 5 of Fig S2, labeled as CL in yellow). The Z-density maps are meant to provide an overall summary of lipid distribution. The contact occupancy structures showing the transverse views and intracellular/ extracellular views provide a better indication of the occupancy across the different leaflets.

In L237 for the comparison of Cav2.2 and Kv7.1 bound to PI(4,5)P2 structures: They do agree well with the PIP1 simulations but not as much for the main PIP2 binding site. If you look in the CG simulations, is there another (not the main) PIP2 binding site at that same location (which might also be stable in AA simulations)?

In some replicates of the CG simulations, we identify stable PIP1 binding via the other orientation (i.e. the one that overlaps with the Cav2.2 and Kv7.1 structures). Since we did not directly observe any PIP2 binding events from the other orientation, we did not run any backmapped atomistic simulations with PIP2 at this position. However, the binding site residues that the PIP1/2 headgroup binds to are the same regardless of which side PIP1/2 approaches from. We would expect that PIP2 bound from the alterative position is also stable.

Two references I want to put for consideration to the authors, for potential inclusion if the authors find their inclusion would strengthen the manuscript. This one gives a good demonstration of using the same PM mixture to define lipid protein fingerprints with Martini:

https://pubs.acs.org/doi/10.1021/acscentsci.8b00143.

And this one https://pubmed.ncbi.nlm.nih.gov/33836525/ shows how Nav1.4 function could also be affected by general changes in bilayer properties (in addition to the specific lipid interactions explored here).

We thank the reviewer for bringing to our attention these two relevant references that will help to respectively substantiate the use Martini to study membrane protein-lipid interactions, as well as, why Nav channels are interesting to study in the context of their membrane environment (and also the potential implications with drugs that can bind from within the membrane). We have added these citations to the introduction and discussion.

Minor comments and fixes:

L2, Title: A binding site for phosphoinositide modulation of voltage-gated sodium channels described by multiscale simulations

The title reads very strangely to me, should it be "A binding site for phosphoinositide" ; "modulation". We thank the reviewer for this comment - title has been updated to: A binding site for phosphoinositides described by multiscale simulations explains their modulation of voltage gated sodium channels.

L25, Abstract, "The phosphoinositide PI(4,5)P2 decreases Nav1.4 activity by increasing the difficulty of channel opening, accelerating fast activation and slowing recovery from fast inactivation." Assuming this is referring to results from Gada et al JGP, 2023 should this not be "accelerating fast inactivation"?

Corrected in the revised manuscript.

L71 maybe good to write the longer version of IFM on first use e.g. Ile-Phe-Met (IFM), as to not mistake it for some random three letter acronym.

Corrected in the revised manuscript.

L109, Fig.2. Maybe change the upper and lower leaflet to intracellular and cytoplasmic leaflets (or outer / inner). In D "(D) Distribution of PIP binding occupancies (left)" something missing can I assume, for/over all lipids exposed residues. Also, for D I am a little confused how occupancy is defined as the total occupancy per residue dose not add up to 100.

The figure has been updated with intracellular and cytoplasmic leaflet labels. The binding occupancy distribution boxplot shows binding occupancies for all lipid exposed residues. In our analysis, we define contact occupancy as the proportion of simulation time in which a lipid type is within 0.7 nm of a given residue. It is possible for more than one lipid to be within this cut in any given frame – that is, both a PIP and PE can be simultaneously bound.

L160 "occurring the identified site" in the

Corrected in the revised manuscript.

L170 "PIP3 (headgroup charge: -7e) has interacts similarly to PIP1," - remove has Corrected in the revised manuscript.

L194, "reducing system size" the size does not change, I am assuming you want to say reducing the number of particles?

Corrected in the revised manuscript.

L252, Fig.6 "(B) Occupancy of all PIPs (PIP1, PIP2, PIP3) at binding site residues in the three systems" A little confusing, initially was expecting 3x3 data points per residue, maybe change to, Combined occupancy of all PIPs...

Corrected in the revised manuscript.

L253, Fig.6 D, I don't really have a good suggestion for improvement here, so this is just a FYI that this panel was very confusing for me and took some time to figure out what is shown.

We have added to the caption of Fig. 6D to try to clarify this panel.

L257, Fig.6 (F) not in bold

Corrected in the revised manuscript.

L259 "PIP binding, we performed embedded three structures of Nav1.7" something missing?

Corrected in the revised manuscript.

L272, "In triplicate 50 us coarse-grained simulations" us instead of (micro_greek)s

Corrected in the revised manuscript.

L272, that paragraph how long/many simulations only reported for the inactivated Nav1.7 system not the Nav1.7-NavPas chimera, which I am assuming is the same?

Corrected in the revised manuscript.

L297, "marked by both shortened inactivation times", can I assume this is: shortened times to inactivation (i.e. to get inactivated not times in the inactivated states)?

Corrected in the revised manuscript.

L331, "are conserved in Nav1.1-1.9 (Fig. 5D)," Fig.5C Corrected in the revised manuscript.

L353, "channel opening []" [] maybe a missing reference?

Thank you for pointing out this oversight - Goldschen-Ohm et al. has been cited here.

L394, "The composition of the complex mammalian membrane is as reported in Ingólfsson, et al. (38)." Ref 38 is the "Computational lipidomics of the neuronal plasma membrane" which indeed uses the 63 component PM but the original reference for the average 63 lipid mixture PM is dx.doi.org/10.1021/ja507832e.

Corrected in the revised manuscript.

L404, "Additionally, a model Nav1.7 with all four VSDs in the deactivated state using Modeller (40)." Something missing, e.g. was also built and simulated for ...

Corrected in the revised manuscript.

Table S1 "Disease information", I am guessing this should be Disease information; mechanism? Of the x5 entries two have mechanism, one has "; unknown significance ", one has "; unknown" maybe clarify in title and make same if unknown.

Corrected in the revised manuscript.

Table S1 and S2 have different styles.

The tables have been amended to have the same style.

Fig. S3 "for all 12 lipid types in the mammalian membrane " there are many more lipid types in a typical PM (hundreds) and 63 in the PM mixture simulated here, so maybe write: 12 lipid classes?

Corrected in the revised manuscript.

Fig.S6 PIP headgroup, can I assume that is for the bound PIP only, please specify.

Only a single PIP at the identified binding site was backmapped into all cases of atomistic simulations. We have now clarified this point in the methods, results and the FigS6 caption.

Writing of PI(4,5)P2 and PI(4)P1 most of the time use 1 and 2 as subscripts but not always (at least not in SI), also the same with Nav vs Na_v (v subscript) and even NAV (in Table S1).

Subscripts have been implemented in the updated Supplementary Information (as well as within various figures and throughout the manuscript).

In this section Satan is attempting to boost the confidence of his council. The speaker says that they will gain more than they have suffered, that it's not all for loss (ll. 429-45). The speaker goes on to say that the council must prepare with "weapons more violent" (6.439) than before. The speaker seems to be describing his plans for revenge in hopes to boost the council's confidence. Sadly, knowing that one cannot win fighting fire with fire, this could foreshadow Satan and all of Hell's population to continue to suffer eternally.

Lines 149-170 are Satan speaking to the seraph Abdiel. The passage begins with Satan telling Abdiel that he will be the first to face his wrath, saying he will be victim to "the first assay / of this right hand provok'd" (6.153-154). As we discussed earlier in class, before Christ, Satan was God's right hand, so the use of "right hand provok'd" works both physically with the image of an attack, as well as in the sense of his former title. Satan then goes on to reference a group or "Synod" of gods who agreed that "while they feel / Vigor divine within them, can allow / Omnipotence to none" (6.158-159). This is an important part of Satan's speech, both because of his rejection of singular omnipotence, as well as his assertion of multiple gods. This reminded me of a key pillar of Christianity: a singular God. It is important to note, though, the biblical quote "Thou shalt have no other gods before me" (Exodus 20:3), which can be interpreted as there being multiple gods, but a more powerful, singular God. So, when Satan mentions other gods rejecting omnipotence and implies that God embraced it, his portrayal of a more sinister, power-hungry God can be understood.

Further in this section, Satan says that Abdiel has only confronted him out of a desire to be praised by God. He states, "But well thou com'st / Before thy fellows, ambitious to win / From me some Plume, that thy success may show / Destruction to the rest" (6.159-162). According to the OED, "Plume" in this instance refers to an adornment received for an accomplishment or merit (as opposed to the modern definition of colourful feathers). Following his assertion about Abdiel's motivations, Satan explains that he used to think that freedom could exist in Heaven, but that he realized that servitude to God removes the possibility for true freedom. He mocks those who have remained faithful to God, saying "Minist'ring Spirits, train'd up in Feast and Song; / Such hast thou arm'd, the Minstrelsy of Heav'n, / Servility with freedom to contend" (6.167-169). These lines serve the dual purpose of mocking the power of God's army, and making Satan's followers represent the positive attribute of freedom, as opposed to the [generally] negative attribute of servitude.

the speaker is describing the hierarchy of Earth, starting with humans, then animals, and then slowly moving down the line to the inanimate, "Corporeal to incorporeal" (413) as the poem says. The speaker goes on to say that there needs to be some type of food chain in the world and relates the chain to the elements as it lists which element feeds another as the cycle continues. They then compare the moon and the sun and state how the moon does not give or take from anything, "unpurg'd / Vapors not yet into her substance turn'd. / Nor doth the Moon no nourishment exhale" (419-421), while the sun both gives and takes from the world, "The Sun that light imparts to all, receives / From all his alimental recompense / In humid exhalations" (423-425). In the last bit of the passage the speaker describes the food in heaven and is comparing them with the new foods that God has created on earth.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

The authors of this study seek to visualize NS1 purified from dengue virus infected cells. They infect vero cells with DV2-WT and DV2 NS1-T164S (a mutant virus previously characterized by the authors). The authors utilize an anti-NS1 antibody to immunoprecipitate NS1 from cell supernatants and then elute the antibody/NS1 complex with acid. The authors evaluate the eluted NS1 by SDS-PAGE, Native Page, mass spec, negative-stain EM, and eventually Cryo-EM. SDS-PAGE, mas spec, and native page reveal a >250 Kd species containing both NS1 and the proteinaceous component of HDL (ApoA1). The authors produce evidence to suggest that this population is predominantly NS1 in complex with ApoA1. This contrasts with recombinantly produced NS1 (obtained from a collaborator) which did not appear to be in complex with or contain ApoA1 (Figure 1C). The authors then visualize their NS1 stock in complex with their monoclonal antibody by CryoEM. For NS1-WT, the major species visualized by the authors was a ternary complex of an HDL particle in complex with an NS1 dimer bound to their mAB. For their mutant NS1-T164S, they find similar structures, but in contrast to NS1-WT, they visualize free NS1 dimers in complex with 2 Fabs (similar to what's been reported previously) as one of the major species. This highlights that different NS1 species have markedly divergent structural dynamics. It's important to note that the electron density maps for their structures do appear to be a bit overfitted since there are many regions with electron density that do not have a predicted fit and their HDL structure does not appear to have any predicted secondary structure for ApoA1. The authors then map the interaction between NS1 and ApoA1 using cross-linking mass spectrometry revealing numerous NS1-ApoA1 contact sites in the beta-roll and wing domain. The authors find that NS1 isolated from DENV infected mice is also present as a >250 kD species containing ApoA1. They further determine that immunoprecipitation of ApoA1 out of the sera from a single dengue patient correlates with levels of NS1 (presumably COIPed by ApoA1) in a dose-dependent manner.

In the end, the authors make some useful observations for the NS1 field (mostly confirmatory) providing additional insight into the propensity of NS1 to interact with HDL and ApoA1. The study does not provide any functional assays to demonstrate activity of their proteins or conduct mutagenesis (or any other assays) to support their interaction predications. The authors assertion that higher-order NS1 exists primarily as a NS1 dimer in complex with HDL is not well supported as their purification methodology of NS1 likely introduces bias as to what NS1 complexes are isolated. While their results clearly reveal NS1 in complex with ApoA1, the lack of other NS1 homo-oligomers may be explained by how they purify NS1 from virally infected supernatant. Because NS1 produced during viral infection is not tagged, the authors use an anti-NS1 monoclonal antibody to purify NS1. This introduces a source of bias since only NS1 oligomers with their mAb epitope exposed will be purified. Further, the use of acid to elute NS1 may denature or alter NS1 structure and the authors do not include controls to test functionality of their NS1 stocks (capacity to trigger endothelial dysfunction or immune cell activation). The acid elution may force NS1 homo-oligomers into dimers which then reassociate with ApoA1 in a manner that is not reflective of native conditions. Conducting CryoEM of NS1 stocks only in the presence of full-length mAbs or Fabs also severely biases what species of NS1 is visualized since any NS1 oligomers without the B-ladder domain exposed will not be visualized. If the residues obscured by their mAb are involved in formation of higher-order oligomers then this antibody would functionally inhibit these species from forming. The absence of critical controls, use of one mAb, and acid elution for protein purification severely limits the interpretation of these data and do not paint a clear picture of if NS1 produced during infection is structurally distinct from recombinant NS1. Certainly there is novelty in purifying NS1 from virally infected cells, but without using a few different NS1 antibodies to purify NS1 stocks (or better yet a polyclonal population of antibodies) it's unclear if the results of the authors are simply a consequence of the mAb they selected.

Data produced from numerous labs studying structure and function of flavivirus NS1 proteins provide diverse lines of evidence that the oligomeric state of NS1 is dynamic and can shift depending on context and environment. This means that the methodology used for NS1 production and purification will strongly impact the results of a study. The data in this manuscript certainly capture one of these dynamic states and overall support the general model of a dynamic NS1 oligomer that can associate with both host proteins as well as itself but the assertions of this manuscript are overall too strong given their data, as there is little evidence in this manuscript, and none available in the large body of existing literature, to support that NS1 exists only as a dimer associated with ApoA1. More likely the results of this paper are a result of their NS1 purification methodology.

Suggestions for the Authors:

Major:

(1) Because of the methodology used for NS1 purification, it is not clear from the data provided if NS1 from viral infection differs from recombinant NS1. Isolating NS1 from viral infection using a polyclonal antibody population would be better to answer their questions. On this point, Vero cells are also not the best candidate for their NS1 production given these cells do not come from a human. A more relevant cell line like U937-DC-SIGN would be preferable.

We performed an optimization of sNS1 secretion from DENV infection in different cell lines (Author response image 1 below) to identify the best cell line candidate to obtain relatively high yield of sNS1 for the study. As shown in Author response image 1, the levels of sNS1 in the tested human cell lines Huh7 and HEK 293T were at least 3-5 fold lower than in Vero cells. Although using a monocytic cell line expressing DC-SIGN as suggested by the reviewer would be ideal, in our experience the low infectivity of DENV in monocytic cell lines will not yield sufficient amount of sNS1 needed for structural analysis. For these practical reasons we decided to use the closely related non-human primate cell line Vero for sNS1 production supported by our optimization data.

Author response image 1.

sNS1 secretion in different mammalian and mosquito cell lines after DENV2 infection. The NS1 secretion level is measured using PlateliaTM Dengue NS1 Ag ELISA kit (Bio-Rad) on day 3 (left) and day 5 (right) post infection respectively.

(2) The authors need to support their interaction predictions and models via orthogonal assays like mutagenesis followed by HDL/ApoA1 complexing and even NS1 functional assays. The authors should be able to mutate NS1 at regions predicted to be critical for ApoA1/HDL interaction. This is critical to support the central conclusions of this manuscript.

In our previous publication (Chan et al., 2019 Sci Transl Med), we used similarly purified sNS1 (immunoaffinity purification followed by acid elution) from infected culture supernatants from both DENV2 wild-type and T164S mutant (both also studied in the present work) to carry out stimulation assay on human PBMCs as described by other leading laboratories investigating NS1 (Modhiran et al., 2015 Sci Transl Med). For reader convenience we have extracted the data from our published paper and present it as Author response image 2 below.

Author response image 2.

(A) IL6 and (B) TNFa concentrations measured in the supernatants of human PBMCs incubated with either 1µg/ml or 10µg/ml of the BHK-21 immunoaffinity-purified WT and TS mutant sNS1 for 24 hours. Data is adapted from Chan et al., 2019.

Incubation of immunoaffinity-purified sNS1 (WT and TS) with human PBMCs from 3 independent human donors triggered the production of proinflammatory cytokines IL6 and TNF in a concentration dependent manner (Author response image 2), consistent with the published data by Modhiran et al., 2015 Sci Transl Med. Interestingly the TS mutant derived sNS1 induced a higher proinflammatory cytokines production than WT virus derived sNS1 that appears to correlate with the more lethal and severe disease phenotype in mice as also reported in our previous work (Chan et al., 2019). Additionally, the functionality of our immune-affinity purified infection derived sNS1 (isNA1) is now further supported by our preliminary results on the NS1 induced endothelial cell permeability assay using the purified WT and mutant isNS1 (Author response image 3). As shown in Author response image 3, both the isNS1wt and isNS1ts mutant reduced the relative transendothelial resistance from 0 to 9 h post-treatment, with the peak resistance reduction observed at 6 h post-treatment, suggesting that the purified isNS1 induced endothelial dysfunction as reported in Puerta-Guardo et al., 2019, Cell Rep.) It is noteworthy that the isNS1 in our study behaves similarly as the commercial recombinant sNS1 (rsNS1 purchased from the same source used in study by Puerta-Guardo et al., 2019) in inducing endothelial hyperpermeability. Collectively our previous published and current data suggest that the purified isNS1 (as a complex with ApoA1) has a pathogenic role in disease pathogenesis that is also supported in a recent publication by Benfrid et al., EMBO 2022). The acid elution has not affected the functionality of NS1.

Author response image 3.

Functional assessment of isNS1wt and isNS1ts on vascular permeability in vitro. A trans-endothelial permeabilty assay via measurement of the transendothelial electrical resistance (TEER) on human umbilical vascular endothelial cells (hUVEC) was performed, as described previously (Puerta-Guardo et al., 2019, Cell Rep). Ovalbumin serves as the negative control, while TNF-α and rsNS1 serves as the positive controls.

We agree with reviewer about the suggested mutagnesis study. We will perform site-directed mutagenesis at selected residues and further structural and functional analyses and report the results in a follow-up study.

(3) The authors need to show that the NS1 stocks produced using acid elution are functional compared to standard recombinantly produced NS1. Do acidic conditions impact structure/function of NS1?

We are providing the same response to comments 1 & 2 above. We would like to reiterate that we have previously used sNS1 from immunoaffinity purification followed by acid elution to test its function in stimulating PBMCs to produce pro-inflammatory cytokines (Chan et al., 2019; Author response image 2). Similar to Modhiran et al. (2015) and Benfrid et al. (2022), the sNS1 that we extracted using acid elution are capable of activating PBMCs to produce pro-inflammatory cytokines. We have now further demonstrated the ability of both WT and TS isNS1 in inducing endothelial permeability in vitro in hUVECs, using the TEER assay (Author response image 3). Based on the data presented in the rebuttal figures as well as our previous publication we do not think that the acid elution has a significant impact on function of isNS1.

We performed affinity purification to enrich the complex for better imaging and analysis (Supp Fig. 1b) since the crude supernatant contains serum proteins and serum-free infections also do not provide sufficient isNS1. The major complex observed in negative stain is 1:1 (also under acidic conditions which implies that the complex are stable and intact). We agree that it is possible that other oligomers can form but we have observed only a small population (74 out of 3433 particles, 2.15%; 24 micrographs) of HDL:sNS1 complex at 1:2 ratio as shown in the Author response image 4 below and in the manuscript (p. 4 lines 114-117, Supp Fig. 1c). Other NS1 dimer:HDL ratios including 2:1 and 3:1 have been reported by Benfrid et al., 2022 by spiking healthy sera with recombinant sNS1 and subsequent re-affinity purification. However, this method used an approximately 8-fold higher sNS1 concentration (400 ug/mL) than the maximum clinically reported concentration (50 ug/mL) (Young et al., 2000; Alcon et al., 2002; Libraty et al., 2002). In our hands, the sNS1 concentration in the concentrated media from in vitro infection was quantified as 30 ug/mL which is more physiologically relevant.

We conclude that the integrity of the HDL of the complex is not lost during sample preparation, as we are able to observe the complex under the negative staining EM as well as infer from XL-MS. Our rebuttal data and our previous studies with our acid-eluted isNS1 from immunoaffinity purification clearly show that our protein is functional and biologically relevant.

Author response image 4.

(A) Representative negative stain micrograph of sNS1wt (B) Representative 2D averages of negative stained isNS1wt. Red arrows indicating the characteristic wing-like protrusions of NS1 inserted in HDL. (C) Data adapted from Figure 2 in Benfrid et al. (2022).

(4) Overall, the data obtained from the mutant NS1 (contrasted to WT NS1) reveals how dynamic the oligomeric state of NS1 proteins are but the authors do not provide any insight into how/why this is, some additional lines of evidence using either structural studies or mutagenesis to compare WT and their mutant and even NS1 from a different serotype of DENV would help the field to understand the dynamic nature of NS1.

The T164S mutation in DENV2 NS1 was proposed as the residue associated with disease severity in 1997 Cuban dengue epidemic (Halsted SB. “Intraepidemic increases in dengue disease severity: applying lessons on surveillance and transmission”. Whitehorn, J., Farrar. J., Eds., Clinical Insights in Dengue: Transmission, Diagnosis & Surveillance. The Future Medicine (2014), pp. 83-101). Our previous manuscript examined this mutation by engineering it into a less virulent clade 2 DENV isolated in Singapore and showed that sNS1 production was higher without any change in viral RNA replication. Transcript profiling of mutant compared to WT virus showed that genes that are usually induced during vascular leakage were upregulated for the mutant. We also showed that infection of interferon deficient AG129 mice with the mutant virus resulted in disease severity, increased complement protein expression in the liver, tissue inflammation and greater mortality compared to WT virus infected mice. The lipid profiling in our study (Chan et al., 2019) suggested small differences with WT but was overall similar to HDL as described by Gutsche et al. (2011). We were intrigued by our functional results and wanted to explore more deeply the impact of the mutation on sNS1 structure which at that stage was widely believed to be a trimer of NS1 dimers with a central channel (~ X Å) stuffed with lipid as established in several seminal publications (Flamand et al., 1999; Gutsche et al., 2011; Muller et al., 2012). In fact “This Week in Virology” netcast (https://www.microbe.tv/twiv/twiv-725/) discussed two back-to-back publications in Science (Modhiran et al., 371(6625)190-194; Biering et al., Science 371(6625):194-200)) which showed that therapeutic antibodies can ameliorate the NS1 induced pathogenesis and expert discussants posed questions that also pointed to the need for more accurate definition of the molecular composition and architecture of the circulating NS1 complex during virus infection to get a clearer handle on its pathogenic mechanism. Our current studies and also the recent high resolution cryoEM structures (Shu et al., 2022) do not support the notion of a central channel “stuffed with lipid”. Even in the rare instances where trimer of dimers are shown, the narrow channel in the center could only accommodate one molecule of lipoid molecule no bigger than a typical triglyceride molecule. This hexamer model cannot explain the lipid proeotmics data in the literature.

In our study we observed predominantly 1:1 NS1 dimer to HDL (~30 μg/mL) mirroring maximum clinically reported concentration of sNS1 in the sera of DENV patients (40-50 μg/mL) as we highlighted in our main text (P. 18, lines 461-471). What is often quoted (also see later) is the recent study of Flamand & co-workers which show 1-3 NS1 dimers per HDL (Benfrid et al, 2022) by spiking rsNS1 (400 μg/mL) with HDL. This should not be confused with the previous models which suggested a lipid filled central channel holding together the hexamer. The use of physiologically relevant concentrations is important for these studies as we have highlighted in our main text (P. 18, lines 461-471).

Our interpretation for the mutant (isNS1ts) is that it is possible that the hydrophilic serine at residue 164 located in the greasy finger loop may weaken the isNS1ts binding to HDL hence the observation of free sNS1 dimers in our immunoaffinity purified (acid eluted sample). The disease severity and increased complement protein expression in AG129 mice liver can be ascribed to weakly bound mutant NS1 with fast on/off rate with HDL being transported to the liver where specific receptors bind to free sNS1 and interact with effector proteins such as complement to drive inflammation and associated pathology. Our indirect support for this is that the XL-MS analysis of purified isNS1ts identified only 7 isNS1ts:ApoA1 crosslinks while 25 isNS1wt:ApoA1 crosslinks were identified from purified isNS1wt (refer to Fig. 4 and Supp. Fig. 8).

Taken together, the cryoEM and XL-MS analysis of purified isNS1ts suggest that isNS1ts has weaker affinity for HDL compared to isNS1wt. We welcome constructive discussion on our interpretation that we and others will hopefully obtain more data to support or deny our proposed explanation. Our focus has been to compare WT with mutant sNS1 from DENV2 and we agree that it will be useful to study other serotypes.

Reviewer #2:

CryoEM:

Some of the neg-stain 2D class averages for sNS1 in Fig S1 clearly show 1 or 2 NS1 dimers on the surface of a spherical object, presumably HDL, and indicate the possibility of high-quality cryoEM results. However, the cryoEM results are disappointing. The cryo 2D class averages and refined EM map in Fig S4 are of poor quality, indicating sub-optimal grid preparation or some other sample problem. Some of the FSC curves (2 in Fig S7 and 1 in Fig S6) have extremely peculiar shapes, suggesting something amiss in the map refinement. The sharp drop in the "corrected" FSC curves in Figs S5c and S6c (upper) indicate severe problems. The stated resolutions (3.42 & 3.82 Å) for the sNS1ts-Fab56.2 are wildly incompatible with the images of the refined maps in Figs 3 & S7. At those resolutions, clear secondary structural elements should be visible throughout the map. From the 2D averages and 3D maps shown in the figures this does not seem to be the case. Local resolution maps should be shown for each structure.

The same sample is used for negative staining and the cryoEM results presented. The cryoEM 2D class averages are similar to the negative stain ones, with many spherical-like densities with no discernible features, presumably HDL only or the NS1 features are averaged out. The key difference lies in the 2D class averages where the NS1 could be seen. The side views of NS1 (wing-like protrusion) are more obvious in the negative stain while the top views of NS1 (cross shaped-like protrusion) are more obvious under cryoEM. HDL particles are inherently heterogeneous and known to range from 70-120 Å, this has been highlighted in the main text (p. 8, lines 203 and 228). This helps to explain why the reviewer may find the cryoEM result disappointing. The sample is inherently challenging to resolve structurally as it is (not that the sample is of poor quality). In terms of grid preparation, Supp Fig 4b shows a representative motion-corrected micrograph of the isNS1ts sample whereby individual particles can be discerned and evenly distributed across the grid at high density.

We acknowledge that most of the dips in the FSC curves (Fig S5-7) are irregular and affect the accuracy of the stated resolutions, particularly for the HDL-isNS1ts-Fab56.2 and isNS1ts-Fab56.2 maps for which the local resolution maps are shown (Fig S7d-e). Probable reasons affecting the FSC curves include (1) the heterogeneous nature of HDL, (2) preferred orientation issue (p 7, lines 198 -200), and (3) the data quality is intrinsically less ideal for high resolution single particle analysis. Optimizing of the dynamic masking such that the mask is not sharper than the resolution of the map for the near (default = 3 angstroms) and far (12 angstroms) parameters during data processing, ranging from 6 - 12 and 14 - 20 respectively, did not help to improve the FSC curves. To report a more accurate global resolution, we have revised the figures S5-7 with new FSC curve plots generated using the remote 3DFSC processing server.

Regardless, the overall architecture and the relative arrangement of NS1 dimer, Fab, and HDL are clearly visible and identifiable in the map. These results agree well with our biochemical data and mass-spec data.

The samples were clearly challenging for cryoEM, leading to poor quality maps that were difficult to interpret. None of the figures are convincing that NS1, Ab56.2 or Fab56.2 are correctly fit into EM maps. There is no indication of ApoA1 helices. Details of the fit of models to density for key regions of the higher-resolution EM maps should be shown and the models should be deposited in the PDB. An example of modeling difficulty is clear in the sNS1ts dimer with bound Fab56.2 (figs 3c & S7e). For this complex, the orientation of the Fab56.2 relative to the sNS1ts dimer in this submission (Fig 3c) is substantially different than in the bioRxiv preprint (Fig 3c). Regions of empty density in Fig 3c also illustrate the challenge of building a model into this map.

We acknowledge the modelling challenge posed by low resolution maps in general, such as the handedness of the Fab molecule as pointed out by the reviewer (which is why others have developed the use of anti-fab nanobody to aid in structure determination among other methods). The change in orientation of the Fab56.2 relative to the sNS1ts dimer was informed by the HDX-MS results which was not done at the point of bioRxiv preprint mentioned. With regards to indication of ApoA1 helices, this is expected given the heterogeneous nature of HDL. To the best of our knowledge, engineered apoA1 helices were also not reported in many cryoEM structures of membrane proteins solved in membrane scaffold protein (MSP) nanodiscs. This is despite nanodiscs, comprised of engineered apoA1 helices, having well-defined size classifications.

Regions of weak density in Fig 3c is expected due to the preferred orientation issue acknowledged in the results section of the main text (p. 9, line 245). The cryoEM density maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession codes EMD-36483 (isNS1ts:Fab56.2) and EMD-36480 (Fab56.2:isNS1ts:HDL). The protein model files for isNS1ts:Fab56.2 and Fab56.2:isNS1ts:HDL model are available upon request. Crosslinking MS raw files and the search results can be downloaded from https://repository.jpostdb.org/preview/14869768463bf85b347ac2 with the access code: 3827. The HDX-MS data is deposited to the ProteomeXchange consortium via PRIDE partner repository51 with the dataset identifier PXD042235.

Mass spec:

Crosslinking-mass spec was used to detect contacts between NS1 and ApoA1, providing strong validation of the sNS1-HDL association. As the crosslinks were detected in a bulk sample, they show that NS1 is near ApoA1 in many/most HDL particles, but they do not indicate a specific protein-protein complex. Thus, the data do not support the model of an NS1-ApoA1 complex in Fig 4d. Further, a specific NS1-ApoA1 interaction should have evidence in the EM maps (helical density for ApoA1), but none is shown or mentioned. If such exists, it could perhaps be visualized after focused refinement of the map for sNS1ts-HDL with Fab56.2 (Fig S7d). The finding that sNS1-ApoA1 crosslinks involved residues on the hydrophobic surface of the NS1 dimer confirms previous data that this NS1 surface engages with membranes and lipids.

We thank the reviewer for the comment. The XL-MS is a method to identify the protein-protein interactions by proximity within the spacer arm length of the crosslinker. The crosslinking MS data do support the NS1-ApoA1 complex model obtained by cryo-EM because the identified crosslinks that are superimposed on the EM map are within the cut-off distance of 30 Å. We agree that the XL-MS data do not dictate the specific interactions between specific residues of NS1-ApoA1 in the EM model. We also do not claim that specific residue of NS1 in beta roll or wing domain is interacting with specific residue of ApoA1 in H4 and H5 domain. We claim that beta roll and wing domain regions of NS1 are interacting with ApoA1 in HDL indicating the proximity nature of NS1-ApoA1 interactions as warranted by the XL-MS data.

As explained in the previous response on the lack of indication of ApoA1 helical density, this is expected given the heterogeneous nature of HDL. It is typical to see lipid membranes as unstructured and of lower density than the structured protein. In our study, local refinement was performed on either the global map (presented in Fig S7d) or focused on the NS1-Fab region only. Both yielded similar maps as illustrated in the real space slices shown in Author response image 5. The mask and map overlay is depicted in similar orientations to the real space slices, and at different contour thresholds at 0.05 (Author response image 5e) and 0.135 (Author response image 5f). While the overall map is of poor resolution and directional anisotropy evident, there is clear signal differences in the low density region (i.e. the HDL sphere) indicative of NS1 interaction with ApoA1 in HDL, extending from the NS1 wing to the base of the HDL sphere.

Author response image 5.

Real Space Slices of map and mask used during Local Refinement for overall structure (a-b) and focused mask on NS1 region (c-d). The corresponding map (grey) contoured at 0.05 (e) and 0.135 (f) in similar orientations as shown for the real space slices of map and masks. The focused mask of NS1 used is colored in semi-transparent yellow. Real Space Slices of map and mask are generated during data processing in Cryosparc 4.0 and the map figures were prepared using ChimeraX.

Sample quality:

The paper lacks any validation that the purified sNS1 retains established functions, for example the ability to enhance virus infectivity or to promote endothelial dysfunction.

Please see detailed response for question 2 in Reviewer #1’s comments. In essence, we have showed that both isNS1wt and isNS1ts are capable of inducing endothelial permeability in an in vitro TEER assay (Rebuttal Fig 3) and also in our previous study that quantified inflammation in human PBMC’s (Rebuttal Fig 2).

Peculiarities include the gel filtration profiles (Fig 2a), which indicate identical elution volumes (apparent MWs) for sNS1wt-HDL bound to Ab562 (~150 kDa) and to the ~3X smaller Fab56.2 (~50 kDa). There should also be some indication of sNS1wt-HDL pairs crosslinked by the full-length Ab, as can be seen in the raw cryoEM micrograph (Fig S5b).

Obtaining high quality structures is often more demanding of sample integrity than are activity assays. Given the low quality of the cryoEM maps, it's possible that the acidification step in immunoaffinity purification damaged the HDL complex. No validation of HDL integrity, for example with acid-treated HDL, is reported.

Please see detailed response for question 3 in Reviewer #1’s comments.

Acid treatment is perhaps discounted by a statement (line 464) that another group also used immunoaffinity purification in a recent study (ref 20) reporting sNS1 bound to HDL. However the statement is incorrect; the cited study used affinity purification via a strep-tag on recombinant sNS1.

We thank the Reviewer for pointing this out and have rewritten this paragraph instead (p 18, line 445-455). We also expanded our discussion to highlight our prior functional studies showing that acid-eluted isNS1 proteins do induce endothelial hyperpermeability (p 18-19, line 470-476).

Discussion:

The Discussion reflects a view that the NS1 secreted from virus-infected cells is a 1:1 sNS1dimer:HDL complex with the specific NS1-ApoA1 contacts detected by crosslinking mass spec. This is inconsistent with both the neg-stain 2D class average with 2 sNS1 dimers on an HDL (Fig S1c) and with the recent study of Flamand & co-workers showing 1-3 NS1 dimers per HDL (ref 20). It is also ignores the propensity of NS1 to associate with membranes and lipids. It is far more likely that NS1 association with HDL is driven by these hydrophobic interactions than by specific protein-protein contacts. A lengthy Discussion section (lines 461-522) includes several chemically dubious or inconsistent statements, all based on the assumption that specific ApoA1 contacts are essential to NS1 association with HDL and that sNS1 oligomers higher than the dimer necessarily involve ApoA1 interaction, conclusions that are not established by the data in this paper.

We thank the Reviewer and have revised our discussion to cover available structural and functional data to draw conclusions that invariably also need further validation by others. One point that is repeatedly brought up by Reviewer 1 & 2 is the quality and functionality of our sample. Our conclusion now reiterates this point based on our own published data (Chan et al., 2019) and also the TEER assay data provided as Author response image 3.

Reviewer #1 (Recommendations For The Authors):

Minor:

(1) Fig. S3B, should the label for lane 4 be isNS1? In figure 1C you do not see ApoA1 for rsNS1 but for S3B you do? Which is correct?

This has been corrected in the Fig. S3B, the label for lane 4 has been corrected to isNS1 and lane 1 to rsNS1, where no ApoA1 band (25 kDa) is found.

(2) Line 436, is this the correct reference? Reference 43?

This has been corrected in the main text. (p 20, Line 507; Lee et al., 2020, J Exp Med).

Reviewer #2 (Recommendations For The Authors):

The cryoEM data analysis is incompletely described. The process (software, etc) leading to each refined EM map should be stated, including the use of reference structures in any step. These details are not in the Methods or in Figs S4-7, as claimed in the Methods. The use of DeepEMhancer (which refinements?) with the lack of defined secondary structural features in the maps and without any validation (or discussion of what was used as "ground truth") is concerning. At the least, the authors should show pre- and post-DeepEMhancer maps in the supplemental figures.

The data processing steps in the Methods section have been described with improved clarity. DeepEMhancer is a deep learning solution for cryo-EM volume post-processing to reduce noise levels and obtain more detailed versions of the experimental maps (Sanchez-Garcia, et al., 2021). DeepEMhancer was only used to sharpen the maps and reduce the noise for classes 1 and 2 of isNS1wt in complex with Ab56.2 for visualization purpose only and not for any refinements. To avoid any confusion, the use of DeepEMhancer has been removed from the supp text and figures.

Line 83 - "cryoEM structures...recently reported" isn't ref 17

This reference has been corrected in to Shu et al. (2022) in p 3, line 83.

Fig. S3 - mis-labeled gel lanes

This has been corrected in the Fig. S3B, the label for lane 4 has been corrected to isNS1 and lane 1 to rsNS1.

Fig S6c caption - "Representative 2D classes of each 3D classes, white bar 100 Å. Refined 3D map for classes 1 and 2 coloured by local resolution". The first sentence is unclear, and there is no white scale bar and no heat map.

Fig S6c caption has been corrected to “Representative 3D classes contoured at 0.06 and its particle distribution as labelled and coloured in cyan. Scale bar of 100 Å as shown. Refined 3D maps and their respective FSC resolution charts and posterior precision directional distribution as generated in crysosparc4.0”.

Collaboration.

Interesting to read this after just watching Oppenheimer on the plane to London.

Definitely my activity page!

This paragraph talks about how social media, especially Instagram, might affect mental health, especially for teenage girls. It mentions different opinions, a study by Meta, anecdotes from cosmetic surgeons, and thoughts from comedian Bo Burnham. The passage acknowledges the complexity of measuring social media's impact and mentions a "digital detox." It doesn't label social media as entirely good or bad, aiming for a more nuanced view. I find it interesting, but the whole social media and mental health issue is really complicated.

The examination of social media's impact, particularly its detrimental effects on the mental health of teenage girls, highlights a critical area of concern within the digital age's societal framework. The revelation from Meta's internal study about Instagram underscores the ethical dilemma faced by social media corporations: the responsibility to safeguard the mental health of their users while balancing the inherent drive for engagement and growth. This conundrum is further complicated by the rising trend of individuals seeking to emulate digitally altered self-images, which distorts perceptions of body image and exacerbates mental health issues.

I think the discussion concerning social media and mental health is not about deciding between blatant rejection and unquestioning acceptance. It is about achieving a balanced relationship with digital technology, in which the advantages are maximized while the hazards are actively addressed through informed use, supporting communities, and responsible platform administration.

When you have an animated face, it conveys that you take interest in what you are saying, even if that may not be the case. It also makes it easier to connect with your audience. Jim Carey is a good exaggerated example of this. In a speech, it's easy to get so caught up in what you have to say. How you present yourself is just as important in getting your message across.

Author Response

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

Responses to the reviewers

We thank the editor and reviewers for their insightful feedback and valuable suggestions on our revised manuscript. In this reply, we provided further clarifications and made changes accordingly. Reviewers’ comments are in bold, and our responses are immediately below. Changes in the main text are presented in italics, accompanied by the specific line numbers in the revised manuscript where these changes can be found. Below, we respond to each reviewer’s comments in turn.

Reviewer #1 (Public Review):

Ps observed 24 objects and were asked which afforded particular actions (14 action types). Affordances for each object were represented by a 14-item vector, values reflecting the percentage of Ps who agreed on a particular action being afforded by the object. An affordance similarity matrix was generated which reflected similarity in affordances between pairs of objects. Two clusters emerged, reflecting correlations between affordance ratings in objects smaller than body size and larger than body size. These clusters did not correlate themselves. There was a trough in similarity ratings between objects ~105 cm and ~130 cm, arguably reflecting the body size boundary. The authors subsequently provide some evidence that this clear demarcation is not simply an incidental reflection of body size, but likely causally related. This evidence comes in the flavour of requiring Ps to imagine themselves as small as a cat or as large as an elephant and showing a predicted shift in the affordance boundary. The manuscript further demonstrates that ChatGPT (theoretically interesting because it's trained on language alone without sensorimotor information; trained now on words rather than images) showed a similar boundary.

The authors also conducted a small MRI study task where Ps decide whether a probe action was affordable (graspable?) and created a congruency factor according to the answer (yes/no). There was an effect of congruency in posterior fusiform and superior parietal lobule for objects within body size range, but not outside. No effects in LOC or M1.

The major strength of this manuscript in my opinion is the methodological novelty. I felt the correlation matrices were a clever method for demonstrating these demarcations, the imagination manipulation was also exciting, and the ChatGPT analysis provided excellent food for thought. These findings are important for our understanding of the interactions between action and perception, and hence for researchers from a range of domains of cognitive neuroscience.

The major element that limits conclusions is that an MRI study with 12 P in this context can really only provide pilot data. Certainly the effects are not strong enough for 12 P to generate much confidence. The others of my concerns have been addressed in the revision.

Reviewer #1 (Recommendations For The Authors):

I think that the authors need to mention in the abstract that the MRI study constitutes a small pilot.

Response: We appreciate the reviewer’s positive evaluation and constructive suggestions. In response to the concern about the limited number of participants in the fMRI study, we fully acknowledge the implications this has on the generalizability and robustness of our findings related to the congruency effect. To clarity, we have explicitly stated its preliminary nature of the MRI study in the abstract [line 22]: “A subsequent fMRI experiment offered preliminary evidence of affordance processing exclusively for objects within the body size range, but not for those beyond.”

Reviewer #2 (Public Review):

Summary

In this work, the authors seek to test a version of an old idea, which is that our perception of the world and our understanding of the objects in it are deeply influenced by the nature of our bodies and the kinds of behaviours and actions that those objects afford. The studies presented here muster three kinds of evidence for a discontinuity in the encoding of objects, with a mental "border" between objects roughly of human body scale or smaller, which tend to relate to similar kinds of actions that are yet distinct from the kinds of actions implied by human-or-larger scale objects. This is demonstrated through observers' judgments of the kinds of actions different objects afford; through similar questioning of AI large-language models (LLMs); and through a neuroimaging study examining how brain regions implicated in object understanding make distinctions between kinds of objects at human and larger-than-human scales.

Strengths 

The authors address questions of longstanding interest in the cognitive neurosciences -- namely how we encode and interact with the many diverse kinds of objects we see and use in daily life. A key strength of the work lies in the application of multiple approaches. Examining the correlations among kinds of objects, with respect to their suitability for different action kinds, is novel, as are the complementary tests of judgments made by LLMs. The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity hints that action affordances may be computed dynamically, "on the fly", during actual action behaviours with objects in the real world.

Weaknesses 

A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs shows human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than providing independent evidence for affordance boundaries.

The relatively small sample size of the brain imaging experiment, and some design features (such as the task participants performed, and the relatively narrow range of objects tested) provide some limits on the extent to which it can be taken as support for the authors' claims.

Response: We thank the reviewer for the positive evaluation and the constructive comments. We agree that how LLMs work is a “black box”, and thus it is speculative to assume them to possess any human-like ability, because, as the reviewer pointed out, “these models demonstrate an affordance discontinuity may simply reflect those statistics.” Indeed, our manuscript has expressed a similar idea [line 338]: “We speculated that ChatGPT models may have formed the affordance boundary through a human prism ingrained within its linguistic training corpus.” That is, our intention was not to suggest that such information could replace sensorimotor-based interaction or achieve human-level capability, but rather to highlight that embodied interaction is necessary. Additionally, the scope of the present study does not extend to elucidating the mechanisms behind LLMs’ resemblance of affordance boundary, whether through statistical learning or actual comprehension. To clarify this point, in the revised manuscript, we have clarified that the mechanisms underlying the observed affordance boundary in LLMs may be different from human cognitive processes, and advocated future studies to explore this possibility [line 415]: “Nevertheless, caution should be taken when interpreting the capability of LLMs like ChatGPT, which are often considered “black boxes.” That is, our observation indicates that certain sensorimotor information is embedded within human language materials presumably through linguistic statistics, but it is not sufficient to assert that LLMs have developed a human-like ability to represent affordances. Furthermore, such information alone may be insufficient for LLMs to mimic the characteristics of the affordance perception in biological intelligence. Future studies are needed to elucidate such limitation.”

Regarding the concern about the models’ results not “providing independent evidence for affordance boundaries”, our objective in employing LLMs was to explore if an affordance boundary could emerge from conceptual knowledge without direct sensorimotor experience, rather than to validate the existence of the affordance boundary per se.

As for the concern about the limitations imposed by the small sample size and certain design features of our brain imaging experiment, please see our reply to Reviewer #1.

Reviewer #3 (Public Review):

Summary:

Feng et al. test the hypothesis that human body size constrains the perception of object affordances, whereby only objects that are smaller than the body size will be perceived as useful and manipulable parts of the environment, whereas larger objects will be perceived as "less interesting components."

To test this idea, the study employs a multi-method approach consisting of three parts:

In the first part, human observers classify a set of 24 objects that vary systematically in size (e.g., ball, piano, airplane) based on 14 different affordances (e.g., sit, throw, grasp). Based on the average agreement of ratings across participants, the authors compute the similarity of affordance profiles between all object pairs. They report evidence for two homogenous object clusters that are separated based on their size with the boundary between clusters roughly coinciding with the average human body size. In follow-up experiments, the authors show that this boundary is larger/smaller in separate groups of participants who are instructed to imagine themselves as an elephant/cat.

In the second part, the authors ask different large language models (LLMs) to provide ratings for the same set of objects and affordances and conduct equivalent analyses on the obtained data. Some, but not all, of the models produce patterns of ratings that appear to show similar boundary effects, though less pronounced and at a different boundary size than in humans.

In the third part, the authors conduct an fMRI experiment. Human observers are presented with four different objects of different sizes and asked if these objects afford a small set of specific actions. Affordances are either congruent or incongruent with objects. Contrasting brain activity on incongruent trials against brain activity on congruent trials yields significant effects in regions within the ventral and dorsal visual stream, but only for small objects and not for large objects.

The authors interpret their findings as support for their hypothesis that human body size constrains object perception. They further conclude that this effect is cognitively penetrable, and only partly relies on sensorimotor interaction with the environment (and partly on linguistic abilities).

Strengths:

The authors examine an interesting and relevant question and articulate a plausible (though somewhat underspecified) hypothesis that certainly seems worth testing. Providing more detailed insights into how object affordances shape perception would be highly desirable. Their method of analyzing similarity ratings between sets of objects seems useful and the multi-method approach is original and interesting.

Weaknesses:

The study presents several shortcomings that clearly weaken the link between the obtained evidence and the drawn conclusions. Below I outline my concerns in no particular order:

(1) It is not entirely clear to me what the authors are proposing and to what extent the conducted work actually speaks to this. For example, in the introduction, the authors write that they seek to test if body size serves not merely as a reference for object manipulation but also "plays a pivotal role in shaping the representation of objects." This motivation seems rather vague motivation and it is not clear to me how it could be falsified.

Overall, the lack of theoretical precision makes it difficult to judge the appropriateness of the approaches and the persuasiveness of the obtained results. I would strongly suggest clarifying the theoretical rationale and explaining in more detail how the chosen experiments allow them to test falsifiable predictions.

(2) The authors used only a very small set of objects and affordances in their study and they do not describe in sufficient detail how these stimuli were selected. This renders the results rather exploratory and clearly limits their potential to discover general principles of human perception. Much larger sets of objects and affordances and explicit data-driven approaches for their selection would provide a more convincing approach and allow the authors to rule out that their results are just a consequence of the selected set of objects and actions.

(3) Relatedly, the authors could be more thorough in ruling out potential alternative explanations. Object size likely correlates with other variables that could shape human similarity judgments and the estimated boundary is quite broad (depending on the method, either between 80 and 150 cm or between 105 to 130 cm). More precise estimates of the boundary and more rigorous tests of alternative explanations would add a lot to strengthen the authors' interpretation.

(4) While I appreciate the manipulation of imagined body size, as a clever way to solidify the link between body size and affordance perception, I find it unfortunate that it is implemented in a between-subjects design, as this clearly leaves open the possibility of pre-existing differences between groups. I certainly disagree with the authors' statement that their findings suggest "a causal link between body size and affordance perception."

(5) The use of LLMs in the current study is not clearly motivated and I find it hard to understand what exactly the authors are trying to test through their inclusion. As it currently stands, I find it hard to discern how the presence of perceptual boundaries in LLMs could constitute evidence for affordance-based perception.

(6) Along the same lines, the fMRI study also provides little evidence to support the authors' claims. The use of congruency effects as a way of probing affordance perception is not well motivated. Importantly (and related to comment 2 above), the very small set of objects and affordances in this experiment heavily complicates any conclusions about object size being the crucial variable determining the occurrence of congruency effects.

Overall, I consider the main conclusions of the paper to be far beyond the reported data. Articulating a clearer theoretical framework with more specific hypotheses as well as conducting more principled analyses on more comprehensive data sets could help the authors obtain stronger tests of their ideas.

Response: We appreciate the insightful inquiries regarding our manuscript. Below, we explained the theoretical motivation and rationale of each part of our experiments.

In response to the reviewer’s insights, we have modified the expression “plays a pivotal role in shaping the representation of objects” in the revised manuscript and have restated the general question of our study in the introduction. Our motivation is on the long-lasting debate over the representation versus direct perception of affordance, specifically examining the “representationalization” of affordance. That is, we tested whether object affordance simply covaried directly with continuous constraints such as object size, a perspective aligned with the representation-free (direct perception) view, or whether affordance became representationalized, adhering to the representation-based view, constrained by body size. Such representationalization would generate a categorization between objects that are affordable and the environment that exceeds affordance.

To test these hypotheses, we first delineated the affordance of various objects. We agree with the reviewer that in this step a broader selection of objects and actions could mitigate the risk of our results being influenced by the specific selection of objects and actions. However, our results are unlikely to be biased, because our selection was guided by two key criteria, rather than being arbitrary. First, the objects were selected from the dataset in Konkle and Oliva's study (2011), which systematically investigated object size’ impact on object recognition, thus providing a well-calibrated range of sizes (i.e., from 14 cm to 7,618 cm) reflective of real-world objects. Second, the selected actions covered a wide range of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing) based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a representative sampling of typical human experiences.

Upon demonstrating a trough in perceived affordance similarity, we recognized the location of the affordance boundary coincidentally fell within the range of human body size. We agree with the reviewer that this observation of the coincidence between body size and the location of boundary alone is not sufficient for a mechanistic explanation, because variables co-varying with object sizes might also generate this coincidence. The identification of a more precise location for the boundary unlikely rules out alternative explanations of this kind. To establish a causal link between body size and the affordance boundary, we opted for a direct manipulation of body sizes through imagination, while keeping all other variables constant across conditions. This approach allowed us to examine whether and how the affordance boundary shifts in response to body size changes.

Regarding the between-subjects design of the imagination experiment, we wish to clarify that this design aimed to prevent carryover effects. Although a within-subjects design indeed is more sensitive in detecting manipulation effects by accounting for subject variability, it risks contamination across conditions. Specifically, transitioning immediately between different imagined body sizes poses a challenge, and sequential participation could induce undesirable response strategies, such as deliberately altering responses to the same objects in different conditions. The between-subjects design, which susceptible to participant variability (e.g., “pre-existing differences between groups” suggested by the reviewer), avoids such contamination. In addition, we employed random assignment of participants to different conditions (cat-size versus elephant-size).

The body imagination experiment provided causal evidence of an embodied discontinuity, suggesting the boundary is tied to the agent’s motor capacity, rather than amodal sources. The LLMs experiment then sought to test a prediction from the embodied theories of cognition: the supramodality of object perception. Especially, we asked whether the embodied discontinuity is supramodally accessible, using LLMs to assess whether affordance perception discretization is supramodally accessible beyond the sensorimotor domain through linguistic understanding. From this perspective, our LLM experiment was employed not to affirm affordance-based perception but to examine and support a prediction by the embodied theories of cognition.

Finally, our preliminary fMRI study aimed to conceptually replicate the perceptual discontinuity and explore it neural correlates using a subset of objects and actions from the behaviour experiments. This approach was chosen to achieve stable neural responses and enhance study power, employing the congruent effect (congruent - incongruent) as a metric for affordance processing (e.g., Kourtis et al., 2018), which reflects facilitated responses when congruent with objects’ affordances (e.g., Ellis & Tucker, 2000). Nevertheless, we recognize the limitation of a relatively small sample sizes, for details please see our reply to the reviewer #1.

In summary, our findings contribute to the discourse on computationalism’s representation concept and influence of these representations, post-discretization, on processes beyond the sensorimotor domain. We hope that these additional explanations and revisions effectively address the concerns raised and demonstrate our commitment to enhancing the quality of our work in light of your valuable feedback. By acknowledging these limitations and directions for future research, we hope to further the discourse on affordance perception and embodied cognition.

References

Ellis, R., & Tucker, M. (2000). Micro‐affordance: The potentiation of components of action by seen objects. British Journal of Psychology, 91(4), 451-471.

Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., ... & Zisserman, A. (2017). The kinetics human action video dataset. arXiv preprint arXiv:1705.06950.

Konkle, T., & Oliva, A. (2011). Canonical visual size for real-world objects. Journal of Experimental Psychology: human perception and performance, 37(1), 23.

Kourtis, D., Vandemaele, P., & Vingerhoets, G. (2018). Concurrent cortical representations of function-and size-related object affordances: an fMRI study. Cognitive, Affective, & Behavioral Neuroscience, 18, 1221-1232.

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

Responses to the reviewers

We deeply appreciate the reviewers’ comments. In response to the concerns raised, we have revised the manuscript accordingly. Below we address each of the reviewers’ comments in turn. Reviewers’ comments are in bold, and our responses are immediately below. Changes in the main text are presented in italics, followed by corresponding page and line numbers in the revised manuscript. We also highlighted tracks of change in the revised manuscript.

Reviewer #1 (Public Review):

(1) The main behavioural work appears well-powered (>500 Ps). This sample reduces to 100 for the imagination study, after removing Ps whose imagined heights fell within the human range (100-200 cm). Why 100-200 cm? 100 cm is pretty short for an adult. Removing 80% of data feels like conclusions from the imagination study should be made with caution.

R1: Sorry for the confusion. We did not remove 80% of the participants; instead, a separate sample of participants was recruited in the imagination experiment. The size of this sample (100 participants) was indeed smaller than the first experiment (528 participants), because the first experiment was set for exploratory purposes and was designed to be over-powered. Besides, inspection of the data of the first sample showed that the affordance pattern became stable after the first 50 participants. We explained this consideration in the revised manuscript:

(p 21, ln 490) “…, another one hundred and thirty-nine participants from the same population were recruited from the same platform. We chose a smaller sample size for the imagination experiment compared to that for the object-action relation judgement task, because inspection of the data of the first sample showed that the affordance pattern became stable after the first 50 participants.”

The average adult human height ranges from 140-170 cm for women and 150180 cm for men (NCD-RisC, 2016). Accordingly, the criterion of 100-200 cm covered this range and was set to ensure that participants unambiguously imagined a body schema different from that of human, as the tallest domestic cat below 100 cm according to the Guinness World Records and an elephant above 200 cm according to Crawley et al. (2017). We clarified these considerations in the revised manuscript:

(p 21, ln 494) “To maximize the validity of the manipulation, data from participants whose imagined height fell within the average human size range (100cm - 200cm) were excluded from further analysis. Consequently, 100 participants (49 males, aged from 17 to 39 years, mean age = 23.2 years) remained in the analysis. This exclusion criterion was broader than the standard adult human height range of 140cm to 180cm (NCD-RisC, 2016). This approach ensured that our analysis focused on participants who unambiguously imagined a body schema different from humans, yet within the known height range of cats and elephants.”

In addition, we also reanalysed the data with a more conservative criterion of 140cm to 180cm, and the results remained.

(2) There are only 12 Ps in the MRI study, which I think should mean the null effects are not interpreted. I would not interpret these data as demonstrating a difference between SPL and LOC/M1, but rather that some analyses happened to fall over the significance threshold and others did not.

R2: We would like to clarify that the null hypothesis of this fMRI study is the lack of two-way interaction between object size and object-action congruency, which was rejected by the observed significant interaction. That is, the interpretation of the present study did not rely on accepting any null effect.

Having said this, we admit that the fMRI experiment is exploratory and the sample size is small (12 participants), which might lead to low power in estimating the affordance effect. In the revision, we acknowledge this issue explicitly:

(p 16, ln 354) “…, supporting the idea that affordance is typically represented only for objects within the body size range. While it is acknowledged that the sample size of the fMRI study was small (12 participants), necessitating cautious interpretation of its results, the observed neural-level affordance discontinuity is notable. That is, qualitative differences in neural activity between objects within the affordance boundary and those beyond replicated our behavioral findings. This convergent evidence reinforced our claim that objects were discretized into two broad categories along the continuous size axis, with affordance only being manifested for objects within the boundary.”

(3) I found the MRI ROI selection and definition a little arbitrary and not really justified, which rendered me even more cautious of the results. Why these particular sensory and motor regions? Why M1 and not PMC or SMA? Why SPL and not other parietal regions? Relatedly, ROIs were defined by thresholding pF and LOC at "around 70%" and SPL and M1 "around 80%", and it is unclear how and why these (different) thresholds were determined.

R3: Our selection of these specific sensory and motor regions was based on prior literature reporting their distinct contribution to affordance perception (e.g., Borghi, 2005; Sakreida et al., 2016). The pFs was chosen as a representative region of the ventral visual stream, involved in object identification and classification, and the SPL was chosen as a representative region of the dorsal visual stream, involved in object perception and manipulation. The primary motor cortex (M1) has also been reported involved in affordance processing (e.g., McDannald et al., 2018), and we chose this region to probe the affordance congruency effect in the motor execution stage of the sense-think-act pathway. We did not choose the premotor cortex (PMC) and the supplementary motor area (SMA) because they were proposedly also involved in processes beyond motor execution (e.g., Hertrich et al., 2016; Kantak et al., 2012), and if any effect was observed, one cannot exclusively attribute the effect to motor execution. As for the parietal regions, our choice of the SPL not IPL/IPS is based on the meta-analysis of affordance processing areas where only the SPL shows consistent activation for both stable and variable affordances (Sakreida et al., 2016). We chose the SPL to capture effects on either type of affordances. In revision, we explained these considerations in the revised manuscript:

(p 14, ln 280) “In addition to the pFs and SPL, we also examined the congruency effect in the lateral occipital cortex (LO), which is involved in object representation (e.g., Grill-Spector et al., 2000; Konkle & Caramazza, 2013) and provides inputs to both the pFs and SPL (Hebart et al., 2018). Meanwhile, the primary motor cortex (M1), which receives inputs from the dorsal stream (Vainio & Ellis, 2020), is involved in affordance processing (e.g., McDannald et al., 2018) and action executions (Binkofski et al., 2002).”

(p 29, ln 684) “We chose the pFs, LO, SPL, and M1 as ROIs based on existing literature highlighting their distinct contributions to affordance perception (Borghi, 2005; Sakreida et al., 2016).”

Regarding ROI thresholding, we apologize for the lack of clarity in reporting the thresholds in the original manuscript. The thresholds were different between ventral regions (from Zhen et al., 2015) and dorsal regions (from Fan et al., 2016) because they are from two different atlases. The former was constructed by probability maps of task-state fMRI activity during localizer contrast with stationary images and the latter by a parcellation of the brain's functional connectivity; therefore, the numerical values in these two atlases are not comparable. To extract ROIs with comparable sizes, we selected a threshold of 55% for the pFs, 90% for the LO, 78% for the SPL, and 94% for the M1 in the original manuscript.

To rule out the possibility that the results were distorted by the specific choice of thresholds, we re-ran the analysis with a threshold 80% for all ROIs (resulting in 456 voxels in the lpFs, 427 voxels in the rpFs, 1667 voxels in the lLO, 999 voxels in the rLO, 661 voxels in the lSPL, 310 voxels in the rSPL, 231 voxels in the lM1, and 327 voxels in the rM1) with the 2-by-2 repeated-measures ANOVA. Our results remained the same qualitatively. A significant interaction between object type and congruency was observed in the pFs (F(1,11) = 24.87, p <.001, 𝜂2=.69) and SPL (F(1,11) = 14.62, p =.003, 𝜂2=.57). The simple effect analysis revealed the congruency effect solely for objects within body size range (pFs: p =.003; SPL: p <.001), not for objects beyond (ps >.30). For the M1 and LO, neither significant main effects (ps >.11) nor interactions were found (ps >.20).

We clarified our choice of thresholds in the methods section in the revised manuscript:

(p 29, ln 686) “Eight ROIs depicted in Fig. 3b were constructed based on the overlap between the whole-brain map activated by both objects within and beyond and corresponding functional atlases (the pFs and LO from Zhen et al., 2015; the SPL and M1 from Fan et al., 2016). To achieve ROIs of similar sizes, we applied varying thresholds to each cortical area: for the pFs and LO, the atlases were thresholded at 55% and 90%, resulting in 266 voxels in the lpFs, 427 in the rpFs, 254 in the lLO and 347 in the rLO; for the SPL and M1, the atlases were thresholded at 78% and 94%, resulting in 661 voxels in the lSPL, 455 in the rSPL, 378 in the lM1, and 449 in the rM1. In the subsequent analysis, homologous areas spanning both cortical hemispheres were merged.”

(4) Discussion and theoretical implications. The authors discuss that the MRI results are consistent with the idea we only represent affordances within body size range. But the interpretation of the behavioural correlation matrices was that there was this similarity also for objects larger than body size, but forming a distinct cluster. I therefore found the interpretation of the MRI data inconsistent with the behavioural findings.

R4: We speculated that the similarity in action perception among objects beyond the body size range may be due to these objects being similarly conceptualized as ‘environment’, in contrast to the objects within the body size range, which are categorized differently, namely as the ‘objects for the animal.’ Accordingly, in cortical regions involved in object processing, objects conceptualized as ‘environment’ unlikely showed the congruency effect, distinct from objects within the body size range. We have explained this point in the revised manuscript:

(p 17, ln 370) “…which resonates the embodied influence on the formation of abstract concepts (e.g., Barsalou, 1999; Lakoff & Johnson, 1980) of objects and environment. Consistently, our fMRI data did not show the congruency effect for objects beyond the body size range, distinct from objects within this range, suggesting a categorization influenced by objects’ relative size to the human body.”

(5) In the discussion, the authors outline how this work is consistent with the idea that conceptual and linguistic knowledge is grounded in sensorimotor systems. But then reference Barsalou. My understanding of Barsalou is the proposition of a connectionist architecture for conceptual representation. I did not think sensorimotor representation was privileged, but rather that all information communicates with all other to constitute a concept.

R5: We are sorry for the confusion. We do not intend to argue that the sensorimotor representation is privileged. Instead, we would like to simply emphasize their engagement in concept. According to our understanding, Barsalou’s Perceptual Symbol Theory proposes that grounded concepts include sensorimotor information, and conceptual knowledge is grounded in the same neural system that supports action (Barsalou, 1999). This is consistent with our proposal that the affordance boundary locked to an animal’s sensorimotor capacity might give rise to a conceptual-ish representation of object-ness specific to the very animal. We have clarified this point in the introduction and discussion on the conceptual knowledge and sensorimotor information:

In the introduction (p 2, ln 59) “…, and the body may serve as a metric that facilitates meaningful engagement with the environment by differentiating objects that are accessible for interactions from those not. Further, grounded cognition theory (see Barsalou, 2008 for a review) suggests that the outputs of such differentiation might transcend sensorimotor processes and integrate into supramodal concepts and language. From this perspective, we proposed two hypotheses...”

In the discussion (p 18, ln 392) “Indeed, it has been proposed that conceptual knowledge is grounded in the same neural system that supports action (Barsalou, 1999; Glenberg et al., 2013; Wilson & Golonka, 2013), thereby suggesting that sensorimotor information, along with other modal inputs, may be embedded in language (e.g., Casasanto, 2011; Glenberg & Gallese, 2012; Stanfield & Zwaan, 2001), as the grounded theory proposed (see Barsalou, 2008 for a review).”

(6) More generally, I believe that the impact and implications of this study would be clearer for the reader if the authors could properly entertain an alternative concerning how objects may be represented. Of course, the authors were going to demonstrate that objects more similar in size afforded more similar actions. It was impossible that Ps would ever have responded that aeroplanes afford grasping and balls afford sitting, for instance. What do the authors now believe about object representation that they did not believe before they conducted the study? Which accounts of object representation are now less likely?

R6: We thank the reviewer for this suggestion. The theoretical motivation of the present study is to explore whether, for continuous action-related physical features (such as object size relative to the agents), affordance perception introduces discontinuity and qualitative dissociation, i.e., to allow the sensorimotor input to be assigned into discrete states/kinds, as representations envisioned by the computationalists; alternatively, whether the activity may directly mirror the input, free from discretization/categorization/abstraction, as proposed by the Replacement proposal of some embodied theories on cognition.

By addressing this debate, we hoped to shed light on the nature of representation in, and resulted from, the vision-for-action processing. Our finding of affordance discontinuity suggests that sensorimotor input undergoes discretization implied in the computationalism idea of representation. Further, not contradictory to the claims of the embodied theories, these representations do shape processes out of the sensorimotor domain, but after discretization.

We have now explained our hypotheses and alternatives explicitly in the revised introduction and discussion:

In the introduction (p 2, ln 45) “However, the question of how object perception is influenced by the relative size of objects in relation to the human body remains open. Specifically, it is unclear whether this relative size simply acts as a continuous variable for locomotion reference, or if it affects differentiating and organizing object representation based on their ensued affordances.”

In the discussion (p 14, ln 295) “One long-lasting debate on affordance centers on the distinction between representational and direct perception of affordance. An outstanding theme shared by many embodied theories of cognition is the replacement hypothesis (e.g., Van Gelder, 1998), which challenges the necessity of representation as posited by computationalism’s cognitive theories (e.g., Fodor, 1975). This hypothesis suggests that input is discretized/categorized and subjected to abstraction or symbolization, creating discrete stand-ins for the input (e.g., representations/states). Such representationalization would lead to a categorization between the affordable (the objects) and those beyond affordance (the environment), in contrast to the perspective offered by embodied theories. The present study probed this ‘representationalization’ of affordance by examining whether affordance perception introduces discontinuity and qualitative dissociation in response to continuous action-related physical features (such as object size relative to the agents), which allows sensorimotor input to be assigned into discrete states/kinds, in line with the representation-based view under the constraints of body size. Alternatively, it assessed whether activity directly mirrors the input, free from discretization/categorization/abstraction, in line with the representation-free view.

First, our study found evidence demonstrating discretization in affordance perception. Then, through the body imagination experiment, we provided causal evidence suggesting that this discretization originates from sensorimotor interactions with objects rather than amodal sources, such as abstract object concepts independent of agent motor capability. Finally, we demonstrated the supramodality of this embodied discontinuity by leveraging the recent advances in AI. We showed that the discretization in affordance perception is supramodally accessible to disembodied agents such as large language models (LLMs), which lack sensorimotor input but can access linguistic materials built upon discretized representations. These results collectively suggest that sensorimotor input undergoes discretization, as implied in the computationalism’s idea of representation. Note that, these results are not contradictory to the claim of the embodied theories, as these representations do shape processes beyond the sensorimotor domain but after discretization.

This observed boundary in affordance perception extends the understanding of the discontinuity in perception in response to the continuity of physical inputs (Harnad, 1987; Young et al., 1997).”

Reviewer #1 (Recommendations For The Authors):

a) I would recommend providing further justification for why 100-200 cm were used as the cut-offs reflecting acceptable imagined body size. Were these decisions preregistered anywhere? If so, please state.

Ra: Please see R1.

b) I would encourage the authors to call the MRI a small pilot study throughout, including in the abstract.

Rb: We completely agree and have indicated the preliminary nature of this study in the revised version:

(p 11, ln 236) “To test this speculation, we ran an fMRI experiment with a small number of participants to preliminarily investigate the neural basis of the affordance boundary in the brain by measuring neural activity in the dorsal and ventral visual streams when participants were instructed to evaluate whether an action was affordable by an object (Fig. 3a).”

c) Please provide much further justification of ROI selection, why these thresholds were chosen, and therefore why they are different across regions.

Rc: Please see R3.

d) Further elucidation in the discussion would help the reader interpret the MRI data, which should always be interpreted also in light of the behavioural findings.

Rd: Please see R4.

e) The authors may wish to outline precisely what they claim concerning the nature of conceptual/linguistic representation. Is sensorimotor information privileged or just part of the distributed representation of concepts?

Re: This is a great point. For details of corresponding revision, please see R5.

f) There are some nods to alternative manners in which we plausibly represent objects (e.g. about what the imagination study tells us) but I think this theoretical progression should be more prominent.

Rf: We thank the reviewer for this suggestion. For details of corresponding revision, please see R6.

Reviewer #2 (Public Review):

(1) A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs show human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than evidence that affordance boundaries can arise independently even without "organism-environment interactions" as the authors claim here.

R1: We agree that how LLMs work is a “black box”, and thus it is speculative to assume them to possess any human-like ability, because, as the reviewer pointed out, “these models demonstrate an affordance discontinuity may simply reflect those statistics.” Indeed, our manuscript has expressed a similar idea: “We speculated that ChatGPT models may have formed the affordance boundary through a human prism ingrained within its linguistic training corpus. (p 16 ln 338)”. That is, we did not intend to claim that such information is sufficient to replace sensorimotor-based interaction, or to restore human-level capability, for which we indeed speculated that embodied interaction is necessary. In the revised manuscript, we have clarified our stand that the mechanism generating the observed affordance boundary in LLMs might be different from that in human cognition, and urged future studies to explore this possibility:

(p 18, ln 413) “…, as well as alignment methods used in fine-tuning the model (Ouyang et al., 2022). Nevertheless, caution should be taken when interpreting the capabilities of LLMs like ChatGPT, which are often considered “black boxes.” That is, our observation indicates that some degree of sensorimotor information is embedded within human language materials presumably through linguistic statistics, but it is not sufficient to assert that LLMs have developed a human-like ability to represent affordances. Furthermore, such information alone may be insufficient for LLMs to mimic the characteristics of the affordance perception in biological intelligence. Future studies are needed to elucidate such limitation.”

Indeed, because of this potential dissociation, our LLM study might bear novel implications for the development of AI agents. We elaborated on them in the revised discussion on LLMs:

(p 19, ln 427) “…, represents a crucial human cognitive achievement that remains elusive for AI systems. Traditional AI (i.e., task-specific AI) has been confined with narrowly defined tasks, with substantial limitations in adaptability and autonomy. Accordingly, these systems have served primarily as tools for humans to achieve specific outcomes, rather than as autonomous agents capable of independently formulating goals and translating them into actionable plans. In recent years, significant efforts have been directed towards evolving traditional AI into more agent-like entities, especially in domains like navigation, object manipulation, and other interactions with the physical world. Despite these advancements, the capabilities of AI still fall behind human-level intelligence. On the other hand, embodied cognition theories suggest that sensorimotor interactions with the environment are foundational for various cognitive domains. From this point of view, endowing AI with human-level abilities in physical agent-environment interactions might provide an unreplaceable missing piece for achieving Artificial General Intelligence (AGI). This development would significantly facilitate AI’s role in robotics, particularly in actions essential for survival and goal accomplishment, a promising direction for the next breakthrough in AI (Gupta et al., 2021; Smith & Gasser, 2005).

However, equipping a disembodied AI with the ability for embodied interaction planning within a specific environment remains a complex challenge. By testing the potential representationalization of action possibilities (affordances) in both humans and LLMs, the present study suggests a new approach to enhancing AI’s interaction ability with the environment. For instance, our finding of supramodal affordance representation may indicate a possible pathway for disembodied LLMs to engage in embodied physical interactions with their surroundings. From an optimistic view, these results suggest that LLM-based agents, if appropriately designed, may leverage affordance representations embedded in language to interact with the physical world. Indeed, by clarifying and aligning such representations with the physical constitutes of LLM-based agents, and even by explicitly constructing an agent-specific object space, we may foster the sensorimotor interaction abilities of LLM-based agents. This progression could lead to achieving animal-level interaction abilities with the world, potentially sparking new developments in the field of embodied cognition theories.”

(2) The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity suggests a different interpretation of the authors' main findings. It may be that action affordance is not a dimension that stably characterises the long-term representation of object kinds, as suggested by the authors' interpretation of their brain findings, for example. Rather these may be computed more dynamically, "on the fly" in response to direct questions (as here) or perhaps during actual action behaviours with objects in the real world.

R2: We thank the reviewer for pointing out the dynamic nature of affordance perception in our study. This feature indeed reinforced our attribution of the boundary into an affordance-based process instead of a conceptual or semantic process, the latter of which would predict the action possibilities being a fixed belief about the objects, instead of being dynamically determined according to the feature of the agent-object dyads. In addition, this dynamic does not contradict with our interpretation of the observed boundary in affordance perception. With this observation, we speculated that continuous input was abstracted or representationalized into discontinued categories, and the boundary between these categories was drawn according to the motor capacity of the agent. The finding of the boundary adapting to manipulation on body schema suggests that the abstraction/representationalization dynamically updates according to the current belief of motor capacity and body schema of the animal. In addition, we agree that future studies are needed to examine the dynamics of the abstraction/representationalization of affordance, probably by investigating the evolvement of affordance representation during ongoing actual interactions with novel objects or manipulated motor capability. These points are now addressed in the revision:

(p 17, ln 380) “Therefore, this finding suggests that the affordance boundary is cognitively penetrable, arguing against the directness of affordance perception (e.g., Gibson, 1979; Greeno, 1994; Prindle et al., 1980) or the exclusive sensorimotor origin of affordances (e.g., Gallagher, 2017; Thompson, 2010; Hutto & Myin, 2012; Chemero, 2013). Further, this finding that the boundary adapted to manipulation on body schema suggests that the abstraction/representationalization may be dynamically updated in response to the current motor capacity and body schema of the agent, suggesting that the affordance-based process is probably determined dynamically by the nature of the agent-object dyads, rather than being a fixed belief about objects. Future studies could explore the dynamics of affordance representationalization, probably by investigating how affordance representations evolve during active interactions with novel objects or under conditions of altered motor capabilities. Finally, our findings also suggest that disembodied conceptual knowledge pertinent to action likely modulates affordance perception.”

Reviewer #2 (Recommendations For The Authors):

a) As described, I think the authors could improve their discussion of the LLM work and consider more deeply possible different interpretations of their findings with those models. Are they really providing an independent data point about how objects may be represented, or instead is this a different, indirect way of asking humans the same questions (given the way in which these models are trained)?

Ra: Please see R1.

b) Some of the decisions behind the design of the fMRI experiment, and some of the logic of its interpretation, could be made clearer. Why those four objects per se? What kinds of confounds, such as familiarity, or the range of possible relevant actions per object, might need to be considered? Is there the possibility that relative performance on the in-scanner behavioural task may be in part responsible for the findings? Why were those specific regions of interest chosen and not others? The authors find that the dorsal and ventral regions make a univariate distinction between congruent and incongruent trials, but only for human-scale objects, but it was not clear from the framework that the authors adopted why that distinction should go in that direction (e.g. congruent > incongruent) nor why there shouldn't also be a distinction for the "beyond" objects? Finally, might some of these brain questions better be approached with an RSA or similar approach, as that would seem to better map onto the behavioural studies?

Rb: We thank the reviewer for the detailed suggestions.

Regarding the fMRI study, we have provided further justification on its rationale in the revised manuscript:

(p 11, ln 231) “The distinct categories of reported affordances demarcated by the boundary imply that the objects on either side of the boundary may be represented differently in the brain. We thus speculated that the observed behavioral discontinuity is likely underpinned by distinct neural activities, which give rise to these discrete ‘representations’ separated by the boundary.”

The objects used in the fMRI study were selected by taking into account the objective of the fMRI study, which was to provide the neural basis for the affordance discontinuity found in behaviour experiments. In other words, the fMRI study is not an exploratory experiment, but a validation experiment. To this end, we deliberately selected a small range of common objects to ensure that participants were sufficiently familiar with them, as confirmed through their oral reports. Furthermore, to ensure a fair comparison between the two categories of objects in terms of action possibility range, we predetermined an equal number of congruent and incongruent actions for each category. This arrangement was intended to eliminate any bias that might arise from different amount of action choices associated with each category. Therefore, the present object and action sets in the fMRI study, which were based on the behavior experiments, are sufficient for its purpose.

Regarding the possibility that the performance of the in-scanner behavioural task may be in part responsible for the findings, we analysed participants’ performance. Not surprisingly, participants demonstrated high consistency and accuracy in their responses:

𝑀𝑒𝑎𝑛𝐶𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝑊𝑖𝑡ℎ𝑖𝑛 = 0.991, SD = 0.018;

𝑀𝑒𝑎𝑛𝐼𝑛𝑐𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝑊𝑖𝑡ℎ𝑖𝑛 = 0.996, SD = 0.007;

𝑀𝑒𝑎𝑛𝐶𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡_𝑂𝑏𝑗𝑒𝑐𝑡𝐵𝑒𝑦𝑜𝑛𝑑 = 0.996, SD = 0.004;

𝑀𝑒𝑎𝑛𝐼𝑛𝑐𝑜𝑛𝑔𝑟𝑢𝑒𝑛𝑡𝑂𝑏𝑗𝑒𝑐𝑡𝐵𝑒𝑦𝑜𝑛𝑑 = 0.998, SD = 0.002

in all conditions, suggesting constant active engagement with the task. Thus, the inscanner behaviour unlikely resulted in the lack of congruency effect for the ‘beyond’ objects observed in the brain.

Regarding the selection of ROIs, our decision to focus on these specific sensory and motor regions was based on existing literature highlighting their distinct contribution to affordance perception (Borghi, 2005; Sakreida et al., 2016). The pFs was chosen for its role in object identification and classification, while the SPL was chosen for its involvement in object manipulation. Additionally, the primary motor cortex (M1) is known to be engaged in affordance processing (e.g., McDannald et al., 2018), which was included to investigate the affordance congruency effect during the motor execution stage of the sense-think-act pathway. These considerations are detailed in the revised manuscript:

(p 14, ln 280) “In addition to the pFs and SPL, we also examined the congruency effect in the lateral occipital cortex (LO), which is involved in object representation (e.g., Grill-Spector et al., 2000; Konkle & Caramazza, 2013) and provides inputs to both the pFs and SPL (Hebart et al., 2018). Meanwhile, the primary motor cortex (M1), which receives inputs from the dorsal stream (Vainio & Ellis, 2020), is involved in affordance processing (e.g., McDannald et al., 2018) and action executions (Binkofski et al., 2002).”

(p 29, ln 684) “We chose the pFs, LO, SPL, and M1 as ROIs based on existing literature highlighting their distinct contributions to affordance perception (Borghi, 2005; Sakreida et al., 2016).”

Regarding the congruency effect, in our study, we followed the established fMRI research paradigm of employing the congruent effect as a measure of affordance processing (e.g., Kourtis et al., 2018), and the rationale behind the directionality of the distinction in our framework (congruent > incongruent) is grounded in the concept of affordance, in which the mere perception of a graspable object facilitates motor responses that are congruent with certain qualities of the object (e.g., Ellis & Tucker, 2000). From the interaction of congruency by object type, we observed only congruency effect for objects within rather than objects beyond. We speculate that the objects beyond the affordance boundary is generally beyond the motor capacities of the very animal, being too large for the animal to manipulate, thus no congruency effect was found. We have added these clarifications in the revised manuscript:

(p 11, ln 244) “The congruency effect, derived from the contrast of Congruent versus Incongruent conditions, is a well-established measure of affordance processing (e.g., Kourtis et al., 2018).”

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to the smaller objects. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

Regarding the RSA analysis, we agree with the reviewer that RSA may offer a more direct comparison with similarities among objects. However, our primary objective in this fMRI study was to explore the neural basis of the affordance boundary observed in the behavioural study, rather than explaining the similarities in neural responses between different objects. For this reason, we did not conduct RSA analysis.

c) Page 4 Re statistical evaluation of the discontinuity in judgments, the authors might consider a Bayesian approach, which would be stronger than using "all ps > 0.05" to argue that within-boundary similarities are consistent and high.

Rc: We thank the reviewer for the suggestion on the Bayesian approach for significance tests, which has been now added in the revised manuscript:

In the results (p 4, ln 105) “This trough suggested an affordance boundary between size rank 4 and 5, while affordance similarities between neighboring ranks remained high (rs > 0.45) and did not significantly differ from each other (ps > 0.05, all 𝐵𝐹10 < 10) on either side of the boundary (Fig. 1d, left panel, green lines).”

In the methods (p 25, ln 597) “Pearson and Filon’s (1898) Z, implemented in R package “cocor” (Diedenhofen & Musch, 2015) was used to evaluate the significance of these similarities (alpha level = .05, one-tail test). For significance tests, Bayesian statistical analyses were conducted using the web version of the “bayesplay” R package (Colling, 2021). Specifically, the data (likelihood) model was specified as a normal distribution, where the correlation coefficients were transformed to Fisher’s z. The null hypothesis was specified as a standard normal distribution centred at zero. Conversely, the alternative hypothesis was specified as a normal distribution centred at 2. Bayes factors (BF10) were calculated and interpreted using the classification scheme suggested by Wagenmakers et al. (2011), wherein a Bayes factor greater than 10 is considered strong evidence for accepting H1 over H0.”

d) Page 4 One question I had about the big objects is whether their internal similarity and dissimilarity to smaller objects, might largely arise if most of the answers about actions for those larger objects are just "no"? This depends on the set of possible actions that were considered: the authors chose 14 from a previous study but did not describe these further or consider possible strengths/limitations of this selection. This is a very important point that needs addressing - to what extent are these findings "fragile" in that they relate only to that specific selection of 14 action kinds?

Rd: The action judgements for objects beyond body size were not mostly “no”; in fact, there was no significant difference between average action possibilities related to objects beyond (25%) and within (26%). Rather, the dissimilarity between objects within and those beyond likely arose from the difference in most-plausible action set they related. For example, the top three actions related to objects within are “grasp”, “hold” and “throw”, while those related to objects beyond are “sit”, “lift” and “stand”, as stated in our original manuscript: “A further analysis on the affordances separated by the boundary revealed that objects within human body size range were primarily subjected to hand-related actions such as grasping, holding and throwing. These affordances typically involve object manipulation with humans’ effectors. In contrast, objects beyond the size range of human body predominantly afforded actions such as sitting and standing, which typically require locomotion or posture change of the whole body around or within the objects (p 11 ln 229)”.

Regarding the validity of action selection, the selection of the objects and affordances in this study was guided by two key criteria. First, the objects were selected from the dataset published in Konkle and Oliva's study (2011), which systematically investigates the effect of object size on object recognition. Therefore, the range of object sizes, from 14 cm to 7,618 cm, is well-calibrated and represents a typical array of object sizes found in the real world. Second, the actions were selected to cover a wide range of daily humans-objects/environments interactions, from singlepoint movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a sufficiently representative of typic human experiences. In revision, we have clarified these two criteria in the methods section:

(p 22, ln 517) “The full list of objects, their diagonal size, and size rankings were provided in Supplementary Table S6. The objects were selected from the dataset in Konkle and Oliva’s study (2011) to cover typic object sizes in the world (ranging from 14 cm to 7,618 cm), and actions related to these objects were selected to span a spectrum of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the Kinetics Human Action Video Dataset (Kay et al., 2017).”

Having said this, we agree with reviewer that a larger set of objects and actions will facilitate finer localization of the representational discontinuity, which can be addressed in future studies

(p 16, ln 344): “…, due to their impracticality for direct interactions. Future studies should incorporate a broader range of objects and a more comprehensive set of affordances for finer delineation of the representational discontinuity between objects and the environment.”

e) Page 12 "no region showed the congruency effect for objects beyond the body size" in a whole brain analysis. What about a similar analysis for the humanscale objects? We must also keep in mind that with N=12 there may be relatively little power to detect such effects at the random-effects level, so this null finding may not be very informative.

Re: We thank the reviewer for this advice. The whole brain analysis on the congruency effect for human-scale objects (objects within) has now been included in the supplementary materials (please see Author response figure 1d (New Supplementary Fig. S4d) and Author response table 1 (New Supplementary Table S5) below).

Author response image 1.

Significant brain activations of different contrasts in the whole-brain level analysis. a, the effect of object type, positive values (warm color) indicated higher activation for objects within than objects beyond and negative values (cold color) indicated the opposite. b, the effect of congruency, positive values indicated higher activation in congruent than incongruent condition. c, the effect of interaction between object type and congruency, positive values indicated the larger congruency effect for objects within than beyond. d, the congruency effect for objects within. All contrasts were corrected with cluster-level correction at p < .05. The detailed cluster-level results for each contrast map can be found in Supplementary Table S2 to S5.

Author response table 1.

Cortical regions showing significant congruency effect (congruent versus incongruent) for objects within, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected)

Regarding the power of the fMRI study, we would like to clarify that, the critical test of this fMRI study is the two-way interaction of congruency effect by object size instead of the (null) congruency effect for the object beyond. Having said this, we agree that the sample size is small which might lead to lack of power in the fMRI study. In the revision we have now acknowledged this issue explicitly:

(p 16, ln 354) “…supporting the idea that affordance is typically represented only for objects within the body size range. While it is acknowledged that the sample size of the fMRI study was small (12 participants), necessitating cautious interpretation of its results, the observed neural-level affordance discontinuity is notable. That is, qualitative differences in neural activity between objects within the affordance boundary and those beyond replicated our behavior findings. This convergent evidence reinforced our claim that objects were discretized into two broad categories along the continuous size axis, with affordance only being manifested for objects within the boundary.”

f) Page 14 [the fMRI findings] "suggest that affordance perception likely requires perceptual processing and is not necessarily reflected in motor execution". This seems a large leap to make from a relatively basic experiment that tests only a small set of (arbitrarily chosen) objects and actions. It's important to keep in mind too that none of the studies here actually asked participants to interact with objects; that objects were shown as 2D images; and that the differences between real-world sizes of objects were greatly condensed by the way they are scaled for presentation on a computer screen (and such scaling is probably greater for the larger-than-human objects).

Rf: The action-congruency judgement task is widely used in the studies of affordance processing (e.g., Kourtis et al., 2018; Peelen & Caramazza, 2012), so does the practice of not including actual interaction with the objects and using 2D instead of 3D objects (e.g., Peelen & Caramazza, 2012; Matić et al., 2020). However, we are aware that alternative practice exists in the field and we agree that it would be interesting for future studies to test whether actual interactions and 3D objects presentation may bring any change on the affordance boundary observed in our study.

Our inference “affordance perception likely requires perceptual processing and is not necessarily reflected in motor execution” was based on the fMRI finding that the congruency effect only in cortical regions proposedly engaged in perceptual processing, but not in the M1 which is associated with motor execution. This significant two-way interaction pointed to a possibility that affordance processing may not necessarily manifest in motor execution.

We acknowledge the scaling issue inherent in all laboratory experiments, but we doubt that it significantly influenced our results. In fact, it is a common practice in studies on object size to present objects of different physical sizes as constantly sized images on a screen (e.g., Konkle & Oliva, 2012; Huang et al., 2022). Moreover, scaling does not change the smoothness of object sizes, whereas the affordance boundary represents a singularity point that disrupts this smoothness. Finally, regarding the limited variety of objects and actions, please see Rd.

g) Page 15 Why are larger objects "less interesting"? They have important implications for navigation, for example?

Rg: We are sorry for the confusion. Our intention was to express that objects beyond the affordance boundary are generally beyond motor capacities of the animal in question. As such, compared to smaller objects within the environment, these larger objects may not typically be considered as potential targets for manipulation. We have now corrected the wording in the revised text:

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to smaller objects in the environment. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

h) Page 15 At several places I wondered whether the authors were arguing against a straw man. E.g. "existing psychological studies...define objects in a disembodied manner..." but no citations are given on this point, nor do the authors describe previous theoretical positions that would make a strong counter-claim to the one advocated here.

Rh: We are sorry for not presenting our argument clearly. Previous studies often define the object space based on object features alone, such as absolute size or function, without reference to the knowledge and the abilities of the agent (e.g., de Beeck et al., 2008; Konkle & Oliva, 2011). This perspective overlooks the importance of the features of the animal-object pairs. Gibson (1979) highlighted that an object’s affordance, which includes all action possibilities it offers to an animal, is determined by the object’s size relative to the animal’s size, rather than its real-world size. Under this embodied view, we argue that the object space is better defined by the features of the agent-object system, and this is the primary assumption and motivation of the present study. We have now clarified this point and added the references in the revision:

(p 2, ln 35) “A contemporary interpretation of this statement is the embodied theory of cognition (e.g., Chemero, 2013; Gallagher, 2017; Gibbs, 2005; Wilson, 2002; Varela et al., 2017), which, diverging from the belief that size and shape are inherent object features (e.g., de Beeck et al., 2008; Konkle & Oliva, 2011), posits that human body scale (e.g., size) constrains the perception of objects and the generation of motor responses.”

(p 17, ln 365) “Existing psychological studies, especially in the field of vision, define objects in a disembodied manner, primarily relying on their physical properties such as shape (e.g., de Beeck et al., 2008) and absolute size (e.g., Konkle & Oliva, 2011).”

Reviewer #3 (Public Review):

(1) Even after several readings, it is not entirely clear to me what the authors are proposing and to what extent the conducted work actually speaks to this. In the introduction, the authors write that they seek to test if body size serves not merely as a reference for object manipulation but also "plays a pivotal role in shaping the representation of objects." This motivation seems rather vague motivation and it is not clear to me how it could be falsified.

Similarly, in the discussion, the authors write that large objects do not receive "proper affordance representation," and are "not the range of objects with which the animal is intrinsically inclined to interact, but probably considered a less interesting component of the environment." This statement seems similarly vague and completely beyond the collected data, which did not assess object discriminability or motivational values.

Overall, the lack of theoretical precision makes it difficult to judge the appropriateness of the approaches and the persuasiveness of the obtained results. This is partly due to the fact that the authors do not spell out all of their theoretical assumptions in the introduction but insert new "speculations" to motivate the corresponding parts of the results section. I would strongly suggest clarifying the theoretical rationale and explaining in more detail how the chosen experiments allow them to test falsifiable predictions.

R1: We are sorry for the confusion about the theoretical motivation and rationale. Our motivation is on the long-lasting debate regarding the representation versus direct perception of affordance. That is, we tested whether object affordance would simply covary with its continuous constraints such as object size, in line with the representation-free view, or, whether affordance would be ‘representationalized’, in line with the representation-based view, under the constrain of body size. In revision, we have clarified the motivation and its relation to our approach:

In the introduction (p 2, ln 45): “However, the question of how object perception is influenced by the relative size of objects in relation to the human body remains open. Specifically, it is unclear whether this relative size simply acts as a continuous variable for locomotion reference, or if it affects differentiating and organizing object representations based on their ensued affordances.”

In the discussion (p 14, ln 295): “One long-lasting debate on affordance centers on the distinction between representational and direct perception of affordance. An outstanding theme shared by many embodied theories of cognition is the replacement hypothesis (e.g., Van Gelder, 1998), which challenges the necessity of representation as posited by computationalism’s cognitive theories (e.g., Fodor, 1975). This hypothesis suggests that input is discretized/categorized and subjected to abstraction or symbolization, creating discrete stand-ins for the input (e.g., representations/states). Such representationalization would lead to a categorization between the affordable (the objects) and those beyond affordance (the environment). Accordingly, computational theories propose the emergence of affordance perception, in contrast to the perspective offered by embodied theories. The present study probed this ‘representationalization’ of affordance by examining whether affordance perception introduces discontinuity and qualitative dissociation in response to continuous action-related physical features (such as object size relative to the agents), which allows sensorimotor input to be assigned into discrete states/kinds, in line with the representation-based view under the constraints of body size. Alternatively, it assessed whether activity directly mirrors the input, free from discretization/categorization/abstraction, in line with the representation-free view.

First, our study found evidence demonstrating discretization in affordance perception. Then, through the body imagination experiment, we provided causal evidence suggesting that this discretization originates from sensorimotor interactions with objects rather than amodal sources, such as abstract object concepts independent of agent motor capability. Finally, we demonstrated the supramodality of this embodied discontinuity by leveraging the recent advances in AI. We showed that the discretization in affordance perception is supramodally accessible to disembodied agents such as large language models (LLMs), which lack sensorimotor input but can access linguistic materials built upon discretized representations. These results collectively suggest that sensorimotor input undergoes discretization, as implied in the computationalism’s idea of representation. Note that, these results are not contradictory to the claim of the embodied theories, as these representations do shape processes beyond the sensorimotor domain but after discretization.

The observed boundary in affordance perception extends the understanding of the discontinuity in perception in response to the continuity of physical inputs (Harnad, 1987; Young et al., 1997).”

We are also sorry for the confusion about the expression “proper affordance representation”. We intended to express that the neural responses to objects beyond the boundary in the whole brain failed to reflect affordance congruency, and therefore did not show evidence of affordance processing. We have clarified this expression in the revised manuscript:

(p 12, ln 265) “Taken together, the affordance boundary not only separated the objects into two categories based on their relative size to human body, but also delineated the range of objects that evoked neural representations associated with affordance processing.”

Finally, we agree with the reviewer that the expressions, such as “not…inclined to interact” and “probably considered a less interesting component of the environment”, may be misleading. Rather, we intended to express that the objects beyond the affordance boundary is generally beyond the motor capacities of the very animal, being too large for the very animal to manipulated, as comparing to the smaller objects in the environment, may not be a typical target object for manipulation for the animal. We have revised these expressions in the manuscript and clarified their speculative nature:

(p 16, ln 340) “In contrast, objects larger than that range typically surpass the animal’s motor capabilities, rendering them too cumbersome for effective manipulation. Consequently, these larger objects are less likely to be considered as typical targets for manipulation by the animal, as opposed to the smaller objects. That is, they are perceived not as the “objects” in the animal’s eye, but as part of the background environment, due to their impracticality for direct interactions.”

(2) The authors used only a very small set of objects and affordances in their study and they do not describe in sufficient detail how these stimuli were selected. This renders the results rather exploratory and clearly limits their potential to discover general principles of human perception. Much larger sets of objects and affordances and explicit data-driven approaches for their selection would provide a far more convincing approach and allow the authors to rule out that their results are just a consequence of the selected set of objects and actions.

R2: The selection of the objects and affordances in this study was guided by two key criteria. First, the objects were selected from the dataset published in Konkle and Oliva's study (2011), which systematically investigates the effect of object size on object recognition. Therefore, the range of object sizes, from 14 cm to 7,618 cm, is well-calibrated and represents a typical array of object sizes found in the real world. Second, the actions were selected to cover a wide range of daily humans objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the kinetics human action video dataset (Kay et al., 2017). Thus, this set of objects and actions is a sufficiently representative of typic human experiences. In revision, we have clarified these two criteria in the methods section:

(p 22, ln 517) “The full list of objects, their diagonal sizes, and size rankings were provided in Supplementary Table S6. The objects were selected from the dataset in Konkle and Oliva’s study (2011) to cover typic object sizes in the world (ranging from 14 cm to 7,618 cm), and actions related to these objects were selected to span a spectrum of daily humans-objects/environments interactions, from single-point movements (e.g., hand, foot) to whole-body movements (e.g., lying, standing), based on the Kinetics Human Action Video Dataset (Kay et al., 2017).”

Having said this, we agree with reviewer that a larger set of objects and actions will facilitate finer localization of the representational discontinuity, which can be addressed in future studies

(p 16, ln 344): “…, due to their impracticality for direct interactions. Future studies should incorporate a broader range of objects and a more comprehensive set of affordances for finer delineation of the representational discontinuity between objects and the environment.”

(3) Relatedly, the authors could be more thorough in ruling out potential alternative explanations. Object size likely correlates with other variables that could shape human similarity judgments and the estimated boundary is quite broad (depending on the method, either between 80 and 150 cm or between 105 to 130 cm). More precise estimates of the boundary and more rigorous tests of alternative explanations would add a lot to strengthen the authors' interpretation.

R3: We agree with the reviewer that correlation analyses alone cannot rule out alternative explanations, as any variable co-varying with object sizes might also affect affordance perception. Therefore, our study experimentally manipulated the imagined body sizes, while keeping other variable constant across conditions. This approach provided evidence of a causal connection between body size and affordance perception, effectively ruling out alternative explanations. In revision, the rationale of experimentally manipulation of imagined body sizes has been clarified

(p 7, ln 152): “One may argue that the location of the affordance boundary coincidentally fell within the range of human body size, rather than being directly influenced by it. To rule out this possibility, we directly manipulated participants’ body schema, referring to an experiential and dynamic functioning of the living body within its environment (Merleau-Ponty & Smith, 1962). This allowed us to examine whether the affordance boundary would shift in response to changes in the imagined body size. This experimental approach was able to establish a causal link between body size and affordance boundary, as other potential factors remained constant. Specifically, we instructed a new group of participants to imagine themselves as small as a cat (typical diagonal size: 77cm, size rank 4, referred to as the “cat condition”), and another new group to envision themselves as large as an elephant (typical diagonal size: 577 cm, size rank 7, referred to as the “elephant condition”) throughout the task (Fig. 2a).”

Meanwhile, with correlational analysis, precise location of the boundary cannot help ruling out alternative explanation. However, we agree that future studies are needed to incorporate a broader range of objects and a more comprehensive set of affordances. For details, please see R2.

(4) Even though the division of the set of objects into two homogenous clusters appears defensible, based on visual inspection of the results, the authors should consider using more formal analysis to justify their interpretation of the data. A variety of metrics exist for cluster analysis (e.g., variation of information, silhouette values) and solutions are typically justified by convergent evidence across different metrics. I would recommend the authors consider using a more formal approach to their cluster definition using some of those metrics.

R4: We thank the reviewer for the suggestion. We performed three analyses on this point, all of which consistently indicated the division of objects into two distinct groups along the object size axis.

First, a hierarchical clustering analysis of the heatmaps revealed a two-maincluster structure, which is now detailed in the revised methods section (p 25, ln 589) “A hierarchical clustering analysis was performed, employing the seaborn clustermap method with Euclidean distance and Complete linkage (Waskom, 2021).”

Second, the similarity in affordances between neighbouring size ranks revealed the same two-main-cluster structure. In this analysis, each object was assigned a realworld size rank, and then Pearson’s correlation was calculated as the affordance similarity index for each pair of neighbouring size ranks to assess how similar the perceived affordances were between these ranks. Our results showed a clear trough in affordance similarity, with the lowest point approaching zero, while affordance similarities between neighbouring ranks on either side of the boundary remained high, confirming the observation that objects formed two groups based on affordance similarity.

Finally, we analysed silhouette values for this clustering analysis, where 𝑎𝑖 represents the mean intra-cluster distance, and 𝑏𝑖 represents the mean nearest-cluster distance for each data point i. The silhouette coefficient is calculated as (Rousseeuw, 1987):

The silhouette analysis revealed that the maximum silhouette value coefficient corresponded to a cluster number of two, further confirming the two-cluster structure (please see Author response table 2 below).

Author response table 2.

The silhouette values of a k-means clustering when k (number of clusters) = 2 to 10

(5) While I appreciate the manipulation of imagined body size, as a way to solidify the link between body size and affordance perception, I find it unfortunate that this is implemented in a between-subjects design, as this clearly leaves open the possibility of pre-existing differences between groups. I certainly disagree with the authors' statement that their findings suggest "a causal link between body size and affordance perception."

R5: The between-subjects design in the imagination experiment was employed to prevent contamination between conditions. Specifically, after imagining oneself as a particular size, it can be challenging to immediately transition to envisioning a different body size. In addition, participating sequentially participate in two conditions that only differ in imagined body sizes may lead to undesirable response strategies, such as deliberately altering responses to the same objects in the different conditions. The reason of employing the between-subjects design is now clarified in the revised text (p 7, ln 161): “A between-subject design was adopted to minimize contamination between conditions. This manipulation was effective, as evidenced by the participants’ reported imagined heights in the cat condition being 42 cm (SD = 25.6) and 450 cm (SD = 426.8) in the elephant condition on average, respectively, when debriefed at the end of the task.”

Further, to address the concern that “pre-existing differences between groups” would generate this very result, we adhered to standard protocols such as random assignment of participants to different conditions (cat-size versus elephant-size). Moreover, experimentally manipulating one variable (i.e., body schema) to observe its effect on another variable (i.e., affordance boundary) is the standard method for establishing causal relationships between variables. We could not think of other better ways for this objective.

(6) The use of LLMs in the current study is not clearly motivated and I find it hard to understand what exactly the authors are trying to test through their inclusion. As noted above, I think that the authors should discuss the putative roles of conceptual knowledge, language, and sensorimotor experience already in the introduction to avoid ambiguity about the derived predictions and the chosen methodology. As it currently stands, I find it hard to discern how the presence of perceptual boundaries in LLMs could constitute evidence for affordance-based perception.

R6: The motivation of LLMs is to test the supramodality of this embodied discontinuity found in behavioral experiments: whether this discontinuity is accessible beyond the sensorimotor domain. To do this, we leveraged the recent advance in AI and tested whether the discretization observed in affordance perception is supramodally accessible to disembodied agents which lack access to sensorimotor input but only have access to the linguistic materials built upon discretized representations, such as large language models (LLM). The theoretical motivation and rationale regarding the LLM study are now included in the introduction and discussion:

In the introduction (p 2, ln 59) “…, and the body may serve as a metric that facilitates meaningful engagement with the environment by differentiating objects that are accessible for interactions from those not. Further, grounded cognition theory (see Barsalou, 2008 for a review) suggests that the outputs of such differentiation might transcend sensorimotor processes and integrate into supramodal concepts and language. From this perspective, we proposed two hypotheses...”

In the introduction (p 3, ln 70) “Notably, the affordance boundary varied in response to the imagined body sizes and showed supramodality. It could also be attained solely through language, as evidenced by the large language model (LLM), ChatGPT (OpenAI, 2022).”

For details in the discussion, please see R1.

(7) Along the same lines, the fMRI study also provides very limited evidence to support the authors' claims. The use of congruency effects as a way of probing affordance perception is not well motivated. What exactly can we infer from the fact a region may be more active when an object is paired with an activity that the object doesn't afford? The claim that "only the affordances of objects within the range of body size were represented in the brain" certainly seems far beyond the data.

R7: In our study, we followed the established fMRI research paradigm of employing the congruent effect as a measure of affordance processing (e.g., Kourtis et al., 2018). The choice of this paradigm has now been clarified in the revised manuscript (p 11, ln 244): “The congruency effect, derived from the contrast of Congruent versus Incongruent conditions, is a well-established measure of affordance processing (e.g., Kourtis et al., 2018).”

The statement that “only the affordances of objects within the range of body size were represented in the brain” is based on the observed interaction of congruency by object size. In the revised text, we have weakened this statement to better align with the direct implications of the interaction effect (p 1 ln 22): “A subsequent fMRI experiment revealed evidence of affordance processing exclusively for objects within the body size range, but not for those beyond. This suggests that only objects capable of being manipulated are the objects capable of offering affordance in the eyes of an organism.”

(8) Importantly (related to my comments under 2) above), the very small set of objects and affordances in this experiment heavily complicates any conclusions about object size being the crucial variable determining the occurrence of congruency effects.

R8: The objective of the fMRI study was to provide the neural basis for the affordance discontinuity found in behaviour experiments. In other words, the fMRI study is not an exploratory experiment, and therefore, the present object and action sets, which are based on the behaviour experiments, are sufficient.

(9) I would also suggest providing a more comprehensive illustration of the results (including the effects of CONGRUENCY, OBJECT SIZE, and their interaction at the whole-brain level).

R9: We agree and in revision, we have now included these analyses in the supplementary material (p 30, ln 711): “For the whole-brain analyses on the congruency effect, the object size effect, and their interaction, see Supplementary Fig. S4 and Table S2 to S5.” Please see Author response image 2 (New Supplementary Fig. S4) and Author responses tables 3 to 5 (New Supplementary Table S2 to S4) below.

Author response image 2.

Significant brain activations of different contrasts in the whole-brain level analysis. a, the effect of object type, positive values (warm color) indicated higher activation for objects within than objects beyond and negative values (cold color) indicated the opposite. b, the effect of congruency, positive values indicated higher activation in congruent than incongruent condition. c, the effect of interaction between object type and congruency, positive values indicated the larger congruency effect for objects within than beyond. d, the congruency effect for objects within. All contrasts were corrected with cluster-level correction at p < .05. The detailed cluster-level results for each contrast map can be found in Supplementary Table S2 to S5.

Author response table 3.

Cortical regions reaching significance in the contrasts of (A) objects within versus object beyond and (B) objects beyond versus objects within, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected).

Author response table 4.

Cortical regions reaching significance in contrasts of (A) congruent versus incongruent and (B) incongruent versus congruent, whole-brain analysis (R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected).

Author response table 5.

Review Table 5 (New Supplementary Table S4). Cortical regions showing significant interaction between object type and congruency, whole-brain analysis (OW = Objects within, OB = Objects beyond; R = right hemisphere, L = left hemisphere; Z > 2.3, p = 0.05, cluster corrected)

Reviewer #3 (Recommendations For The Authors):

a. >a) Clarify all theoretical assumptions already within the introduction and specify how the predictions are tested (and how they could be falsified).

Ra: Please see R1.

b. >b) Explain how the chosen experimental approach relates to the theoretical questions under investigation (e.g., it is not clear to me how affordance similarity ratings can inform inference about which part of the environment is perceived as more or less manipulable).

Rb: We thank the reviewer for the suggestion, and the theoretical motivation and rationale are now clarified. For details, please see R1.

c. >c) Include a much larger set of objects and affordances in the behavioural experiments (that is more generalizable and also permits a more precise estimation of the boundary), and use a more rigorous methodology to justify a particular cluster solution.

Rc: Please see R2 for the limited variance of objects and actions, and R4 for more analyses on the boundary.

d. >d) Clearly motivate what the use of LLMs can contribute to the study of affordance perception.

Rd: Please see R6.

e) Clearly motivate why congruency effects are thought to index "affordance representation in the brain" Re: Please see R7.

e) Include a much larger set of objects and affordances in the fMRI study.

Re: Please see R7.

f) Consider toning down the main conclusions based on the limitations outlined above.

Rf: We have toned down the main conclusions accordingly.

We are profoundly grateful for the insightful comments and suggestions provided by the three reviewers, which have greatly improved the quality of this manuscript.   References

Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22(4), 637-660.

de Beeck, H. P. O., Torfs, K., & Wagemans, J. (2008). Perceived shape similarity among unfamiliar objects and the organization of the human object vision pathway. Journal of Neuroscience, 28(40), 10111-10123.

Borghi, A. M. (2005). Object concepts and action. Grounding cognition: The role of perception and action in memory, language, and thinking, 8-34.

Colling, L.J. (2021). ljcolling/go-bayesfactor: (Version v0.9.0).Zenodo. doi: 10.5281/zenodo.4642331

Crawley, J. A. H., Mumby, H. S., Chapman, S. N., Lahdenperä, M., Mar, K. U., Htut, W., ... & Lummaa, V. (2017). Is bigger better? The relationship between size and reproduction in female Asian elephants. Journal of Evolutionary Biology, 30(10), 1836-1845.

Ellis, R., & Tucker, M. (2000). Micro‐affordance: The potentiation of components of action by seen objects. British Journal of Psychology, 91(4), 451-471.

Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., ... & Jiang, T. (2016). The human brainnetome atlas: a new brain atlas based on connectional architecture. Cerebral Cortex, 26(8), 3508-3526.

Fodor, J. A. (1975). The Language of Thought (Vol. 5). Harvard University Press.

Gibson, J. J. (1979). The ecological approach to visual perception: Classic edition.

Hertrich, I., Dietrich, S., & Ackermann, H. (2016). The role of the supplementary motor area for speech and language processing. Neuroscience & Biobehavioral Reviews, 68, 602-610.

Huang, T., Song, Y., & Liu, J. (2022). Real-world size of objects serves as an axis of object space. Communications Biology, 5(1), 1-12.

Kantak, S. S., Stinear, J. W., Buch, E. R., & Cohen, L. G. (2012). Rewiring the brain: potential role of the premotor cortex in motor control, learning, and recovery of function following brain injury. Neurorehabilitation and Neural Repair, 26(3), 282-292.

Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., ... & Zisserman, A. (2017). The kinetics human action video dataset. arXiv preprint arXiv:1705.06950.

Konkle, T., & Oliva, A. (2011). Canonical visual size for real-world objects. Journal of Experimental Psychology: human perception and performance, 37(1), 23.

Kourtis, D., Vandemaele, P., & Vingerhoets, G. (2018). Concurrent cortical representations of function-and size-related object affordances: an fMRI study. Cognitive, Affective, & Behavioral Neuroscience, 18, 1221-1232.

Matić, K., de Beeck, H. O., & Bracci, S. (2020). It's not all about looks: The role of object shape in parietal representations of manual tools. Cortex, 133, 358-370.

McDannald, D. W., Mansour, M., Rydalch, G., & Bolton, D. A. (2018). Motor affordance for grasping a safety handle. Neuroscience Letters, 683, 131-137.

NCD Risk Factor Collaboration (NCD-RisC). (2016). A century of trends in adult human height. Elife, 5, e13410.

Peelen, M. V., & Caramazza, A. (2012). Conceptual object representations in human anterior temporal cortex. Journal of Neuroscience, 32(45), 15728-15736.

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.

Sakreida, K., Effnert, I., Thill, S., Menz, M. M., Jirak, D., Eickhoff, C. R., ... & Binkofski, F. (2016). Affordance processing in segregated parieto-frontal dorsal stream sub-pathways. Neuroscience & Biobehavioral Reviews, 69, 89-112.

Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21(5), 615-628.

Wagenmakers, E.-J., Wetzels, R., Borsboom, D. & van der Maas, H. L. J. Why psychologists must change the way they analyze their data: the case of psi: Comment on Bem (2011). Journal of Personality and Social Psychology, 100(3), 426–432.

Zhen, Z., Yang, Z., Huang, L., Kong, X. Z., Wang, X., Dang, X., ... & Liu, J. (2015). Quantifying interindividual variability and asymmetry of face-selective regions: a probabilistic functional atlas. NeuroImage, 113, 13-25.

Author Response

Public reviews:

Reviewer 1:

Weaknesses:

While I generally agree with the author's interpretations, the idea of Saccorhytida as a divergent, simplified off-shot is slightly contradictory with a probably non-vermiform ecdysozoan ancestor. The author's analyses do not discard the possibility of a vermiform ecdysozoan ancestor (importantly, Supplementary Table 4 does not reconstruct that character),

Reply: Thanks for the comments. Saccorhytids are only known from the early Cambrian and their unique morphology has no equivalent among any extinct or extant ecdysozoan groups. This prompted us to consider them as a possible dead-end evolutionary off-shot. The nature of the last common ancestor of ecdysozoan (i.e. a vermiform or non-vermiform animal with capacities to renew its cuticle by molting) remains hypothetical. At present, palaeontological data do not allow us to resolve this question. The animal in Fig. 4b at the base of the tree is supposed to represent an ancestral soft-bodied form with no cuticle from which ecdysozoan evolved via major innovations (cuticular secretion and ecdysis). Its shape is hypothetical as indicated by a question mark. Our evolutionary model is clearly intended to be tested by further studies and hopefully new fossil discoveries.

and outgroup comparison with Spiralia (and even Deuterostomia for Protostomia as a whole) indicates that a more or less anteroposteriorly elongated (i.e., vermiform) body is likely common and ancestral to all major bilaterian groups, including Ecdysozoa. Indeed, Figure 4b depicts the potential ancestor as a "worm". The authors argue that the simplification of Saccorhytida from a vermiform ancestor is unlikely "because it would involve considerable anatomical transformations such as the loss of vermiform organization, introvert, and pharynx in addition to that of the digestive system". However, their data support the introvert as a specialisation of Scalidophora (Figure 4a and Supplementary Table 4), and a pharyngeal structure cannot be ruled out in Saccorhytida. Likewise, loss of an anus is not uncommon in Bilateria. Moreover, this can easily become a semantics discussion (to what extent can an animal be defined as "vermiform"? Where is the limit?).

Reply: We agree with you that “vermiform” is an ill-defined term that should be avoided. “Elongated” might be a better term to designate the elongation of the body along the antero-posterior axis. Changes have been made in the text to solve this semantic problem. Priapulid worms or annelids are examples of extremely elongated, tubular animals. In saccorhytids, the antero-posterior elongation is present (as it is in the vast majority of bilaterians) but extremely reduced, Saccorhytus and Beretella having a sac-like or beret-shape, respectively. That such forms may have derived from elongated, tubular ancestors (e.g. comparable with scalidophoran worms) would require major anatomical transformations that have no equivalent among modern animals. We agree that further speculation about the nature of these transformations is unnecessary and should be deleted simply because the nature of these ancestors is purely hypothetical. We also agree that the loss of anus and the extreme simplification of the digestive system is common among extant bilaterians. The single opening seen in Saccorhytus and possibly Beretella may result from a comparable simplification process. In Figure 4b, the hypothetical pre-ecdysozoan animal is slightly elongated (antero-posterior axis and polarity) but in no way comparable with a very elongated and cylindrical ecdysozoan worm (e.g. extant or extinct priapulid).

Therefore, I suggest to leave the evolutionary scenario more open. Supporting Saccorhytida as a true group at the early steps of Ecdysozoa evolution is important and demonstrates that animal body plans are more plastic than previously appreciated. However, with the current data, it is unlikely that Saccorhytida represents the ancestral state for Ecdysozoa (as the authors admit), and a vermiform nature is not ruled out (and even likely) in this animal group. Suggesting that the ancestral Ecdysozoan might have been small and meiobenthic is perhaps more interesting and supported by the current data (phylogeny and outgroup comparison with Spiralia).

Reply: We agree the evolutionary scenario should be more open, especially the evolutionary process that gave rise to Saccorhytida. Again, we know nothing about the morphology of the ancestral ecdysozoan (typically the degree of body elongation, whether it had a differentiated introvert or not, whether it had a through gut or not). Simplification appears as one possible option, but which assumes that the ancestral ecdysozoan was an elongated animal with a through gut. Changes will be made in Fig.4A accordingly. Alternatively, the ancestral ecdysozoan might have been small and meiobenthic.

Reviewer 2:

Weaknesses:

The preservations of the specimens, in particular on the putative ventral side, are not good, and the interpretation of the anatomical features needs to be tested with additional specimens in the future. The monophyly of Cycloneuralia (Nematoida + Scalidophora) was not necessarily well-supported by cladistic analyses, and the evolutionary scenario (Figure 4) also needs to be tested in future works.

Reply: Yes, we agree that our MS is the first report on an enigmatic ecdysozoan. Whereas the dorsal side of the animal is well documented (sclerites), uncertainties remain concerning its ventral anatomy (typically the mouth location and shape). Additional better-preserved specimens will hopefully provide the missing information. Concerning Cycloneuralia, their monophyly is generally better supported by analyses based on morphological characters than in molecular phylogenies. I

Reviewer 3:

Weaknesses: I, as a paleontology non-expert, experienced several difficulties in reading the manuscript. This should be taken into consideration when assuming a wide range of readers including non-experts.

Reply: We have ensured that the text is comprehensible to biologists. Our main results are summarized in relatively simple diagrams (e.g. Fig. 4). We are aware that technical descriptive terms may appear obscure to non-specialists. However, we think that our text-figures help the reader to understand the morphology of these ancient animals.

Author Response

eLife assessment

The manuscript explores the ways in which the genetic code evolves, specifically how stop codons are reassigned to become sense codons. The authors present phylogenetic data showing that mutations at position 67 of the termination factor are present in organisms that nevertheless use the UGA codon as a stop codon, thereby questioning the importance of this position in the reassignment of stop codons. Alternative models on the role of eRF1 would reflect a more balanced view of the data. Overall, the data are solid and these findings will be valuable to the genomic/evolution fields.

Public Reviews:

Reviewer #1 (Public Review):

The issue:

The ciliates are a zoo of genetic codes, where there have been many reassignments of stop codons, sometimes with conditional meanings which include retention of termination function, and thus > 1 meaning. Thus ciliate coding provides a hotspot for the study of genetic code reassignments.

The particular issue here is the suggestion that translation of a stop (UGA) in Blastocritihidia has been attributed to a joint change in the protein release factor that reads UGA's and also breaking a base pair at the top of the anticodon stem of tRNATrp (Nature 613, 751, 2023).

The work:

However, Swart, et al have looked into this suggestion, and find that the recently suggested mechanism is overly complicated.

The broken pairing at the top of the anticodon stem of tRNATrp indeed accompanies the reading of UGA as Trp as previously suggested. It changes the codon translated even though the anticodon remains CCA, complementary to UGG. A compelling point is that this misreading matches previous mutational studies of E coli tRNA's, in which breaking the same base pair in a mutant tRNATrp suppressor tRNA stimulated the same kind of miscoding.

This is a fair characterization, and we would also note the additional positive aspect: that we observed there is consistency in the presence of 4 bp tRNA-Trp anticodon stems in those ciliates which translate UGA as tryptophan, and generally 5 bp anticodon stems in those that do not (including Euplotes with UGA=Cys).

But the amino acid change in release factor eRF1, the protein that catalyzes termination of protein biosynthesis at UGA is broadly distributed. There are about 9 organisms where this mutation can be compared with the meaning of UGA, and the changes are not highly correlated with a change in the meaning of the codon. Therefore, because UGA can be translated as Trp with or without the eRF1 mutation, Swart et al suggest that the tRNA anticodon stem change is the principal cause of the coding change.

We do think multiple lines of evidence support the shorter tRNA anticodon stem promoting UGA translation, but also think other changes in the translation system may be important. For instance, structural studies suggest interaction of ribosomal RNA with extended stop codons (particularly the base downstream of the triplet) during translation termination (Brown et al. 2015, Nature). As we noted, previous studies have sought to correlate individual eRF1 substitutions with genetic code changes, but the proposed correlations have invariably disappeared once new tranches of eRF1 sequences and alternative genetic codes for different species became available. This is why we concluded that there needs to be more focus on obtaining and understanding molecular structures during translation termination, particularly in the organisms with alternative codes.

The review:

Swart et al have a good argument. I would only add that eRF1 participation is not ruled out, because finding that UGA encodes Trp does not distinguish between encoding Trp 90% of the time and encoding it 99% of the time. The release factor could still play a measurable quantitative role, but the major inference here seems convincing.

We agree that eRF1 may participate and compete with the tRNA, but we question the hypothesis that the particular amino acid position/substitution proposed by Kachale et al. 2023 is the key. There is experimental evidence in the form of Ribo-seq for the ciliate Condylostoma magnum (A67), which does appear to efficiently translate UGA sense codons (Swart et al. 2016, Figure S3: https://doi.org/10.1016/j.cell.2016.06.020): we observed no dip in ribosome footprints downstream of these codons, as there would be in the case of classical translational readthrough in standard genetic code organisms (which is usually relatively inefficient - certainly well below 50% of upstream translation from our reading of the literature). Ribo-seq also supports efficient termination at those Condylostoma UGA codons that are stops.

Of course, the entire translation system may have evolved to be as efficient as what we currently observe, and it is not unreasonable to consider that it may have been less efficient in the past. However, not so inefficient that the error rate incurred would have been strongly deleterious. Importantly also, we believe the role of multiple eRF1 paralogs in translation termination in the ciliates really needs to be investigated, given that translation is inherently probabilistic with any of these proteins potentially being incorporated into the ribosome.

Reviewer #2 (Public Review):

The manuscript raises interesting observations about the potential evolution of release factors and tRNA to readdress the meaning of stop codons. The manuscript is divided into two parts: The first consists of revealing that the presence of a trp tRNA with an AS of 5bp in Condylostoma magnum is probably linked to contamination in the databases by sequences from bacteria. This is an interesting point which seems to be well supported by the data provided. It highlights the difficulty of identifying active tRNA genes from poorly annotated or incompletely assembled genomes.

We will consider adding subheadings in revising the manuscript to make the structure more explicit, as it really has three parts to it, with the third largely in the supplement. The “good” was that there is a range of support for the 4 bp AS stem, with new evidence we supplied from ciliates and older studies with E. coli tRNAs. The “bad” is that scrutiny of eRF1 sequences, with the addition of ones we provided, contradicts the hypothesis by Kachale et al. that a S67A/G substitution is necessary for genetic code evolution in Blastocrithidia and certain ciliates. The “ugly” is that a tRNA shown in a main figure in Kachale et al. 2023, and which was investigated in a number of subsequent experiments, is almost certainly a bacterial contaminant.

Proper scrutiny of the bacterial tRNA should have led to its immediate recognition and rejection, as one of us did years ago in searches of tRNAs in a preliminary Condylostoma genome assembly (only predicted 4 bp AS tRNA secondary structures were shown in Swart et al. 2016, Fig S4B and C). Evidence for the bacterial nature of this tRNA was placed in the supplement of the present manuscript, as the meat of the critique was the consideration of the evidence for and against its good and bad aspects. The bacterial tRNA secondary structure has been removed from the main figure by Kachale et al. 2023, and downstream experiments based on synthetic constructs for this tRNA have also been revised (https://www.nature.com/articles/s41586-024-07065-0).

Much of the rest of the supplement served to correct multiple errors in genetic codes in public sequence databases that led to additional errors and difficulties in interpreting the eRF1 substitutions in Kachale et al. 2023. It is important that these codes get corrected. If not they create multiple headaches for users besides those investigating genetic codes, as we found out in communications with authors and a colleague of Kachale et al. 2023 (in particular, leading to thousands of missing genes in the macronuclear genome of the standard code ciliate Stentor coeruleus that were removed in automated GenBank processing due to incorrectly having an alternative genetic code specified).

Recently the NCBI Genetic Codes curators reinstated a genetic code incorrectly attributed to the ciliate Blepharisma (“Blepharisma nuclear genetic code”) (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi#SG15), despite us requesting a reasonable fix years ago. This would be very confusing for those that are not in the know. We have explained this confusion in our supplement too. Thus we also hope that this paper will aid in communication with the genetic code database curators and in correcting such issues.

The second part criticises the fact that a mutation at position S67 of eRF1 is required to allow the UGA codon to be reassigned as a sense codon. As supporting evidence, they provide a phylogenetic study of the eRF1 factor showing that there are numerous ciliates in which this position is mutated, whereas the organism shows no trace of the reassignment of the UGA codon into a sense codon. While this criticism seems valid at first glance, it suffers from the lack of information on the level of translation of UGA codons in the organisms considered.

Firstly, we not only showed that there are organisms with the S67 substitution but no UGA reassignment, but also provided evidence for the converse: organisms with a UGA=Trp reassignment but without the S67 substitution (both ciliates and a non-ciliate). So, two related lines of substitutions were not consistent with the eRF1 substitution hypothesis proposed.

Secondly, we disagree that there is a “lack of information about UGA translation in the organisms considered”. Evolution has already supplied information as to whether UGA codons are translated at an appreciable level in the organisms of interest, in the form of codon frequencies within their protein-coding sequences and those ending them. If UGA was translated at appreciable levels, it would be found at a corresponding frequency in coding sequences. In genomes with thousands of genes, if not predicted as amino acids, they likely primarily serve as stops. Low levels of potential readthrough of actual stops would not change the arguments. With the exception of selenocysteine translation (which is restricted to a limited number of genes by the condition of requiring a specific mRNA secondary structure) there is no expectation of meaningful levels of UGA translation when this codon is missing from the bulk of coding sequences (CDSs).

This is well illustrated by the heterotrichs, a clade of ciliates that use a variety of genetic codes. In heterotrichs that use the standard code, UGA is virtually absent from coding sequences, only appearing at the 3’ end of transcripts in the predicted stop codon and 3’-UTR (Seah et al. 2022, Figure 5). This contrasts notably with other genera like Blepharisma where appreciable levels of UGA codons occur throughout coding sequences, upstream of the predicted UAA and UAG stops (Seah et al. 2022, Figure 5: https://www.biorxiv.org/content/biorxiv/early/2022/07/12/2022.04.12.488043/F5.large.jpg). The difference in the UGA, UAG and UAA codon frequencies in 3’ UTRs compared to the upstream frequencies in CDSs of standard genetic code heterotrichs is stark. Frequencies of all three codons are elevated in the 3’ UTRs of all heterotrich ciliates, irrespective of their genetic codes (Seah et al. 2022, Figure 5), according with these codons not being deleterious in this region and strongly selected against upstream, within CDSs.

The reviewer raises the possibility that UGA may appear to be a stop codon but still have biologically significant translational readthrough. We think that this is unlikely in the heterotrich ciliate species discussed here, which have extremely short (median 21-26 bp) and AU-rich 3’-UTRs compared to yeast and animals (Seah et al. 2022). Therefore, in heterotrichs where UGA is predicted to be a stop, translational readthrough would lead to extensions of only a few amino acids and be relatively inconsequential, as there are plenty of secondary UAA, UAG and UGA codons downstream of the typical stop.

If one were to consistently pursue the reviewer’s line of argumentation, one would also have to argue against the very reasoning used in Kachale et al. 2023 about all the stop codon predictions/reassignments in protists for which experiments were not conducted in S. cerevisiae or other translation systems, as well as decades of prior work using sequence conservation in multiple sequence alignments to infer alternative genetic codes.

Furthermore, experimental information for UGA translation levels is available for the ciliate Condylostoma magnum, predominantly in the form of Ribo-seq (Swart et al. 2016). Similarly to Condylostoma’s UAA and UAG codons, Ribo-seq shows that the UGA codons are generally either efficiently translated when present in the bodies of CDSs or terminate translation as actual stops close to mRNA 3’ termini/poly(A) tails (Swart et al. 2016). Thus, irrespective of the presence of the hypothesized eRF1 substitution there is an example of relatively discrete reading of UGA codons in ciliates as either stops or amino acids. This contrasts with Kachale et al 2023’s experiments in yeast with yeast eRF1 S67G or Blastocrithida eRF1 which also has glycine at the equivalent position that appear to lead to modest readthrough. In addition, efficient reading of codons in either of two ways also occurs in the ciliate genus Euplotes in which “stop” codons can either serve as frameshift sites during translation within coding sequences or be actual stops when they are close to 3’ mRNA termini (Lobanov et al. 2017), as verified by Ribo-seq and protein mass spectrometry.

It has been clearly shown that S67G or S67A mutations allow a strong increase in the reading of UGA codons by tRNAs, so this point is not in doubt. However, this has been demonstrated in model organisms, and we now need to determine whether other changes in the translational apparatus could accompany this mutation by modifying its impact on the UGA codon. This is a point partly raised at the end of the manuscript.

There is no doubt that S67G or S67A mutations lead to increased translational readthrough, but this is restricted to experiments with or in baker’s yeast or other standard genetic code surrogate model organisms. Experiments introducing eRF1 sequences from alternative genetic code eukaryotes into translation systems of such standard genetic code eukaryotes are not compelling because the rest of the associated translation system has also evolved tremendously. As far as we are aware, no in vivo experiments with ciliate eRF1s have been conducted to determine if position 67 or other substitutions have any effect. These considerations are critical given the vast evolutionary distances between yeasts, Blastocrithidia, the ciliates and Amoebophrya sp. ex Karlodinium veneficum. On the other hand, the evolutionary information presented contradicts the importance of this substitution in the Amoebophyra species and ciliates. We will consider how to incorporate these ideas in the revised version of the manuscript.

Indeed, it is quite possible that in these organisms the UGA codon is both used to complete translation and is subject to a high level of readthrough. Actually, in the presence of a mutation at position 67 (or elsewhere), the reading of the UGA can be tolerated under specific stress conditions (nutrient deficiency, oxidative stress, etc.), so the presence of this mutation could allow translational control of the expression of certain genes.

As explained a couple replies above, it is not constructive to invoke the additional complexity of conditional translation or any other kinds of factors that lead to enhanced readthrough, because the translation of UGA sense codons in the ciliate Condylostoma, where we have supporting experimental evidence, does not resemble translational readthrough. These codons occur in constitutively expressed single-copy genes, like a tryptophan tRNA synthetase and an eRF1 protein (Swart et al. 2016), not ones that might be expected to be conditionally translated.

On the other hand, it seems obvious to me that there are other ways of reading through a stop codon without mutating eRF1 at position S67. So the absence of a mutation at this position is not really indicative of a level of reading of the UGA codon.

It may seem obvious to the reviewer, but that is neither what Kachale et al. originally proposed nor what we questioned. Kachale et al. hypothesized that mutation of S67 to A or G is necessary for UGA=Trp translation, but we provided evidence that it is not: multiple organisms with S67 or C67 that translate UGA as tryptophan. Kachale et al. also originally suggested that the S67 to A/G substitution is also necessary in Condylostoma for UGA translation as tryptophan by weakening its recognition of this codon as a stop (from their abstract: “Virtually the same strategy has been adopted by the ciliate Condylostoma magnum.”). However, as we have stated, Condylostoma (A67) is both able to efficiently terminate at UGA stop codons and to efficiently translate (other) UGA sense codons, which does not fit this hypothesis.

Before writing such a strong assertion as that found on page 3, experiments should be carried out. The authors should therefore moderate their assertion.

Experiments should be carried out in the organisms in which stop codon reassignments have readily occurred and their close relatives that have not, not distantly related ones where they rarely, if ever, occur, like yeasts. We made this point in the conclusion. There is too much emphasis on models for investigation of genetic code evolution via stop codon reassignments in questionable models and too little investigation in the really good ones, particularly the ciliates. This clade has genera that are amenable to molecular experiments including Paramecium, Tetrahymena and Oxytricha. We plan to add some text about these considerations in revision.

To make a definitive conclusion, we would need to be able to measure the level of termination and readthrough in these organisms. So, from my point of view, all the arguments seem rather weak.

We reiterate: there is experimental information about translation and termination in two ciliate species worth considering, including one that translates UGA codons depending on their context. If one chooses to ignore the evolutionary information presented, this not only ignores all prior approaches to infer genetic codes, but also the fact that there is experimental verification and other lines of evidence supporting these approaches.

Moreover, the authors themselves indicate that the conjunction between a Trp tRNA that is efficient at reading the UGA codon and an eRF1 factor that is not efficient at recognising this stop codon could be the key to reassignment.

This does not convey well what we wrote, since the main consideration was overall eRF1 structure, rather than individual amino acid substitutions. Here are the key sentences:

“Instead, in a transitional evolutionary phase, codons may be interpreted in two ways, with potential eRF1-tRNA competition. With time, beneficial mutations or modifications in either the tRNA or eRF1 (or other components of translation) that reduce competition may be selected.

Instead of focusing on individual eRF1 substitutions, we propose future investigations should more generally explore the structure of non-standard genetic code eRF1’s captured in translation termination in the context of their own ribosomes.”

I think this collaborative problem-solving dynamic not only fosters a sense of community but also demonstrates the versatility of social media platforms as spaces for collective creativity. It goes beyond mere individual expression and taps into the collective intelligence of the user base. By identifying a problem or challenge, users can come together to contribute unique perspectives, skills, or talents, ultimately leading to the co-creation of content that may not have been possible without the collaborative efforts of the community.

Author Response

The authors' responses to the public reviews can be found here

The following is the authors’ response to the most recent recommendations.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I appreciate the effort that the authors have put into this revised version of the manuscript. Before going into details, I would suggest that, in the future, the authors include enough information in their response to allow reviewers to follow the changes made. Not simply "Fixed", but instead "we have modified the description of these results and now state on lines XXX to XXX (revised text)".

We greatly apologize, we certainly did not wish to cause more work for the reviewer to find the necessary changes. We will list the line number and our changes in the following response.

The authors' response to my comments was confined to the minor points, with no attention to more important questions regarding speculations about mechanism which were (and still are) presented as factual conclusions. I do not consider the responses adequate.

We responded to each of your comments and where we disagree, we have explained in detail.

With respect to the meaning of "above" and "below" in the context of an intracellular organelle, I think that referring to up and down in a figure is fine, provided that the cytoplasmic and luminal sides are indicated in that figure. I think that labeling to that effect in each figure would be immensely helpful for the reader.

We agree with this point and have updated all the figures to include these labels.

The statement on lines 333-335 about non-competitive inhibition is a bit naïve. The only thing ruled out by this type of inhibition is that substrate and TBZ binding do not share the same binding process, in which case they would compete. It doesn't show that TBZ gets to its binding site from the lumen or from the bilayer, or by any other process that isn't shared with substrate. It also doesn't rule out kinetic effects, such as slow inhibitor dissociation, that result in non-competitive kinetics. Please rewrite this sentence to indicate that one explanation of the non-competitive nature of TBZ inhibition would be that TBZ diffuses into the vesicle and binds from the lumen. It's not the only explanation.

We have changed this sentence lines 334-336 to be more speculative and not include any statement about non-competitive inhibition. Please see, “Studies have proposed that TBZ first enters VMAT2 from the lumenal side, binding to a lumenal-open conformation.”

The revised version integrates the MD simulations into a plausible mechanism for luminal release of substrate. A key element in this mechanism is the protonation of D33, E312 and D399, which allows substrate to leave following water entry into the binding site. The acidic interior of synaptic vesicles should facilitate such protonation, but the fate of those protons needs to be considered. Are any of them predicted to dissociate prior to the return to a cytoplasm-facing conformation? If so, are all 3 released in that conformation? Postulating protonation events at one point in the reaction cycle requires some accounting for those protons - or at least recognition of the problem of reconciling their binding with the known stoichiometry of VMAT.

We completely agree with this point and while we cannot account for all protons with a single structure and simulation of neurotransmitter release, some discussion of the fate of the protons is warranted. We have included a highly speculative statement in the discussion on this point, see lines 462-465, “Given the known transport stoichiometry of two protons per neurotransmitter, we speculate that two protons may dissociate back into the lumen, perhaps driven by the formation of salt bridges between D33 and K138 or R189 and E312 for example in an cytosol-facing state.”

Reviewer #3 (Recommendations For The Authors):

On page 13, line 238, the statement "The protonation states of titratable residues D33, E312, D399, D426, K138 and R189, which are in close proximity to TBZ, also impact its binding stability (Table 4)" is misleading. Table 4 only shows that D426 is charged and what the pKa values are. This should be rephrased to separate out which residues are in close proximity from what is known about how their protonation states affect TBZ stability.

We agree with this statement and have rephrased this on line 290-294 on page 13 to read, “Several titratable residues, including D33, E312, D399, D426, K138, and R189, line the central cavity of VMAT2 and impact TBZ binding stability (Table 4). We found that maintaining an overall neutral charge within the TBZ binding pocket, as observed in system TBZ_1, most effectively preserves the TBZ-bound occluded state of VMAT2. Residues R189 and E312 in particular are within close proximity of TBZ and participate directly in binding.” We note that given the acidic pH of the vesicle lumen (5.5), it is likely all four residues may be protonated to a significant degree in this state.

Typos:

• luminal is another name for the drug generically known as phenobarbital, lumenal means in the lumen. (This typo seems to have crept into the published literature now too).

Thank you for pointing this out. Indeed, we had considered carefully whether to use ‘lumenal’ or ‘luminal’ in our revised text. In fact, both are used interchangeably throughout the scientific literature and luminal is the more commonly used term. Please also see: https://www.merriam-webster.com/medical/luminal we do agree that there may be confusion because ‘Luminal’ is a trademark of phenobarbital. Therefore, we have changed the text to read ‘lumenal’ throughout.

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

Reviewer #1 (Recommendations For The Authors):

I congratulate the authors on this study, which I enjoyed reading. Overall, the study reports a novel and exciting new structure for a member of the SLC18 family of vesicular monoamine transporters. Associated MD, binding and transport assays provide support for the hypothesis and firm up the modelled pose for the TBZ drug. The main strengths of the study largely sit with the structure, which, as the authors say, provides additional and essential insights above those available from AF2. The structures also reveal several potentially interesting observations concerning the mechanism of gating and proton-driven transport. The main weakness lies in the limited mutational data and studies into the role of pH in regulating ligand binding. As detailed below, my main comment would be to spend a little extra time expanding the mutational data (perhaps already done during the review?) to enable more evidence-based conclusions to be drawn.

We thank reviewer #1 for their helpful comments and suggestions. We agree that mutational analysis specifically of neurotransmitter transport would strengthen the mechanistic conclusions of the work. We also agree with reviewer #1 and #3 that the role of pH and the protonation state of charged residues was a weakness in the first version of the manuscript. Therefore, we have expanded our mutational and computational data as detailed below and we believe that this has further solidified our findings.

Specific comments & suggestions:

It is an interesting strategy to fuse the mVenus and anti-GFP nanobody to the N-/C-termini. The authors should also include in SI Fig. 1 a full model for the features observed in these maps and deposit this in the PDB.

Great point, we have made a main text panel describing the construct. Figure S1 includes a full description of the construct. The reviewer will note that the PDB entry contains the entire amino acid sequence of the construct and while the GFP and GFP-Nb cannot be well modeled into the density, we have included all of the relevant information for the reader.

Difficult to make out the ligand in Fig. 2b, I would suggest changing the color of the carbon atoms.

Fixed.

It is difficult to make out the side chains in ED Fig. 5d.

This is now its own supplemental figure and is presented larger.

ED Figures are called out of order in the manuscript. For example, in line 143 ED Fig.6 is called before ED Fig. 5d (line 152), and then ED 5d is called before ED 5a. This makes it rather confusing to follow the description, analysis, and data when reading the paper. Although there are other examples. I would suggest trying to order the figure callouts to flow with the narrative of the study.

Agreed. Fixed.

It wasn't clear to me what the result was produced by just imaging the ligand-free chimaera protein. It would be useful to say whether this resulted in low-resolution maps and whether the presence of the TBZ compound was essential for high-resolution structure determination.

The ligand is likely required for structure determination. We have not, however, made such a statement largely because we have yet to determine an apo reconstruction.

The role of E127 and W318 on EL1 in gating the luminal side of the transporter is very intriguing. As the authors suggest, this may represent an atypical gating mechanism for the MFS (line 182). I did wonder if the authors had considered providing more insight into this potentially novel mechanism. Additional experiments would be further mutations of W318 to F, Y, V, and I to see if they can identify a non-dead variant that could be analysed kinetically. They may have more luck with variants of E127, as they suggest this stabilises W318. If these side chains are important for gating and transport regulation, one might expect to see interesting effects on the transport kinetics.

This is a fantastic suggestion. We have done this, and we think that the reviewer will find the results to be quite interesting. Some VMAT2 sequences have an R or an H at position 318 while VPAT has an F at the equivalent position. We have made these mutants including the E127A mutant and analyzed them using TBZ binding and transport experiments. Interestingly the W318R, H, and F mutants preserve activity in varying degrees with the R mutant closely resembling wild type. W318A has no transport activity. Only the W318F mutant retains some TBZ binding. The E127A mutant also has little transport activity but nearly wild type like TBZ binding which we believe suggests a role for this residue also in stabilizing W318.

The authors identify an interesting polar network, which is described in detail and shown in Fig. 2d. However, the authors present no experimental data to shed further mechanistic insight into how these side chains contribute to monoamine transport or ligand binding. Additional experiments that would be helpful here might include repeating the binding and competition assays shown in Fig. 1c under different pH conditions for the WT and different mutations of this polar network. At present, this section of the manuscript is very descriptive without providing much novel insight into the mechanism of VMAT transport. I did wonder whether a similar analysis of pH effects on DTBZ binding might also provide insight into the role of E312 and the role of protons in the mechanism.

Thank you, we have addressed this point in several different ways. The first is that many of these residues have already been characterized in several earlier studies, see refs 31, 32, and 42 and we have incorporated this into our discussion where appropriate. With respect to E312, the reviewers’ comments are again very appropriate. We have addressed this using computational experiments exploring the protonation status of E312 and other residues as well as TBZ. Our simulations and Propka calculations clearly show that E312 must be protonated and TBZ must be deprotonated to maintain TBZ binding. We have also extended these computational studies toward understanding the protonation status of residues which orchestrate dopamine binding and release.

The authors then describe the binding pose for TBZ. This section also provides some biochemical characterisation of the binding site, in the form of the binding assay introduced in Fig. 1. However, the insights are again somewhat reduced as the mutants were chosen to show reduced binding. Could the authors return to this assay and try more conservative mutations of the key side chains to illuminate more detail? For example, does an R189K mutant still show binding but not transport? Similarly, what properties does an E312D have? The authors speculate that K138 might play a role in coupling ligand binding/transport to the protonation, possibly through an interaction with D426 and D33 (line 236). Given the presence of D33 in the polar network described previously, I was left wondering how this might occur. I feel that some of the experiments with pH and conservative mutants might shed some light on this important aspect. Please label the data points in Fig. 3d.

Indeed, alanine mutants at these positions while valuable do not provide the level of detailed insight into mechanism that we also would have liked to obtain. Thus, we have made more conservative and targeted mutants like the R189K mutant and various mutants at N34 for example and tested them in both transport and binding assays. We have also made a mutant at K138 and found that it is not transport competent or able to bind TBZ to a significant degree. With respect to labels and color codes, we have made the color codes consistent between the bar graphs and the curves. We have also labeled the data points in the figure legends.

The manuscript currently doesn't present a hypothesis for how TBZ induces the 'dead-end' complex compared to physiological ligands. Does the MD shed any light on this aspect of the study? If the authors place the physiological ligand in the same location as the TBZ and run the simulation for 500ns, what do they observe? 100ns is also a very short time window. I appreciate the comment about N34 in line 303, but is this really the answer? It would be very interesting to provide more evidence on this important aspect of VMAT pharmacology.

MD with a natural ligand (dopamine) provides substantial insight into why TBZ is a dead-end complex. Since water cannot penetrate into the binding site in the TBZ bound complex, this does not allow for substantial luminal release. In contrast, simulations conducted in the presence of DA bound to the occluded VMAT2 show the propensity of that structure to accommodate an influx of water molecules that promote the release of DA to the lumen. The new results are illustrated in Figure 5 (main text) as well as supplemental figure 8 panels d-h. The new simulations further emphasized the importance of the protonation state of acidic residues near the substrate-binding pocket.

Reviewer #2 (Recommendations For The Authors):

Line 68, "both sides of the membrane" -> "alternately to either side of the membrane".

Fixed. Thanks.

Transmembrane proteins in intracellular organelles present unique issues of nomenclature. I suggest the authors refer to cytoplasmic and luminal faces of the protein (not intracellular or extracellular (line 124)) and adhere to these names to avoid confusion. This creates problems for loops called IL and EL, but they could be defined on first use.

We agree with this point and had initially gone with the conventional definitions used in the literature. We have now changed this throughout the text to be luminal and cytosolic.

Lines 135-6, are these residue numbers correct? The pdb file lists 126 as Asp and 333 as Ala.

Thank you. This is fixed.

ED Fig. 6 is not clear. A higher-resolution figure is needed.

We have updated this figure and hope that the reviewer will find it to be much clearer.

Lines 158-9, Is there any data to support effects on dynamics or folding? If not, please indicate that this is speculation.

Fixed.

Line 174, Should "I315" be "L315"?

Fixed.

Line 179, Please indicate what is meant by "inner" and "below" (also lines 183 and 258).

We have added Figure calls here where needed.

Line 192, S197 is listed as part of polar network 1, but not discussed further. Is it actually involved, or just in the neighborhood?

It is part of the network, but we did not discuss in further detail because we do not have data indicating its precise function and thus have left this as a description.

Line 199, E312, and N388 are fairly distant from each other. Do you want to clarify why they represent a network?

While they are not within hydrogen bonding distance, we nevertheless include them as part of the same network because they may come into closer proximity in a different conformational state.

Line 206, Protonation of all 3? VMAT2 doesn't transport 3 protons per cycle. Please clarify.

We believe that these residues may be protonated, but they may not necessarily all be involved in proton transport.

Line 219, Do you mean the aspartate unique to DAT, NET, and SERT? This is Gly in all the amino acid transporters in the NSS family. Please be specific.

Fixed. Thank you.

Line 224, "mutation of E312 to Q" or "mutation of Glu312 to Gln".

Fixed. Thank you.

Fig. 3d, Normally, one would expect full saturation curves for each mutant. How can a reader distinguish between low affinity or a decrease in the number of binding sites? Would full binding curves be prohibitive for the mutants because of the cost or availability of the ligand? These points should be addressed. A couple of the curves are not visible. Would an expanded scale inset show them more clearly? Also, would it be possible to include chemical structures for all ligands discussed?

Many if not most of these mutants bind TBZ with such low affinity that it is not possible to measure a full saturation curve either because of ligand availability (radioactive ligand concentration is only in µM) or due to technical issues with being able to measure such low affinity binding. We have changed the presentation of the curves and have split the gating and binding site mutants into their own figures. We feel this improves the readability of these curves. We have also included a table with the respective Kd values determined for each of the mutants where possible.

Line 235, The distances are long for a direct interaction between K138 and the TBZ methoxy groups. The unusual distances should be mentioned if an interaction is being proposed.

We do not think that K138 is directly involved in TBZ binding, however this was written in a confusing way and has been now changed.

Line 243, Please give a quantitative estimate of the affinity difference. "modestly" is vague.

It is an approximately 2-fold difference. Fixed in the text.

Line 248, 150 nM is, at best, a Kd, not an affinity.

Agreed, this is changed.

Reviewer #3 (Recommendations For The Authors):

The (3 x ~100ns-long) molecular dynamics simulations provided suggest some instability of the pose identified by cryo-EM. While it is not unreasonable that ligands shift around and adopt multiple conformations within a single binding site (in a reversible manner), the present results do raise questions about the assumptions made when starting the simulations, in particular (1) the protonation states of charged residues in the TBZ binding sites; (2) the parameters used for tetrabenazine; (3) the conformations of acidic side chains that are notoriously difficult to resolve in cryoEM maps; and (4) any contributions of the truncated regions truncated in the simulated structure, namely the cysteine cross-linked loop and the terminal domains. The authors should examine and/or discuss these contributions before attributing mechanistic insights into the newly observed binding orientation.

In order to estimate the effects of protonation states on TBZ binding, we now added three new systems with altered protonation on TBZ and binding pocket lining residues (see Table 3 in the revised vision); and for each system, we performed multiple MD runs to address the question and concerns raised by reviewer.

Regarding the protonation states: Propka3.0 was used to determine the protonation states, finding that E312 and D399 should be protonated. If I am not mistaken, this version of ProPka cannot account for non-protein ligands (https://github.com/jensengroup/propka). Given their proximity to the binding site, these protonation states will be critical factors for the stability of the simulations. The authors could test their assumption by repeating the calculations with Propka 3.1 or higher, to establish sensitivity to the ligand. Beyond this, showing the resultant hydrogen bond networks will help to reassure the reader that the dynamics in the lumenal gates do not arise from an artifact.

We thank the reviewer for suggestion of using higher version of Propka. We used the most recent Propka3.5 and carried out protonation calculations in the presence and absence of TBZ. The new calculations are presented in Table 4 and SI Figure 8c of the revised version.

It should be possible to assess whether waters penetrate the ligand binding site during the simulations if that is of concern.

We now added the number of waters within the ligand binding pockets for all MD simulations we performed, which are presented in Table 3 and Table 5 of the revised version.

Finally, I didn't fully understand the conclusion based on the simulations and the "binding affinity" calculations: do they imply that the pose identified in the EM map is not stable? What is the value of the binding affinity histogram?

We apologize for this confusion. For each MD snapshot, we calculated TBZ binding affinity using PRODIGY-LIG (Vangone et al., Bioinformatics 2019), which is a contact-based tool for computing ligand binding affinity. The binding affinity histogram shown in the original submission was the histogram of those binding affinities calculated for MD snapshots. In the revision, we replaced binding affinity histogram by time evolution of binding affinity changes (SI Fig 6c in the revision). The simulations confirmed that the pose identified in the EM map is stable, with a flattened binding affinity of -9.4 ± 0.3 kcal/mol in all three runs.

Recommendations regarding writing/presentation:

The authors use active tense terminology in attributing forces to elements of structure (cinching, packing tightly, locking). While appealing and commonplace in structural biology, this style frequently overstates the understanding obtained from a static structure and can give a rather misleading picture, so I encourage rephrasing.

We appreciate this point; the use of these words is not meant to overstate or provide a misleading picture but rather to aid the reader in mechanistic understanding of the proposed processes.

I would also recommend replacing the terms "above" and "below" for identifying aspects of the structure; the protein's location in the vesicular membrane makes these terms particularly difficult to follow.

These terms refer specifically to the Figures themselves which we have always oriented with the luminal side at the top of the page and the cytosolic on the bottom. We have indicated in Figure 1 the orientation of VMAT2. The Figures are the point of reference which we refer to, and the ‘above’ and ‘below’ terms have been used to assist the reader to make the manuscript easier for a more casual or non-expert reader to follow.

Minor corrections:

• the legend in Figure 2 lacks details, e.g. how many simulation frames are shown, how were the electrostatic maps calculated?

We revised Figure 2 and moved simulation frames to SI figure 6e. A total of 503 simulation frames are shown.

• how were the TBZ RMSDs calculated? using all atoms or just the non-hydrogen atoms?

For TBZ RMSDs, we used non-hydrogen atoms. This information is presented in the Methods section.

MD simulation snapshots and input files can be provided via zenodo or another website.

We will upload snapshots and input files to Zenodo upon acceptance of the manuscript.

Reviewing editor specific points:

Specific points

L.97: Remove "readily available"

Fixed.

L.99: The authors are not measuring competition binding. It is well known that reserpine and substrates inhibit TBZ binding only at concentrations 100 times higher than their respective KD and KM values. It is, therefore, surprising that the authors use this isotherm and refrain from commenting on the significance of the finding. Moreover, the presentation of results as "Normalized Counts" does not provide any information about the fraction of VMAT molecules binding the ligand. At least, the authors should provide the specific activity of the ligand, and the number of moles bound per mole of protein should be calculated.

The point was not to infer any details about the conformations that TBZ and reserpine bind but merely to point out that both constructs have a similar behavior with respect to their Ki for reserpine. We have added a sentence to say that reserpine binding stabilizes cytoplasmic-open so the reader is aware of the significance of this competition experiment.

L.102: The characterization of serotonin transport activity needs to be more satisfactory. The Km in rVMAT2 is 100-200 nM, so why are the experiments done at 1 and 10 micromolar? Is the Km of this construct very different? The results provided (counts per minute at the steady state) need to give more information.

The Km of human VMAT2 varies somewhat according to the source but has generally been reported to be between 0.6 to 1.4 µM for serotonin according to these references.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019297/ https://www.cell.com/cell/pdf/0092-8674(92)90425-C.pdf https://www.pnas.org/doi/abs/10.1073/pnas.93.10.5166

Fig 1B could be more informative. I suggest adding a cartoon model with TMs labeled, similar to ED Fig6a.

This panel is to aid the reader in accessing the overall map quality and thus we do not wish to add additional labels/fits which would distract from that point. Instead, we have added overall views of the model in Figs 2,3.

L.179: The authors claim that the inner gate is located "below" (whatever this could mean) the TBZ ligand. In L.214, they claim that TBZ adopts a pose.....just "below" the location of the luminal gating residues. Please clarify and use appropriate terminology.

This refers to the position of these residues in the Figures themselves. We have added figure calls where appropriate here.

Fig. 4: The cartoon could be more informative.

We have added more information to the mechanism cartoon which is now Figure 6. This incorporates some of our new data and we believe it will be more informative.

L. 213: The paragraph describes residues involved in TBZ binding. Mutagenesis is used to validate the structural information. However, the results (ED fig. 5B) must be corrected for protein expression levels. In the Methods section, the authors state (L.444), "Mutants were evaluated similarly from cell lysates of transfected cells." Without normalization of protein expression levels, the results are meaningless even if they agree with predictions.

In fact, we have normalized the concentrations of protein in our binding experiments. This was noted in the methods section. And to account for these differences, experiments were conducted using 2.5 nM of VMAT2 protein as assessed by FSEC.

L.220: The referral to ED Fig.7 is not appropriate here. The figure shows docking-predicted poses of dopamine and serotonin.

Figure call has been changed.

L.226: The referral to Fig. 3b needs to be corrected. The figure shows TBZ and not the neurotransmitter.

This has been corrected.

L. 337: "The neurotransmitter substrate is bound at the central site." What do the authors mean in this cartoon? Do they have evidence for this? Tetrabenazine is not a substrate.

This cartoon drawing is meant to illustrate the elements of structure. Similar drawings are presented throughout the literature such as here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940252/ Figure 3 and here: https://pubs.acs.org/doi/10.1021/acs.chemrev.0c00983 Figure 2.

The same compound is mentioned with different names: 3H-dihydrotetrabenazine and 3H-labeled DTBZ.

Fixed.

ED fig 1d is illegible.

The high-resolution figure is completely legible. We will provide this to the journal upon publication.

Figure 2d: A side view would be more visual.

We have updated this figure and believe that it is much easier to understand now.

L. 179: The inner gate is located 'below' the TBZ ligand

Please see above response, this refers to the figures themselves. The figures are our point of reference.

L. 213-215: Tetrabenazine binding site just 'below' the location of the luminal gating residues.

See above.

Throughout the paper, results are given as cpm or counts. The reader can only estimate the magnitude of the binding/transport by knowing the specific activity of the radiolabel. I recommend switching to nano/picomoles or supplying enough information to understand what the given cpm values could mean.

Binding experiments were done using scintillation proximity assays and therefore converting the CPMs to values in pmol of bound ligand is simply not possible. For the transport experiments (now Fig 1d) the point was to show that the wild type was similar in activity to the chimera. In our new transport experiments we have presented for the mutants, many experiments were combined together and therefore, we have normalized the counts to the relative activity of wild type VMAT2.

I believe my very first school held true to this. We had our principal and five teachers. That was the whole staff and we truly worked as a team. Not once did my principal ever feel like a "boss" and the team of teachers I was a part of really steered the school. And I like to think that that model was for the better. The students were at the heart of every decision made and many of the things we engaged in came from a collective decision we made as a team rather than an order given from the top down. I felt as if being a first year teacher, I had just as much respect from my principal and colleagues as the veteran teacher of twenty-five years had. Unfortunately, I have since learned that this is not the culture of many schools and is very hard to achieve. Why? I'm not quite sure. I think the small nature of the school is what kept the team so close and respectful.

Author Response

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

eLife assessment

The manuscript from Richter et al. is a very thorough anatomical description of the external sensory organs in Drosophila larvae. It represents an important tool for investigating the relationship between the structure and function of sensory organs. Using improved electron microscopy analysis and digital modeling, the authors provide compelling evidence offering the basis for molecular and functional studies to decipher the sensory strategies of larvae to navigate through their environment.

Public Reviews:

Summary

This is a very meticulous and precise anatomical description of the external sensory organs (sensillia) in Drosophila larvae. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Improved serial electron microscopy and digital modeling are used to the fullest to provide a definitive and clear picture of the sensory organs, the sensillia, and adjacent ganglia, providing an integral and accurate map, which is dearly needed in the field. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments and construct a complete structural and neuronal map of the external larval sensilla.

Strengths

It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely use it as a reference paper. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. It will allow for investigating sensory processing in depth. Serial electron microscopy and digital modeling are used to the fullest to provide a comprehensive, definitive, and clear picture of the sensory organs. The discussion places the anatomical data into a functional and developmental frame. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

Weaknesses

There are no apparent weaknesses, although it is not a complete novel anatomical study. It revisits many data that already existed, adding new information. However, the repetitiveness of some data and prior studies may be avoided for easy readability.

We would like to thank the reviewers for their respective reviews. The detailed comments and efforts have helped us to improve our manuscript. In the following, we have listed the comments one by one and provide the respective information on how we addressed the concerns.

Recommendations for the authors:

We have tried to address every single comment as far as possible. In order to structure our response a little better, we have listed the relevant page number and the original comments once again. Directly following this you will find our response and a description of what we have changed in the manuscript.

REVIEWER #1 (Recommendations For The Authors):

I have a few comments that will help the reader navigate this long and detailed paper.

REVIEWER 1.1. page 4

The final section of "the Structural organization of Drosophila larvae" needs some reorganization.

Specifically:

"The DO and the TO are prominently located on the tip of the head lobes" Can the authors rewrite the sentence in a way that it is clear that there is one DO and one VO on each side of the head? Check at the beginning of each section, please. There is a mention about hemi-segments but it is still confusing.

Done – replaced with “The largest sense organs of Drosophila larvae are arranged in pairs on the right and left side of the head.”

REVIEWER 1.2. page 5

"The sequence of sensilla is always similar for and different between T1, T2-T3, and A1-A7" This sentence is not clear, please break it into two sentences.

Done – replaced with: “We noticed varying arrangements for T1, T2-T3, and A1-A7, with a consistent sequence of sensilla in each configuration.”

REVIEWER 1.3. figures page 4

Double hair can't be found in Figure 1B or C (is it h3, h4?) - please clarify.

Done - changed to double hair organ in page 11, included double hair sketch in legend in figure 1B. We changed the name of the structure to double hair organ, to clarify that this is a compound sensillum consisting of two individual sensilla.

REVIEWER 1.4. page 5

The authors go back and forth in their descriptions of the different sensory organs. Knob sensilla and then papilla sensilla are discussed and then a few lines later a further description is done. Please unify the description of each separately.

Done – we restructured the whole section.

REVIEWER 1.5. figures page 6

"We found three hair sensilla on T1-T3, and "two" on A1-A7" - in the figure there seem to be "four" on A1-A7.

Done – we included the two hair sensilla of the double hair organ

REVIEWER 1.6. figures page 6

DORSAL ORGAN:

Can the authors explain the colour map meaning in Figure 2A? It is explained in 2C but the image already has colours. Add your sentence "Color code in A applies to all micrographs in this Figure".

Done – we added a sentence to explain that the color code in A applies to the whole figure.

REVIEWER 1.7. page 6

Page 10: which comprises seven olfactory sensilla "composing" three dendrites each: replace this with"with". At the end, we want to think 7 X 3= 21 ORNs.

Done – replaced.

REVIEWER 1.8. page 9

CHORDOTONAL ORGANS:

"We find these these DO associated ChO (doChO).. .". Please remove one "these"

Done – removed.

REVIEWER 1.9. page 8

Is the DO associated ChO part of the dorsal ganglion???? It does not look like it. Could you clarify?

Done – we added a sentence that clarifies that the ChO neuron is not iside the DOG.

REVIEWER 1.10. page 9 VENTRAL ORGAN: A figures page 12

Please add to the Figure 8 legend the description of 8c' and 8c'?

Done – added description in figure legend.

B page 9

8H, what are the *, arrows? Please clarify - it is hard to interpret the figure.

Done – we added parentheses in the figure legend that state which structures the asterisks and arrows indicate.

C page 9

"Three of them are innervated by a single neuron () and one by two neurons () (Figure 8F-I). Please add which are innervated by 1 (VO1, VO2-VO4) and which by 2 (VO3).

Done – we added parentheses that clarify which sensilla are innervated by 1 or 2 neurons.

REVIEWER 1.11. page 9

Can you add something (or speculate) about the difference in sensory processing of the different types of sensilla?

Done – new sentence in discussion:

‘Their different size and microtubule organization likely correlate with processing of different stimulus intesities applied to the mechanotransduction apparatus (Bechstedt et al. 2010).’

REVIEWER 1.12. figures page 16

PAPILLA AND HAIR SENSILLA:

FIGURE 10a, please add the name of each sensillum from p1, p2, px py, etc... (if not we have to go back to figure 1 when you describe specific ps.)

Thanks for the comment, it really makes it a lot easier for the reader.

REVIEWER 1.13. figures page 18 Figure 11, can you add the name of each hair, please?

Done – updated figure.

REVIEWER 1.14. figures pages 16, 18, 20

In Figures 10, 11, and 12 you clearly draw an area on the internal side that I assume is what you call the "electron-dense sheath". It is wider in papilla sensilla than in hair sensilla, most likely due to the difference in stimuli sensed that you explain in detail in the discussion. Can you say in the figure what this "internal" thing is? Can you add this difference to your list "Apart from the difference in outer appearance and structure of the tubular body"?

This is the basal septum, but it is not certain that it is wider in the papillae sensillae, at least we could not observe this in our data sets. The impression could have been created by different scales in the 3D reconstructions and a perspective view. Therefore, we do not want to list this as a difference here, as we are not sure.

However, we have now specified the socket septum in the figure legends and in Figures 10A, 11A and 12A.

REVIEWER 1.15. page 11

KNOB SENSILLA:

Page 25;" Knob sensilla have been described under "vaious" names such as": add various.

Done

REVIEWER 1.16. page 12

"reveals that the three hair and the two papilla sensilla are associated with a single dendrite." Can you write that "reveals THAT EACH OF the three hair and the two papilla sensilla" if not it seems that there is only one dendrite.

Done

REVIEWER 1.17. figures page 25 TERMINAL SENSORY CONES:

Please name the t1-t7 cones in Figure 15A.

Done – we updated the figure.

REVIEWER 1.18. page 13

The spiracle sense organ deserves a new paragraph. As does the papilla sensillum of the anal plate.

Done – we added subtitles before the prargraphs.

Discussion:

REVIEWER 1.19. page 15

Page 38: "v'entral" correct typo

PAGE 15

Done – we have updated the nomenclature  ventral 1 (v), ventral 2 (v’) and ventral 3 (v’’)

REVIEWER #2 (Recommendations For The Authors):

I have only a few comments:

REVIEWER 2.1. page 5

p.5, right column, middle: the use of trichoid, campaniform, and basiconical (sensilla) in previous works were based on even older papers and reviews that attempted to link EM architecture to function (e.g., KEIL, T. A. & STEINBRECHT, R. A. (1986). Mechanosensitive and olfactory sensilla of insects. In Insect Ultrastructure, vol. 2. (ed. R. C. King & H. Akai), pp. 477-516. New York/London: Plenum Press). Trichoid sensilla can be mechano-sensitive, olfactory, or gustatory; trichoid simply refers to the shape (hair). The same applies to basiconical sensilla. The use of "campaniform", which Ghysen et al called "papilla sensilla", was the only really problematic case, because these (Drosophila larval) sensilla did not really resemble closely the classical campaniform sensilla (e.g., adult haltere). The only reason we called them campaniform is because they were not more similar to any other type of (previously named) sensillum.

Thank you for the explanation. The nomenclature of structures is generally always a complex topic with often different approaches and principles. We are aware of this and have therefore tried to be as careful as possible. We were not sure from this comment whether you were suggesting to change the text or whether you wanted to explain how these names were assigned to the sensilla in the past. However, we hope that the current version is in line with your understanding, but could of course make changes if necessary (see also comments of reviewer 1).

REVIEWER 2.2. page 9

p.21, Labial Organ: the ventral lip is the labium; the dorsal one is the labrum.

Done – replaced labrum with labium.

REVIEWER 2.3. page 9

p.20/21, Ventral organ and labial organ: here, the projection of the axons could be mentioned as an ordering principle. In the previous literature, for larva and embryo, a labial organ (lbo) was described that most likely corresponds to the labial organ presented here. This (previously mentioned) lbo characteristically projects along the labial nerve to the labial segment (hence the name). It fasciculates with axons of another sensory complex, also generated by the labial segment, namely the ventral pharyngeal sensory organ (VPS). Does the labial organ described here share this axonal path?

Yes, it has the same axonal pathway and is the same organ as the lbo. We have tried to standardise the nomenclature for all important external head organs (DO, TO, VO, LO) and have therefore used abbreviations with two letters. However, to avoid confusion, we have now added that the LO was also called lbo in the past.

For the ventral organ, the segmental origin (to my knowledge) was never clarified. The axons of the ventral organ project along the maxillary nerve (which carries axons of the terminal=maxillary organ). This nerve, closely before entering the VNC, splits into a main branch to the maxillary segment (TO axons) and a thinner branch that appears to target the mandibular segment. This branch could contain the axons of the ventral organ (as described previously and in this paper). Could the authors confirm this axonal projection of the VO?

In this work, we did not focus on the axonal projections into the SEZ. This is also not a simple and fast process, as in the entire larval dataset, the large head nerves unfortunately exhibit a highly variable quality of representation. Therefore, the reconstruction of nerves and individual neurons within it is often challenging and very time-consuming. The research question is, of course, very intriguing, and one could also attempt to match each sensory neuron of the periphery with the existing map of the brain connectome. However, this is a project in itself, exceeding the scope of this work, and is therefore more feasible as a subsequent project.

REVIEWER #3 (Recommendations For The Authors):

Minor suggestions that the authors might consider:

REVIEWER 3.1. figures all

Recheck the scale bar in figures and figure legends. Missing in a few places.

Done – we replaced or added some (missing) scale bars in figures and figure legends (see annotated figure document).

REVIEWER 3.2. figures page 4

The color schematic in Figure 1 can be improved for readability.

Done – we changed the color schematic, especially for the head region to improve readability.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. The authors find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

Strengths & Weaknesses:

Various analyses nicely support the authors' claims. Accordingly, I have only one significant comment and several minor comments that focus on wordsmithing - e.g., clarifying the interpretation of statistical results and requesting additional citations to support claims in the introduction.

We are pleased the reviewer found the analyses to support the claims. We have addressed comments related to clarifying interpretations as suggested in the Recommendations to the Authors. For example, we have added discussion and clarification on the other parameters that could affect the strength of ERC correlations.

Reviewer #2 (Public Review):

Summary:

The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction types that all fall under the umbrella explanation of 'co-functional interactions'.

Strengths:

Very smart experimental design that leverages individual protein domains for increased resolution.

Weaknesses:

Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

We appreciate the reviewer’s acknowledgement of the experimental design. We have addressed the nuance of the results by changing the title and clarifying other statements throughout the manuscript as suggested in the reviewer’s recommendations. We have also addressed reviewer comments asking for further explanation on using Fisher transformations when normalizing the Pearson correlations for branch counts.

Reviewer #3 (Public Review):

Summary:

The paper makes a convincing argument that physical interactions of proteins do not cause substantial evolutionary co-variation.

Strengths:

The presented analyses are reasonable and look correct and the conclusions make sense.

Weaknesses:

The overall problem of the analysis is that nobody who has followed the literature on evolutionary rate variation over the last 20 years would think that physical interactions are a major cause of evolutionary rate variation. First, there have been probably hundreds of studies showing that gene expression level is the primary driver of evolutionary rate variation (see, for example, [1]). The present study doesn't mention this once. People can argue the causes or the strength of the effect, but entirely ignoring this body of literature is a serious lack of scholarship. Second, interacting proteins will likely be co-expressed, so the obvious null hypothesis would be to ask whether their observed rates are higher or lower than expected given their respective gene expression levels. Third, protein-protein interfaces exert a relatively weak selection pressure so I wouldn't expect them to play much role in the overall evolutionary rate of a protein.

We thank the reviewer for their comments and suggestions. A point to immediately clarify is that the methods studied in this manuscript deal with rate variation of individual proteins over time, and if that variation correlates with that of another protein.. The numerous studies the reviewer refers to deal with explaining the differences in average rate between proteins. These are different sources of variation. It has not, to our knowledge, been shown that variation in the expression level of a single protein over time is responsible for its variation in evolutionary rate over time, let alone to a degree that allows its variation to correlate with that of a functionally related protein. That question interests us, but it is not the focus of this study.

In our study, we sought to test for a contribution of physical interaction to the correlation of evolutionary rate changes as they vary over time, i.e. between branches. We made many changes to clarify this distinction in our revisions.

We agree that the manuscript would be more clear to define the forces proposed to lead to difference in rate in general, which includes expression levels. We had generally considered expression level as one of the many potential non-physical forces, but failed to make that explicit and instead focused on selection pressure. In our revision we describe expression level as another potential driver of evolutionary rate variation over time. References to previous literature have been made in the introduction. We also added a more explicit explanation of the rate covariation over time that we are measuring in contrast with the association between expression level and rate differences between proteins that was studied in previous literature.

On point 3, the authors seem confused though, as they claim a co-evolving interface would evolve faster than the rest of the protein (Figure 1, caption). Instead, the observation is they evolve slower (see, for example, [2]). This makes sense: A binding interface adds additional constraint that reduces the rate at which mutations accumulate. However, the effect is rather weak.

The values in Fig 1B are a measure of correlation, specifically a Fisher transformed correlation coefficient. They are not evolutionary rates, so they are not reflecting faster or slower evolution, rather more or less covariation of evolutionary rates over time. We are not predicting that physically interacting interfaces evolve faster than the rest of the protein, but rather that if physical interaction drives covariation in evolutionary rates over time, their correlation would be stronger between pairs of physically interacting domains. In response, we have used clearer language in the figure caption and reorganized labels in Figure 1B to clearly show that the values are correlations. Revised Figure 1 Legend:

“Overview of experimental schema and hypotheses. Proteins that share functional/physical relationships have similar relative rates of evolution across the phylogeny, as shown in (A) with SMC5 and SMC6. The color scale along the bottom indicates the relative evolutionary rate (RER) of the specific protein for that species compared to the genome-wide average. A higher (red) RER indicates that the protein is evolving at a faster rate than the genome average for that branch. Conversely, a lower (blue) RER indicates that protein is evolving at a slower rate than the genome average. The ERC (right) is a Pearson correlation of the RERs for each shared branch of the gene pair. (B) Suppose the correlation in relative evolutionary rates between two proteins is due to compensatory coevolution and physical interactions. In that case, the correlation of their rates (ie. ERC value) would be higher for just the amino acids in the physically interacting domain. (C) Outline of experimental design. Created with Biorender.com”

All in all, I'm fine with the analysis the authors perform, and I think the conclusions make sense, but the authors have to put some serious effort into reading the relevant literature and then reassess whether they are actually asking a meaningful question and, if so, whether they're doing the best analysis they could do or whether alternative hypotheses or analyses would make more sense.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523088/

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854464/

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments

(1) Numerous parameters influence ECR calculation. The authors note that their use of a large dataset of budding yeast provides sufficient statistical power to calculate ECR. I agree with that. However, a discussion of other parameters needs to be improved, especially when comparing the present study to others like Kann et al., Hakes et al., and Jothi et al.. For example, what is the evolutionary breadth and depth used in the Kann, Hakes, Jothi and other studies? How does that compare to the present study? Budding yeast evolve rapidly with gene presence/absence polymorphisms observed in genes otherwise considered universally conserved. Is there any reason to expect different results in a younger, slower-evolving clade such as mammals? There is potential to acknowledge and discuss other parameters that may influence ECR, such as codon optimization and gene/complex "essentiality," among others.

More discussion of these parameters is a good idea. We have added the number and phylogeny of species used in the previous studies in the discussion paragraph starting with “Previous studies attributed varying degrees of evolutionary rate covariation signal to physical interactions between proteins.” We also like the idea of studying the effect of younger and more slowly evolving clades as opposed to the contrary, but currently we lack the required number of datasets to do this.

We have also added more discussion and clarification of potential non-physical forces leading to ERC correlations in the introduction.

Minor comments

(1) It would be good to add a citation to the second sentence of the first paragraph, which reads, "It has been observed that some genes have rates that covary with those of other genes and that they tend to be functionally related."

Added citation to Clark et al. 2012

(2) In the last sentence of the first paragraph of the introduction, ERC is discussed in the context of only amino acid divergence, however, there is no reason that DNA sequences can't be used, especially if ERC is being calculated among species that are less ancient than, for example, Saccharomycotina yeasts. Thus, it may be more accurate to suggest that ERC measures how correlated branch-specific rates of sequence divergence are with those of another gene.

Nice suggestion to generalize. We have made this change.

(3) ERC was not calculated in reference #2. For the sentence "Protein pairs that have high ERC values (i.e., high rate covariation) are often found to participate in shared cellular functions, such as in a metabolic pathway2 or meiosis3 or being in a protein complex together," I think more appropriate citations (including inspiring work by the corresponding author) would be

a) Coevolution of Interacting Fertilization Proteins (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000570)

b) Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007720)

c) An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function (https://www.science.org/doi/10.1126/sciadv.abn0105)

d) PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data (https://academic.oup.com/bioinformatics/article/37/16/2325/6131675)

Thank you for pointing out these works. We agree that there are more appropriate citations and we have referenced your suggested b-d.

(4) The dataset of 343 yeast species also includes outgroup taxa. Therefore, indicating that 332 species are Saccharomycotina yeast and 11 are closely related outgroup taxa may be more accurate.

Thank you for the suggestion, the following sentence has been added, citing the Shen et. al 2018 paper that the dataset was derived from:

“To investigate the discrepancy between contributions to ERC signal from co-function and physical interaction, we used a dataset of 343 evolutionarily distant yeast species. 332 of the species are Saccharomycotina with 11 closely related outgroup species providing as much evolutionary divergence as humans to roundworms3”

(5) Are there statistics/figures to support the claim that "Almost all complexes and pathways had mean ERC values significantly greater than a null distribution consisting of random protein pairs"?

This is shown in supplementary figure 1. A reference to this figure was added as well as quantification within the text.

(6)Similar to the previous comment, can quantitative values be added to the statement "While protein complexes appear to have higher mean ERC scores than the pathways..."?

The median of the mean ERC scores for protein complexes is 5.366 while the median for the mean ERC score in pathways is 4.597. This quantification has been included in the text: “While protein complexes have higher mean ERC scores (median 5.366) than the pathways (median 4.597), the members of a given complex are also co-functional, making interpretation of the relative contribution of physical interactions to the average ERC score difficult”

These quantifications are were also added to the figure caption for figure 2A

(7) A semantic point: In the sentence "The lack of significance in the global permutation test shows that the...", I recommend saying that the analysis suggests, not shows, because there is potential for a type II error.

Good suggestion, we have made this change.

(8) The authors suggest that shared evolutionary pressures, "and hence shared levels of constraint," drive signatures of coevolution. The manuscript does not delve into selection measures (e.g., dN/dS). Perhaps it would be more representative to remove any implication of selection.

We have added better language to clarify that discussion of selection is purely a hypothesis and that selection is not probed in our analyses.

“Previous work finds evidence that relaxation of selective constraint can lead to drastic rate variation and hence covariation6. Rather, the greater and consistent contribution comes from non-physical interaction drivers that could include variation in essentiality, expression level, codon adaptation, and network connectivity. These non-physical forces would be under shared selective pressures and hence shared levels of constraint, the result of which was elevated ERC between non-interacting proteins, as visible in our study of genetic pathways that do not physically interact (Figure 2).”

Reviewer #2 (Recommendations For The Authors):

Major comments:

-Title: In my opinion, the title of the manuscript is a somewhat misleading summary of the results of this paper. In the majority of the analyses in this paper, physical interactions do account for a significantly outsized portion of the ERC signature. The current title downplays the consistent (although sometimes small effect-sized) result that physically interacting domains do show higher ERC than non-physically interacting domains by every statistical measure employed in this paper to compare physical vs non-physical interactions. The authors' interpretation of their results within the manuscript body is that the effect of physical interactions is an inconsistent, weak, and non-generalizable driver of ERC. I generally agree with the authors' interpretations, but the nuance of these interpretations is lost in the title of the paper. I would suggest rewording the title to try to capture the nuance or at least be subjectively accurate. For example, stating that "...physical interactions are not the sole driving force.." is inarguably accurate based on these results.

As an alternative title, I would suggest focusing on an important takeaway from the paper: ERC is a reliable predictor of co-functional interactions but not necessarily physical interactions. I agree with the statement that "there is not a strong enough signal to confidently call an interaction physical or not and would be of little value to an experimentalist wanting to infer interacting domains" and I think that a title that emphasizes this idea would be more accurate and impactful.

Great suggestion. We agree that the current title is downplaying the minutiae of the method and the signal we capture with it, we have used your suggested title.

There are an outsized number of complexes that had ROC-AUCs greater than 0.5 which is why we performed the permutation tests to determine how significant each of the individual ROC-AUCs were given the differing number of protein/domain pairs in each complex. Between the statistical methods used only 3 of the 17 complexes ranked physical interactions significantly higher than non-physically interacting domains in every analysis. Even among the 3 that were statistically significant some of the physically interacting domains still fell among the bottom portion of the ERC scores for that complex (Figure 5: MCM and CUL8 complexes) This is why we concluded that physical interactions are not the sole driving force of the signal captured by ERC.

-Abstract: related to my preceding comment, the word "negligible" in the abstract is misleading. If physical interactions were truly entirely negligible, the comparisons of physically interacting vs non-physically interacting domains would yield 0.5. Instead, these comparisons always yielded results greater than 0.5. Consider rewording.

Thank you for the suggestion this phrasing has been changed to “Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC”

We agree that “negligible” may be too strong of a word, however, the comparisons do not always yield results greater than 0.5.

5 of the 17 complexes do not reach the 0.5 threshold for the initial ROC analysis and even among those that do, only 4 had significantly high ROC-AUCs. You are correct that the signal is not completely negligible which is why we continued by determining if the physical interaction was driving high ERC only within proteins (Figure 5)

-Figure 3: I think there may be an error in the domain labeling in Figure 3. The comparison between OKP1_2 and AME1_3 is the highest ERC value in the matrix. From the complex structure, it appears that OKP1_2 and AME1_3 are two helix domains that appear to physically interact. However, in the ERC matrix, they are not shaded to indicate they are a physical interaction pair. Please double-check that the interacting domains are properly annotated, since mis-annotation would have a large impact on the interpretation of this figure with respect to the overall question the paper addresses.

Thank you for catching this - fixed.

Minor comments:

-Methods: "The full ERC pipeline can be found at (Github)." Provide github URL here? Thanks for the catch, fixed

-Discussion: "Evidence for physical coevolution however was tempered by a global permutation test, which did not reach significance, indicating that this inference is sensitive to approach and further underlines the relatively weak contribution of physical coevolution." The word "relatively" may not be a good choice of words. In comparison to what? As is, the phrasing could be interpreted as implying "in comparison to non-physical interactions". This would not be accurate, because the results show that in general, physical interactions are a stronger contributor to ERC (consistent trend but varied significance, depending on methodology) than non-physical interactions.

Thank you for your help with clarification. The word relatively was removed.

However, we do not agree that in general physical interactions are a stronger contributor to ERC than non-physical interactions (such as gene expression, codon adaptation, etc.). In all of our statistical tests a maximum of 5 of the 17 complexes ranked physical interactions significantly higher than non-physical interactions. While the ROC-AUC is greater than 0.5 for 12 of the 17 complexes only 4 of those were significant.

-I have not seen Fisher-transformed correlation coefficients used in the context of ERC. I understand that it's helpful in normalizing the results so that they are comparable between ERC comparisons with differing numbers of overlapping branches (i.e. points on a linear correlation plot). A reference of where the authors got this idea or a little more verbiage to describe the rationale would be helpful. On a related note, I would expect that using linear correlation p-value instead of R-squared would account for differences in overlapping branches, eliminating the need to apply fisher-transformation. It would be helpful for the authors to outline their rationale for using a correlation coefficient rather than a P-value.

We agree that this method could be made clearer. We made a methodological choice to use Fisher transformation over linear correlation p-value. Both methods should achieve the same end result by taking the number of branches into consideration. We have added additional explanation to the results section “Both protein pathways and complexes have elevated ERC”:

“ERC was calculated for all pairs of the 12,552 genes. For each pair the correlation is Fisher transformed to normalize for the number of shared branches that contribute to the correlation. This normalization is necessary to reduce false positives that have high correlation solely due to a small number of data points. This normalization also allows for direct comparison of ERC between gene pairs that have differing numbers of branches contributing to the score.”

We also added additional explanation in the methods section including the formula used to calculate the Fisher transformation

-Did the authors use Pearson or Spearman correlation coefficient?

Pearson. We clarified this in the methods section, “Calculating evolutionary rate covariation” : “Evolutionary rate covariation is calculated by correlating relative evolutionary rates (RERs) between two gene trees using a Pearson correlation.”

-Did the authors explore ERC between domains within a single protein? Do domains within a protein exhibit ERC? I would expect that they do. If they do, this could likely be attributed to linkage/genetic hitchhiking, representing a new angle/factor beyond physical interaction that could lead to ERC. This is just an idea for a future analysis, not necessarily a request within the scope of the present paper.

We did calculate the ERC between domains of a single protein but did not include them in the analysis since they didn’t address the specific question we posed. As expected they are highly correlated, and past unpublished studies in the lab do find a very weak, but detectable genome-wide, signature of rate covariation between neighboring colinear genes on a chromosome. That signal was however so weak as to be eclipsed by true functional relationships, when present.

Reviewer #3 (Recommendations For The Authors):

Please read the literature and revise accordingly.

We understand the confusion surrounding previous literature on the relationship between expression levels and evolutionary rates when comparing between different proteins. Those studies clearly showed how expression level is highly predictive of a given protein’s average evolutionary rate. However, we are studying the change in evolutionary rate over branches for single proteins. This is inherently different because we’re following rate fluctuations in the same protein over time. To our knowledge it has not yet been shown that expression level commonly varies enough over time to produce large rate variations over time in the same protein, and if it is responsible for the correlations of rate we observe between co-functional proteins. It is however reasonable to expect that what governs between-protein differences in rate could also contribute to between-branch differences (over time for a single protein). In fact, our earlier study approached this (Clark et al. Genome Research 2012). We expect expression level could influence rate over time and lump its effect together with general non-physical forces, such as selection pressures. We recognize we could do better in defining more of the non-physical forces and the past literature. We added the following section to the introduction and many other clarifying statements throughout the manuscript:

“For the purposes of this study, the forces that contribute to correlated evolutionary rates are grouped into two bins, physical and non-physical. The physical force is coevolution occurring at physical interaction interfaces. Non-physical forces include gene co-expression, codon adaptation, selective pressures, and gene essentiality. There is a well accepted negative relationship between gene expression and rate of protein evolution where genes that are highly expressed generally have slower rates of evolution14,15. However, Cope et al.16 found that there is a weak relationship between both gene expression and the number of interactions a protein has with the coevolution of expression level. Conversely, they found a strong relationship between proteins that physically interact and the coevolution of gene expression. These findings illuminate the difference between the strong relationship of gene expression level on the average evolutionary rate of a protein and the weak contribution of gene expression level to correlated evolutionary rates of proteins across branches. The finding that physically interacting proteins have strong expression level coevolution brings to question how much coevolution of physically interacting proteins contributes to overall covariation in protein evolutionary rates.”

Author Response

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

eLife assessment

This manuscript provides novel and important findings regarding the impact of noradrenergic signaling from the locus coeruleus on hippocampal gene expression. The locus coeruleus is the sole source of noradrenaline to the hippocampus and many rapid molecular changes induced by stress are regulated by noradrenaline. This manuscript provides a rigorous investigation into hippocampal genes uniquely regulated by noradrenaline in the presence or absence of stress. Data were collected and analyses were performed using solid methodology, and the results mostly convincingly support the conclusion made with few weaknesses. The study would benefit from a more comprehensive analyses of sex differences.

Response: We thank the reviewers and the editors for the positive evaluation of our work and for the constructive feedback. To address some of the key criticisms, we have performed several new experiments and analyses. Importantly, we now provide a much more rigorous comparison of males and females, which strongly suggests that there are no major sex differences in the transcriptomic response to stress and noradrenaline in the hippocampus. We think that these - and other additions discussed below - significantly strengthen the manuscript. We provide detailed responses to all the reviewers comments. We have added numbers to the reviewers’ comments for easier referencing.

Reviewer #1 (Public Review):

Comment 1: Privitera et al., provide a comprehensive and rigorous assessment of how noradrenaline (NA) inputs from the locus coeruleus (LC) to the hippocampus regulate stress-induced acute changes in gene expression. They utilize RNA-sequencing with selective activation/inhibition of LC-NA activity using pharmacological, chemogenetic and optogenetic manipulations to identify a great number of reproducible sets of genes impacted by LC activation. It is noteworthy that this study compares transcriptomic changes in the hippocampus induced by stress alone, as compared with selective circuit activation/inhibition. This reveals a small set of genes that were found to be highly reproducible. Further, the publicly available data will be highly useful to the scientific community.

Response: We are very grateful for this positive evaluation.

Comment 2: A major strength of the study is the inclusion of both males and females. However, with this aspect of the study also lies the biggest weakness. While the experiments tested males and females, they were not powered for identifying sex differences. There are vast amounts of literature documenting the inherent sex differences, both under resting and stress-evoked conditions, in the LC-NA system and this is a major missed opportunity to better understand if there is an impact of these sex-specific differences at the genetic level in a major LC projection region. There are many instances whereby sex effects are apparent, but do not pass multiple testing correction due to low n's. The authors highlight one of them (Ctla2b) in supplemental figure 6. This gene is only upregulated by stress in females. It is appreciated that the manuscript provides an incredible amount of novel data, making the investigation of sex differences ambitious. Data are publicly available for others to conduct follow up work, and therefore it may be useful if a list of those genes that were different based on targeted interrogation of the dataset be provided with a clear statement that multiple testing corrections failed. This will aid further investigations that are powered to evaluate sex effects.

Response: The assessment of the reviewers and the editorial feedback encouraged us to look more thoroughly into potential sex differences, because we believe it would indeed be a major additional strength if our manuscript could make a firm statement on this important issue. To this end, we have expanded the manuscript in two major ways:

(1) To expand the analysis of sex effects also to the dorsal hippocampus, and to increase robustness of the data, we have performed RNA-seq in 32 additional samples of male and female mice exposed to stress (or control) and propranolol (or saline) injection. Figure 1fh and Supplementary Figure 1d-f have been updated to reflect this new addition, and the results are presented in a new section on Pages 3-4 (pasted below for ease of reviewing). In summary, the strongly support our initial observation that the effects of stress on gene expression, as well as the effects of propranolol on blocking stress-induced effects, are highly similar in both sexes.

(2) To further increase the power for detection of sex-effects, we have performed a small meta-analysis. For this, we combined several RNAseq datasets from the current manuscript and published datasets from our previous work (Floriou-Servou et al., 2018; von Ziegler et al., 2022), which also investigated transcriptomic sex-differences in the hippocampus 45 min after cold swim stress exposure in the same setup as used for the current manuscript. This approach increased our sample size to 51 males and 20 females. In summary, this well-powered approach shows no evidence for sex differences in the transcriptional response to stress, even when more lenient analyses were applied. These results are described in a new section on page 4, and summarized in Supplementary Figures 1f+g. This section is pasted below for ease of reviewing.

"While blocking β-adrenergic receptors was able to block stress-induced gene expression, we did not test whether propranolol might decrease gene expression already at baseline, independent of stress. Additionally, all tests had thus far been conducted in male mice, raising the question about potential sex differences in NA-mediated transcriptomic responses. To address these two issues, we repeated the experiment in both sexes and included a group that received a propranolol injection but was not exposed to stress (Fig. 1f). Combining the data from both experiments, we repeated the analysis for each region, to identify genes whose response to stress was inhibited by propranolol (Figure 1g). As in the previous experiment, we found that many of the stress-induced gene expression changes were blocked by propranolol injection in both dHC (Figure 1g, left panel) and vHC (Figure 1g, right panel). Importantly, propranolol did not change the expression level of these genes in the absence of stress. We then directly compared the genes sensitive to stress and propranolol treatment in both dHC and vHC. To this end, we plotted the union of genes showing a significant stress:propranolol interaction in either region in one heatmap across both dHC and vHC (Supplementary Figure 1d). This showed again that the stress-induced changes were very similar in dHC and vHC, and that propranolol similarly blocked many of them. Finally, we asked whether the response differs between males and females. Despite clear sex differences in gene expression at baseline (data not shown), we found no significant sex differences in response to stress or propranolol between male and female mice (FDR<0.05; Fig. 1g). To more directly visualize this, we compared females and males by plotting the log2-fold changes of the stress:propranolol interaction across all stress-induced genes that were blocked by propranolol. We find very similar regulation patterns in both sexes (Figure 1h). Although none of these sex differences are significant, some genes seem to show quantitative differences, so we plotted the expression patterns of the 5 genes showing the largest difference in interaction term as box-plots, which suggest that these spurious differences are likely due to noisy coefficient estimates (Supplementary Fig. 1e). To address concerns that our analysis of sex differences might not have been sufficiently powered, we performed a meta-analysis of the experiments shown here along with previously published datasets from our lab (Floriou-Servou et al. 2018; von Ziegler et al. 2022). In all these experiments, the vHC of male and female mice was profiled 45 min after exposure to an acute swim stress challenge. This resulted in a sample size of 51 males and 20 females. Despite this high number of independent samples, we could not identify any statistically significant interaction between sex and the stress response. To identify candidates that might not reach significance while discounting differences due to noise in fold-change estimates, we reproduced the same analysis using DESeq2 with Approximate Posterior Estimation for generalized linear model (apeglm) logFC shrinkage (A. Zhu, Ibrahim, and Love 2018). This analysis also did not reveal any sex differences in the stress response (Supplementary Fig. 1f). We then tailored the meta-analysis specifically to the set of stress-responsive genes that were blocked by propranolol, and also for these genes the response to stress was strikingly similar in both sexes (Supplementary Fig. 1g). Altogether, we conclude that there are no major sex differences in the rapid transcriptomic stress response in the hippocampus, and that blocking beta-receptors prevents a large set of stress-induced genes in both females and males."

To put these findings in context with existing literature, we agree with the reviewer that there are many studies that have reported sex differences in the LC-circuitry as summarized by Bangasser and colleagues (Bangasser et al., 2016, 2019). However, these studies primarily focus on the LC itself, suggesting that female rats have more LC neurons, denser LC-dendrites in the peri-LC region, and that LC neurons are more readily activated by stress in females because of heightened sensitivity to CRF-signaling. A recent study in mice reports, in contrast, that females have fewer TH-positive neurons in the LC, but they also find enhanced excitability of LC neurons in females (Mariscal et al., 2023). Similarly, one study has suggested molecular differences in the makeup of the LC (Mulvey et al., 2018). Our experiments, however, focus on the impact of NA release in a projection region (hippocampus). Further, we use a strong stress induction protocol (swim stress) and various potent modes of direct LC activation, so differences in "LC-excitability" are likely less relevant in this context. We added evidence showing that we trigger powerful NA release in both sexes (Supplementary Figure 2c-h; see response to Reviewer #2, Comment #3 for more details). In addition, we show that the intensity or pattern of LC stimulation does not appear to alter the molecular response (Figure 3a-b), and that various stressors (mild or intense) all trigger the same NA-dependent molecular changes (Figure 4a-b). Therefore, our results suggest that once NA is released (in the hippocampus), the molecular downstream effects on gene expression are very similar - independent of stimulation intensity, sex, or hippocampal subregion (dorsal/ventral). This does not mean that there are no sex differences for activation of LC, but rather that the transcriptional response to NA release in the hippocampus is robust across sexes, and that propranolol seems to block NA-dependent effects similarly in both sexes. This does not rule out quantitative differences between sexes that only emerge with targeted analyses of individual genes, or once fluctuations in ovarian hormones are taken into account. We have updated the section in the discussion to summarize these considerations in light of the new results (see pages 20-21, section: "A uniform molecular response to stress and noradrenaline release in both sexes").

Comment 3: A major finding of the present study is the involvement of noradrenergic transcriptomic changes occurring in astrocytic genes in the hippocampus. Given the stated importance of this finding within the discussion, it seems that some additional dialogue integrating this with current literature about the role of astrocytes in the hippocampus during stress or fear memory would be important.

Response: We thank the reviewer for giving us an opportunity to add a more detailed discussion about the role of astrocytes and thyroid hormones in the hippocampus during learning and memory formation. We have added these statements to the discussion:

“Within the hippocampus, astrocytic pathways are emerging as important players for learning and memory processes (Gibbs, Hutchinson, and Hertz 2008; Bohmbach et al. 2022). In fact, it is well-known that NA enhances memory consolidation (Schwabe et al. 2022; McGaugh and Roozendaal 2002), and recent work suggests that these effects are mediated by astrocytic β-adrenergic receptors (Gao et al. 2016; Iqbal et al. 2023). Our transcriptomic screens revealed Dio2 as the most prominent target influenced by LC activity. Dio2 is selectively expressed in astrocytes and encodes for the intracellular type II iodothyronine deiodinase, which converts thyroxine (T4) to the bioactive thyroid hormone 3,3',5-triiodothyronine (T3) and therefore regulates the local availability of T3 in the brain (Bianco et al. 2019). Enzymatic activity of DIO2 has further been shown to be increased by prolonged noradrenergic transmission through desipramine treatment in LC projection areas (Campos-Barros et al. 1994). This suggests that the LC-NA system and its widespread projections could act as a major regulator of brain-derived T3. Notably, T3-signaling plays a role in hippocampal memory formation (Rivas and Naranjo 2007; Sui et al. 2006), raising the possibility that NA-induced Dio2 activity in astrocytes might mediate some of these effects.”

Comment 4: The comparison of the candidate genes activated by the LC in the present study (swim) with datasets published by Floriou-Servou et al., 2018 (Novelty, swim, restraint, and footshock) is an interesting and important comparison. Were there other stressors identified in this paper or other publications that do not regulate these candidate genes? Further, can references be added to clarify to the reader, that prior studies have identified that novelty, restraint and footshock all activate LC-NA neurons.

ponse: Thank you for the positive feedback. We have only tested the stressors reported in Figure 4a-b (novelty, swim, restraint, and footshock). It is known that all these stressors trigger noradrenaline release, in fact we are not aware of stressors that do not trigger NA release. This reproducible finding supports the notion that the identified set of genes is indeed highly NAresponsive. As suggested, we have now included references that show increased NA release in response to all these stressors:

“Therefore, we assessed their expression in a dataset comparing the effect of various stressors on the hippocampal transcriptome (Floriou-Servou et al., 2018). The stressors included restraint, novelty and footshock stress, which have all previously been shown to increase hippocampal NA release (HajósKorcsok et al., 2003; Lima et al., 2019; Masatoshi Tanaka et al., 1982).”

Comment 5: Comparisons are made between chemogenetic studies and yohimbine, stating that fewer genes were activated by chemogenetic activation of LC neurons. There is clear justification for why this may occur, but a caveat may need to be mentioned, that evidence of neuronal activation in the LC by each of these methods were conducted at 90 (yohimbine) versus 45 (hM3Dq) minutes, and therefore it cannot be ruled out that differences in LC-NA activity levels might also contribute.

Response: The reviewer raises an important point about some inconsistencies between the time points chosen in our study, an aspect that was also pointed out by Reviewer #2. We have chosen the 45 and 90 min time points for two different reasons. On the one hand, cFos changes on the protein level are known to peak 90 min after neuronal activation, and we wanted to capture the strongest possible cFos signal in the LC. On the other hand, we wanted to measure gene expression changes triggered by NA release, which already occur 45 min after noradrenergic activation (Roszkowski et al., 2016). Thus, when the experimental design allowed separate experiments (e.g. systemic yohimbine injection), we chose to measure gene expression after 45 min, but to validate cFos activation in the LC separately after 90min. In response to DREADD activation, however, we wanted to confirm within the same animal that LC activation was successful, and thus we collected LC and hippocampus simultaneously (Figure 2c,d). While the cFos increase is already very pronounced at the 45min time point (Figure 2g), the quality of IHC is slightly lower because the tissue cannot be perfused in this experimental design. Therefore, we do not think that the time point for cFos sampling matters in this context. However, we agree with the reviewer that it remains unclear whether yohimbine and DREADDs activate the LC with similar potency. To directly compare NA release would require a set of photometry-based experiments to measure NA release using genetically-encoded NA-sensors. While we have added such experiments for LC activation with DREADDs and optogenetics to show rapid NA release indeed occurs in the hippocampus (see Reviewer #2, Comment 3; Supplementary Figure 2c-h), yohimbine interferes with the NA-sensors as explained in detail in response to Reviewer 2, Comment 3. Thus, it was too challenging for us to directly compare the release dynamics in response to DREADDs and yohimbine, which was also not the main focus of our work. To explicitly address this caveat, we have extended the corresponding section in the discussion:

"Finally, our observation that systemic administration of the α2-adrenergic receptor antagonist yohimbine very closely recapitulates the transcriptional response to stress stands in contrast to the much more selective transcriptional changes observed after chemogenetic or optogenetic LC-NA activation. This difference could be due to various factors. First, it remains unclear how strong the LC gets activated by yohimbine versus hM3Dq-DREADDs. However, given the potent LC activation observed after DREADD activation, it seems unlikely that yohimbine would lead to a more pronounced LC activation, thus explaining the stronger transcriptional effects. Second, contrary to LC-specific DREADD-activation, systemic yohimbine injection will also antagonize postsynaptic α2-adrenergic receptors throughout the brain (and periphery). More research is needed to determine whether this could have a more widespread impact on the hippocampus (and other brain regions) than isolated LC-NA activation, further enhancing excitability by preventing α2-mediated inhibition of cAMP production. Finally, systemic yohimbine administration and noradrenergic activity have been shown to induce corticosterone release into the blood (Johnston, Baldwin, and File 1988; Leibowitz et al. 1988; Fink 2016). Thus, yohimbine injection could have broader transcriptional consequences, including corticosteroid-mediated effects on gene expression."

Comment 6: Please add information about how virus or cannula placement was confirmed in these studies. Were missed placements also analyzed separately?

Response: Pupillometry recordings were performed with all animals involving optogenetic or chemogenetic manipulations of the LC, before subjecting them to stress experiments. These assessments account for both correct optic fiber placement and virus expression (Privitera et al., 2020). If an animal did not show a clear pupil response, it was not included any further in the study. To demonstrate correct cannula placement for drug infusion of isoprotenerol in the dorsal hippocampus, we added a representative image of cannula placement in Supplementary Figure 1h.

Comment 7: Time of day for tissue collection used in genetic analysis should be reported for all studies conducted or reanalyzed.

Response: Thank you for pointing out this omission. Tissue collection for RNA-seq analysis was always performed between 11am and 5pm during the dark phase of the reversed light-dark cycle. We have added this information to the corresponding method section (“Tissue collection”).

Reviewer #1 (Recommendations For The Authors):

Comment 8: This is a well written, comprehensive and rigorous manuscript that will be of great interest to those in the scientific community.

Response: Thank you for the positive evaluation of our work and for the constructive feedback.

Reviewer #2 (Public Review):

Comment 1: The present manuscript investigates the implication of locus coeruleus-noradrenaline system in the stress-induced transcriptional changes of dorsal and ventral hippocampus, combining pharmacological, chemogenetic, and optogenetic techniques. Authors have revealed that stress-induced release of noradrenaline from locus coeruleus plays a modulatory role in the expression of a large scale of genes in both ventral and dorsal hippocampus through activation of β-adrenoreceptors. Similar transcriptional responses were observed after optogenetic and chemogenetic stimulation of locus coeruleus. Among all the genes analysed, authors identified the most affected ones in response to locus coeruleus-noradrenaline stimulation as being Dio2, Ppp1r3c, Ppp1r3g, Sik1, and Nr4a1. By comparing their transcriptomic data with publicly available datasets, authors revealed that these genes were upregulated upon exposure to different stressors. Additionally, authors found that upregulation of Ppp1r3c, Ppp1r3g, and Dio2 genes following swim stress was sustained from 90 min up to 2-4 hours after stress and that it was predominantly restricted to hippocampal astrocytes, while Sik1 and Nr4a1 genes showed a broader cellular expression and a sharp rise and fall in expression, within 90 min of stress onset.

Overall, the paper is well written and provides a useful inventory of dorsal and ventral hippocampal gene expression upregulated by activation of LC-NA system, which can be used as starting point for more functional studies related to the effects of stress-induced physiological and pathological changes.

Response: We thank the reviewer for the careful assessment of our work.

Comment 2: However, I believe that the study would have benefited of a more comprehensive analyses of sex differences. Experiments in females were conducted only in one experiment and analyses restricted to the ventral hippocampus.

Response: In response to the comments by the reviewer, as well as Reviewer #1 and the editors, we have sequenced an additional 32 brain samples to expand the comparison of sex effects in females and males across dorsal and ventral hippocampus, and we included a new meta-analysis of 3 experimental datasets (51 male and 20 female) samples, to thoroughly assess sex differences in the transcriptomic response to stress. We refer the reviewer to our detailed response provided above to Reviewer #1, comment #2, and the updated results section on pages 3-4.

Comment 3: Although, the experiments were overall sound and the results broadly support the conclusion made, I think some methodological choices should be better explained and rationalized. For instance, the study focuses on identifying transcriptional changes in the hippocampus induced by stress-mediated activation of the LC-NA system, however NA release following stress exposure and pharmacological or optogenetic manipulation was mostly measured in the cortex.

Response: Because the hippocampus was used for RNA-sequencing, we could not assess NA release in the hippocampus (as this would require fiber implants that would interfere with molecular measures, or different tissue processing for HPLC). Nonetheless, we wanted to assess the transcriptional changes in the hippocampus, while simultaneously measuring successful stimulation of the LC-NA system in the same animals. To achieve this, we pursued 3 routes: 1) we used pupillometry to confirm functional LC activation; 2) we measured cFOS in the LC to directly demonstrate LC activation; 3) we assessed NA release using uHPLC (which requires larger tissue samples) and we chose the cortex because both cortex and hippocampus receive NA predominantly from the LC (Samuels & Szabadi, 2008). Importantly, we had previously shown that chemogenetic LC activation leads to a similar NA turnover in both the cortex and hippocampus, as measured by uHPLC (Zerbi et al., 2019). The relevant figure from that paper is inserted below to quickly show the striking similarity between hippocampus and cortex.

Author response image 1.

Levels of noradrenaline (NE) turnover (MHPG/NE ratio) in the cortex (CTX) and hippocampus (HC), measured in whole tissue with uHPLC 90min after hM3Dq-DREADD activation of the LC (copied and cropped from Zerbi et al, 2019, Neuron).

In response to the reviewers comment, we performed additional experiments to directly demonstrate that LC-activation with DREADDs as well as optogenetics causes an increase in hippocampal NA-release. We recorded NA release in the hippocampus (using fiber photometry combined with genetically encoded NA sensors). For DREADD activation, we observed a strong increase in hippocampal noradrenaline that started a few minutes after clozapine administration, and this increase was sustained throughout the duration of the 21 minute recording (see Supplementary Figure2c-e). For optogenetic LC activation, we find a rapid and immediate sharp increase in NA levels in the hippocampus (Supplementary Figure 2f-h). These experiments were performed in females and males and triggered similar responses. An adapted and cropped version of Supplementary Figure 2 is pasted below for ease of reading.

Please note that we could not perform a similar experiment using yohimbine, because the GRABNE sensors are based on the alpha-2 adrenergic receptor, thus yohimbine administration interferes with the photometry recording. However, we believe that it is clear from this response that strong activation of the LC leads to uniform release of NA in the hippocampus and cortex.

Author response image 2.

c, Schematic of fiber photometry recording of hippocampal NA during chemogenetic activation of the LC. After 5 min baseline recording in the homecage animals were injected with clozapine (0.03mg/kg, i.p.) and placed in the OFT for 21min. d, Average ΔF/F traces of GRABNE2m photometry recordings in response to chemogenetic activation of the LC (mean±SEM for hM3DGq+ and hM3DGq- split into females and males, n=3/group/sex). e, Peak ΔF/F response of fiber photometry trace. f, Schematic of fiber photometry recording of hippocampal NA during optogenetic activation of the LC. Animals were lightly anesthetized (1.5% isoflurane) and recorded in a stereotaxic frame. After 1 min baseline recording, animals were stimulated three times with 5Hz for 10s (10ms pulse width, ~8mW laser power) and recorded for 2 min post-stimulation. g, Average ΔF/F traces of the NA sensors GRABNE1m and nLightG in response to optogenetic activation of the LC (mean±SEM for females and males, n(females)= 10, n(males)=5. h, Peak ΔF/F response of fiber photometry trace.

Comment 4: Furthermore, behavioral changes following systemic pharmacologic or chemogenetic manipulation were observed in the open field task immediately after peripheral injections of yohimbine or CNO, respectively. Is this timing sufficient for both drugs to cross the blood brain barrier and to exert behavioral effects?

Response: We have previously shown that chemogenetic activation of the LC through clozapine elicits pupil responses within 1-2 minutes after injection (Privitera et al., 2020; Zerbi et al., 2019). This indicates that clozapine rapidly crosses the blood brain barrier and affects LC activity within a few minutes after injection. Our additional experiments using genetically encoded sensors in the hippocampus show this even more directly (Supplementary Figure 2d), see also the response to Comment 3 above.

Similarly, yohimbine also rapidly crosses the blood brain barrier within the same time frame (Hubbard et al., 1988). These observations are consistent with the rapid behavioral effects that can be detected within a few minutes after injection of clozapine for LC-DREADD activation (Zerbi et al., 2019), and for yohimbine as well (von Ziegler et al., 2023). In response to another comment of this reviewer, we have also re-analyzed the behavior presented in the current manuscript in time-bins of 3 minutes, which also shows the rapid onset of effects in response to yohimbine (within the first 3 min) and DREADDs (within 6 min), see Supplementary Fig. 3.

Comment 5: Finally, the study shows that activation of noradrenergic hippocampus-projecting LC neurons is sufficient to regulate the expression of several hippocampal genes, although the necessity of these projection to induce the observed transcriptional effects has been tested to some extent through systemic blockade of beta-adrenoceptor, I believe the study would have benefited of more selective (optogenetic or chemogenetic) necessity experiments.

Response: We understand the reviewer's point that blocking the LC during stress exposure would be an interesting experiment. However, it is very hard to completely silence the LC during intense stressors. In fact, despite intense efforts, we have not been able to silence the LC during swim stress exposure using DREADDs or other chemogenetic approaches (PSAM/PSEM). We were in fact able to silence the LC with the optogenetic inhibitor JAWS (and others have reported successful LC silencing with GtACR2), but there is a major issue involving the "rebound effect", where more NA is released once the inhibition is stopped. We would thus have had to optogenetically silence the LC for 45-90 min, which would create heat artifacts, and require challenging control experiments to draw firm conclusions. Given all these issues, we reasoned that blocking adrenergic receptors is a simple and elegant solution, which provides clear evidence for the necessity of beta-adrenergic signaling.

Reviewer #2 (Recommendations For The Authors):

Major concerns:

Comment 6: The study focuses on the identification of transcriptional changes in the hippocampus induced by stress-mediated activation of the LC-NA system, however, noradrenaline release following stress exposure or yohimbine injection was measured in the cortex. Authors should consider measuring NA concentrations in the hippocampus after exposure to swim stress or administration of yohimbine, or at least explain their choice to analyse to cortex in the manuscript.

Response: We have addressed this issue in detail in Response to "Reviewer 2, Comment #3", where we provided an overview of the additional data that support our approach. As mentioned before, measuring NA release after yohimbine is not compatible with our GRABNE-photometry approach, as the GRAB-sensor is based on alpha2-adrenoceptor. Here, we would like to add that measuring NA release using photometry during swim stress is also challenging. The challenge is the vigorous movement (swimming, typically in one direction), which creates pressure on the cables/implants. We felt that overcoming these experimental challenges (setup, troubleshooting and controls) would be beyond the scope of the paper, given that it is already known that this stressor leads to strong NA release in the hippocampus. We have now included references that demonstrate that all the stressors used in our work trigger NA increase in the hippocampus (see response to Reviewer 1, Comment 3): “Therefore, we assessed their expression in a dataset comparing the effect of various stressors on the hippocampal transcriptome (Floriou-Servou et al., 2018). The stressors included restraint, novelty and footshock stress, which have all previously been shown to increase hippocampal NA release (Hajós-Korcsok et al., 2003; Lima et al., 2019; Masatoshi Tanaka et al., 1982).”

Comment 7: Concerning the experiment aimed at investigating sex differences in gene expression, it is not clear the reason why authors decided to restrict their analyses in females to the ventral hippocampal only. The explanation that in males they did not detect major differences between the dorsal and ventral hippocampus is not sufficient, because there could have been different effects in females. Therefore, the conclusion made by the authors that their "results suggest that the transcriptomic response is independent of sex" is not entirely correct, since sex differences were only evaluated in the ventral hippocampus.

Response: We appreciate the reviewer's critique. As described above, we have now also sequenced the dorsal hippocampal tissue from the propranolol experiment (males and females, 32 samples) and additionally added an extensive meta-analysis of three large datasets (n=71) to compare transcriptional sex differences in response to stress. A detailed description of these experiments and how they have extended/supported our conclusions have been provided in response to Reviewer #1, Comment #2.

Comment 8: Besides the effects on females, the same experiment examined whether propranolol by itself (in the absence of stress) would have been able to alter gene expression: such effects were not examined in the dorsal hippocampus. In contrast, in a different experiment, the effects of isoproterenol on genes expression were restricted to the dorsal hippocampus only. Furthermore, related to this latter experiment, intra-dorsal hippocampal injection of isoproterenol should presumably mimic the rise in NA observed after stress exposure, why was gene expression measured 90 min after isoproterenol central injections while in the other experiments gene expression was determined 45 min after stress, that is when authors observe the peak NA concentration?

Response: We have addressed the reviewer's critique of dorsal vs ventral hippocampus by reanalyzing 32 additional samples from dorsal hippocampus of male and female mice after propranolol (or saline) injection. Please see response to Reviewer #1, comment #2.

Regarding the time points: We have chosen the 45 and 90 min time points mainly for two reasons. First, cFos protein changes are known to be strongest 90 min after neuronal activation. Second, because we wanted to capture gene expression changes triggered by NA release, we reasoned that these effects must be fast and should thus be measured at an early transcriptional time-point (45min). However, after performing the time-course experiment after swim stress exposure (Figure 4d,c), we observed that the LC-NA-sensitive genes (e.g. Dio2 and several PP1-subunits) show the strongest changes 90 min after stress exposure. Therefore, in some of our experiments we opted to analyze gene expression changes at 90min, converging with the time-point we typically use for cFos staining. Contrary to the reviewer's statement, peak NA concentrations are not observed 45 min after the various interventions, but rather the peak in the main metabolite (MHPG) is observed then, due to the temporal dynamics of NA release and breakdown. NA release occurs immediately upon stress exposure (or direct LC activation), which we also show in the new photometry data described above. Thus, rapid NA release triggers intracellular cascades that lead to downstream transcriptional changes, which peak presumably between 4590 min later.

Comment 9: Behavioral changes following systemic pharmacologic or chemogenetic manipulation were observed in the open field task immediately after peripheral injections of yohimbine or CNO, respectively. Is this timing sufficient for both drugs to cross the blood brain barrier and to exert behavioral effects? It is also not immediately clear the reason why the open field tasks have different durations depending on the experiments, which can also impact the results. Authors might also consider to split the open field data analyses in 2 or 3 min time-bins, to allow for a better comparison across the different results.

Response: We thank the reviewer for the suggestion to plot the behavior data as time-bins. We have implemented this change for the yohimbine and DREADD experiments, and updated the corresponding figure accordingly (Supplementary Figure 3, pasted below for ease of reading). The new visualization clearly shows that yohimbine injection triggers rapid behavioral effects already in the first three minutes, whereas the LC-DREADD activation triggers behavioral changes within 3-6 minutes after injection. Thus, clear drug effects are visible in the first 10 minutes, which is comparable to the standard OFT test (10min testing) shown in response to swim stress exposure (Suppl. Figure 3a). The choice to expose mice to the OFT for 21 minutes in total was due to the fact that we based our experimental approach on the optogenetic LC-stimulation protocol first published by McCall and colleagues (McCall et al, Neuron, 2015), in which the LC is stimulated for 3 min followed by 3 min pauses (see Suppl. Figure 3d). Because of this on-off design, we decided to keep the optogenetic analysis simple and show the overall effect (Supplementary Figure 3d), particularly as we know that NA dynamics do not recover rapidly enough after 3 min continuous stimulation to justify a bin-analysis (unpublished data).

Author response image 3.

Effects of acute stress and noradrenergic stimulation on anxiety-like behaviour in the open field test. a, Stress-induced changes in the open field test 45 min after stress onset. Stressed animals show overall reductions in distance traveled (unpaired t-test; t=3.55, df=22, p=0.0018), time in center (welch unpaired t-test; t=3.50, df=13.61, p=0.0036), supported rears (unpaired t-test; t=3.39, df=22, p=0.0026) and unsupported rears (unpaired t-test; t=5.53, df=22, p = 1.47e-05) compared to controls (Control n = 12; Stress n = 12). This data have been previously published (von Ziegler et al., 2022). b, Yohimbine (3 mg/kg, i.p.) injected animals show reduced distance traveled (unpaired t-test; t=2.39, df=10, p=0.03772), reduced supported rears (unpaired t-test; t=6.56, df=10, p=0.00006) and reduced unsupported rears (welch unpaired t-test; t=3.69, df=4.4, p = 0.01785) compared to vehicle injected animals (Vehicle n = 6; Yohimbine n = 7). c, Chemogenetic LC activation induced changes in the open field test immediately after clozapine (0.03 mg/kg, i.p.) injection. hM3Dq+ animals show reduced distance traveled (unpaired t-test; t=6.28, df=13, p=0.00003), reduced supported rears (unpaired t-test; t=4.28, df=13, p=0.0009), as well as reduced unsupported rears (welch unpaired t-test; t=4.28, df=13, p = 0.00437) compared to hM3D- animals (hM3Dq- n = 7; hM3Dq+ n = 8). d, Optogenetic 5 Hz LC activation induced changes during the open field test. ChR2+ animals show reduced supported rears (unpaired t-test; t=2.42, df=64, p=0.0185) and reduced unsupported rears (unpaired ttest; t=2.91, df=64, p = 0.00499) compared to ChR2- animals (ChR2- n = 32; ChR2+ n = 36). Data expressed as mean ± SEM. p < 0.05, p < 0.01, p < 0.001, **p < 0.0001.

Comment 9: The study shows that activation of noradrenergic hippocampus-projecting LC neurons is sufficient to regulate the expression of several hippocampal genes. I believe the study would have benefited of more selective necessity experiments. Authors might consider adding optogenetic (or chemogenetic) experiments aimed at inhibiting LC-NA hippocampal projections during stress exposure (or, alternatively, perform intrahippocampal pharmacological blockade of β-adrenoreceptors during stress exposure), and determine the effects on gene expression.

Response: We kindly refer the reviewer to our previous response to Comment #2 above.

Minor concerns:

There is a typo in the abstract. Please correct "LN-NA" with "LC-NA"

Response: Thank you, we have corrected it.

References

Bangasser, D. A., Eck, S. R., & Ordoñes Sanchez, E. (1/2019). Sex differences in stress reactivity in arousal and attention systems. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 44(1), 129–139.

Bangasser, D. A., Wiersielis, K. R., & Khantsis, S. (06/2016). Sex differences in the locus coeruleusnorepinephrine system and its regulation by stress. Brain Research, 1641, 177–188.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

All comments made in the public section.

We would like to thank the reviewer for their assessment of our study and for suggestions for additional experiments to follow up our studies.

Reviewer #2 (Recommendations For The Authors):

‐ Preparation of spike proteins and VLPs. Although Triton‐X114 extraction was done to remove endotoxin from the recombinant spike protein preparations, its removal efficiency depends on the levels of endotoxin in the samples. Therefore, the residual endotoxin levels in each of the test samples and batches should be measured. Even very low but varying levels of residual endotoxin would substantially impact the reported results, as they create inconsistent data that are not interpretable.

Certainly, endotoxin contamination in instilled materials is always an issue. Established protocols for inducing acute inflammatory responses using endotoxin outline specific ranges of endotoxin levels in the instillation materials. To induce acute lung inflammation in mice at least 2 µg of endotoxin must be instilled. We have endeavored to reduce the possibility of endotoxin contamination in our recombinant proteins by using a mammalian expression system; careful aseptic culture and protein purification techniques; and a final Triton-X114 partitioning protocol. We assessed the possibility of endotoxin contamination using the Pierce™ Chromogenic Endotoxin Quant Kit, which is based on the amebocyte lysate assay. Our analysis revealed that the endotoxin level in the purified recombinant protein preparation is below 1.0 EU/ml, which closely aligns with the levels specified for recombinant proteins. An endotoxin concentration of 1.0 EU/ml is equivalent to approximately 0.1 ng/ml. Throughout all mouse nasal instillation experiments, the total volume of recombinant protein administered did not exceed 6 µl. The amount of contaminant endotoxin instilled did not exceed 1 pg (50 µl of 0.02 ng/ml of endotoxin). Consequently, we can confirm that the extent of endotoxin contamination is at trace levels. Moreover, our study reveals multiple results indicating that the level of endotoxin contamination in the recombinant protein was inadequate to independently induce neutrophil recruitment in the cremaster muscle, lymph nodes, and liver. For further insights, refer to Figure 5.

‐ Doses of spike and VLPs: The amount of spike protein incorporated into HIV Gag‐based VLPs should be determined and compared to that found in the native SARS‐CoV‐2 virus particles. This should provide more physiologic doses (or dose ranges/titration) of spike than the arbitrary doses (3 ug or 5 ug) used in the mouse experiments.

To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein in the tissue samples. We determined that instillation of 3 µg of Alexa 488 labeled spike protein yielded the optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher were commonly used. Regarding the titer of spike-incorporated VLPs, it is important to highlight that we did not directly compare the quantity of spike protein present in NL4.3 VLPs to that of the naïve SARS-CoV-2 virus. HIV-1 and SARS-CoV-2 viruses typically carry around 70 gp120 spikes and 30 spikes, respectively. We estimated that SARS-CoV-2 spike-incorporated NL4.3 VLPs may display twice the number of spikes compared to naïve SARS-CoV-2. Notably, our measurements of SARS-CoV-2 spike on NL4.3 VLPs demonstrated similar behavior to SARS-CoV-2 in terms of specific binding to ACE2-expressing 293T cells, indicating their functional similarity in this context.

Author response image 1.

Spike protein-incorporated NL4.3 VLPs test with human ACE2-transfected HEK293 cells. The wild-type spike protein-incorporated VLPs and delta envelope NL4.3 VLPs were analyzed using human ACE2-transfected HEK293 cells. The first plot shows ACE2 expression levels in HEK293 cells. The second plot displays the binding pattern of Delta Env NL4.3 VLPs on ACE2-expressing HEK293 cells. The third plot illustrates the binding pattern of wild-type spike protein-incorporated NL4.3 VLPs on ACE2expressing HEK293 cells. The histogram provides a comparison of VLP binding strength to ACE2expressing HEK293 cells.

‐ The PNGase F‐treated protein was not studied in Fig 1. In Fig 2, glycan‐removal by PNGaseF has little effects on cell uptake and cell recruitment in the lung. If binding to one of the Siglec lectins is a critical initial step, experiments should be designed to evaluate this aspect of the spike‐cell interaction in a greater depth.

As the reviewer states results with the PNGase F-treated protein were not shown in Fig. 1 although we showed results in Figs. 2 & 3. See discussion below about our preparation of the PNGase F-treated protein. Perhaps because we elected to use a purified fraction that retained ACE2 binding, the protein we used likely retained some complex glycans. As the reviewer notes the PNGase F treated protein had similar overall cellular recruitment and uptake profiles compared to the untreated spike protein. The PNGase Ftreated fraction we used no longer bound Siglec-F in the flow-based assay, shown in Fig. 7. This argues that the initial uptake and cellular recruitment following intranasal instillation of the Spike protein did not depend upon the engagement of Siglec-F. While Siglec-F on the murine alveolar macrophage can likely efficiently capture the spike proteins other cellular receptors contribute and the overall impact of the spike protein on alveolar macrophages likely reflects its engagement of multiple receptors.

• Enzymatic removal of sialic acids from spike may be one parameter to explore. The efficiency of enzymatic removal should also be verified prior to experiments. Finally, the authors need to assess whether the proteins remained functional, folded properly, and did not aggregate.

To obtain the de-glycosylated form of the SARS-CoV-2 spike protein, we employed PNGase F enzymatic digestion to remove glycans. Subsequently, the spike protein was purified using a size exclusion column. During this purification process, the PNGase F-treated spike protein segregated into two distinct fractions, specifically fraction 6 to 8 and fraction 9 to 11 (see revised Figure 1- figure supplement 1).

Author response image 2.

Size exclusion chromatography. The peak lines represent the absorbance at 280 nm. PNGase F-treated spike proteins were loaded onto a Superdex 26/60 column, resolved at a flow rate of 1.0 ml/min, and collected in 1 ml fractions.

The Coomassie blue staining of an SDS-PAGE gel revealed that fractions 6 to 8 likely underwent a more pronounced de-glycosylation by PNGase F compared to fractions 9 to 11. Additionally, during the size column purification, we noticed that fraction 6 to 8 exhibited a faster mobility than the untreated spike protein, implying a potentially substantial modification of the protein's conformation. To probe the functional characteristics of the de-glycosylated spike protein in fraction 6 to 8, we conducted binding tests with human ACE2. Strikingly, the spike protein in fraction 6 to 8 completely lost its binding affinity to ACE2, indicating a loss of its ACE2-binding capability. Conversely, the protein in fraction 9 to 11 showed partial de-glycosylation but still retained its original functionality to bind to ACE2 and its antibody.

Author response image 3.

FACS analysis of various spike protein-bound beads. Protein bound beads were detected with labeled spike antibody, recombinant human ACE2, and recombinant mouse Siglec-F.

Based on these results, we concluded that fraction 9 to 11 would be the most suitable choice for further studies as the de-glycosylated spike protein, considering its retained functional properties relevant for ligating ACE2 and antibody motifs yet had lost Siglec-F binding. In the revised manuscript we have describe in more detail the purification of the PNGase F treated Trimer and its functional assessment.

‐ Increases in macrophages and alveolar macrophages by Kifunensine Tx spike in Fig 2A suggest effects that are not related to Siglec lectins. These effects are not seen with the wild type or D614 spike trimers, so the relevance of high‐ mannose spike is unclear. On the other hand, there were clear differences between Wuhan and D614 trimers seen in Fig 2A and 2B, but there was no verification to ascertain whether these differences were indeed due to strain differences and not due to batch‐to‐batch variability of the recombinant protein production. The overall glycan contents of the Wuhan and D614 spike protein samples should be measured. If Siglec interaction is the main interest in this study, the terminal sialic acid contents should be determined and compared to those in the corresponding strains in the context of native SARS‐CoV‐2 virions.

Our initial observation that Siglec-F positive alveolar macrophages (AMs) avidly acquired spike proteins followed by a rapid leukocyte recruitment provided the rational for us to examine the impact of modifying the glycosylation pattern on the spike protein (de-glycosylated and spike variants) on their binding tropism and their cellular recruitment profiles in the lung. In this context, we examined the influence of several glycan modification on spike proteins, hypothesizing that these modifications would alter the acquisition of the spike protein by mouse AMs compared to the wild-type trimer. While we did not conduct an indepth analysis of the glycan composition and terminal sialic acid contents of the SARS-CoV-2 spike proteins we used we did verify that the different proteins behaved as expected. Most of the biochemical studies were performed in Jim Arthos’ laboratory, which has a long interest in the glycosylation of the HIV envelope protein. On SDS-PAGE the SARS-CoV-2 spike protein purified from the Kifunesine treated CHO cells exhibited a 12 kDa reduction. It bound much better to L-Sign, DC-Sign, and maltose binding lectin, and poorly to Siglec-F. In the cellular studies it bound less well to most of the cellular subsets examined including murine alveolar macrophages. In studies with human blood leukocytes, it relied on cations for binding. However, it retained its toxicity directed at mouse and human neutrophils and it elicited a similar cytokine profile when added to human macrophages. The D614G mutation increased the spike protein binding to P-Selectin, CD163, and snowdrop lectin (mannose binding) suggesting that the mutation had altered the glycan content of the protein. We used the D614G spike protein in a limited number of experiments as it behaved like the wild-type protein except for a slightly altered cellular retention pattern 18 hrs after intranasal instillation. In the revised manuscript we have included its binding to peripheral blood leukocytes. The D614G mutation conferred stronger binding to human monocytes than the original Spike protein. As discussed above, we recovered two fractions following the PNGase F treatment, one with a 40 kDa reduction on SDS-PAGE and the other a 60 kDa decrease and we chose to evaluate the fraction with a 40 kDa reduction in subsequent experiments. Consistent with a loss of N-linked glycans the PNGase F treatment reduced the binding to the lectin PHA, which recognizes complex carbohydrates, and it resulted in a sharp reduction in Siglec-F binding. The lower molecular weight fraction recovered after PNGase F treatment no longer bound ACE2. While our studies showed that alveolar macrophages likely employ Siglec-F as a capturing receptor they possess other receptors that also can capture the spike protein. The downstream consequences of engaging SiglecF and other Siglecs by the SARS-CoV-2 spike protein will require additional studies.

While acknowledging the possibility of some batch-batch variation in recombinant protein preparation, we don’t think this was a major issue. We have noted some batch-batch variations in yield- efficiency, however the purified proteins consistently gave similar results in the various experiments.

‐ Fig 3: The same concern described above applies to the hCoV‐HKU1 spike protein. In Panel D, the PNGase and Kifunensine treatment did not appear to abrogate the neutrophil recruitment. Panel A did not include PNGase and Kif Tx spike proteins. Quantification of images in panel D is missing and should be done on many randomly selected areas.

We analyzed the neutrophil count of images in panel D and the results are presented. (Figure 3-figure supplement 1C). The Kifunensine treatment reduced the neutrophil recruitment at 3 hours, while the PNGase F treated Spike protein recruited as well or slightly more neutrophils. The hCoV-HKU1 S1 domain did not differ much from the saline control.

‐ Fig 4: Kifunensine Tx spike caused more increase in neutrophil damage after intrascrotal injections. PNGase Tx spike was not tested. Connection between Siglec‐spike binding and neutrophil recruitment/damage is lacking.

Exteriorized cremaster muscle imaging functions as a model system for monitoring neutrophil behavior recruited by spike proteins within the local tissue, distinct from Siglec F-positive alveolar macrophages residing in lung tissue. Hence, our primary focus was not on investigating the Siglec/Spike protein interaction. Consequently, we did not utilize PNGase F-treated spike protein in these experiments. To clarify this issue, we added a sentence in main text ‘Although this model lacks Siglec F-positive macrophages, it is worth monitoring the effect of the SARS-CoV-2 Spike protein on neutrophils recruited in the inflammatory local tissue.’

‐ Fig 5. Neutrophil injury was also seen after inhalation (intranasal) of spike protein in mice and in vitro with human neutrophils. Panel B shows no titrating effects of spike (from 0.1 to 2) on Netosis of murine neutrophils. Panel C: Netosis was seen with human neutrophils at 1 but not 0.1. Is this species difference important?

Given the observation of neutrophil NETosis in the mouse imaging experiment, our objective was to characterize the direct impact of the spike protein on human and murine neutrophils. The origins of the neutrophils are different as the murine neutrophils were purified from mouse bone marrow while the human neutrophils were purified from human blood. Both purification protocols led to greater than 98% neutrophils. However, the murine neutrophils contain many more immature cells (50-60%) because the bone marrow served as their source. Furthermore, the murine neutrophils are from 6–8-week-old mice while the human neutrophils are from 30-50 year-old humans. More work would be needed to sort out whether there is any difference between human and mouse neutrophils in their propensity to undergo netosis in response to Spike protein.

‐ Kifunensine Tx again did not cause any reduction, indicating the lack of involvement of sialic acid. How was this related to Siglec participation directly or indirectly? There was no quantification for Panel D.

We do not think that Siglecs play a role in the induction of neutrophil netosis as the Spike proteins lacking Siglec interactions induced similar levels of netosis. Likely other neutrophil receptors are important. As noted in the text,

"human neutrophils express several C-type lectin receptors including CLEC5A, which has been implicated in SARS-CoV-2 triggered neutrophil NETosis." Our goal with the data in Panel D was to visualize human neutrophil NETosis on trimer-bearing A549 cells we relied on the flow cytometry assays for quantification.

‐ The rationale for testing cation dependence is unclear and should be described. What is the significance of "cations enhanced leukocyte binding particularly so with the high mannose protein"? Are there cationdependent receptors for spike independent of glycans and huACE‐2? If so, how is this relevant to the main topic of this paper?

It is well known that many glycan bindings by C-type lectins are calcium-dependent, involving specific amino acid residues that coordinate with calcium ions and bind to the hydroxyl groups of sugars. As discussed in our previous draft, the C-type lectin receptor L-SIGN has been suggested as a calciumdependent receptor for SARS-CoV-2, specifically interacting with high-mannose-type N-glycans on the SARS-CoV-2 spike protein. Therefore, it was worthwhile to investigate the calcium-dependent manner of spike protein binding to various types of immune cells. We added some data to this figure. It now includes the binding profile of the D614G protein. In addition, we corrected the binding data by subtracting the fluorescent signal from the unstained control cells.

‐ Fig 7: human Siglec 5 and 8 were studied in comparison with mouse Siglec F. Recombinant protein data are not congruent with transfected 293 cell data. Panel A, the best binding to hSiglec 5 and 8 are the PNGase F Tx spike protein; how to interpret these data? Panel B: only the WT and D614G spike proteins binding to Siglec 5 and 8 on transfected cells. It made sense that kif Tx (high‐mannose) and PNGaseF Tx (no glycan) spike would not bind to the Siglecs, but they did not bind to ACE2 either, indicative of nonfunctional spike proteins.

We discussed this as follows: ‘The closest human paralog of mouse Siglec-F is hSiglec-8 (reference 40). While expressed on human eosinophils and mast cells, human AMs apparently lack it. In contrast, human AMs do express Siglec-5 (reference 37). Along with its paired receptor, hSiglec-14, Siglec-5 can modulate innate immune responses (reference 41). When tested in a bead binding assay, in contrast to Siglec-F, neither hSiglec-5 or -8 bound the recombinant spike protein, yet their expression in a cellular context allowed binding. The in vitro bead binding assay we established demonstrated the specific binding of the bait molecule to target molecules. However, it does have limitations in replicating the complexities of the actual cellular environment. As discussed previously the PNGase Tx fraction we used in these experiments retained ACE2 binding, but loss binding to Siglec-F in the bead assay. In a biacore assay, not shown, the PNGase Tx fraction bound L-Sign and DC-Sign better than the untreated trimer, and it retained human ACE2 binding although it bound less well than wild type-trimer. Why the PNGase Tx fractions bound poorly to the human ACE2 transfected HEK293 cells is unclear. A higher density of recombinant ACE2 on the beads compared to that expressed on the surface of HEK293 may explain the difference. Alternatively in the bead assay we used a recombinant human ACE2-Fc fragment fusion protein purified from HEK293 cells, while in the transfection assay, we expressed human full length ACE2. The biacore, the bead binding, and the functional assays we performed all suggest that we had used intact recombinant proteins.

‐ Fig 8: This last set of experiment was to measure cytokine release by different types of macrophage cultures treated with spike from different cells with vs without Kifunensine Tx. The connection of these experiments to the rest is tenuous and is not explained. This is one of the examples where bits of data are presented without tying them together.

Dysregulated cytokine production significantly contributes to the pathogenesis of severe COVID-19 infection. Since we had observed strong binding of the spike protein to human monocytes and murine alveolar macrophages, we tested whether the spike protein altered cytokine production by human monocyte-derived macrophages. Depending on the culture conditions human monocytes can be differentiated M0, M1, or M2 phenotypes. Each type of macrophage responds differently to stimulants, often leading to distinct patterns of cytokine secretion. These patterns offer valuable insights into the immune response. The cytokine profiling conducted in this study enhances our understanding of how distinct macrophage types react to the spike protein.

‐ Discussion section did not describe how the various experiments and data are tied together. The authors explained the interactions of spike with different cell types in each paragraph separately, leaving this reviewer really confused as to what the authors want to convey as the main message of the paper.

We have modified discussion to address this issue.

Reviewer #3 (Recommendations For The Authors):

‐ The authors may want to refer to "intranasal instillation" to distinguish it from inhalation of an aerosolised liquid. How was the dose of the spike protein selected? There is some dose information in different settings, but usually between 0.1‐1 µg/ml or 0.1 µg‐5 µg range for in vivo injection, but the rationale for these ranges should be discussed. Is this mimicking a real situation during infections or a condition that might be used for vaccines?

While inhalation of aerosolized liquid closely mimics the natural route of human exposure to respiratory infectious materials, intranasal instillation with a liquid inoculum remains a widely accepted standard approach for virus or vaccine inoculation across various laboratory species. To clearly define our mouse model, we are changing the term 'inhalation' to 'instillation'. We previously answered to Reviewer #2 as following: To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa Fluor 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein on the tissue samples. Through our investigations, we determined that an instillation of 3 µg of Alexa Fluor 488 labeled spike protein yielded the most optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV-2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher was commonly used. Hence, based on these references and our preliminary studies, we selected 3 µg as the optimal concentration of instilled spike protein per mouse.

‐ Controls are not evenly applied. In some cases, the control for the large and complex SARS‐CoV2 spiker trimer is PBS. This seems insufficient to control against effects of injecting such complex proteins that can undergo significant conformational changes after uptake by a cell. In some cases, human coronavirus spike proteins from different viruses are used, but not much is said about these proteins and the different glycoforms are not explored. Are these prepared in the same way and do they have similar glycoforms. For example, if the Siglecs bind sialic acid on N‐linked glycans, then why do the purified Siglecs or Siglecs expressed in cells not bind the HKU‐1 spike, which would have such sialic acids if expressed in the same way as the CoV2 spike?

We have taken careful consideration to select an appropriate control material for these experiments. Initially, we opted to employ Saline or PBS for intranasal instillation as a vehicle control, a choice aligned with the approach taken in numerous previous studies involving lung inflammation mouse models. However, as the reviewer pointed out, we share the concern for achieving more meaningful and comparable control materials, particularly considering the size and complexity of the recombinant protein. In accordance with this perspective, we introduced glycan-modified spike proteins and the HCoV-HKU1 S1 subunit. Figure 3 illustrates our comprehensive evaluation of various spike proteins in terms of their impact on neutrophil recruitment. The diversity of sialic acid structures observed on recombinant proteins expressed within the same cell emerges from the intricate interplay of multiple factors within the cellular glycosylation machinery. This complex enzymatic process empowers cells to finely modulate glycan structures and sialic acid patterns, tailoring them to suit the diverse biological functions of distinct proteins. Despite structural similarities between the HCoV-HKU1 and SARS-CoV-2 spike proteins, their glycan modifications vary, thereby leading to distinct binding properties with various Siglec subtypes. All recombinant proteins used in this study except for the S1 subunits were generated within our laboratory. These include the wild-type spike protein, the D614G Spike protein, the Kifunensine-treated high mannose spike proteins, and the PNGase F-treated deglycosylated spike proteins. All the proteins were produced using the same protocol using CHO cells or on occasion HEK293F cells. We have indicated in the manuscript where we used HEK293F cells for the protein production otherwise they were produced in CHO cells.

‐ Figure 1 F‐I, there should be a control for VLP without SARS‐CoV2 spike as the VLP will contain other components that may be active in the system.

We tested the delta Env VLP for alveolar macrophage acquisition and neutrophil recruitment. We found a similar alveolar macrophage acquisition of the VLPs, but significantly less neutrophil recruitment compared to the free Spike protein. Since the uptake pattern with the VLPs matched that of the spike protein we did not consider adding a non-spike bearing VLP as a control. The rapid VLPs clearance into the lymphatics shortly after instillation may account for the reduced neutrophil recruitment following their instillation (Figure 1 figure supplement 2B, C).

‐ In Figure 1H, that do they mean by autofluorescence? Is this the cyan signal?

Is the green signal also autofluorescence as this is identified as the VLP?

We appreciate reviewer pointing out the typo regarding autofluorescence in the figure image. To provide clarity regarding the background in all lung section images, we have included additional supplemental data. During the fixation process of lung tissue, various endogenous elements in the tissue sample contribute to autofluorescence when exposed to lasers in the confocal microscope. Specifically, collagen and elastin present in the lung vasculature, including airways and blood vessels, are dominant structures that generate autofluorescence. To address this issue, we have implemented optimizations to distinguish between real signals and the noise caused by autofluorescence. We inadvertently failed to indicate the source of the strong cyan signal. The signal is due to Evans Blue dye delineating lung airway structures, which contain collagen and elastin—known binding materials for Evans Blue dye. This explains the strong fluorescence signals observed in the airways. We conjugated the recombinant spike protein with Alexa Fluor 488, and viral-like particles (VLPs) were visualized with gag-GFP. (Figure 1 figure supplement 2A, D)

‐ The control for SARS‐CoV2 spike trimer is PBS, but how can the authors distinguish patterns specific to the spike trimer from any other protein delivered by intranasal instillation. Could they use another channel with a control glycoprotein to determine if there is anything unique about the pattern for spike trimer?

Alveolar macrophages employ numerous receptors to capture glycoproteins that have mannose, Nacetylglucosamine, or glucose exposed. Galactose-terminal glycoproteins are typically not bound. We do not think that the Spike protein is unique in its propensity to target alveolar macrophages.

‐ What is the parameter measured in Figure S2B?

The percentage of the different cell types that have retained the instilled Spike protein at the three-hour time point. .

‐ The Spike trimer with high mannose oligosaccharides may gain binding to the mannose receptor. It may be helpful to state the distribution of this receptor and comment is it could be responsible for this having the largest effect size for some cell types.

We agree that the spike trimer with high mannose should target cells bearing the mannose receptor. We have modified the discussion to address this point and have mentioned some of the cell types likely to bind the high mannose bearing spike protein.

‐ A key experiment is the Evans Blue measure of lung injury in Figure 3A. A control with the HKU‐1 spike is also performed, but more details on the matching of this proteins production to the SARS‐CoV2 spike trimer and the quantification of these comparative result should be provided. To show that the SARSCoV2 spike trimer can cause tissue injury on its own seems like a very important result, but the impact is currently reduced by the inconsistent application of controls and quantification of key results. Furthermore, if these results can be repeated in the B6 and B6 K18‐hACE2 mouse model it might further increase the impact by demonstrating whether or not hACE2 contributes to this effect.

We repeated the lung permeability assay using the S1 subunit from the original SARS-CoV-2 and the S1 subunit from HCoV-HKU1. Both proteins were made by the same company using a similar expression system and purification protocol. Consistent with our original data, the instillation of the SARS-CoV-2 S1 subunit led to an increase in lung vasculature permeability, whereas the HCoV-HKU-1 S1 subunit had a minimal impact. (Figure 3 figure supplement 1A). This experiment suggests that it the S1 subunit that leads to the increase in vascular permeability. To address the contribution of hACE2 in this phenomenon, we conducted a lung permeability assay using K18-hACE2 transgenic mice. The K18-hACE2 transgenic mice exhibited a slight increase in lung vasculature permeability upon SARS-CoV-2 trimer instillation compared to the non-transgenic mice. This suggests that the hACE2-Spike protein interaction may contribute to an increase in lung vascular permeability during SARS-CoV-2 lung infection (Figure 3 figure supplement 1B).

‐ For Figure 4A, could they provide quantification. The neutrophil extravasation with Trimer appears quite robust, but the authors seem to down‐play this and it's not clear without quantification.

To address this issue, we analyzed and graphed the neutrophil numbers in each image. Injection of the trimer along with IL-1β significantly increased neutrophil infiltration. (Figure 4 figure supplement 1)

‐ In Figure 4B, there are no neutrophils at all in the BSA condition. Is this correct? Intravascular neutrophils were detected with PBS injection in Figure 4A.

We demonstrated that the neutrophil behaviors occur within the infiltrated tissue rather than within the blood vessels. Even when examining the blood vessels in all other images, it is challenging to identify neutrophils adhering to the endothelium of the blood vessels. Neutrophils observed in the PBS 3-hour control group are likely acute responders to the local injection, as a smaller number of neutrophils were observed in the 6-hour image.

‐ In Figure 5A the observation of neutrophil response in lung slices seems to be presented an anecdotal account. The neutrophil appears to polarize, but is this a consistent observation? How many such observations were made?

We have consistent observations across three different experiments. In addition, highly polarized and fragmented neutrophils were consistently observed in the fixed lung section images.

‐ The statement: "human Siglec‐5 and Siglec‐8 bound poorly despite being the structural and functional equivalents of Siglec F, respectively (37)". How can one Siglec be the structural and the other the functional equivalent of Siglec‐F? It might help to provide a little more detail as to how these should be seen.

Mouse Siglec-F has two distinct counterparts in the human Siglec system, both in terms of structure and function. In the context of domain structure, human Siglec-5 serves as the counterpart to mouse Siglec-F. However, it's important to note that while human Siglec-8 is not a genetic ortholog of mouse Siglec-F, it is expressed on similar cellular populations and functions as a functional paralog.

‐ The assay using purified proteins and proteins expressed in cells don't fully agree. For example, it's very surprising that recombinant Siglec 5 and 8 bind better to the non‐glycosylated form than to the glycosylated trimer. It appears from Figure S1 that the PNGaseF treated Spike contains at least partly glycosylated monomers and it also appears that the Kifunesine effect may be partial. PNGaseF may have a hard time removing some glycans from a native protein.

We were also surprised by the results using the PNGase F treated Spike protein in that it lost binding to Siglec-F and retained binding to human Siglec-5 and 8 in the bead assay, shown in Figure 7A. As explained above we used a purified fraction of the PNGase F treated protein that retained some functional activity as assessed in the ACE2 binding assay and in biacore assays not shown. The persistent binding of Siglec-5 and Siglec-8 suggests that removal of some of the complex glycans had revealed sites capable of binding Siglec-5 and 8. We would agree with the reviewer that the PNGase treatment we used only removed some of the glycans from the native protein. In data not shown the high mannose spike protein behaved as predicted in biacore assays, binding better to DC-SIGN and maltose binding lectin, but less well to PHA and less well to ACE2. The high mannose trimer also bound less to the HEK293 cells expressing ACE2, Siglec-5, or Siglec-8 as well as peripheral blood leukocytes.

Author Response

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

Public Reviews:

Reviewer #1:

Summary:

In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

Strengths:

(1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

(2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

Weaknesses:

The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

(1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

We are very grateful that Reviewer #1 proposed the possibility that MTA and MTB form a receptor-ligand complex in which one acting as the ligand and the other as the receptor. We considered this hypothesis when asking how dose MTRC function, too. However, our current results do not support this idea. For instance, if MTA were a ligand and MTB a receptor, we would expect a mating signal upon treatment with MTAxc protein, but not with MTBxc. Contrary to this expectation, our experiments revealed that both MTAxc and MTBxc exhibit very similar effects (Figure 5, green and blue), and their combined treatment produces a stronger effect (Figure 5, teal). This suggests a mixed function for both proteins. (We incorporated this discussion into the revised version [line 120-121, 240-244].) It is pity that our current knowledge does not provide a detailed molecular mechanism for this intricate system. We are actively investigating the protein structures of MTA, MTB, and the entire MTRC, hoping to gain deeper insights into the molecular functions of MTA and MTB.

Additionally, we also realized that the expression we used in the previous version, “simple receptor-ligand model”, is not clearly defined. As Reviewer #1 pointed out, in this section, we examined whether the individual proteins of MTA and MTB act as a couple of receptor and ligand. We think this is the simplest possibility as a null hypothesis for Tetrahymena mating-type recognition. We have clarified it in the revised version (line 90-91, 104-106). According to this section, we proposed that MTA and MTB may form a complex that serves as a recognizer (functioning as both ligand and receptor) (line 117-118).

(2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

Several factors could contribute to the inability of MTA/Bxc to induce costimulation. It is highly likely that additional components are necessary, given that MTA/B form a protein complex with other proteins. Moreover, the expression of MTA/Bxc in insect cells, compared with Tetrahymena, might result in differences in post-translational modifications. Additionally, there are variations in protein conditions; on the Tetrahymena membrane, these proteins are arranged regularly and concentrated in a small area, while MTA/Bxc is randomly dispersed in the medium. The former condition could be more efficient. If there is a threshold required to stimulate a costimulation marker, MTA/Bxc may fail to meet this requirement. Much more studies are needed to fully answer this question. We acknowledged this limitation in the revised version (line 244-248).

Reviewer #2:

This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

Thanks for the valuable comments and suggestions. We are currently in the process of constructing deletion strains for these genes. As of now, we have successfully obtained ΔMRC1-3 and MRC4-6 knockdown strains. Our preliminary observations indicate that ΔMRC1-3 strains are unable to undergo mating. However, we prefer not to include these results in the current manuscript, as we believe that more comprehensive studies are still needed.

Reviewer #3:

The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

We express our gratitude to Reviewer #3 for proposing the occupation model and have incorporated this possibility into the manuscript. We believe it is possible that occupation may serve as the molecular mechanism through which self-recognition negatively regulates mating. If there is a physical interaction between mating-type proteins of the same type, but this interaction blocks the recognition machinery rather than initiating mating, it can be considered a form of self-recognition. This aligns with the observation that strains expressing MTA/B6 and MTB2 mate normally with WT cells of all mating types except for VI and II (line 203-204). A concise discussion on this topic is included in the manuscript (line 288-293, 659-661). We are actively investigating the downstream aspects of mating-type recognition, and we hope to provide further insights into this question soon.

The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

We are very grateful that Reviewer #3 provide this insightful view and relevant papers. In response to the feedback, we removed the sentences regarding “multiple mating types greatly complicate the process” in the revised version. Instead, we have introduced a discussion section comparing the mating systems of yeasts and Tetrahymena (line 279-286).

The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013.

In T. thermophila, the MIC (diploid) contains several mating-type gene pairs (mtGP, i.e., MTA and MTB) organized in a tandem array at the mat locus on each chromosome. In sexual reproduction, the new MAC of the progeny develops from the fertilized MIC through a series of genome editing events, and its ploidy increases to ~90 by endoreduplication. During this process, mtGP loss occurs, resulting in only one mtGP remaining on the MAC chromosome. The mating-type specificity of mtGPs on each chromosome within one nucleus becomes relatively pure through intranuclear coordination. After multiple assortments (possibly caused by MAC amitosis during cell fission), only mtGPs of one mating-type specificity exist in each cell, determining the cell’s mating type.

It is pity that the exact mechanisms involved in this complicated process remain a black box. The loss of mating-type gene pairs is hypothesized to involve a series of homologous recombination events, but this has not been completely proven. Furthermore, there is no clear understanding of how intranuclear coordination and assortment are achieved. While we have made observations confirming these events, a breakthrough in understanding the molecular mechanism is yet to be achieved.

We included more information in the revised version (line 672-683). Given the complexity of these unusual processes, we recommend an excellent review by Prof. Eduardo Orias (PMID: 28715961), which offers detailed explanations of the process and related concepts (line 685-686).

Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

Sorry that we didn’t realize the term “contact” is not precise enough. In Tetrahymena, physical contact is indeed necessary, but it can refer to temporary interactions. Unlike yeast, Tetrahymena cells exhibit rapid movement, swimming randomly in the medium. Occasionally, two cells may come into contact, but they quickly separate instead of sticking together. Even newly formed loose pairs often become separated. As a result, one cell can come into contact with numerous others and stimulate them. We have clarified this aspect in the revised version (line 50-51, 57).

Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

We have clarified the expression in the revised version (line 20, 38, 40, 45).

Recommendations for the authors:

Reviewer #1:

I really enjoyed reading this manuscript and I think a few tweaks in the writing/data presentation could greatly improve the experience for the reader:

(1) The information about your previous work in identifying downstream proteins CDK19, CYC9, and CIP1 (lines 170-173) could be directly presented in the introduction.

We have moved this information in the introduction in the revised version (line 74-77).

(2) For a reader who is not familiar with Tetrahymena, a few more details on how reporter and knock-out lines are generated would be beneficial.

We introduced the knock-out method in Figure 2 – figure supplement 1B, HA-tag method in Figure 3A, and MTB2-eGFP construction method in Figure 4E. In addition, we introduced how co-stimulation markers observed in Materials and Methods (line 404-410)

(3) Figures 5 and 6: clarify the types of pairing and treatments that were done directly in the figure (eg. adding additional labels). As of now, it is necessary to go through the text and legend to try and understand in detail what was done.

Cell types and treatments were directly introduced in the revised figure (Figure 5 and 6).

(4) The logical transition in lines 136-142 is hard to follow.

We rewrote this paragraph in the revised version (lines 143-156). Additionally, we added a figure to illustrate the theoretical mating-type recognition model between WT cells and ΔCDK19, ΔCYC9 cells, MTAxc, MTBxc proteins, and ΔMTA, ΔMTB cells (Figure 2 – figure supplement 1D-G).

(5) Lines 191-196: the fact that cells expressing multiple mating types can self goes against an active self-rejection system - if this is the case there should be self-rejection among all expressed mating types. Unless non-self recognition is an active process and self-recognition is simply the absence of non-self recognition. The authors briefly mention this in lines 263-265, but it would be interesting to expand and clarify this.

We appreciate that Reviewer #1 notice the interesting selfing phenotype of the MTB2-eGFP (MTVI background) strain. We further discussed it in the revised manuscript (line 298-306).

(6) The authors briefly mention the possibility of different mating types using different recognition mechanisms (lines 255-260), based on the big differences in the size of the mating-specific region of MT proteins. Following this and the weakness nr. 2, I think it would be pertinent to gather and present more information on the properties and structures of the mating-type specific regions of MT proteins. Simple in silico analysis of motifs, structure, etc. could help clarify the role of these regions. It seems more parsimonious that MT proteins would have variable mating type specific regions that account for the recognition of the different mating types, and conserved cytoplasmic functions that could trigger a single downstream signaling cascade. It would be interesting to know the authors' opinion on this.

We are very grateful for this suggestion. Actually, we are currently working on determining the 3D structure of MTRC. The Alphafold2 prediction indicates that the MT-specific region is comprised of seven global β-sheets, resembling the structure of immunoglobulins (Ig). Our most recent cryo-EM results have revealed a ~15Å structure, aligning well with the prediction. However, the main challenge lies in the low expression levels, both in Tetrahymena and insect/mammal cells. We anticipate obtaining more detailed results soon. Therefore, we prefer to present the MT recognition model with robust experimental evidence in the future, and didn’t discuss too much on this aspect in the current manuscript.

(7) Adding a figure including a proposed model, as well as expanding the discussion on the points presented as "weaknesses" would help clarify the ideas/hypothesis on how the mating recognition works. I think this would really elevate the paper and help highlight the results.

We added a figure to introduce the model and the weaknesses in the revised version (Figure 7, line 656-665).

(8) Line 202-203: It is far-fetched to infer subcellular localization based on the data presented here, couterstaining with other dyes and antibodies specific to certain cell components, as well as negative control images, are required.

Thanks for the suggestion. We attempted to stain cell components using various dyes and antibodies. Unfortunately, we found that cell surface and cilia (especially oral cilia) is very easy to give a false positive signal. We think this issue seriously affects the credibility of the results. It may seem like splitting hairs, but we are trying to be precise.

Meanwhile, we still believe the mating-type proteins localizes to cell surface because MTA-HA is identified in the isolated cell surface proteins.

Regarding negative control, as shown in Fig. 4G, where a MTB2-eGFP cell is pairing with a WT cell, no GFP signal is observed in the WT cell.

(9) Lines 131: clarify the sentence - expression of Con-A receptors requires both MTA and MTB (MTA to receive the signal).

We modified the sentence in the revised version (line 139-140).

Reviewer #2:

Minor points.

(1) Line 194-196. Why are these cells able to self?

These cells able to self may because the MTRC contain heterotypic mating-type proteins (MTA6 and MTB2), which activate mating when they interact with another heterotypic MTRC (line 207-208).

(2) Line 232. What do the authors mean by the term synergistic effect here? Definition and statistics?

Sorry about the confusion. The synergistic effect refers to the effect of MTAxc and MTBxc become stronger when using together. We clarified it in the revised version (line 232).

(3) For Figure 4 panel D, are there antibodies that are available as a control for cilia? If so, then blotting this membrane would show that cilia-associated proteins are in the cilia preparation, which is a standard control for sub-cellular fractionation.

Thanks for the suggestion. Unfortunately, we didn’t find a suitable cilia-specific antibody yet. Instead, we employed MS analysis to confirm the presence of cilia proteins in this sample (line 195-196, Figure 4–Source data 1). We also observed the sample under the microscope, which directly revealed the presence of cilia (Figure 4C).

(4) At least one reference cited in the text was not present in the reference list. The authors should go through the references cited to ensure that all have made it into the reference list.

We have checked all the references.

Some minor edits:

(1) MTA and MTB are presented in both roman and italics (e.g. line 209) in the manuscript. Maybe all should be in italics? Or is this a distinction between the gene and the protein?

The italics word (MTA) refers to gene, and non-italics word (MTA) refers to protein.

(2) Line 251. Change "achieving" to "achieve".

We have corrected this word (line 266).

Reviewer #3:

Line 101. It would help to explain this expectation earlier in this paragraph.

We explained the expectation in the revised version (line 92-97, 104-106).

Line 109. How is a co-receptor different from the MTRC complex?

We have rewritten the relevant sentences to enhance clarity (line 116-119). The molecular function of the MTRC complex could involve acting as a co-receptor or recognizer (functioning as both ligand and receptor). Based on the results presented in this section, we propose that MTA and MTB may function as a complex, but the confirmation of this hypothesis (MTRC) is provided in a later section. Therefore, we did not use the term “MTRC” here. These sentences briefly discuss the molecular function of this complex and explain why MTRC does not appear to function as a co-receptor.

Line 251: which "dual approach" is referred to?

Dual approach is referred to both self and non-self recognition. We explained it in the revised version (line 265-266).

Line 258: what "different mechanisms" do the authors have in mind? Why would a different mechanism be expected? The different sizes could have evolved for (coevolutionary?) selection on the same mechanism.

Sorry about the confusion. We clarified it in the revised version (line 269-278).

What we intended to express is that we are uncertain whether the mating-type recognition model we discovered in T. thermophila is applicable to all Tetrahymena species due to significant differences in the length of the mating-type-specific region. We believe it is important to highlight this distinction to avoid potential misinterpretations in future studies involving other Tetrahymena species. At the same time, we look forward to future research that may provide insights into this question.

Fig 2 C&D. Is it correct that these figures show the strains only after 'preincubation'? This is not apparent from the caption of the text. Additionally, the order of the images is very confusing. Write in the figures (so not just in the caption) what the sub-script means.

These panels are re-organized in the revised version (Fig. 2C&D). There are three kinds of pictures: “not incubated”, “WT pre-incubated by mutant” and “mutant pre-incubated by WT”.

The methods used to generate Figure 5 are not clearly described. I understand that the obtained xc proteins were added to the cells, and then washed, after which a test was performed mixing WT-VI and WT-VII cells. Were both cells treated? Or only one of the strains? The explanation for the reused washing medium is not clear and the method is not indicated.

Both cells are treated. More details are provided in the revised manuscript (line 230-231, 633-634, 637-639, Fig. 5). To prepare the starvation medium containing mating-essential factors, cells were starved in fresh starvation medium for ~16 hours. Subsequently, cells were removed by three rounds of centrifugation (1000 g, 3 min) (line 330-332).

In general, the figures are difficult to understand without repeated inquiries in the captions. Give more information in the figures themselves.

More information is introduced in the figure (Fig. 2C, Fig. 3B, Fig. 4A, B, D, Fig. 5 and Fig. 6).

Given the author's thesis, I was wondering if it is a reasonable task to determine/ define the correctness of a belief in such a sense, since many people hold beliefs to be deeply personal and private. Not all beliefs that a person has may necessarily be true, and this knowledge may be possessed by the person themselves as well, but that does not make the belied any less of one. A belief is not fact, and facts are not beliefs--normatively too, when we speak of beliefs, there is an aspect of the personal attached to it. We do not use "believes" as a placeholder for "knows", nor is it a moniker for fact--and i think that inherently suggests that the nature of the correctness of a belief is more subjective, and thus cannot really be said to be determined through the truth value of its propositions. A belief may seem irrational on the surface, but if it holds great value for the person who upholds said belief, it seems uncharitable to suspend judgement on the correctness of it--especially if it a belief that does not have to do with how things are in the world.

As we engage with viral content, whether as creators, participants, or observers, these value statements require us to reflect on the broader implications of online interactions. Behind every viral phenomenon lies a complex web of human stories, aspirations and responsibilities that deserve our thoughtful consideration.

Its interesting how he put the word hear in quotes because it shows that we have no way of knowing if badgers perceive sound the way that we do and process it in the way that we can. What we think of as hearing may not be the same process for animals. Even though we can scientifically know the hearing levels of a badger, we can't ever really know what hearing is like for them

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: #RC-2023-02281

Corresponding author(s): Maurizio Molinari

Point-by-point description of the revisions

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

• *

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

In this manuscript from Fasana et al., the authors present data that investigates potential compensatory degradation pathways for misfolded glycoproteins in the ER - postulating that the ER-to-lysosome associated degradation (ERLAD) pathway becomes employed in the absence of a path for substrates to reach the ER-associated degradation (ERAD) mechanism. Using the classic ERAD substrate alpha1-antitrypsin NHK variant (NHK), the authors first demonstrate that pharmacologically preventing access of NHK to ERAD either with KIF (early) or PS-341 (late) elevates the number of LAMP-1 positive endolysosomes also immunoreactive for NHK (via HA), similar to what is observed for the ATZ variant that forms polymers in the ER (Fig 2). The authors next use shRNAs that silence essential ERAD factors (EDEM1, OS-9) involved in glycan recognition to demonstrate comparable enrichment of NHK in endolysosomes through genetic disruption (Fig 3). Next, the authors employ FAM134B-deficient MEFs to demonstrate the requirement for this ER-phagy receptor when ERAD is unavailable (Fig 4). Reconstituting FAM134B-/- MEFs treated with KIF/PS-341 + Baf, with a full length FAM134B rescue plasmid restores endolysosomal accumulation of NHK while a FAM134B-∆LIR does not, providing supporting evidence for substrate rerouting to ERLAD. Finally, the authors use knockouts of Atg7 and Atg13 to demonstrate dependence on LC3 lipidation and independence from macro-ERphagy (Fig 6), that points towards a pathway that is like that used to remove ATZ polymers. From these data, the authors conclude that ERLAD is increasingly engaged for substrate degradation when ERAD is impaired.

MAJOR COMMENTS 1. All assays rely on quantification of the NHK-HA substrates by microscopy. Would it be possible for the authors to also include biochemical analysis of NHK - potentially including data assessing its changing abundance and glycosylation state?

To consider this, and other comments, the new submission includes biochemical data (pulse-chase analyses) on NHK (new panels A-D in Fig. 2) and on BACE457delta, an additional ERAD substrate (new Fig. 6). Please also refer to Comment 3.

In Figure 3D, the knockdown of OS-9.1/2 is modest compared to that of EDEM1 (Fig 3A). Moreover, there is only data from single shRNAs presented. Could the authors please at least include another shRNA to confirm and demonstrate whether the targeting to ERLAD is accordingly scaled to loss of access to ERAD (based on the degree of OS-9 or EDEM1 remaining)?

__The reviewer is right. The phenotype (i.e., lysosomal delivery of NHK, Figs. 3B, 3C) is quite modest upon EDEM1 silencing. However, one has to consider that in contrast to OS9 lectins, EDEM1 is an enzyme, and residual protein may partially facilitate NHK de-mannosylation and access to the ERAD pathways and therefore reduce the ERLAD contribution for NHK clearance in these cells. Moreover, cells also express EDEM2 and 3 that may partially compensate the loss of EDEM1. __

While degradation is implied, it is not specifically demonstrated at any point in the manuscript. Perhaps the authors might include some demonstration of NHK stabilization in one of the figures via a translational shutoff or pulse-chase assay.

__In the new submission, we show biochemical analyses (pulse-chase) that reveal the decay of radiolabeled NHK (Fig. 2A, lanes 1-3) and BACE457delta (Fig. 6A, lanes 1-3), the inhibition by PS341 (lanes 4, 5) and by KIF (lanes 8, 9), and the intervention of lysosomal enzymes when ERAD is inhibited (lanes 6, 7 and 10, 11). Moreover, we confirm that the protein delivered to the endolysosome is eventually degraded by performing a Bafilomycin washout experiment (new Fig. 2J-2O). __

10-30% of NHK-HA positive endolysosomes are detected even with Baf alone (e.g. Fig 2E)? Does this mean that Baf impairs ERAD to some extent since or is it evidence for continuous ERLAD involvement when ERAD is intact? If so, how much is its contribution?

Pulse-chase analyses (new Fig. 2D) and published data show that BafA1 or chloroquine do not inhibit clearance of the ERAD substrates NHK and BACE457delta (e.g., Liu et al 1999, Molinari et al 2002, references in the manuscript). A basal level of endolysosomal delivery between the 20 and 30% as quantified with LysoQuant is observed in all experiments (Figs. 2I, 2O, 3C, 3F, 4C, 4K, 5H, 6G, 6O), which have been performed in 3 different cell lines (3T3, HEK293, MEF). We measure similar basal levels also when ER-phagy is monitored on quantification of lysosomal delivery of endogenous ER marker proteins (e.g., CNX), possibly to be ascribed to constitutive ER phagy that controls physiologic ER turnover.

An accounting of how much ERLAD is contributing to NHK degradation with or without ERAD impairment is not really present.. Effectively, how much degradation capacity is ERLAD making up? These would be interesting data to include if possible as they would speak to the "division of labour" for ER substrate degradation its potentially dynamic nature.

The biochemical analyses show the contribution of ERLAD on NHK (new Figs 2B, 2C, grey zones) and BACE457delta (new Figs. 6B,C, grey zones) clearance, when ERAD is dysfunctional.

MINOR COMMENTS 1. In Figure 4, an increase is observed for the rescue of FAM134B-/-MEFs with WT FAM134B that is 50% greater that of WT MEFs, suggesting that its availability might be rate limiting. Could the authors compare the relative levels of FAM134B for the WT and KO-rescue MEFs to address this possibility?

__The referee is right in assuming that FAM134B, expressed at low levels in these cells, is limiting. We now show the levels of endogenous FAM134B and of recombinant FAM134B in WB (new Fig. 4A). __

In Figures 1 and 6, the terms siOS9 and siEDEM1 are used but Figure 3 shows data from shRNAs and not siRNAs.

We apologize for the mistake. We have corrected this in the new Figures 1 and 7.

Samples from Figure 3 treated with Baf but this is not indicated in the figure or figure legend.

We have corrected this, thank you.

VCP/p97 inhibitors typically stabilize ERAD glycoprotein substrates better than proteasome inhibitors do. Is the same degree of endolysosomal targeting present ?

__For the convenience of the reviewer (we did not put these data in the new manuscript). In our experiments, the p97 inhibitor DBeQ is less efficient in deviating NHK to the endolysosomal degradative compartments, if compared with KIF (see below). At higher doses, DBeQ also inhibits other AAA-ATPases (e.g., VPS4, which plays a role in certain types of autophagy). This, or other cross-reactivities of DBeQ could explain the moderate capacity to activate ERLAD pathways as a response of ERAD inhibition, if compared with the phenotypes observed when ERAD is inhibited with KIF or PS341. __

Reviewer #1 (Significance (Required)):

Deconvolution of the different pathways taken by misfolded proteins to escape the ER is of great interest not only to the ER community but also represents consequences to consider for those interested in therapeutics involving UPS inhibition. While concise, this manuscript does a good job of trying to demonstrate the principal of substrate rerouting and the prioritisation of degradation pathways. Overall, the manuscript is well written, the experiments presented are performed to a sufficient standard, the data are lean but of good quality, and the appropriate statistical analyses have mostly been included where necessary and are described. The Methods and Materials is brief but describes the experiments that have been performed. The manuscript is brief in its results and would obviously benefit from additional complementary assays that would strengthen and broaden the authors arguments for rerouting. But too their credit, the authors do not grossly overstate their findings and merely present the culmination of a set of experiments to answer a single question - what happens to a misfolded glycoprotein substrate when ERAD is impaired. This is a key question with broad implications.

While their limited data clearly demonstrates an acquired dependence on ERLAD, one can't help but wonder how broadly these findings hold true, as only a single glycoprotein substrate example is used.

We have now added a complete set of experiments (imaging + biochemical to monitor clearance of the model polypeptides by pulse-chase analyses) performed with a second ERAD substrate (BACE457delta, Fig. 6). These data fully recapitulate the results obtained with NHK.

Moreover, it is not clear what percentage ERLAD contributes to overall NHK degradation (with or without ERAD) as the total NHK amount remaining is not assessed or measured.

Pulse-chase analyses (new Fig. 2D) and published data (e.g., Liu et al 1999, Molinari et al 2002, references in the manuscript) show that BafA1 or chloroquine do not inhibit clearance of the ERAD substrates NHK and BACE457delta. The biochemical analyses now show the contribution of ERLAD on NHK (new Figs 2B, 2C, grey zones) and BACE457delta (new Figs. 6B,C, grey zones) clearance, when ERAD is dysfunctional.

Nevertheless, the manuscript is an advancement of understanding of the fate of substrates unable to access ERAD and raises many future questions of interdependency between the ERAD and ERLAD pathways. The data just need a bit of shoring up.

Expertise - ERAD, UPS, protein quality control

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

The endoplasmic reticulum (ER) is a crucial site for protein synthesis and folding within the cell, and strict protein quality control is essential for maintaining ER homeostasis. In this context, ER-associated degradation (ERAD) and the unfolded protein response (UPR) play pivotal roles. Recent researches have highlighted the significance of ER-phagy in protein quality control. In this manuscript, the authors demonstrate the role of FAM134B in degrading misfolded proteins such as ATZ through the ER-phagy pathway when the ERAD pathway is obstructed. This work partially addresses a prominent issue in the field, unveiling the interconnections between different regulatory pathways in maintaining ER homeostasis.

Major issues: 1: In a multitude of experiments, the authors employed Bafilomycin A1 (BafA1) to block the fusion between autophagosomes and lysosomes, attempting to demonstrate that the clearance of misfolded proteins mediated by FAM134B is independent of autolysosomes. However, in Figure 4, the lack of rescue of FAM134B knockout by overexpressing FAM134B△LIR suggests a dependence on the interaction between FAM134B and LC3. The conclusions drawn before and after appear contradictory.

We apologize if our explanations were unclear. We have now modified the text and performed new experiments to clarify these issues.

__The inhibitor of the V-ATPase BafA1 is used here to inhibit the activity of lysosomal hydrolases and to accumulate undegraded material in the LAMP1-endolysosomes (note that these endolysosomes also display RAB7 at their limiting membrane) (Fregno et al 2018, Forrester et al 2019, Fregno et al 2021, …). __

__In Figs. 2A-2D, we now monitor the lack of NHK stabilization by cell exposure to BafA1 (Fig. 2D), which correlates with lack of accumulation of NHK in the LAMP1-positive compartment (e.g., Fig. 2F, 2J, and quantifications in 2I and 2O). The biochemical data also show that BafA1 stabilizes NHK in cells where ERAD has been inactivated with PS341 or KIF (Fig. 2A, lanes 6, 7, 10, 11 and grey zones in Figs. 2B and 2C), which correlates with accumulation of NHK in LAMP1-positive organelles (Figs. 2G, 2H, 2I, 2K, 2M, 2O). __

__In Figs. 2J-2O, we have now added panels showing that NHK clearance from the LAMP1-positive endolysosome lumen is restored upon BafA1 washout. __

Importantly, the involvement of the lipidation machinery, of the ER-phagy receptor FAM134B and of the LC3-binding function of FAM134B (the LIR), does not necessarily imply the involvement of autophagosomes in the process under investigation, as the comment by the referee seems to suggest. For example, both the clearance from the ER of ATZ polymers and of mutant forms of procollagen rely on the LC3 lipidation machinery and on the LC3-binding function of FAM134B, but ERLAD of ATZ polymers does not rely on autophagosomes intervention (new Fig. 1B, arrow 1 and Fregno et al 2018), whereas ERLAD of procollagen relies on intervention of autophagosomes (new Fig. 1B, arrow 2 and Forrester et al 2019).

2: Some Western blot data are insufficient to substantiate the author's conclusions. For instance, in Figure 5D, the ATG7 KO line is inadequately supported

The WB show____s the absence of ATG7 in the ATG7-KO cells (a well-established cell line generated in the lab of Masaaki Komatsu (____Komatsu M, et al. J Cell Biol 169: 425-434_) and used in many_ laboratories, including our lab in Fumagalli et al 2016, Fregno et al 2018, Fregno et al 2021, Loi et al 2019, Kucinska et al 2023). We agree with the reviewer that the anti-Atg7 shows cross-reactions. We have now added a WB showing the lack of LC3 lipidation in the Atg7-KO cells exposed to nutrient deprivation (new Fig. 5D).

3: The author employed Lamp1 antibody for lysosomal staining in cells and observed a significant abundance of lysosomes in some experiments, as depicted in Figure 2C, 2D, 4I, etc. Is the phenomenon of lysosomes extensively filling the entire cell a common occurrence? Is it indicative of a normal physiological state?

There may be variations depending on the cell type used for the experiments. In the new version of the manuscript, we now present imaging data for 3 cell lines (NIH 3T3 with stable expression of NHK and ATZ (Figs. 2E-2H), MEF (Figs. 2J-2N, 4, 5, 6) and HEK293 with transient expression of ERAD clients (Figs. 3).

Minor issues： 1: Some immunofluorescence experimental data are unclear. Please request the authors to replace these with more distinct images, as seen in Figure 3B and 3E.

We hope that the quality of the new images will be considered sufficient for publication.

2: Some expressions appear to be questionable. For instance, the necessity of utilizing endolysosomes requires clarification.

For the use of endolysosomes (lysosome would be incorrect in our opinion to indicate these LAMP1/RAB7-positive degradative organelles), we now refer to the papers by Bright et al ____Endolysosomes Are the Principal Intracellular Sites of Acid Hydrolase Activity_ Curr Biol 2016, and the original definition by Huotari and Helenius _Endosome maturation EMBO J 2011 (Introduction, page 2).

3: Some writing lacks precision, such as referring to FAM134B as FAM134.

__Corrected, thank you____ __ Reviewer #2 (Significance (Required)):

o General assessment: o Advance: provide an meaningful evidence that how two degradative pathways are coordinated in maintaining ER homeostasis. o Audience: cell biologist o Reviewer's expertise: autophagy, vesicle trafficking, organelle biolgy Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In their study, Fasana and colleagues investigate protein quality control in the ER. Specifically, they test whether folding-incompetent proteins that are normally cleared by ER-associated degradation (ERAD) can also be targeted for degradation by direct vesicular transport from the ER to lysosomes in case ERAD is blocked. They show that blocking ERAD pharamacologically or genetically indeed leads to re-rerouting of an ERAD model substrate (the NHK variant of alpha-antitrypsin) to lysosomes and that this pathway requires the reticulon-like protein FAM134B, the ability of FAM134B to interact with the ubiquitin-like protein LC3 and the machinery for LC3 lipidation.

The paper is, for the most part, easy to follow. There are, however, a few minor issues and I think the authors could do more to connect their work with similar studies in the literature. Accordingly, I have some general and specific suggestions to make the manuscript more accessible for the reader.

General suggestions

1. To avoid confusion, it would be helpful to more clearly distinguish between vesicular transport to endolysosomes and autophagy. Previous work by the authors has defined a trafficking pathway from the ER to endolysosomes that appears to rely on conventional vesicle-mediated transport (Fregno et al, EMBO J 2018). This pathway delivers material from the ER lumen to the lumen of endolysosomes, which are both topologically equivalent to the extracellular space. Hence, this pathway is distinct from autophagy, which is the transport of cytoplasmic components to endolysosomes and thus the transport of material from intracellular to extracellular space. This distinction is particularly important as both vesicular ER-to-lysosome transport and autophagy of the ER involve LC3 and FAM134B, which is typically referred to as an ER-phagy receptor. To make this less confusing, it may be helpful to explain that FAM134B appears to be a multifunctional molecule that can function as a receptor for macroautophagy but also in the vesicular transport pathway studied here. In addition, it would be helpful to point out that LC3 appears to also have roles unrelated to autophagosome formation.

The reviewer is referring to the original definition of ERLAD to describe the mechanisms of clearance of ATZ polymers (Fregno et al 2018). The definition of ERLAD has now been expanded and is given, for example, in Klionsky DJ, et al (2021) Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition). Autophagy 17: 1-382 and is explained in detail in our recent review Rudinskiy M, Molinari M (2023) ER-to-lysosome-associated degradation in a nutshell: mammalian, yeast, and plant ER-phagy as induced by misfolded proteins. Febs Letters: 1928-1945.

__Notably, the acronym ERAD for ER-associated degradation has originally been used to describe ____the proteasomal clearance from the ER of misfolded pro-alpha factor in a reconstituted yeast system in McCracken AA, Brodsky JL (1996) Assembly of ER-associated protein degradation in vitro: dependence on cytosol, calnexin, and ATP. The Journal of cell biology 132: 291-298. Only later on, the acronym has been used as an umbrella term that now covers all the pathways that control proteasomal clearance of misfolded proteins from the ER. A short historical excursus is presented in the new introduction to better explain these issues. __

It is well established that LC3 and the LC3 lipidation machinery have functions that go beyond macroautophagy (which involves double membrane autophagosomes). Micro-autophagy (or micro-ER-phagy to remain on the topic of our paper) is an example of autophagic pathway relying on ER-phagy receptor that engage LC3, on the LC3 lipidation machinery, without involving autophagosomes. This is schematically represented in the new Fig. 1B.

Several recent papers that appear relevant to the present study are not mentioned. In particular, Sun et al., Dev Cell 2023 (PMID: 37922908) appears worthy of discussion, as does Gonzalez et al., Nature 2023 (PMID: 37225996).

Thank you. Both papers are not directly linked to our study addressing the intervention of ERLAD pathways when ERAD activity is impaired. In particular the work of Gonzales et al describes post-translational modification of ER-phagy receptors for their activation. The Sun et al paper is not really related to the topic covered in our manuscript, but we cite it as an alternative pathway that removes ATZ from the ER (page 8).

Specific suggestions

1. Abstract: The abstract begins with "About 40% of the eukaryotic cell's proteome is synthesized ... in the ER." Similar statements can be found in many papers and purportedly reflect common knowledge. However, it is unclear where the figure of 'about 40%' comes from. It would be proper to provide a reference and demonstrate that giving such a fairly precise estimate is supported by experimental data. Alternatively, the statement could be modified to avoid being precise than is justified.

No reference is allowed in the abstract. We therefore modified the sentence as suggested by the reviewer.

1. p2: "The ER is site of gene expression in nucleated cells and ... native proteins to be delivered at their site of activity ...". There is something missing at the beginning of this sentence. Also, it should be 'delivered to their site of activity', not 'delivered at'.

Thank you

1. p2: "... by mechanistically distinct ER-phagy pathways collectively defined as ER-to-lysosome-associated degradation ERLAD." This statement suggests that all pathways subsumed under the term ERLAD are ER-phagy pathways, which I believe is misleading (see comment above on the distinction between autophagy and vesicular transport pathway).

See point 1.

1. p2: "KIF selectively ...". Please spell out KIF and explain what kind of compound it is.

Thank you, we changed to “_The alkaloid kifunensine (KIF) is a cell permeable selective inhibitor of the members of the glycosyl hydrolase 47 family of a____1,2-mannosidases_”____ __ 5. p3: "Notably, ERAD inhibition delays, rather than blocking degradation of ERAD clients ...". Please correct, for example: Notably, ERAD inhibition delays rather than blocks degradation of ERAD clients ...

Thank you

Figures 2 - 5: The number of quantified cells is given but it is not clear if experiments were done once or in biological replicates. Please indicate this in the figure legends.

__N is now given for all panels in the corresponding figure legends.____ __ 7. p4: "To verify if ERAD inactivation ..." sounds odd. Less ambiguous would be 'To test whether' or 'To ask if'.

Thank you

1. p7, beginning of discussion: Please correct "delivered at" to 'delivered to'.

Thank you

Reviewer #3 (Significance (Required)):

This is a concise and convincing manuscript with a clear message. The idea that proteins that cannot be processed by ERAD can be eliminated by other means, for instance by autophagy, is not new. Similarly, the FAM134B- and LC3-dependent pathway for ER-to-lysosome transport has been described by the authors before (Fregno et al, EMBO J 2018). Furthermore, the study exclusively relies on microscopy and does not attempt to tackle new mechanistic questions. Still, this study presents a definite functional advance in our understanding of the interplay of various ER quality control pathways.

The findings presented here will be of interest mainly to molecular cell biologists working on protein quality control and organelle homeostasis. However, given the disease-relevance of misfolded proteins, and alpha-antitrypsin in particular, the impact of this study may eventually go beyond basic research and may also interest translational researchers.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

This is significant work, and you should certainly make the best case you can on the weaknesses discussed.

We thank reviewer for this positive comment on the significance of our work. The referee indicates as weaknesses (i) that the force involving the bent or straight αI-helix is not readily apparent, (ii) the residue types were not varied in the helix mutations, and (iii) that the chemical shift perturbations are indirect observations.

We think we have tried to address a large part of these questions by being very careful in our analysis and by the discussion in the manuscript. The following remarks may help to clarify this further:

(i) The force emanating from the helix is e.g. visualized in the PC2 loadings in Figure 6E of the PCA carried on all observed SH3-SH2-KD resonances for all apo forms of the helix mutants. The SH2 residues identified by these loadings are in direct vicinity to the αI-helix. The respective PC2 scores correlate to 98% with the vmax of the catalytic reaction and to 94 % with the PC1 scores found for imatinib-induced opening. Importantly, the structure of the KD with the straight αI-helix indicates that mostly residues F516, Q517, S520, and I521 would clash with the SH2 domain in a closed core (Figure 6F). Thus, the expected clashes are in direct vicinity of the SH2 residues identified by the PC2 loadings as correlated to vmax and imatinib-induced opening. These data are completely orthogonal and show that most of the force is coming from residues F516, Q517, S520, and I521 in the αI-αI’ turn.

(ii) We agree that we mainly used truncations of the αI-helix to study its involvement in activation. Point (i) makes it clear that a larger part of the αI-helix effects is caused by steric clashes of the residues in the αI-αI’ turn. In the latter region, we don’t expect strong amino acid type-specific effects besides excluded volume. Due to expression problems, we could not vary the helix length between residues 519 and 534. However, in this region we introduced the amino acid type mutation E528K. The latter showed a clear specific effect. Further amino acid type-specific effects may be possible in this region. However, we expect that the identified electrostatic E528-R479 interaction is one of the most important interactions in this region.

(iii) We agree that chemical shift changes of individual resonances are often hard to interpret. However, we want to stress that our conclusions are all drawn from principal component analyses, which in all cases had as input well over 100 if not over 200 1H-15H resonances. The first two principal components of these analyses are robust averages over many residues, which reveal general correlated structural trends.

We assume that chemical shift deposition etc will be pursued.

We are currently depositing a larger collection of our Abl data to the “Biological Magnetic Resonance Data Bank (BMRB)”, which includes the NMR chemical shift data of the present work. A ‘collection’ will be a new feature of the BMRB, and we are in discussion with their staff. We will provide the accession codes as soon as possible (probably within the next month) to be included into the final version of the manuscript. We have amended the Data Availability Section accordingly.

Reviewer #2 (Recommendations For The Authors):

1) The overall discussion of the implications of the described allostery on kinase activation is provided through lenses of imatinib binding, which is used as an experimental trigger to disassemble the autoinhibited core. Can the authors elaborate in the Discussion on what event would play this role in the kinase catalytic cycle, communicating to helix I? Would dissociation of the myristate from the active site be hypothesized to be the first step in kinase activation? While I understand that certainty may be challenging to attain, it would be good to introduce some ideas into the Discussion.

We appreciate the reviewer’s suggestions for the discussion and added the following text to the Conclusion section:

"We have used here imatinib binding to the ATP-pocket as an experimental tool to disassemble the Abl regulatory core. Our previous analysis (Sonti et al., 2018) of the high-resolution Abl transition-state structure (Levinson et al., 2006) indicated that due to the extremely tight packing of the catalytic pocket, binding and release of the ATP and tyrosine peptide substrates is only possible if the P-loop and thereby the N-lobe move towards the SH3 domain by about 1–2 Å. This motion is of similar size and direction as the motion of the N-lobe observed in complexes with imatinib and other type II inhibitors (Sonti et al., 2018). From this we concluded that substrate binding opens the Abl core in a similar way as imatinib. The present NMR and activity data now clearly establish the essential role of the αI-helix both in the imatinib- and substrate-induced opening of the core, thereby further corroborating the similarity of both disassembly processes.

Notably, the used regulatory core construct Abl83-534 lacks the myristoylated N-cap. Although we have previously demonstrated that the latter construct is predominantly assembled (Skora et al., 2013), the addition of the myristoyl moiety is expected to further stabilize the assembled conformation in a similar way as asciminib.

Considering this mechanism, dissociation of myristoyl from the native Abl 1b core may be a first step during activation. However, it should be kept in mind that the Abl 1a isoform lacks the N-terminal myristoylation, and it is presently unclear whether other moieties bind to the myristoyl pocket of Abl 1a during cellular processes."

2) Can the authors comment more on the differentiation between assembled conformations induced by type I inhibitor binding vs apo forms (or AMP-PNP and allosteric inhibitor) reported in Figure 3B? The differences are clearly identified by PCA but not sufficiently discussed.

As indicated in the text, we think two structural effects are intermingled within PC2. Due to this admixture, it is hard to draw strong conclusions and we don’t want to expand on this too much. We have slightly modified the respective paragraph (p.7) as follows):

"As the affected residues react differently to perturbations by type I inhibitors and truncation of the αI’-helix (Figure 3A, right), we attribute this behavior to two effects intermixed into the PC2 detection: (i) a minor rearrangement of the SH3/KD N-lobe interface caused by filling of the ATP pocket with type I inhibitors, which in contrast to the stronger N-lobe motion induced by type II inhibitors does not yet lead to core disassembly and (ii) a small rearrangement of the SH2/KD C-lobe interface caused by shortening and mutations of the αI-helix."

3) The allosteric connection between active site inhibitor binding and the myristate/allosteric inhibitor binding has been observed in the past and noted before, in papers such as Zhang et al, Nature 2010. While the authors reference this paper, they do not acknowledge its specific findings or engage in a broader discussion of how their conclusions relate to this work.

We have modified the beginning of the Conclusion section:

"The allosteric connection between Abl ATP site and myristate site inhibitor binding has been noted before, albeit specific settings such as construct boundaries and the control of phosphorylation vary in published experiments. Positive and negative binding cooperativity of certain ATP-pocket and allosteric inhibitors has been observed in cellular assays and in vitro (Kim et al., 2023; Zhang et al., 2010). Furthermore, hydrogen exchange mass spectrometry has indicated changes around the unliganded ATP pocket upon binding of the allosteric inhibitor GNF-5 (Zhang et al., 2010). Here, we present a detailed high-resolution explanation of these allosteric effects via a mechanical connection between the kinase domain N- and C-lobes that is mediated by the regulatory SH2 and SH3 domains and involves the αI helix as a crucial element.

Specifically, we have established a firm correlation between the kinase activity of the Abl regulatory core, the imatinib (type II inhibitor)-induced disassembly of the core, which is caused by a force FKD–N,SH3 between the KD N-lobe and the SH3 domain, and a force FαI,SH2 exerted by the αI-helix towards the SH2 domain. The FαI,SH2 force is mainly caused by a clash of the αI-αI’ loop with the SH2 domain. Both the FKD–N,SH3 and FαI,SH2 force act on the KD/SH2SH3 interface and may lead to the disassembly of the core, which is in a delicate equilibrium between assembled and disassembled forms. As disassembly is required for kinase activity, the modulation of both forces constitutes a very sensitive regulation mechanism. Allosteric inhibitors such as asciminib and also myristoyl, the natural allosteric pocket binder, pull the αI-αI’ loop away from the SH2 interface, and thereby reduce the FαI,SH2 force and activity. Notably, all observations described here were obtained under nonphosphorylated conditions, as phosphorylation will lead to additional strong activating effects."

4) Figure 6 could do a better job of providing an illustration of steric clashes.

We have revised Figure 6, panel F, in order to better illustrate the steric clashes, and modified the legend accordingly.

5) There is a typo in line 5 from the top on page 11 (dash missing from "83534" superscript).

Thank you. This was fixed.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

Reviewer #2 (Public Review):

Summary: In this manuscript, the authors performed single cell RNA-sequencing of cells from the penises of healthy and diabetes mellitus model (STZ injection-based) mice, identified Lbh as a marker of penis pericytes, and report that penis-specific overexpression of Lbh is sufficient to rescue erectile function in diabetic animals. In public human single cell RNA-sea datasets, the authors report that LBH is similarly specific to pericytes and down regulated in diabetic patients. Additionally, the authors report discovery of CRYAB and VIM1 as protein interacting partners with LBH.

The authors contributions are of interest to the erectile dysfunction community and their Lbh overexpression experiments are especially interesting and well-conducted. However, claims in the manuscript regarding the specificity of Lbh as a pericyte marker, the mechanism by which Lbh overexpression rescues erectile function, cell-cell interactions impaired by diabetes, and protein-interaction partners require qualification or further evidence to justify.

Major claims and evidence:

1) Marker gene specificity and quantification: One of the authors' major contributions is the identification of Lbh as a marker of pericytes in their data. The authors present qualitative evidence for this marker gene relationship, but it is unclear from the data presented if Lbh is truly a specific marker gene for the pericyte lineage (either based on gene expression or IF presented in Fig. 2D, E). Prior results (see Tabula Muris Consortium, 2018) suggest that Lbh is widely expressed in non-pericyte cell types, so the claims presented in the manuscript may be overly broad. Even if Lbh is not a globally specific marker, the authors' subsequent intervention experiments argue that it is still an important gene worth studying.

Answer: We appreciate this comment. In our scRNAseq data for the mouse cavernosum tissues, previously known markers such as Rgs5, Pdgfrb, Cspg4, Kcnj8, Higd1b, and Cox4i2 were found to be expressed not exclusively in pericytes, while Lbh exhibited specific expression patterns in pericytes (Fig. 2 and Supplementary Fig. 5). LBH expression was easily distinguishable from α-SMA, not only in mouse cavernosum but also in dorsal artery and dorsal vein tissues within penile tissues. This distinctive expression pattern of LBH was also observed in the human cavernous pericytes (Fig. 5). Then, we examined Lbh expression patterns in various mouse tissues using the mouse single-cell atlas (Tabula Muris), although endothelial and pericyte clusters were not subclustered in most tissues from Tabula Muris. To identify pericytes, we relied on the expression pattern of known marker genes (Pecam1 for endothelial cells, Rgs5, Pdgfrb, and Cspg4 for pericytes). Lbh was expressed in pericytes of the bladder, heart and aorta, kidney, and trachea but not as specifically in penile pericytes (Supplementary Fig. 6A-D). However, it is worth noting that other known pericyte markers were also did not exhibit exclusive expression in pericytes across all the tissues we analyzed. Therefore, in certain tissues, particularly in mouse penile tissues, Lbh may be a valuable marker in conjunction with other established pericyte marker genes for distinguishing pericytes.

2) Cell-cell communication and regulon activity changes in the diabetic penis: The authors present cell-cell communication analysis and TF regulon analysis in Fig 3 and report differential activities in healthy and DM mice. These results are certainly interesting, however, no statistical analyses are performed to justify claimed changes in the disease state and no validations are performed. It is therefore challenging to interpret these results, and the relevant claims do not seem well supported.

Answer: In response to these helpful suggestions, we calculated statistical significance and performed experimental validation. CellphoneDB permutes the cluster labels of all cells 1000 times and calculates the mean(mean(molecule 1 in cluster X), mean(molecule 2 in cluster Y)) at each time for each interaction pair, for each pairwise comparison between two cell types. We only considered interactions in which the difference in means calculated by these permutations were greater than 0.25-fold between diabetes and normal. Also, we considered that the interactions with P-value < 0.05 were significant.

To assess differential regulon activities of transcription factor (SCENIC) between diabetic and normal pericytes, we utilized a generalized linear model with scaled activity scores for each cell as input. These scaled regulon activity values for angiogenesis-related TFs exhibited differences between diabetic and normal pericytes. The results of the generalized linear model revealed that Klf5, Egr1, and Junb were TFs with significantly altered regulon activities in diabetic pericytes. Experimental data indicated that the expression level of Lmo2, Junb, Elk1, and Hoxd10 was higher (Hoxd10) or lower (Lmo2, Junb, Elk1) in diabetic pericytes compared to normal pericytes (Supplementary Fig. 9). We have added the scaled regulon activity values and statistical significance in Fig. 3E.

3) Rescue of ED by Lbh overexpression: This is a striking and very interesting result that warrants attention. By simple overexpression of the pericyte marker gene Lbh, the authors report rescue of erectile function in diabetic animals. While mechanistic details are lacking, the phenomenon appears to have a large effect size and the experiments appear sophisticated and well conducted. If anything, the authors appear to underplay the magnitude of this result.

Answer: We appreciate this comment. Therefore, we have added relevant clarification in the revised manuscript discussion section to emphasize the importance of LBH overexpression on rescuing ED as follows: “To test our hypothesis, we utilized the diabetes-induced ED mouse model, commonly employed in various studies focusing on microvascular complications associated with type 1 diabetes. We observed that the overexpression of LBH in diabetic mice led to the restoration of reduced erectile function by enhancing neurovascular regeneration. However, this study primarily demonstrated the observed phenomenon without delving into the detailed mechanisms. Nonetheless, these results of LBH on erections provide us with new strategies for treating ED and should be of considerable concern.” (Please see revised ‘Discussion’)

4) Mechanistic claims for rescue of ED by Lbh overexpression: The authors claim that cell type-specific effects on MPCs are responsible for the rescue of erectile function induced by Lbh overexpression. This causal claim is unsupported by the data, which only show that Lbh overexpression influences MPC performance. In vivo, it's likely that Lbh is being over expressed by diverse cell types, any of which could be the causal driver of ED rescue. In fact, the authors report rescue of cell type abundance in endothelial cells and neuronal cells. Therefore, it cannot be concluded that MPC effects alone or in principal are responsible for ED rescue.

Answer: We agree with these claims. Therefore, we have added relevant clarifications in the discussion section of the revised manuscript. Our findings suggest that LBH can affect the function of cavernous pericytes, although we cannot definitively specify which particular cavernous cell types are affected by the overexpressed LBH, whether it be cavernous endothelial cells, smooth muscle cells, or others. Subsequent research will be required to conduct more comprehensive mechanistic investigations, such as in vitro studies using cavernous endothelial cells, smooth muscle cells, and fibroblasts to address these knowledge gaps. (Please see revised ‘Discussion’)

5) Protein interaction data: The authors claim that CRYAB and VIM1 are novel interacting partners of LBH. However, the evidence presented (2 blots in Fig. 6A,B) lack the relevant controls. It is possible that CRYAB and VIM1 are cross-reactive with the anti-LBH antibody or were not washed out completely. The abundance of bands on the Coomassie stain in Fig. 6A suggests that either event is plausible. Therefore, the evidence presented is insufficient to support the claim that CRYAB and VIM1 are protein interacting partners of LBH.

Answer: We agree with these claims. Therefore, we have added the relevant controls(Input) and performed Co-IP (IP: CRYAB or VIM, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Our results show that we can detect the expression of CRYAB and VIM after LBH IP, and we also detect the expression of LBH after CRYAB and VIM IP. In addition, it can be seen from our results that the binding of LBH to VIM is higher than that of CRYAB. Regardless, these results indicate that the binding of CRYAB or VIM to LBH is not a random phenomenon. (Please see revised ‘Result’ and ‘Figure 6B’)

Impact: These data will trigger interest in Lbh as a target gene within the erectile dysfunction community.

Reviewer #3 (Public Review):

Bae et al. described the key roles of pericytes in cavernous tissues in diabetic erectile dysfunction using both mouse and human single-cell transcriptomic analysis. Erectile dysfunction (ED) is caused by dysfunction of the cavernous tissue and affects a significant proportion of men aged 40-70. The most common treatment for ED is phosphodiesterase 5 inhibitors; however, these are less effective in patients with diabetic ED. Therefore, there is an unmet need for a better understanding of the cavernous microenvironment, cell-cell communications in patients with diabetic ED, and the development of new therapeutic treatments to improve the quality of life.

Pericytes are mesenchymal-derived mural cells that directly interact with capillary endothelial cells (ECs). They play a vital role in the pathogenesis of erectile function as their interactions with ECs are essential for penile erection. Loss of pericytes has been associated with diabetic retinopathy, cancer, and Alzheimer's disease and has been investigated in relation to the permeability of cavernous blood vessels and neurovascular regeneration in the authors' previous studies. This manuscript explores the mechanisms underlying the effect of diabetes on pericyte dysfunction in ED. Additionally, the cellular landscape of cavernous tissues and cell type-specific transcriptional changes were carefully examined using both mouse and human single-cell RNA sequencing in diabetic ED. The novelty of this work lies in the identification of a newly identified pericyte (PC)-specific marker, LBH, in mouse and human cavernous tissues, which distinguishes pericytes from smooth muscle cells. LBH not only serves as a cavernous pericyte marker, but its expression level is also reduced in diabetic conditions. The LBH-interacting proteins (Cryab and Vim) were further identified in mouse cavernous pericytes, indicating that these signaling interactions are critical for maintaining normal pericyte function. Overall, this study demonstrates the novel marker of pericytes and highlights the critical role of pericytes in diabetic ED.

Reviewer #1 (Recommendations For The Authors):

1) The methods are poorly written. It lacks specific information on the sample size, experimental design, and data analysis methods employed. The absence of these crucial details makes it difficult to evaluate the robustness and reliability of the findings.

Answer: We agree with the reviewer’s suggestion, now we revised the methods of our manuscript, and added detailed information or references. For sample size we have added detailed information in Figure legend (Please see revised ‘Method’ , Figure Legend, and Supplementary information.)

2) The cell number in the scRNA-seq analysis is small (~12000) and some minor cell types are probably underrepresented. It is not clear whether the authors pooled the cells from different mice as one sample, or replicates in different groups have been included. It will be helpful to label different samples in the UMAP. The authors should repeat the experiments with more replicates to increase the cell number and validate the findings.

Answer: We understand the reviewer's concern, but due to the small size of mouse penile tissue, we had to pool 5 corpus cavernosum tissues for each group (using pooled samples) for scRNA-seq analysis. Moreover, owing to the unique nature of mouse penile tissue, which is highly resistant, it posed challenges for the dissolution and isolation of single cells using conventional single-cell separation methods. Consequently, we had to increase the concentration of the enzyme to finally obtain 12,894 cells. Rather than conducting a repetitive scRNAseq analysis on the same mouse model, we validated our findings in human cavernous single-cell transcriptome data. This analysis allowed us to confirm the presence of pericyte in human corpus cavernosum, specific expression of LBH in human cavernous pericytes, and the identification of relevant GO terms associated with pericyte functions (Figure 5). We have add these information in ‘Method’ (Please see revised ‘Method’).

3) Functional studies are lacking to justify how manipulating LBH expression or its interacting proteins might lead to effective therapeutic approaches for diabetic ED.

Answer: We have performed the functional study to evaluate LBH expression might lead to effective therapeutic approaches for diabetic ED as showed in Figure 4G. Assessment of intracavernous pressure (ICP) is the most representative test for evaluating erectile function. Therefore, we modulated LBH expression in the penis of diabetic mice and assessed the erectile function of the mice by intracavernous pressure. However, we have not performed ICP studies and relative in vitro studies (migration, survival experiment) to assess whether LBH-interacting proteins have the same effect.

4) Although the abstract identifies novel targets for potential interventions, such as LBH and its interacting proteins, the clinical relevance of these findings remains uncertain. The authors should include a discussion regarding the translation of these discoveries into therapeutic strategies or their potential impact on patients with diabetes and ED.

Answer: We appreciate the reviewer's suggestion and have added a discussion as per the reviewer’s recommendation (Please see revised ‘Discussion’).

5) While the study highlights the importance of pericytes in penile erection, it fails to mention the broader context of other cell types involved in the pathogenesis of ED. Neglecting to discuss potential contributions from endothelial cells, smooth muscle cells, or neural elements limits the comprehensive understanding of the cellular interactions underlying diabetic ED.

Answer: We agree with the reviewer's suggestion and have added a discussion regarding the significance of other cell populations in penile tissues, such as endothelial cells, smooth muscle cells fibroblasts, and neural elements, along with the rationale for our focus on pericytes. (Please see revised ‘Discussion’).

Reviewer #2 (Recommendations For The Authors):

We congratulate the authors on an interesting study. We were especially excited to see their Lbh overexpression results. However, we felt other claims in the paper could benefit from additional investigation, analysis, and statistical rigor. We have provided a set of suggestions for improvement below.

Major points:

1) Pericyte marker gene proposal: See public review for commentary on the following suggested experiments. The authors should perform binary classification analysis using Lbh and report the performance of this gene as a marker (e.g. using the area under the receiver operating characteristic, accuracy, precision and recall). Further, they should consider performing this analysis for all other genes in their data to determine whether Lbh is the best marker gene.

Answer: We appreciate this comment. AUC scores of Rgs5, Pln, Ednra, Npylr, Atp1b2, and Gpc3 for ability of a binary classifier to distinguish between pericyte and the other cell types in mouse penile tissues were measured by using FindMarkers function. Rgs5 had the highest AUC, but Rgs5 was also expressed in SMCs in our data. Pln, Ednra, Gpc3, and Npy1r also seemed to be candidate markers, but the literature search excluded these genes as they are also expressed in the SMCs of other tissues or different cell types. The AUC score of Lbh was over 0.7, and expression in SMC was not identified in previous studies, and ultimately, we experimentally identified that Lbh is penis pericyte specific. We have added this to the manuscript.

Author response table 1.

Robust differential expression analysis should also be performed for this gene (if not all) and the statistics should be reported, given known issues with the statistical approach used by the authors for differential expression (see: Squair 2021, 10.1038/s41467-021-25960-2). The authors' should also report the number of cells involved in these comparisons, as the number of pericytes in the data (Fig 1B) appears quite small.

Answer: We appreciate this comment. We used “MAST” to identify differentially expressed genes. This test is often used to find DEGs in single-cell RNA data. However, because the pseudobulk method has advantages over the single cell DEG method (Squair 2021, 10.1038/s41467-021-25960-2), we additionally performed DEG analysis with DESeq2 to confirm whether Lbh can distinguish pericytes from other cell types in the penile. As a result, even when tested with DESeq2, Lbh expression was significantly higher in pericytes than in other cell types in penile (adjusted p-value = 2.694475e-07 in Pericyte vs SMC, adjusted P-value = 3.700118e-58 in Pericyte vs the other cell types). Mouse penile tissue is small in size, and the number of pericytes in mouse penile tissue is relatively smaller compared to fibroblasts and chondrocytes. In our mouse penile scRNAseq data, the number of pericytes is as follows: normal: 58, diabetes: 116. Despite the limited number of cells, we were able to establish statistical significance in our analyses.

Immunostaining results in Fig. 2D, E should likewise be quantified. At present, it's unclear that LBH and aSMA are mutually exclusive as claimed. The authors should also investigate Lbh expression in public single cell genomics data, rather than performing candidate gene literature searches. For example, the Tabula Muris suggests Lbh is expressed widely outside pericytes.

Answer: For Figure 2D and E, the aim of these analyses was to assess the distribution of LBH and other cellular markers to see if they overlap and if they can be distinguished. We think that some of the overlapping staining in the tissue may be caused by multilayered cellular structures, so staining within cells would be more convincing. Therefore, we quantified the percentage of LBH- or α-SMA-expressed pericytes and relative expression in smooth muscle cells in cell staining (Supplementary Fig. 5E). We found that only 3% of smooth muscle cells expressed LBH, 67% of mouse cavernous pericytes (MCPs) expressed α-SMA, and more than 97% of MCPs expressed LBH. Therefore, these results may illustrate the specific expression of LBH in MCPs. These information was added as ‘Supplementary Fig. 5E’ (Please see revised ‘Supplementary information’). We also examined Lbh expression patterns in various mouse tissues using the public mouse single-cell atlas (Tabula Muris), and provided a detailed response in reviewer 2’s public review 1.

Even if Lbh is not the best marker, the authors' intervention experiment still motivates study of the gene, but these analyses would help contextualize the result for readers.

2) Statistical anslyses for cell-cell communication and TF regulon analysis: See public review for context on these comments. The authors should perform statistical tests to evaluate the significance of differences detected for each of these analysis. For example, generalized linear models can be used to assess the significance of TF regulon activity scores from SCENIC, and permutation tests can be used to measure the significance of cell-cell interaction score changes. Without these statistical tests, it's challenging for a reader to interpret whether the results reported are meaningful or within the realm of experimental noise.

Answer: We appreciate this comment. We calculated statistical significance TF regulon analyses as suggested by the reviewer and described a detailed statistical calculation method for cell-cell communication. We provided a detailed response in reviewer 2’s public review 2.

3) Mechanism of ED rescue by Lbh overexpression: To support this claim, the authors would need to perform an experiment where Lbh is over expressed specifically in MPCs (using e.g. a specific promoter on their LTV construct, or a transgenic line with a cell type-specific Cre-Lox system). Absent these data, the claim should be removed.

Answer: We agree with the reviewer's suggestion and we have reworked the claim that ‘LBH overexpression is affected by pericytes during ED recovery’ and have added relevant clarification in the Discussion section to clearly state that LBH overexpression may affect many cavernosum cells, such as cavernous endothelial cells, smooth muscle cells, fibroblasts, and pericytes (Please see revised ‘Result’ and ‘Discussion’)

4) Protein interaction claims: This experiment would require that the authors perform a similar pull-down with LBH KO cells and or a reciprocal Co-IP (e.g. IP: CRYAB or VIM1, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Further, these experiments appear to only have a single replicate for each condition. The authors should either remove associated claims, or perform a Co-IP experiment with the relevant controls with sufficient replication.

Answer: We agree with the claims. Therefore, we have included the necessary controls (Input) and performed Co-IP (IP: CRYAB or VIM1, WB: LBH) to demonstrate that CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Our results show that we can detect the expression of CRYAB and VIM after LBH IP, and we also detect the expression of LBH after CRYAB and VIM IP. In addition, it can be seen from our results that the binding of LBH to VIM is higher than that of CRYAB. Regardless, these results indicate that the binding of CRYAB or VIM to LBH is not a random phenomenon. Additionally, all IP experiments were replicated at least three times. (Please see revised ‘Result’ and ‘Figure 6B’)

Minor Points:

• The reference "especially in men" on line 56 seems odd given that only males can experience penile erectile dysfunction.

Answer: We agree with the reviewer's suggestion and have removed the description 'especially male' (Please see revised ‘Introduction’)

• Line 109, it's unclear what genes showed altered expression in Schwann cells.

Answer: We apologize for the confusion. There was no significant differentially expressed genes between normal and diabetes in Schwann cells. We revised this part in the manuscript. (Schwann cells showed an increased expression compared to normal cells in diabetes, though not significant. In Schwann cells, there were no significant DEGs between diabetic and normal cells.)

• It would be helpful for readers to see an analysis of the cell types that are transduced in the Lbh overexpression experiment in vivo. At present, some pericyte specificity is implied, but not demonstrated.

Answer: We appreciate this comment. Our findings suggest that LBH can affect the function of cavernous pericytes, although we cannot definitively conclude which specific-cavernous cell types are affected by the overexpressed LBH, whether it be cavernous endothelial cells, smooth muscle cells, or others. Subsequent research will be required to conduct more comprehensive mechanistic investigations, such as in vitro studies using cavernous endothelial cells, smooth muscle cells, and fibroblasts to address these knowledge gaps. These were also mentioned in the manuscript.

• To improve clarity and enhance readability, define abbreviations before their initial usage in the text. For instance, in the second paragraph of the Introduction, the abbreviation 'ECs' is used without prior definition. It can be inferred that it is referring to endothelial cells, mentioned in parentheses in the subsequent sentence.

Answer: We agree with the reviewer's suggestion to expand acronyms and ensure that all acronyms are defined in the revised manuscript before they are used for the first time in the text (Please see revised Manuscript).

• It is important to include relevant references that align with the content being discussed. For example, in the Introduction, pericytes are described as being involved in various processes such as angiogenesis, vasoconstriction, and permeability. The text refers to a single reverence, a review by Gerhardt and Besholtz, which primarily focuses on pericyte's role in regulating angiogenesis. Adding additional sources, such as the review by Bergers and Song (Neuro Oncol., 2005) is recommended.

Answer: We agree with the reviewer's suggestion, and have added the reference as reviewer recommended (Please see revised Manuscript and reference).

• Figure 3E: it is stated that a panel of 53 angiogenesis factors were tested, it is stated that only MMP3 showed increased expression. However, various unlabeled spots appear to show changed expression patterns. It would be helpful to show a summary graph with the relative intensities of the full array of factors tested.

Answer: We agree with the reviewer’s suggestion, now we showed all spots density in angiogenesis array as Supplementary Table 1. The condition of the spots we selected was that the expression density was at least above 1500, and the change ratio was greater than 1.2. (Please see revised ‘Supplementary information’)

Reviewer #3 (Recommendations For The Authors):

Detailed statistical power calculation

Data availability statement( were both mouse and human scRNA deposited in GEO with a taken and when will they be released to the public?)

Answer: Human scRNA data have been deposited in GEO under accession number GSE206528. Our mouse scRNA dataset has been uploaded to KoNA and is available for download (https://www.kobic.re.kr/kona/review?encrypt_url=amlod2FucGFya3xLQUQyMzAxMDEz)

Major concerns about this work

1) The single cell RNAseq data collected for mouse diabetic ED(Fig 1B), FB are the most abundant cell population compared to PC, EC, SMC and other clusters. The rationale for studying FB clusters (in Figure 1, D-F) instead of PC cluster is unclear. Which cluster DEG did the authors annotate for Fig 1G-H?

Answer: We understand the reviewer's suggestion and confusion. Although other major cell populations in penile tissue such as smooth muscle cells, endothelial cell, and fibroblasts have been extensively studied, pericytes have mainly been investigated in the context of the central nervous system (CNS). For example, in the CNS, pericytes are involved in maintaining the integrity of the brain's blood-brain barrier (BBB) [PMID: 27916653], regulating blood flow at capillary junctions [PMID: 33051294], and promoting neuroinflammatory processes [PMID: 31316352], whose dysfunction is considered an important factor in the progression of vascular diseases such as Alzheimer's disease [PMID: 24946075]. But little is known about the role of pericytes in penile tissue [PMID: 35865945; PMID: 36009395; PMID: 26044953]. In order to explore the role of pericytes in repairing the corpus cavernosum vascular and neural tissues damaged by DM, we focused on pericytes, which are multipotent perivascular cells that contribute to the generation and repair of various tissues in response to injury. Although recent studies have shown that pericytes are involved in physiological mechanisms of erection, little is known about their detailed mechanisms. We have also added this rationale in discussion.

Single cell level study has not been conducted in mouse penile tissues. Therefore, before delving into pericytes, we aimed to identify overall transcriptome differences between normal and diabetic conditions in mouse penile tissues. We presented the analyses of FB, which make up the largest proportion among the cell types in the mouse penis, in Fig. 1D-F. The analysis of other cell types is provided in Supplementary Fig. 1-4. Fig. 1G-H are GO terms for Fibroblasts clusters. We added this information in the figure.

2) Fig 2 is the critical data to show Lbh is a cavernous PC specific marker. More PC violin plots to identify PC cluster such as Cspg4, Kcnj8, Higd1b, Cox4i2 and more SMC violin plots to identify SMC cluster such as Acta2, Myh11, Tagln, Actg2 should be used for inclusion and exclusion of PC( the same concern applied to human scRNAseq in Fig 5B).

Answer: We appreciate this comment. We examined the expression of other marker genes of pericytes and SMCs. Although some marker genes were rarely expressed in the mouse penis data (Kcnj8, Higd1b), the expression of marker genes tended to be relatively high in each cluster. The expression of Cspg4 and Cox4i2 was higher in pericytes than in SMCs, while the expression of Acta2, Myh11,and Tagln was higher in SMCs than in pericytes. Actag2 was specifically expressed in SMCs. Through the gene set enrichment test as well as the expression of known cell type marker genes, we identified that the annotation of pericyte and SMC was appropriate (Fig. 2B and Fig. 5C). We added the violin plots of these marker genes in Supplementary Fig. 5.

Author response image 1.

(Mouse)

In human penis data, ACTA2 and MYH11 were expressed in SMCs, pericytes, and myofibroblasts, as in the previous paper [PMID: 35879305]. Among pericyte markers, the number of cells expressing KCNJ8 and HIGD1B was small. The cluster we annotated as pericyte was double positive for pericyte markers CSPG4 and COX4I2. ACTG2, a marker for SMC, was expressed more highly in SMC than in pericytes and myofibroblasts. As in the mouse penis data, we identified that the annotation of each cell type was appropriate through the gene set enrichment test in the human penis data. We added the violin plots of CSPG4, COX4I2, and ACTG2 in Supplementary Fig. 11.

Author response image 2.

(Human)

When exploring Lbh expression levels in "Database of gene expression in adult mouse brain and lung vascular and perivascular cells" from https://betsholtzlab.org/VascularSingleCells/database.html, Lbh is not uniquely expressed in PC, suggesting its tissue-specific expression level. This difference should be discussed in the Discussion section.

Answer: We appreciate this valuable comment. For the answer to this comment, we extensively analyzed Lbh expression patterns in various mouse tissues using the public mouse single-cell atlas (Tabula Muris) as also suggested by Reviewer 2. Please see our detailed response in reviewer 2’s public review 1.

3) In prior studies on PC morphology and location (PMID: 21839917), they reside in capillaries (diameter less than 10um) or distal vessels (diameter less than 25um) and have oval cell body and long processes. Due to the non-specificity of Pdgfrb, SMC are positive for Pdgfrb staining (this has been shown in many publications that SMC are Pdgfrb+; unfortunately, NG2 antibody also stains for both PC and SMC). Therefore, the LBH immunostaining (in Fig 2D and 2E of large-sized vessels) are very likely for SMC identity, not PC. PC should be in close contact with CD31+ ECs in healthy conditions. The LBH immunostaining of PC in both mouse and human tissues (Fig 4) must be replaced and better characterized.

Answer: We agree with the reviewer's suggestion. As it is widely known, peicytes are primarily located in capillaries, where they surround endothelial cells of blood vessels. However, recent discoveries have identified cells with pericyte-like characteristics in the walls of large blood vessels, challenging the traditional concept [PMID: 27268036]. In our study, we observed minimal overlap in staining between LBH and α-SMA, suggesting that the cells expressing LBH were not smooth muscle cells but possibly pericyte-like cells in large vessels. In small vessels within the bladder, kidney, and even the aorta, we found LBH-expressing cells surrounding CD31-expressing vessels, consistent with the known characteristics of pericytes. Further research is needed to comprehend the differences in LBH expression and its characteristics in both large and small blood vessels. We have added discussions and references for this issue (Please see revised ‘Discussion’ and ‘Reference’)

4) How do mouse cavernous pericytes isolate? How is purity?

Answer: As the reviewer points out, we isolated mouse spongiform pericytes following our and other previously published methods. We used pigment epithelium-derived factor (PEDF), which removes non-pericytic cells [PMID: 30929324, 23493068]. Although there are no purity study results such as FACS, other staining results thoroughly support the notion that this method yields pericytes with a notably high level of purity. (Please see ‘Method’ section).

5) Can mouse scRNAseq cell-cell communication in Fig 3 be reproducible in human scRNAseq cell-cell communication? The results in human ED are more clinically significant than in mouse data.

Answer: In human scRNAseq data, the difference between angiogenesis-related interactions between normal and diabetes was not as significant as that in mouse data. Because the cell type composition of the human and mouse penis is not completely identical, there are limitations in comparing cell-cell interactions. However, in the human penis data, some interactions related to angiogenesis between pericytes and other cell types were decreased in diabetes compared to normal (boxed parts).

Author response image 3.

6) Fibroblasts also express Vim. Murine PC VIM/CRYAB( should be written as Vim/Cryab as mouse proteins) direct interaction with Lbh is unclear from Lbh IP as Fig 6A red boxes showed a wide range of sizes. Where is the band for Lbh? Do human PC LBH interact with VIM/CRYAB?

Answer: We agree with the reviewer's comment. VIM is a type III intermediate filament protein expressed in many cell types. We have added the relevant controls (Input) and performed Co-IP (IP: CRYAB or VIM, WB: LBH) to demonstrate CRYAB and VIM are not simply cross-reactive antigens to their LBH antibody. In western blot study, the LBH band was expressed between 35 kDa-48 kDa. From Figure 6A, we detected CRYAB in band 1 and VIM in bands 2 and 3. This may be due to the formation of dimers or multimers by VIM. We did not use human PCs for IP studies because IP requires large amounts of protein, making IP studies using human pericyte challenging. Nevertheless, the interaction between LBH and CRYAB in humans has been reported through fluorescent resonance energy transfer assay and affinity chromatography technology assay [PMID:34000384, PMID:20587334].

7) In Fig 6H and I, why does CRYAB expression significantly reduce in vitro and in vivo under diabetic conditions, whereas VIM expression significantly increases?

Answer: As the reviewer pointed out, and we have discussed on this issue in the manuscript, CRYAB is known to promote angiogenesis. Diabetes reduces CRYAB expression, so angiogenesis may be impaired. Furthermore, since VIM is a multifunctional protein, it interacts with several other proteins with multiple functions under various pathophysiological conditions. There are many relevant literatures showing that VIM expression is increased under diabetic conditions [PMID: 28348116 and PMID: 32557212]. And VIM deficiency protects against obesity and insulin resistance in patients with type 2 diabetes. Therefore, we hypothesize that exogenous LBH may have the ability to bind to the increased VIM in diabetic conditions and inactivate the effects of VIM. Thereby achieving the protective effect. This needs to be proved in further studies.

8) The therapeutic strategies targeting (Lbh-Cryab-Vim) on mouse diabetic ED model is not investigated and need to be further validated and discussed.

Answer: As the reviewers pointed out, in this study, we did not evaluate the targeted therapeutic strategy for LBH-CRYAB-VIM in a mouse diabetic ED model. We only identified the binding potential of these three proteins. Evaluation of this treatment strategy requires further study. For example, we can employ shRNA lentivirus, either alone or in combination, to downregulate CRYABexpression [PMID: 31612679] in normal mice, utilize a lentiviral vector CMV-GFP-puro-vimentin to overexpress Vimentin [PMID: 36912679], and then treat it with LBH to evaluate whether the LBH effect still exists (in vivo erectile function study and in vitro angiogenesis assay). We include this information in the Discussion section as a limitation of this study (Please see revised ‘Discussion’).

9) The Discussion of current knowledge of pericytes in diabetic ED and other diseases and the significance of this study as well as clinical implications, should be expanded.

Answer: As the reviewers pointed out, we have expanded the current knowledge of pericytes in diabetic ED and other diseases (CNS disease) and clinical implications as follows: “Although other major cell populations in penile tissue such as smooth muscle cells, endothelial cell, and fibroblasts have been extensively studied, pericytes have mainly been investigated in the context of the central nervous system (CNS). For example, in the CNS, pericytes are involved in maintaining the integrity of the brain's blood-brain barrier (BBB), regulating blood flow at capillary junctions, and promoting neuroinflammatory processes, whose dysfunction is considered an important factor in the progression of vascular diseases such as Alzheimer's disease. But little is known about the role of pericytes in penile tissue.” (Please see revised ‘Discussion’).

10) How many clinical samples were used? How many times did each experiment repeat?

Answer: As the reviewers pointed out, the clinical samples’ information was added in ‘method’ section. A total four human samples were used in this study (‘human corpus cavernosum tissues were obtained from two patients with congenital penile curvature (59-year-old and 47-year-old) who had normal erectile function during reconstructive penile surgery and two patients with diabetic ED (69-year-old and 56-year-old) during penile prosthesis implantation.’). For in vivo study, we quantified four different fields from human samples.

Minor concerns

1) Fig 1A, why normal mouse's body size is the same as DM?

Answer: As the reviewer pointed out, in Figure 1A, while the size of normal mice and DM mice may not appear significantly different, there are indeed notable difference in body weight and size. The normal mice body weigh we used was about 30 grams, while DM mice body weigh was generally less than 24 grams. We found that we missed information on physiological and metabolic parameters from in vivo studies (ICP function study). Therefore, we have added it in Supplementary Table 2 (Please see revised ‘Supplementary information’)

2) The label and negative, and positive controls for Fig 6B are missing.

Answer: We thank for pointing out this. We have added the relevant controls (Input) and performed Co-IP (IP: CRYAB or VIM1, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody and all IP was replicated for at least 3 times. (Please see revised ‘Result’ and ‘Figure 6B’)

3) The limitation of this study and future work should be discussed.

Answer: As the reviewer pointed out, we have added the limitation of this study and future direction in the discussion section (Please see revised ‘Discussion’).

Author Response

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

REVIEWER 1

The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.

Many thanks for your suggestion concerning the lack of ambient flow in the simulations. We revised the section “Perspectives for future work and improvements” (lines 381-392 in our revised version of manuscript). Conducting the simulations without ambient flow can reduce the computational cost and, of course, making the simulation easier, while adding ambient flow can lead to poorer convergency and more technical issues. Meanwhile, we strongly agreed that these (benthic) organisms did not live in stagnant waters, as discussed in Liu et al. 2022. However, reducing computational complexity is not the main reason that the ambient flow was not incorporated in the simulations. As we discussed in section “Perspectives for future work and improvements”, our work focuses on the theoretical effect caused by the dynamics (based on fossil observation and hypothesis) of polyp on ambient environment (i.e., how fast the organism inhales water from ambient environment) rather than effect caused by ambient flow on organism (e.g., drag forces), which was what previous palaeontological CFD simulations mainly focused based on fossil morphology and hydrodynamics. To this end, we mainly concern the flow velocity above or near peridermal aperture (and vorticity computed in this paper) generated only by polyp’s dynamics itself without the interference of ambient flow (as many CFD simulations for modern jellyfish, i.e., McHenry & Jed 2003; Gemmell et al. 2013; Sahin et al. 2009. All those simulations were conducted under hydrostatic conditions). Adding ambient flow to our simulations “biases” the flow velocity profiles we expect to obtain in this case.

Nevertheless, we do agree that the ambient unidirectional laminar current or oscillating current plays an important role in feeding and respiration behavior of Quadrapyrgites. Further investigations need to be realized by designing a set of new insightful simulations and is beyond the scope of this work. We conducted CFD simulations incorporated with a randomly generated surface that imitated uneven seabed, where unidirectional laminar current and oscillating current (or vortex) were formed and exerted on Quadrapyrgites located in different places on the surface (Zhang et al. 2022). We assumed that combining the method we used in Zhang et al. 2022 and the velocity profiles collected in this work to conduct new simulations may be a promising way to further investigate the effect of the ambient current on organisms’ active feeding behavior.

There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Thanks for your suggestion. We revised the section “Perspectives for future work and improvements” (lines 396-404 in our revised version of manuscript).

Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a “breakthrough” in simulation gregarious feeding. However, we modified the corresponding description in section “Perspectives for future work and improvements” to make it more appropriate.

Throughout the manuscript there are portions that are difficult to digest due to grammar, which I suspect is due to being written in a second language. This is particularly problematic when the reader is attempting to understand if the authors are stating an idea is well documented versus throwing out hypotheses/interpretations.

Thanks. Our manuscript was checked and corrected by a native speaker of English again.

Line-by-line:

L023: "Although fossil evidence suggests..."

L026: "demonstrated" instead of "proven"

We corrected them accordingly.

L030: "The hydrostatic simulations show that the..." Maybe I'm confused by the wording, but shouldn't this be the case since it's a set part of the model?

As is demonstrated in our manuscript, all the simulations were conducted under “hydrostatic” environment. We originally intend to use the description “hydrostatic” here to emphasize the simulation condition we set in our work. However, it can literally lead to misunderstanding that some of the simulations we conducted are “hydrostatic” while the others are not. To this end, deleting the word “hydrostatic” here (line 30) may be appropriate to eliminate confusion.

L058: "lacking soft tissue" Haootia preservation suggests it is soft tissue (Liu et al., 2014), unless the preceding sentence is not including Haootia, in which case this section is confusingly worded

Thank you. We deleted the sentence “However, their affinities are not without controversy as the lacking soft tissue.”

L085: change "proxy"

Yes, we changed to “Considering their polypoid shape and cubomedusa-type anatomy, the hatched olivooids appear to a type of periderm-bearing polyp-shaped medusa (Wang et al. 2020) (lines 86-88).”

L092: "assist in feeding" has this been stated before? Citation needed, else this interpretation should primarily be in the discussion

Yes, you are right. We cited the reference at the end of the mentioned sentence (lines 91-94).

L095: Remove "It is suggested that"

Thanks for your suggestions. We corrected it.

L100: "Probably the..." here to the end belongs in the discussion and not introduction.

Thanks for your suggestions. We corrected the sentences.

L108: "an abapical"

Thanks for your suggestions. We revised it in line 107.

L112: "for some distance" be specific or remove

Yes, we deleted “for some distance” in line 111.

L133: I can't find a corresponding article to Zhang et al., 2022. Is this the correct reference?

The article Zhang et al. 2022 (entitled “Effect of boundary layer on simulation of microbenthic fossils in coastal and shallow seas”.) was in press at the time when we first submitted this manuscript. We complemented the corresponding term in References with the doi (10.13745/j.esf.sf.2023.5.32), which may help readers to locate this article easier.

L138: You can't be positive that your simulations "provide a good reproduction of the movement." You have attempted to reconstruct said movement, but the language here is overly firm - as is "pave a new way"

Thanks for your suggestions. We corrected the corresponding description (lines 138-140) to make it more rigorous.

L149: "No significant change" implies statistics were computed that are not presented here.

The statistics were computed by using built-in function of Excel and presented in Table supplement 2 (deposited in figshare, https://doi.org/10.6084/m9.figshare.23282627.v2) rather than in manuscript. To be specific, the error computations are followed by the formula of relative error, which is defined by:

where u_z denotes the velocity profile collected on each cut point z with the current mesh parameters, u_z^* denotes the velocity profile collected on each cut point z with the next finer mesh parameters, i denotes each time step (from 0.01 to 4.0). In this case, the total average error was computed by averaging the sum of each 〖error〗_i on corresponding time step. The results are red marked in Table supplement 2. We revised the corresponding description in lines 140-146

L152: "line graphs" >> "profiles"

Thanks for your suggestions. We corrected it in line 144.

L159: remove "significant" unless statistics are being reported, in which case those need to be explained in detail.

Thanks for your suggestions. We removed "significant" and corrected the corresponding sentences in lines 150-153 to make them more rigorous.

L159: I would recommend including a supplemental somewhere that shows how tall the modeled Quadrapyrgites is and where the cut lines exist above it.

Many thanks for your suggestions. Corresponding complementation was made in the last paragraph of section “Computational fluid dynamics” (line 455 and line 535). We agree that it is appropriate to elucidate the height of modeled Quadrapyrgites and the position of each cut point. Hence, we add a supplementary figure (entitled Figure supplement 1) to illustrate those above.

L183: "The maximum vorticity magnitude was set..." I do not follow what this threshold is based on the current phrasing.

The vorticity magnitude mentioned here is the visualisation range of the color scalebar, which can be set manually set in the software. The positive number represent the vortex rotated counterclockwise, while the negative number represent that rotated clockwise on the cut plane. In this case, the visualisation range is [-0.001,0.001] (i.e., the absolute value of 0.001 is the threshold), as the color scalebar in Figure 7. Decreasing the threshold, for example, setting the visualisation range to [-0.0001,0.0001], can capture smaller vorticity on the cut plane, as the figure below on the left. Otherwise, setting the range to [-0.01,0.01] will focus on bigger vorticity, as the figure below on the right. We found [-0.001,0.001] could be an appropriate parameter to visualize the vortex near periderm based on our trial. To be more rigorous and to avoid confusion, we modified the description in the corresponding place of the manuscript (lines 172-174).

Author response image 1.

L201: "3.9-4 s"

Thanks, we corrected it in line 191.

L269: "Sahin et al.,..." add to the next paragraph

Yes, we rearranged the corresponding two paragraphs (lines 258-289).

L344: "Higher expansion-contraction..." this needs references and/or more justification.

Thanks. We deleted the sentence.

L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

Author response image 2.

L449: similar to comments regarding lines 146-148, key information is missing here. Figure 3C appears to be COMSOL's default meshing routine. While it is true that the domain is discretized in a non-uniform manner, no information is provided as to what mesh parameters were "tuned" to determine "optimal settings" or what those settings are (or how they are optimal).

Many thanks for your question. Specific mesh parameters were listed in Table supplement 3 and corresponding descriptions and modifications were made both in lines 475-479 and lines 542-549. In most CFD cases, the mesh parameters need to be tuned to ensure a balance between computational cost and accuracy. If the difference of the result obtained from present mesh and that obtained from the next finer mesh ranges from 5% -10%, the present mesh is expected to be “optimal”. To achieve this, we prescribed several sets of different mesh (mainly concerning maximum and minimum element size) to each subdomain (domain of the inner cavity, domain of the peridermal aperture and domain outside of fossil model) of the whole computational domain in the test model. Subsequently, we refined the mesh step by step as much as possible and adjust the element size of subdomains to find suitable mesh parameters, that is how the mesh parameters were "tuned". We agree that we should explicit what mesh parameters were tuned and what those settings are.

Figure 7 should have the timesteps included and the scaling of the arrows should be explicit in the caption

Many thanks for your suggestions. We intended to use the white arrows to represent the velocity orientation rather than true velocity scale in Figure 7 (Instead, the white arrows in Animation supplement 1 represent a normalized velocity profile). To avoid confusion, we revised Figure 7 with timesteps and arrows represent a normalized velocity profile, making it consistent with Animation supplement 1. Corresponding modification is also made in the caption of Figure 7.

The COMSOL simulation files (raw data) are missing from the supplemental data. These should be posted to Dryad or here.

We uploaded the files to Dryad (https://datadryad.org/stash/share/QGDSqLh8HOll7ofl6JWVrqM57Rp62ZPjvZU0AQQHwTY), and added the corresponding link to section “Data Availability Statement”.

REVIEWER 2

Lines 319-334: The omission in this paragraph of Paraconularia ediacara Leme, Van Iten and Simoes (2022) from the terminal Ediacaran of Brazil is a serious matter, as (1) the medusozoan affinities of this fossil are every bit as well established as those of anabaritids, Sphenothallus, Cambrorhytium and Byronia, and (2) P. ediacara was a large (centimetric) polyp, the presence of which in Precambrian times is thus a problem for the simple evolutionary scenario (very small polyps followed later in evolutionary history by large polyps) outlined in the paragraph. Thus, Paraconularia ediacara must be mentioned in this paper, both in connection with the early evolution of size in cnidarian polyps and in other places where the early evolution of cnidarians is discussed.

Thanks for your important suggestions. We added some sentences in lines 323-326 as following: “Significantly, the large-bodied, skeletonized conulariids-like Paraconularia found from the terminal Ediacaran Tamengo Formation of Brazil confirmed their ancient predators like the extant medusozoans and suggested the origin of cnidarians even farther into the deep evolutionary scenario (Leme et al. 2022).”

Line 23. Delete the word, been.

Line 25. Replace conjecture with conjectural.

Line 26. Delete the word, the before calyx-like.

Line 32. Replace consisting with consistent.

Thanks for your suggestions. We all corrected them.

Reviewer #1 (Public Review):

Summary:<br /> The authors utilize fluid-structure interaction analyses to simulation fluid flow within and around the Cambrian cnidarian Quadrapyrgites to reconstruct feeding/respiration dynamics. Based on vorticity and velocity flow patterns, the authors suggest that the polyp expansion and contraction ultimately develop vortices around the organism that are like what modern jellyfish employ for movement and feeding. Lastly, the authors suggest that this behavior is likely a prerequisite transitional form to swimming medusae.

Strengths:<br /> While fluid-structure-interaction analyses are common in engineering, physics, and biomedical fields, they are underutilized in the biological and paleobiological sciences. Zhang et al. provide a strong approach to integrating active feeding dynamics into fluid flow simulations of ancient life. Based on their data, it is entirely likely the described vortices would have been produced by benthic cnidarians feeding/respiring under similar mechanisms. However, some of the broader conclusions require additional justification.

Weaknesses:

1. The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.<br /> 2. While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.<br /> 3. There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Despite these weaknesses the authors dynamic fluid simulations convincingly reconstruct the feeding/respiration dynamics of the Cambrian Quadrapyrgites, though the large claims of transitionary stages for this behavior are not adequately justified. Regardless, the approach the authors use will be informative for future studies attempting to simulate similar feeding and respiration dynamics.

The following text is directly in response to the revised version of the manuscript.<br /> Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps

Revision 1

I think this manuscript has been improved by the authors, and I appreciate their time and effort in considering my earlier comments. While most of my line by line comments have been incorporated, I do feel that some of my larger points have been insufficiently addressed. Those are repeated with additional clarifications below.

Original comment: The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

Author response: Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

Reviewer response: This response does not sufficiently address my earlier comment. While the authors are correct that individual Ediacaran affinities are an area of active research and that Olivooids can reasonably be considered cnidarians, this doesn't address the actual critique in my comment. Most (not all) Ediacaran soft-bodied fossils are considered to have been benthic, but pelagic cnidarian life is widely acknowledged to at least be present during later White Sea and Nama assemblages (and earlier depending on molecular clock interpretations). The authors have certainly provided support for the mechanics of this type of feeding being co-opted for eventual jet-propulsion swimming in Olivooids. They have not provided sufficient justifications within the manuscript for this to be broadened beyond this group.

Original comment: There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Author response: Thanks for your suggestion. We revised the section "Perspectives for future work and improvements" (lines 396-404 in our revised version of MS). Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a "breakthrough" in simulation gregarious feeding. However, we modified the corresponding description in section "Perspectives for future work and improvements" to make it more appropriate.

Reviewer response: I think I understand where the authors are trying to take this next step. If the authors were to follow up on this study with the proposed implementation of inhalant/exhalent velocities profiles (or more preferably velocity/pressure fields), then that study would be a breakthrough in simulating such gregarious feeding. Based on what has been done within the present study, I think the term "breakthrough" is instead overly emphatic.<br /> An additional note on this. The authors are correct that incorporating additional models could be used to simulation a population (as has been successfully done for several Ediacaran taxa despite computational limitations), but it's not the only way. The authors might explore using periodic boundary conditions on the external faces of the flow domain. This could require only a single Olivooid model to assess gregarious impacts - see the abundant literature of modeling flow through solar array fields.

Original comment: L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Author response: Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

Reviewer response: As the authors point out in the main text, these organisms are small (millimeters in scale) and certainly lived within the boundary layer range of the ocean. While the boundary layer is not the main point, it still needs to be accurately resolved as it should certainly affect the flow further towards the far field at this scale. I'm not suggesting the authors need to perfectly resolve the boundary layer or focus on using turbulence models more tailored to boundary layer flows (such as k-w), but the flow field still needs sufficient realism for a boundary bounded flow. The authors really should consider quantitatively assessing the number of hexahedral elements within their mesh refinement study.

Reviewer #1 (Public Review):

Questions and concerns:

The abstract is hard to follow. The authors there refer to a previous experiment showing that "overnight fasting diminishes excessive avoidance and speeds up fear extinction by decreasing subjective relief during threat omissions" (L26). They go on to say that "relief tracks the reward prediction error signal that governs safety learning" (L28). This is puzzling. While getting less relief/safety from avoidance actions will surely diminish avoidance (because avoidance actions are less reinforced), getting less relief/safety from omissions of an unconditioned stimulus (US) in fear extinction should slow down (not speed up) fear extinction. In the same vein, why are "lower activations [in fMRI] in the ventromedial prefrontal cortex and nucleus accumbens in response to threat omissions signaled by a safe cue" (L34) associated with "increased effective avoidance and sped up fear extinction" (L33)? This clearly goes against the existing literature on reward prediction errors (PEs) in fear learning paradigms, where these PEs in the mesolimbic dopamine system drive extinction, that is, they are associated with better extinction (and should therefore also be associated with more avoidance). For instance, in the rodent, Luo et al., 2018 (DOI: 10.1038/s41467-018-04784-7) and Salinas-Hernandez et al., 2018 (DOI: https://doi.org/10.7554/eLife.388181 of 25RESEARCH ARTICLE) and 2023 (https://doi.org/10.1016/j.neuron.2023.08.025ll) have in various constellations optogenetically enhanced and diminished, respectively, the PE signal at the time of US omission in extinction in either VTA or nucleus accumbens and thereby sped up and slowed down, respectively, extinction learning. If the results of the current experiment contradict established knowledge, the reader must be clearly informed about this. By contrast, the abstract gives the impressions as if the current results were to be expected and in line with the literature ("since relief tracks the reward prediction error signal ..., we hypothesized ...").

It would also help the reader if it was clarified that the finding of "increased effective avoidance" (L33) went counter to the hypothesis, e.g., by saying "Contrary to our hypothesis, we observed ...".

Introduction:

L51: The presentation of exposure therapy is a bit misleading and may create confusion. While it is probably correct that exposure works by "promoting safety learning", this is generally thought to be the case only for Pavlovian associations (CS-US), that is, for extinction (where safety learning creates the new association of CS and "no US"). It is, however, not generally considered to be the case for the instrumental action-outcome associations that underlie avoidance learning ("I do this or that, then I do not have to experience the feared object or situation"). Therapists try to prevent this type of learning from happening, exactly by promoting the confrontation with fear objects or situations in the absence of any avoidance action.

Generally, I think the introduction suffers from the absence of a short explanation of what avoidance and extinction learning are, behaviorally, and what types of mechanisms are believed to drive them, and that the one (avoidance) is thought to contribute to the maintenance of fears whereas the other (extinction) reduces fear. The non-specialist reader is somehow left in the dark.

In the same vein, on L63, presenting the results of their previous fasting study that serves as a discovery study for the present experiment, the authors make a distinction between "unnecessary avoidance during a signal of safety" and "effective avoidance during a signal of upcoming threat". It is really expecting too much from the reader that they will understand at this stage that a CS can become a signal of safety through extinction or that a CS not paired with a US during conditioning (a "CS-") is a safety signal and that it is not necessary to avoid such a signal, whereas a non-extinguished CS (signaling threat) may well be avoided. (At least, this is how I understood the distinction.)

I was then really confused by the following statement (L65) that "the decrease in unnecessary avoidance was mediated by lower levels of relief ... during omissions of threat". If a CS is already extinguished (has no remaining or only little threat value, that is, is a safety stimulus), there is no longer threat omission when the US does not occur, and no relief. There should also be no relief to US omission after a CS-. More importantly even, if fasted participants reported lower levels of relief from threat omission, why did they not also show less effective avoidance (which is driven by the reinforcement provided by the relief that occurs when a successful avoidance action has prevented a US from occurring after or during the CS)?

L69: Also the statement "a faster decline in relief ... ratings during ... extinction, suggesting faster decrease of threat expectancies" can only be understood by the reader if they already know what a PE is and by what rules PE-driven learning is governed (that is, essentially, if they know Rescorla-Wagner). I think the authors must explain, in order to allow a non-specialist reader to follow their text, that the PE (supposed to be indexed by the relief rating) reflects the discrepancy between the magnitude of an outcome expectation (e.g., here, expectation of the US) and the obtained outcome (here, US or not); that, therefore, a PE is generated when a subject expects a US (as a result of prior conditioning) but does not get it; that this leads to a proportional update (reduction) of the US expectation in the next trial; and that this in turn leads to a diminished PE when the US again does not occur. Notably, the reader must be made aware that the higher the PE, the higher the reduction and the faster the extinction (proportionality).

The reader must also be made aware that the update is additionally determined by some multiplicatory "transmission" function or constant (e.g., learning rate in Rescorla-Wagner) that defines the size of the relationship between the magnitude of the PE and the magnitude of the update (reduction). Hence, in two individuals, even if the magnitude of the PE is identical, the magnitude of the update may differ because of individual differences in the learning rate (to take the Rescorla-Wagner implementation). The authors, however, seem to ignore the possibility that fasting changes the learning rate.

Both the dynamics of the PE and the learning rate, of course, add complexity to the interpretation of the past and present data. But I think the authors cannot avoid this when they want to make sense of a treatment (fasting) that they believe affects safety learning. Speaking of "lower levels of relief" (L66) must be qualified by whether these lower ratings were observed initially (when the first PEs were registered at initial threat omissions, meaning that safety learning should be relatively slowed down by fasting) or on average or later during a safety learning experiment (which could indicate that learning under fasting was relatively quicker/more successful).

Following upon this, in L74, the conclusion from observations of lower levels of relief during avoidance and faster decline in relief during extinction in the previous study that "overnight fasting decreased the reward value of safety (less relief pleasantness)" may be wrong if the faster decline and the resulting lower average levels of relief were the consequence of a higher initial PE in the fasting group, as would be expected from the Rescorla-Wagner rule. If the latter were the case, this would suggest that subjects actually registered more safety (a higher discrepancy to their threat expectation) in early trials. This could also explain why fasting sped up extinction in that study (see Abstract). It might also explain why "effective avoidance" (L64) was at least maintained (although it should actually also be sped up). It might make less parsimonious explanations ("fasting biases .. to focus on food at the expense of safety", L79), requiring the presence of a food source and a utility function of accepting a threat in the obtainment of food, unnecessary.

All this, however, rests on whether I think I have understood what the authors want to say about their relief measurements and the way the operationalized avoidance in the previous study.

More unclarities due to not giving full information: L91: "... extinction and avoidance learning. Accordingly, human fMRI studies have found ... activations in the ventral striatum and the VTA during threat omissions that might contribute to establishing a new safety CS-->noUS memory that reduces the initial fear response." However, in avoidance, it is an action that is reinforced by the US omission and hence an action-->noUS memory that is being formed. The CS keeps its threat value acquired during the preceding conditioning phase, and the reduction of fear during CS presentations is contingent upon the exertion of the avoidance action.

L99: "Because overnight fasting decreased relief rating particularly during omissions after safety signals". Again, if a US is omitted after a safety signal (an extinguished CS or a CS-), there should be no PE and no relief. If there were still relief ratings at US omission after a safety signal, this would suggest extinction did not fully work or differential conditioning was not successful. In any case, it is not clear at all why relief was specifically decreased during omissions after safety signals and not (and much more so) during omissions after threat signals, where there is clearly a PE. If this was not the case, one has to wonder if something went wrong in the discovery study.

The paragraph starting L103 and the associated figure 1 could be a bit more precise and give a bit more information in order to provide the reader a proper understanding of key experimental manipulations, in particular the ART task. Please define abbreviations "CS+unav", "CS+av". L108 ff.: One gets the impression there is only one CS+, whereas there are two. Say explicitly that one CS+ remains unavoidable during the Avoidance phase (CS+unav). What is the purpose of this stimulus? Do participants learn during the Avoidance phase that the CS+unav is unavoidable and the CS+av is avoidable or is this instructed? Do participants have to press the button within a certain time after CS+unav onset in order to avoid the US, or with a certain force? Is avoidance in case of successful button pressing deterministic or probabilistic? Say that the frame with the non-lit lamp is the ITI.

Relief ratings (Figure 1b): The rating says "How pleasant was the relief that you felt?". That is, the experimenter insinuates that the participant will have felt relief and only wants to know how pleasant that relief was. The subjects has no chance to indicate there was no relief. This may be the reason why, in the discovery study, subjects indicated relief to safe stimuli, see above. Why did the authors not simply ask about the degree of relief felt, which would give a subject the chance to say there was no relief? I think this is a major flaw.

L119: "We previously found that overnight fasting reduces avoidance and relief mostly to a safe CS-." If this is really the only thing that the authors found, then the fasting manipulation in their previous study failed to modulate avoidance of CS+s and the PE signaling at the time of US omissions after CS+s, that is, after actual threat stimuli. The procedure then clearly is not suited to study influences of fasting on avoidance learning. Whatever it does manipulate, it is not relief-based avoidance learning.

L130: It makes absolutely no sense to hypothesize that a manipulation reducing relief in extinction learning will decrease activation in the neural PE circuitry at the time of US omission more after the CS- than after the CS+. Of course, the PE is highest when the US is not given after the CS+, and this is where any relief manipulation should have an effect. As said above, the authors must also specify their hypothesis with respect of timing (early or late extinction? See the animal papers cited above.)

Author Response

Thanks to all the reviewers for their insightful and constructive comments, which are very helpful in improving the manuscript. We are encouraged by the many positive comments regarding the significance of our findings and the value of our data. Regarding the reviews’ concern on cell classification, we used several additional marker genes to explain the identification of cell clusters and subclusters. We have further analyzed and rewrote part of the text to address the concerns raised. Here is a point-by-point response to the reviewers’ comments and concerns. Figures R1-R9 were provided only for additional information for reviewers and were not included in the revised manuscript.

Reviewer #1 (Public Review):

In the article "Temporal transcriptomic dynamics in developing macaque neocortex", Xu et al. analyze the cellular composition and transcriptomic profiles of the developing macaque parietal cortex using single-cell RNA sequencing. The authors profiled eight prenatal rhesus macaque brains at five timepoints (E40, E50, E70, E80, and E90) and obtained a total of around 53,000 high-quality cells for downstream analysis. The dataset provides a high-resolution view into the developmental processes of early and mid-fetal macaque cortical development and will potentially be a valuable resource for future comparative studies of primate neurogenesis and neural stem cell fate specification. Their analysis of this dataset focused on the temporal gene expression profiles of outer and ventricular radial glia and utilized pesudotime trajectory analysis to characterize the genes associated with radial glial and neuronal differentiation. The rhesus macaque dataset presented in this study was then integrated with prenatal mouse and human scRNA-seq datasets to probe species differences in ventricular radial glia to intermediate progenitor cell trajectories. Additionally, the expression profile of macaque radial glia across time was compared to those of mouse apical progenitors to identify conserved and divergent expression patterns of transcription factors.

The main findings of this paper corroborate many previously reported and fundamental features of primate neurogenesis: deep layer neurons are generated before upper layer excitatory neurons, the expansion of outer radial glia in the primate lineage, conserved molecular markers of outer radial glia, and the early specification of progenitors. Furthermore, the authors show some interesting divergent features of macaque radial glial gene regulatory networks as compared to mouse. Overall, despite some uncertainties surrounding the clustering and annotations of certain cell types, the manuscript provides a valuable scRNA-seq dataset of early prenatal rhesus macaque brain development. The dynamic expression patterns and trajectory analysis of ventricular and outer radial glia provide valuable data and lists of differentially expressed genes (some consistent with previous studies, others reported for the first time here) for future studies.

The major weaknesses of this study are the inconsistent dissection of the targeted brain region and the loss of more mature excitatory neurons in samples from later developmental timepoint due to the use of single-cell RNA-seq. The authors mention that they could observe ventral progenitors and even midbrain neurons in their analyses. Ventral progenitors should not be present if the authors had properly dissected the parietal cortex. The fact that they obtained even midbrain cells point to an inadequate dissection or poor cell classification. If this is the result of poor classification, it could be easily fixed by using more markers with higher specificity. However, if it is the result of a poor dissection, some of the cells in other clusters could potentially be from midbrain as well. The loss of more mature excitatory neurons is also problematic because on top of hindering the analysis of these neurons in later developmental periods, it also affects the cell proportions the authors use to support some of their claims. The study could also benefit from the validation of some of the genes the authors uncovered to be specifically expressed in different populations of radial glia.

We thank the Reviewer’s comments and apologize for the shortcomings of tissue dissection and cell capture.

We used more marker genes for major cell classification, such as SHOX2, IGFBP5, TAC1, PNYN, FLT1, and CYP1B, in new Figure 1D, to improve the cell type annotation results. We improved the cell type annotation results by fixing cluster 20 from C20 as Ventral LGE-derived interneuron precursor and cluster by the expression of IGFBP5, TAC1, and PDYN; fixing cluster 23 from meningeal cells to thalamus cells by the expression of ZIC2, ZIC4, and SHOX2. These cell types were excluded in the follow-up analysis. Due to EN8 being previously incorrectly defined as midbrain neurons, it resulted in a misunderstanding of the dissection result as a poor dissection. After carefully reviewing the data analysis process, we determined that EN8 was a small group of cells in cluster 23 mistakenly selected during excitatory neuron analysis, as shown in Figure R5(A), which was corrected after revision. In the revised manuscript, we deleted the previous EN8 subcluster and renumbered the rest of the excitatory neuron subclusters in the new Figure 2.

In addition, we also improved the description of sample collection as follows: “We collected eight pregnancy-derived fetal brains of rhesus macaque (Macaca mulatta) at five prenatal developmental stages (E40, E50, E70, E80, E90) and dissected the parietal lobe cortex. Because of the different development times of rhesus monkeys, prenatal cortex size and morphology are different. To ensure that the anatomical sites of each sample are roughly the same, we use the lateral groove as a reference to collect the parietal lobe for single-cell sequencing (as indicated by bright yellow in Figure S1A) and do not make a clear distinction between the different regional parts including primary somatosensory cortex and association cortices in the process of sampling”. As shown in Figure S1A, due to the small volume of the cerebral cortex at early time points, especially in E40, a small number of cells beyond the dorsal parietal lobe, including the ventral cortex cells and thalamus cells, were collected during the sampling process with the brain stereotaxic instrument.

In this study, the BD method was used to capture single cells. Due to the fixed size of the micropores, this method might be less efficient in capturing mature excitatory neurons. However, it has a good capture effect on newborn neurons at each sampling time point so that the generation of excitatory neurons at different developmental time points can be well observed, as shown in Figure 2, which aligns with our research purpose.

To verify the reliability of our cell annotation results, we compared the similarity of cell-type association between our study and recently published research(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652), using the scmap package to project major cell types in our macaque development scRNA-seq dataset to GSE226451. The river plot in Author response image 1 illustrates the broadly similar relationships of cell type classification between the two datasets.

Author response image 1.

Riverplot illustrates relationships between datasets in this study and recently published developing macaque telencephalon datasets major cell type annotation.

Furthermore, bioinformatics analysis is used for the validation of genes specifically expressed in outer radial glia. We verified terminal oRG differentiation genes in the recently published macaque telencephalic development dataset(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652) (GEO accession: GSE226451). The results of Author response image 2 show that the gene expression showed states/stages. Most of the oRG terminal differentiation markers genes identified in our study were also expressed in the oRG cells of the GSE226451 dataset. In particular, the two datasets were consistent in the expression of ion channel genes ATP1A2, ATP1A2, and SCN4B.

Author response image 2.

Heatmap shows the relative expression of genes displaying significant changes along the pseudotime axis of vRG to oRG from the dataset of Nicola Micali et al.2023(GEO: GSE226451). The columns represent the cells being ordered along the pseudotime axis.

Reviewer #2 (Public Review):

Summary:

This manuscript by Xu et al., is an interesting study aiming to identify novel features of macaque cortical development. This study serves as a valuable atlas of single cell data during macaque neurogenesis, which extends the developmental stages previously explored. Overall, the authors have achieved their aim of collecting a comprehensive dataset of macaque cortical neurogenesis and have identified a few unknown features of macaque development.

Strengths:

The authors have accumulated a robust dataset of developmental time points and have applied a variety of informatic approaches to interrogate this dataset. One interesting finding in this study is the expression of previously unknown receptors on macaque oRG cells. Another novel aspect of this paper is the temporal dissection of neocortical development across species. The identification that the regulome looks quite different, despite similar expression of transcription factors in discrete cell types, is intriguing.

Weaknesses:

Due to the focus on demonstrating the robustness of the dataset, the novel findings in this manuscript are underdeveloped. There is also a lack of experimental validation. This is a particular weakness for newly identified features (like receptors in oRG cells). It's important to show expression in relevant cell types and, if possible, perform functional perturbations on these cell types. The presentation of the data highlighting novel findings could also be clarified at higher resolution, and dissected through additional informatic analyses. Additionally, the presentation of ideas and goals of this manuscript should be further clarified. A major gap in the study rationale and results is that the data was collected exclusively in the parietal lobe, yet the rationale and interpretation of what this data indicates about this specific cortical area was not discussed. Last, a few textual errors about neural development are also present and need to be corrected.

We thank you for your comments and suggestions concerning our manuscript. The comments and suggestions are all valuable and helpful for revising and improving our paper and the essential guiding significance to our research. We have studied the comments carefully and made corrections, which we hope to meet with approval. We have endeavored to address the multiple points raised by the referee.

To support the reliability of our data and newly identified features, we verified terminal oRG differentiation genes in the recently published macaque telencephalic development dataset(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652) (GEO accession: GSE226451). The results of Figure R2 show that the gene expression showed states/stages. Most of the oRG terminal differentiation markers genes identified in our study were also expressed in the oRG cells of the GSE226451 dataset. In particular, the two datasets were consistent in the expression of ion channel genes ATP1A2, ATP1A2, and SCN4B.

Our research results mainly explore the conserved features of neocortex development across species. By comparing evolution, we found the types of neural stem cells in the intermediate state, their generative trajectories, and gene expression dynamics accompanying cell trajectories. We further explored the stages of transcriptional dynamics during vRG generating oRG. More analysis was performed through transcriptional factor regulatory network analysis. We performed the TFs regulation network analysis of human vRG with pyscenic workflow. The top transcription factors of every time point in human vRG were calculated, and we used the top 10 TFs and their top 5 target genes to perform interaction analysis and generate the regulation network of human vRG in revised figure 6. In comparison of the pyscenic results of mouse, macaque and human vRG, it was obvious that the regulatory networks were not evolutionarily conservative. Compared with macaque, the regulatory network of transcription factors and target genes in humans is more complex. Some conserved regulatory relationships present in more than one species are identified, such as HMGN3, EMX2, SOX2, and HMGA2 network at an early stage when deep lager generation and SOX10, ZNF672, ZNF672 network at a late stage when upper-layer generation.

Although the parietal lobe is the center of the somatic senses and is significant for interpreting words as well as language understanding and processing. In this study, the parietal lobe area was selected mainly because of the convenience of sampling the dorsal neocortex. As we described in the Materials and Methods section as follows: “Because of the different development times of rhesus monkeys, prenatal cortex size and morphology are different. To ensure that the anatomical sites of each sample are roughly the same, we use the lateral groove as a reference to collect the parietal lobe for single-cell sequencing (as indicated by bright yellow in Figure S1A) and do not make a clear distinction between the different regional parts including primary somatosensory cortex and association cortices in the process of sampling”.

Thanks for carefully pointing out our manuscript's textual errors about neural development. We have corrected them which were descripted in the following response.

Reviewer #3 (Public Review):

Summary: The study adds to the existing data that have established that cortical development in rhesus macaque is known to recapitulate multiple facets cortical development in humans. The authors generate and analyze single cell transcriptomic data from the timecourse of embryonic neurogenesis.

Strengths:

Studies of primate developmental biology are hindered by the limited availability and limit replication. In this regard, a new dataset is useful.

The study analyzes parietal cortex, while previous studies focused on frontal and motor cortex. This may be the first analysis of macaque parietal cortex and, as such, may provide important insights into arealization, which the authors have not addressed.

Weaknesses:

The number of cells in the analysis is lower than recent published studies which may limit cell representation and potentially the discovery of subtle changes.

The macaque parietal cortex data is compared to human and mouse pre-frontal cortex. See data from PMCID: PMC8494648 that provides a better comparison.

A deeper assessment of these data in the context of existing studies would help others appreciate the significance of the work.

We thank the reviewer for these suggestions and constructive comments. We agree with the reviewer that the cell number in our study is lower than in recently published studies. The scRNA sequencing in this study was completed between 2018 and 2019, the early stages of the single-cell sequencing technology application. Besides, we have been unable to get extra macaque embryos to enlarge the sample numbers recently since rhesus monkey samples are scarce. Therefore, the number of cells in our study is relatively small compared to recently published single-cell studies.

The dataset suggested by the reviewers is extremely valuable, and we tried to perform analysis as the reviewer suggested to explore temporal expression patterns in different species of parietal cortex. The dataset from PMCID: PMC8494648 provides the developing human brain across regions from gestation week (GW)14 to gestation week (GW)25. Since this data set only covers the middle and late stages of embryonic neurogenesis, it did not fully match the developmental time points of our study for integration analysis. However, we quoted the results of this study in the discussion section.

The human regulation analysis with pyscenic workflow was added into new figure 6 for the comparison of different species vRG regulatory network. Compared with macaque, the regulatory network of transcription factors and target genes in humans is more complex. Some conserved regulatory relationships present in more than one species are identified, such as HMGN3, EMX2, SOX2, and HMGA2 network at an early stage when deep lager generation and SOX10, ZNF672, ZNF672 network at a late stage when upper-layer generation.

Besides, we performed additional integration analysis of our dataset with the recently published macaque neocortex development datase (GEO accession: GSE226451) to verify the reliability of our cell annotation results and terminal oRG differentiation genes. The river plot in Figure R1 illustrates the broadly similar relationships of cell type classification between the two datasets. The result in Figure R2 showed that most of the oRG terminal differentiation markers genes identified in our study were also expressed in the oRG cells of the GSE226451 dataset. In particular, the two datasets were consistent in the expression of ion channel genes ATP1A2, ATP1A2, and SCN4B.

Reviewer #1 (Recommendations For The Authors):

1) Throughout the manuscript, the term "embryonic" or "embryogenesis" is used in reference to all timepoints (E40-E90) in this study. The embryonic period is a morphologically and anatomically defined developmental period that ends ~E48-E50 in rhesus macaque. Prenatal or developing is a more accurate term when discussing all timepoints of this study.

We thank the reviewer for pointing out this terminology that needs to be clarified. We have now replaced “embryonic” with “prenatal” as a more appropriate description for the sampling time points in the manuscript.

2) Drosophila should be italicized in the introduction.

Thanks for suggesting that we have set the “Drosophila” words to italics in the manuscript.

3) Introduction - "In rodents, radial glia are found in the ventricular zone (VZ), where they undergo proliferation and differentiation." This sentence implies that only within rodents are radial glia found within the ventricular zone. Radial glia are present within the ventricular zone of all mammals.

Thanks for careful reading. This sentence has been corrected “In mammals, radial glial cells are found in the ventricular zone (VZ), where they undergo proliferation and differentiation.”

4) Figure 1A - an image of the E40 brain is missing.

We first sampled the prenatal developmental cortex of rhesus monkeys at the E40 timepoint. Unfortunately, we forgot to save the photo of the sampling at the E40 time point.

5) Figure 1B and 1C - it is unclear why cluster 20 is not annotated in Figure 1 as in the text it is stated "Each of the 28 identified clusters could be assigned to a cell type identity..." This cluster expresses VIM and PAX6 suggestive of ventricular radial glia and is located topographically approximate to IPC cluster 8 and seems to bridge the gap between neural stem cells and the interneuron clusters. Additionally, cluster 20 appears to be subclustered by itself in the progenitor subcluster UMAP (Figure 3A) suggestive of a batch effect or cells with low quality. The investigation, quality control, and proper annotation of this cluster 20 is necessary.

We appreciate for the reviewer’s suggestion. We detected specific expression marker genes of cluster 20, cells in this cluster specifically expressed VIM, IGFBP5 and TAC. According to the cell annotation results from a published study, we relabeled cluster 20 as ventral LGE-derived interneuron precursors (Yu, Yuan et al. Nat Neurosci. 2021. doi:10.1038/s41593-021-00940-3. PMID: 34737447.). Cluster 20 cells have been deleted in the new Figure 3A.

6) Figure 1B UMAP - it is unexpected that meningeal cells would cluster topographically closer to the excitatory neuron cluster (one could even argue that the meningeal cell cluster is located within the excitatory neuron clusters) instead of next to or with the endothelial cell clusters. This is suspicious for a mis-annotated cell cluster. ZIC2 and ZIC3 were used as the principal marker genes for meningeal cells. However, these genes are not specific for meninges (PanglaoDB) and had not been identified as marker genes in a developmental sc-RNAseq dataset of the developing mouse meninges (DeSisto et al. 2020). Additional marker genes (COL1A1, COL1A2, CEMIP, CYP1B1, SLC13A3) may be helpful to delineate the identity of this cluster and provide more evidence for a meningeal origin.

We thank the reviewer for the constructive advice. The violin plot in Author response image 3 has checked additional marker genes, including COL1A1, COL1A2, CEMIP, and CYP1B2. Cluster 23 does not express these marker genes but specifically expresses thalamus marker genes SHOX2(Rosin, Jessica M et al. Dev Biol. 2015. doi:10.1016/j.ydbio.2014.12.013. PMID: 25528224.) and TCF7L2(Lipiec, Marcin Andrzej et al. Development. 2020. doi: 10.1242/dev.190181. PMID: 32675279). According to the gene expression results, we corrected the cell definition of cluster 23 to thalamic cells in the revised manuscript. Specifically, we added marker genes SHOX2 and CYP1B1 in the new Figure 1D violin plot and corrected the cell definition of cluster23 from meninges to thalamus cells in the revised manuscript and figures.

Author response image 3.

Vlnplot of additional markers in cluster 23.

7) From Figure 1A, it appears that astrocytes (cluster 13) are present at E40 and E50 timepoints. This is inconsistent with literature and experimental data of the timing of the neuron-glia switch in primates and inconsistent with the claim within the text that, "Collectively, these results suggested that cortical neural progenitors undergo neurogenesis processes during the early stages of macaque embryonic cortical development, while gliogenic differentiation... occurs in later stages." The clarification of the percentage of astrocytes at each timepoint would clarify this point.

According to the suggestion, we have statistically analyzed the percentage of astrocytes (cluster 13) at each time point. The statistical results showed that the proportion of astrocytes was low to 0.1783% and 0.1046% at E40 and E50 time points, and increased significantly at E80 and E90, suggesting the onset of macaque gliogenesis might be around embryonic 80 days to 90 days. The result was consistent with published research on the timing of the neuron-glial transition in primates (Rash, Brian G et al. Proc Natl Acad Sci U S A. 2019. doi:10.1073/pnas.1822169116. PMID: 30894491). Besides, we thought that the cells in cluster 13 captured at E40 to E50 time points, with a total number of less than 200, maybe astrocyte precursor cells expressing the AQP4 gene (Yang, Lin, et al. Neuroscience bulletin. 2022. doi:10.1007/s12264-021-00759-9. PMID: 34374948).

8) A subcluster of ExN neurons was identified and determined to be of midbrain origin based on expression of TCF7L2. Did this subcluster express other known markers of the developing midbrain (OTX2, LMX1A, NR4A2, etc...)? Additionally, does this subcluster suggest that the limits of the dissection extended to the midbrain in samples E40 and E50?

We apologize for the previous inadequacy of the excitatory neuron cell annotation. In the description of the previous version of the manuscript, we misidentified the cells of the EN8 as midbrain cells. Following the reviewer’s suggestion, we verified the expression of more tissue- specific marker genes of EN8. As the violin diagram in Author response image 4 shows, other developing midbrain markers OTX2, NR4A2, and PAX7 did not express in EN8, but thalamus marker genes SHOX2, TCF7L2, and NTNG1 were highly expressed in EN8. Besides, dorsal cortex excitatory neuron markers NEUROD2, NEUROD6, and EMX1 were not expressed in EN8, which suggests that EN8 might not belong to cortical cells. After carefully reviewing the data analysis process, we determined that EN8 was a small group of cells in cluster 23 mistakenly selected during excitatory neuron analysis, as shown in Figure R5(A), which was corrected after revision. In the revised manuscript, we have removed EN8 from the analysis of excitatory neurons. In the revised manuscript, we have deleted the previous EN8 subcluster and renumbered the left excitatory neuron subclusters in new Figure 2 and Figure S3.

Author response image 4.

(A). Modified diagram of clustering of excitatory neuron subclusters collected at all time points, visualized via UMAP related to Figure 2A. (B) Vlnplot of different marker genes in EN8.

9) "These data suggested that the cell fate determination by diverse neural progenitors occurs in the embryonic stages of macaque cortical development and is controlled by several key transcriptional regulators" The authors present a list of differentially expressed genes specific to the various radial glia clusters along pseudotime. Some of these radial glia DEGs are known and have been characterized by previous literature while other DEGs they have identified had not been previously shown to be associated with radial glia specification/maturation. However, this list of DEGs does not support the claim that cell fate determination is controlled by several key transcriptional regulators. What were the transcriptional regulators of radial glia specification identified in this study and how were they validated?

We agree with the reviewer and honestly admit that the description of this part in the previous manuscript is inaccurate. The description has been deleted in the revised manuscrip.

10) "Comparing vRG to IPC trajectory between human, macaque, and mouse, we found this biological process of vRG-to-IPC is very conserved across species, but the vRG to oRG trajectory is divergent between species. The latter process is almost invisible in mice, but it is very similar in primates and macaque." Firstly, macaques are primates, and the text should be updated to reflect this. Secondly, from Figure 5C., it seems there were no outer radial glia detected at all within the vRG-oRG and vRG-IPC developmental trajectories. This would imply that oRGs are not "almost invisible" in mice, but rather do not exist. The authors need to clarify the presence or absence of identifiable outer radial glia in the integrated dataset and relate the relative abundance of these cells to their interpretation of the developmental trajectories for each species.

We apologize for the description inaccuracies in the manuscript and thank the reviewer for pointing out the expression errors. At your two suggestions, the description has been corrected in the revised manuscript as "Comparing vRG to IPC trajectory between human, macaque, and mouse, we found this biological process of vRG-to-IPC is very conserved across species. However, the vRG to oRG trajectory is divergent between species because the oRG population was not identified in the mouse dataset. The latter process is almost invisible in mice but similar in humans and macaques".

Although several published research has shown that oRG-like progenitor cells were present in the mouse embryonic neocortex(Wang, Xiaoqun et al. Nature neuroscience.2011. doi:10.1038/nn.2807; Vaid, Samir et al. Development. 2018, doi:10.1242/dev.169276. PMID: 30266827). However, oRG cells were barely detected in the scRNA-seq dataset of mice cortical development studies(Ruan, Xiangbin et al. Proc Natl Acad Sci U S A. 2021. doi:10.1073/pnas.2018866118. PMID: 33649223; Di Bella, Daniela J et al. Nature. 2021. doi:10.1038/s41586-021-03670-5. PMID: 34163074; Chen, Ao et al. Cell. 2022. doi:10.1016/j.cell.2022.04.003. PMID: 35512705). There were no oRG populations detected in the mouse embryonic cortical development dataset (GEO: GSE153164) used for integration analysis in our study.

11) "Ventral radial glia cells generate excitatory neurons by direct and indirect neurogenesis" This should be corrected to dorsal radial glia cells as this paper is discussing radial glia of the dorsal pallium.

13) Editorially, gene names need to be italicized in the text, figures, and figure legends.

14) Figure 5B - a scale bar showing the scale of the relative expression denoted by the dark blue color would be beneficial.

15) Figure S7D is mislabeled in the figure legend.

Merged response to points 11 to 15: Thank you for kindly pointing out the errors in our manuscript. We have corrected the above four points in the revised version.

Reviewer #2 (Recommendations For The Authors):

Specific suggestions for authors:

In the abstract the authors state: "thicker upper-layer neurons". I think it's important to be clear in the language by stating either that the layers are thicker or the neurons are most dense.

Thanks for your good comments. The description of “thicker upper-layer neurons” was corrected to “the thicker supragranular layer” in the revised manuscript. The supragranular layer thickness in primates was much higher than in rodents, both in absolute thickness and in proportion to the thickness of the whole neocortex (Hutsler, Jeffrey J et al. Brain research. 2005. doi:10.1016/j.brainres.2005.06.015. PMID: 16018988). Here, we want to describe the supragranular layer of primates as significantly higher than that of rodents, both in absolute thickness and in proportion to the thickness of the whole neocortex.

The introduction needs additional clarification regarding the vRG vs oRG discussion. I was unclear what the main takeaway for readers should be. Similarly, the discussion of previous studies and the importance for comparing human and macaque could be clarified.

We appreciate the suggestion and apologize for the shortcomings of the introduction part. We have rewritten the section and added additional clarification in the revised introduction. In the revised manuscript, the contents of the introduction are as follows:

“The neocortex is the center for higher brain functions, such as perception and decision-making. Therefore, the dissection of its developmental processes can be informative of the mechanisms responsible for these functions. Several studies have advanced our understanding of the neocortical development principles in different species, especially in mice. Generally, the dorsal neocortex can be anatomically divided into six layers of cells occupied by distinct neuronal cell types. The deep- layer neurons project to the thalamus (layer VI neurons) and subcortical areas (layer V neurons), while neurons occupying more superficial layers (upper-layer neurons) preferentially form intracortical projections1. The generation of distinct excitatory neuron cell types follows a temporal pattern in which early-born neurons migrate to deep layers (i.e., layers V and VI), while the later- born neurons migrate and surpass early-born neurons to occupy the upper layers (layers II-IV) 2. In Drosophila, several transcription factors are sequentially explicitly expressed in neural stem cells to control the specification of daughter neuron fates, while very few such transcription factors have been identified in mammals thus far. Using single-cell RNA sequencing (scRNA-seq), Telley and colleagues found that daughter neurons exhibit the same transcriptional profiles of their respective progenitor radial glia, although these apparently heritable expression patterns fade as neurons mature3. However, the temporal expression profiles of neural stem cells and the contribution of these specific temporal expression patterns in determining neuronal fate have yet to be wholly clarified in humans and non-human primates. Over the years, non-human primates (NHP) have been widely used in neuroscience research as mesoscale models of the human brain. Therefore, exploring the similarities and differences between NHP and human cortical neurogenesis could provide valuable insight into unique features during human neocortex development.

In mammals, radial glial cells are found in the ventricular zone (VZ), where they undergo proliferation and differentiation. The neocortex of primates exhibits an extra neurogenesis zone known as the outer subventricular zone (OSVZ), which is not present in rodents. As a result of evolution, the diversity of higher mammal cortical radial glia populations increases. Although ventricular radial glia (vRG) is also found in humans and non-human primates, the vast majority of radial glia in these higher species occupy the outer subventricular zone (OSVZ) and are therefore termed outer radial glia (oRG). Outer radial glial (oRG) cells retain basal processes but lack apical junctions 4 and divide in a process known as mitotic somal translocation, which differs from vRG 5. VRG and oRG are both accompanied by the expression of stem cell markers such as PAX6 and exhibit extensive self-renewal and proliferative capacities 6. However, despite functional similarities, they have distinct molecular phenotypes. Previous scRNA-seq analyses have identified several molecular markers, including HOPX for oRGs, CRYAB, and FBXO32 for vRGs7. Furthermore, oRGs are derived from vRGs, and vRGs exhibit obvious differences in numerous cell-extrinsic mechanisms, including activation of the FGF-MAPK cascade, SHH, PTEN/AKT, and PDGF pathways, and oxygen (O2) levels. These pathways and factors involve three broad cellular processes: vRG maintenance, spindle orientation, and cell adhesion/extracellular matrix production8.

Some transcription factors have been shown to participate in vRG generation, such as INSM and TRNP1. Moreover, the cell-intrinsic patterns of transcriptional regulation responsible for generating oRGs have not been characterized.

ScRNA-seq is a powerful tool for investigating developmental trajectories, defining cellular heterogeneity, and identifying novel cell subgroups9. Several groups have sampled prenatal mouse neocortex tissue for scRNA-seq 10,11, as well as discrete, discontinuous prenatal developmental stages in human and non-human primates 7,12 13,14. The diversity and features of primate cortical progenitors have been explored 4,6,7,15. The temporally divergent regulatory mechanisms that govern cortical neuronal diversification at the early postmitotic stage have also been focused on 16. Studies spanning the full embryonic neurogenic stage in the neocortex of humans and other primates are still lacking. Rhesus macaque and humans share multiple aspects of neurogenesis, and more importantly, the rhesus monkey and human brains share more similar gene expression patterns than the brains of mice and humans17-19. To establish a comprehensive, global picture of the neurogenic processes in the rhesus macaque neocortex, which can be informative of neocortex evolution in humans, we sampled neocortical tissue at five developmental stages (E40, E50, E70, E80, and E90) in rhesus macaque embryos, spanning the full neurogenesis period. Through strict quality control, cell type annotation, and lineage trajectory inference, we identified two broad transcriptomic programs responsible for the differentiation of deep-layer and upper-layer neurons. We also defined the temporal expression patterns of neural stem cells, including oRGs, vRGs, and IPs, and identified novel transcription factors involved in oRG generation. These findings can substantially enhance our understanding of neocortical development and evolution in primates.”

Why is this study focused on the parietal lobe? This should be discussed in the introduction and interpretation of the data should be contextualized in the context of this cortical area.

In this study, samples were collected from the parietal lobe area mainly for the following reasons:

(1) To ensure that the cortical anatomical parts collected at each time point are consistent, we used the lateral cerebral sulcus as a marker to collect the parietal lobe tissue above the lateral sulcus for single-cell sequencing sample collection. Besides, the parietal region is also convenient for sampling the dorsal cortex.

(2) Previous studies have made the timeline of the macaque parietal lobe formation process during the prenatal development stage clear （ Finlay, B L, and R B Darlington.Science.1995. doi:10.1126/science.7777856. PMID: 7777856）, which is also an essential reason for using the parietal lobe as the research object.

Figure 1:

Difficult to appreciate how single cell expression reflects the characterization of layers described in Figure 1A. A schematic for temporal development would be helpful. Also, how clusters correspond to discrete populations of excitatory neurons and progenitors would improve figure clarity. Perhaps enlarge and annotate the UMAPS on the bottom of Figure 1A.

We thank the reviewer for the suggestion and apologize for that Figure 1A does not convey the relationship between single-cell expression and neocortex layer formation. In the revised manuscript, time points information associated with the hierarchy is labeled to the diagram in Figure S1A. The UMAPS on the bottom of Figure 1A was enlarged in the revised manuscript as new Figure 1C.

Labels on top of clusters for 1A/1B would be helpful as it's difficult to see which colors the numbers correspond to on the actual UMAP.

Many thanks to the reviewer for carefully reading and helpful suggestions. We have adjusted the visualization of UMAP in the revised vision. The numbers in the label bar of Figure 1B have been moved to the side of the dot so that the dot can be seen more clearly.

Microglia and meninges are also non-neural cells. This needs to be changed in the discussion of the results.

Thanks for the suggestion. We have fixed the manuscript as the reviewer suggested. The description in the revised manuscript has been fixed as follows: “According to the expression of the marker genes, we assigned clusters to cell type identities of neurocytes (including radial glia (RG), outer radial glia (oRG), intermediate progenitor cells (IPCs), ventral precursor cells (VP), excitatory neurons (EN), inhibitory neurons (IN), oligodendrocyte progenitor cells (OPC), oligodendrocytes, astrocytes, ventral LGE-derived interneuron precursors and Cajal-Retzius cells, or non-neuronal cell types (including microglia, endothelial, meninge/VALC(vascular cell)/pericyte, and blood cells). Based on the expression of the marker gene, cluster 23 was identified as thalamic cells, which are small numbers of non-cortical cells captured in the sample collection at earlier time points. Each cell cluster was composed of multiple embryo samples, and the samples from similar stages generally harbored similar distributions of cell types.”.

It's important to define the onset of gliogenesis in the text and figure. What panels/ages show this?

We identified the onset of gliogenesis by statistically analyzing the percentage of astrocytes (cluster 13) at each time point and added the result in Figure S1. The statistical results showed that the proportion of astrocytes was deficient at E40 and E50 time points and increased significantly at E80 and E90, suggesting the onset of macaque gliogenesis might be around embryonic 80 days to 90 days. The result was consistent with published research on the timing of the neuron-glial transition in primates (Rash, Brian G et al. Proceedings of the National Academy of Sciences of the United States of America 201. doi:10.1073/pnas.1822169116. PMID: 30894491).

Figure 2:

Why are there so few neurons at E90? Is it capture bias, dissociation challenges (as postulated for certain neuronal subtypes in the discussion), or programmed cell death at this time point?

We thought it was because mature neurons at E90 with abundant axons and processes were hard to settle into micropores of the BD method for single cell capture. Due to the fixed size of the BD Rhapsody microwells, this sing-cell capture method might be less efficient in capturing mature excitatory neurons but has a good capture effect on newborn neurons at each sampling time point. In conclusion, based on the BD cell capture method feature, the immature neurons at each point are more easily captured than mature neurons in our study, so the generation of excitatory neurons at different developmental time points can be well observed, as shown in Figure 2, which aligns with our research purpose.

The authors state: "We then characterized temporal changes in the composition of each EN subcluster. While the EN 5 and EN 11 (deep-layer neurons) subclusters emerged at E40 and E50 and disappeared in later stages, EN subclusters 1, 2, 3, and 4 gradually increased in population size from E50 to E80 (Figure 2D)." What about EN7? It's labeled as an upper layer neuron that is proportionally highest at E40. Could this be an interesting, novel finding? Does this indicate something unique about macaque corticogenesis? The authors don't describe/discuss this cell type at all.

We apologize for the manuscript’s lack of detailed descriptions of EN results. In our study, EN7 is identified as CUX1-positive, PBX3-positive, and ZFHX3-positive excitatory neuron subcluster. The results of Fig. 2B show that EN7 was mainly captured from the early time points (E40/E50) samples. Above description was added in the revised manuscript.

The Pbx/Zfhx3-positive excitatory neuron subtype reported in Moreau et al. study on mouse neocortex development progress （ Moreau, Matthieu X et al. Development. 2021. doi:10.1242/dev.197962. PMID: 34170322）. Our study verified that the Pbx3/Zfhx3-positive cortical excitatory neurons also exist in the early stage of prenatal macaque cortex development.

Is there any unique gene expression in identified subtypes that are surprising? Did the comparison against human data, in later figures, inform any unique features of gene expression?

Based on the excitatory neuron subclusters analysis result in our study, we found no astonishing results in excitatory neuron subclusters. In subsequent integrated cross-species analyses, macaque excitatory neurons showed similar transcriptional characteristics to human excitatory neurons. In general, excitatory neurons tend to have a greater diversity in the cortex of animals that are more advanced in evolution (Ma, Shaojie et al. Science. 2022. doi:10.1126/science.abo7257. PMID: 36007006; Wei, Jia-Ru et al. Nat Commun. 2022. doi:10.1038/s41467-022-34590-1. PMID: 36371428; Galakhova, A A et al. Trends Cogn Sci. 2022. doi:10.1016/j.tics.2022.08.012. PMID: 36117080; Berg, Jim et al. Nature. 2021. doi:10.1038/s41586-021-03813-8. PMID: 34616067). Since only single-cell transcriptome data was analyzed in this study, we did not find any unique features of the prenatal developing macaque cortex excitatory neurons in the comparison against the human dataset due to the limitation of information dimension.

Figure 3:

The identification of terminal oRG differentiation genes is interesting. The confirmation of known gene expression as well as novel markers that indicate different states/stages of oRG cells is a valuable resource. As the identification of described ion channel expression is a novel finding, it should be explored more and would be strengthened by validation in tissue samples and, if possible, functional assays.

E is the most novel part of this figure, but it's very hard to read. I think increasing the focus of this figure onto this finding and parsing these results more would be informative.

Thanks for the positive comments. We apologize for the lack of clarity and conciseness in figure visualizations. We hypothesized vRG to oRG cell trajectories into three phases: onset, commitment, and terminal. The leading information conveyed by Figure 3E was the dynamic gene expression along the developmental trajectory from vRG to oRG. Specific genes were selected and shown in the schema diagram of new Figure 3.

We verified terminal oRG differentiation genes in the recently published macaque telencephalic development dataset(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652) (GEO accession: GSE226451). The results of Author response image 2 show that the gene expression showed states/stages. Most of the oRG terminal differentiation markers genes identified in our study were also expressed in the oRG cells of the GSE226451 dataset. In particular, the two datasets were consistent in the expression of ion channel genes ATP1A2, ATP1A2, and SCN4B.

I'm curious about the granularity of the oRG_C12 terminal cluster. Are there ways to subdivide the different cells that seem to be glial-committed vs actively dividing vs neurogenically committed to IPCs? In the text, the authors referred to different oRG populations, but they are annotated as the same cluster and cell type. The authors should clarify this.

According to the reviewer's suggestion, we subdivide the oRG_C12 into eight subclusters. Based on the marker gene in Author response image 5C, subclusters 1,2 and 4 might be glial- committed with AQP4/S100B positive expression; subclusters 3,6,7 might be neurogenically committed to IPCs with NEUROD6 positive expression; subclusters 0,3,5,6,7 might be actively dividing state with MKI67/TOP2A positive expression.

Author response image 5.

Subdivide analysis of oRG_C12. (A)and (B) Subdividing of e oRG_C12 visualized via UMAP. Cells are colored according to subcluster timepoint (A) and subcluster identities (B). (C) Violin plot of molecular markers for the subclusters.

Figure 4:

Annotating/labeling the various EN clusters (even as deep/upper) would help improve the clarity of this and other figures. It's clear what each progenitor subtype is but it's hard to read the transitions. Why are all the EN groups in pink/red? It makes the data challenging to interpret.

In Figure4A, we use different yellow/orange colors for deep-layer excitatory neuron subclusters (EN5 and EN10), and different red/pink colors for upper-layer excitatory neuron subclusters (EN1, EN2, EN3, EN4, EN6, EN7, EN8 and EN9). We add the above information in the legend of Figure 4 in the revised manuscript.

E50 seems to be unique - what's EN11?

Based on the molecular markers for EN subclusters in Author response image 2, we recognized EN11 as a deep-layer excitatory neuron subcluster expressing BCL11B and FEZF2. As explained in the above reply, the microplate of BD has a good effect on capturing newborn neurons at each time point. The EN11 was mainly a newborn excitatory neuron at the E50 timepoint, which makes the subcluster seem unique.

Author response image 6.

Vlnplot of different markers in EN8.

Figure 4E - the specificity of gene expression for deep vs upper layer markers seems to be over stated given the visualized gene expression pattern (ex FEZF2). Could the right hand panels be increased to better appreciate the data and confirm the specificity, as described.

In our study, we used slingshot method to infer cell lineages and pseudotimes, which have been used to identifying biological signal for different branching trajectories in many scRNA- seq studies. We apologize for the lack of visualization clarity in the figure 4E. Due to the size limitation of the uploaded file, the file was compressed, resulting in a decrease in the clarity of the image. Below, we provided figure 4E with a higher definition and increased several genes’ slingshot branching tree results according to the reviewer's suggestion.

Figure 5:

There are some grammatical typos at the bottom of page 8. In this section, it also feels like there is a missing logical step between expansion of progenitors through elongated developmental windows that impact long-term expansion of the upper cortical layers.

We apologize for the grammatical typos and have corrected them in the revised manuscript. We understand the reviewer’s concern. Primates have much longer gestation than rodents, and previous study evidence had shown that extending neurogenesis by transplanting mouse embryos to a rat mother increases explicitly the number of upper-layer cortical neurons, with concomitant abundant neurogenic progenitors in the subventricular zone(Stepien, Barbara K et al. Curr Biol. 2020. doi:10.1016/j.cub.2020.08.046. PMID: 32888487). We thought this mechanism could also explain primates' much more expanded abundance of upper-layer neurons.

I'm curious about the IPCs that arise from the oRGs. Lineage trajectory shows vRG decision to oRG or IPC, but oRGs also differentiate into IPCs. Could the authors conjecture why they are not in this dataset or are indistinguishable from vRG-derived IPCs.

Several published experiments have proved that oRG can generate IPC in human and macaque developing neocortex. (Hansen, David V et al. Nature. 2010. doi:10.1038/nature08845. PMID: 20154730; Betizeau, Marion et al. Neuron. 2013. doi:10.1016/j.neuron.2013.09.032. PMID: 24139044). Clearly identifying the difference between IPC generated from vRG and oRG at the transcriptional level in our single-cell transcriptome dataset is difficult. We hypothesized that the IPCs produced by both pathways have highly similar transcriptional features. Due to the limit of the scRNA data analysis algorithm used in this study, we didn’t distinguish the two kinds of IPC, which could not be in terms of pseudo-time trajectory reconstruction and transcriptional data.

Figure 6 :

How are the types 1-5 in 6A defined? Were they defined in one species and then applied across the others?

We applied the same analysis to each species. We first picked up vRG cells in each species dataset and screened the differentially expressed genes (DEGs) between adjacent development time points using the “FindMarkers” function (with min. pct = 0.25, logfc. threshold = 0.25). After separate normalization of the DEG expression matrix from different species datasets, we use the “standardise” function from the Mfuzz package to standardize the data. The DEGs of vRG in each species were grouped into five clusters using the Mfuzz package in R with fuzzy c- means algorithm.

The temporal dynamics in the highlighted section in B have interesting, consistent patterns of gene expression of the genes described, but what about the genes below that appear less consistent temporally? What processes do not appear to be conserved, given those gene expression differences?

Many thanks for the constructive comments. The genes in Figure 6B below are temporal dynamics non-conserved transcription factors among the three species vRG. We performed a functional enrichment analysis on the temporal dynamics of non-conserved transcription factors with the PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System(https://www.pantherdb.org/), and the analysis results are shown in Author response image 7. The gene ontology (GO) analysis results show that unconserved transcription factors were related to different biological processes, cellular components, and molecular functions. However, subsequent experiments are still needed to verify specific genes.

Author response image 7.

Gene Ontology (GO) analysis of unconserved temporal patterns transcription factors among mouse, macaque and human vRG cells.

The identification of distinct regulation of gene networks, despite conservation of transcription factors in discrete cell types, is interesting. What does the comparison between humans and macaques indicate about regulatory differences evolutionarily?

We appreciate the reviewer for the comments. We performed the TFs regulation network analysis of human vRG with pyscenic workflow. The top transcription factors of every time point in human vRG were calculated, and we used the top 10 TFs and their top 5 target genes to perform interaction analysis and generate the regulation network of human vRG in revised figure 6. In comparison of the pyscenic results of mouse, macaque and human vRG, it was obvious that the regulatory networks were not evolutionarily conservative. Compared with macaque, the regulatory network of transcription factors and target genes in humans is more complex. Some conserved regulatory relationships present in more than one species are identified, such as HMGN3, EMX2, SOX2, and HMGA2 network at an early stage when deep lager generation and SOX10, ZNF672, ZNF672 network at a late stage when upper-layer generation.

Reviewer #3 (Recommendations For The Authors):

The data should be compared to a similar brain region in human and mouse, if available. (See data from PMCID: PMC8494648).

We appreciate the reviewer’s suggestions. In Figure 6, the species-integration analysis, the mouse data were from the perspective of the somatosensory cortex, macaque data were mainly from the parietal lobe in this study, and human data including the frontal lobe (FL), parietal lobe (PL), occipital lobe (OL), and temporal lobe (TL). PMC8494648 offered high-quality data covering the period of gestation week 14 to gestation week 25. However, our study's development stage of rhesus monkeys is E40-E90 days, corresponding to pcw8-pcw21 in humans. The quality of data from PMC8494648 is particularly good. However, the developmental processes covered by PMC8494648 don’t perfectly match the development time of the macaque cortex that we focused on in this study. Therefore, it is challenging to integrate the dataset (PMCID: PMC8494648) into the data analysis part. However, we have cited the results of this precious research (PMCID: PMC8494648) in the discussion part of the revised manuscript.

A deeper assessment of these data in the context of existing studies would help distinguish the work and enable others to appreciate the significance of the work.

We appreciate the reviewer’s constructive suggestions. The human regulation analysis with pyscenic workflow was added into new figure 6 for the comparison of different species vRG regulatory network. Analysis of the regulatory activity of human, macaque and mouse prenatal neocortical neurogenesis indicated that despite commonalities in the roles of classical developmental TFs such as GATA1, SOX2, HMGN3, TCF7L1, ZFX, EMX2, SOX10, NEUROG1, NEUROD1 and POU3F1. The top 10 TFs of the human, macaque, and mouse vRG each time point and their top 5 target genes identified by pySCENIC as an input to construct the transcriptional regulation network (Figure 6 D, F and H). Some conserved regulatory TFs present in more than one species are identified, such as HMGN3, EMX2, SOX2, and HMGA2 at an early stage when deep- lager generation and SOX10, ZNF672, and ZNF672 at a late stage when upper-lay generation.

Besides, we performed some comparative analysis with our macaque dataset and the newly published macaque telencephalon development dataset. The results were only used to provide additional information to reviewers and were not included in the revised manuscript.

To verify the reliability of our cell annotation results, we compared the similarity of cell-type association between our study and recently published research(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652), using the scmap package to project major cell types in our macaque development scRNA-seq dataset to GSE226451. The river plot in Author response image 1 illustrates the broadly similar relationships of cell type classification between the two datasets. Otherwise, we used more marker genes for cell annotation to improve the results of cell type definition in new Figure 1D. Besides, the description of distinct excitatory neuronal types has been improved in the new Figure 2.

Furthermore, we verified terminal oRG differentiation genes in the recently published macaque telencephalic development dataset(Micali N, Ma S, Li M, et al. Science. doi:10.1126/science.adf3786.PMID: 37824652) (GEO accession: GSE226451). The results of Authro response image 2 show that the gene expression showed states/stages. Most of the oRG terminal differentiation markers genes identified in our study were also expressed in the oRG cells of the GSE226451 dataset. In particular, the two datasets were consistent in the expression of ion channel genes ATP1A2, ATP1A2, and SCN4B.

Author Response

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

eLife assessment

This valuable study reports on the potential of neural networks to emulate simulations of human ventricular cardiomyocyte action potentials for various ion channel parameters with the advantage of saving simulation time in certain conditions. The evidence supporting the claims of the authors is solid, although the inclusion of open analysis of drop-off accuracy and validation of the neural network emulators against experimental data would have strengthened the study. The work will be of interest to scientists working in cardiac simulation and quantitative pharmacology.

Thank you for the kind assessment. It is important for us to point out that, while limited, experimental validation was performed in this study and is thoroughly described in the work.

Reviewer 1 - Comments

This manuscript describes a method to solve the inverse problem of finding the initial cardiac activations to produce a desired ECG. This is an important question. The techniques presented are novel and clearly demonstrate that they work in the given situation. The paper is well-organized and logical.

Strengths:

This is a well-designed study, which explores an area that many in the cardiac simulation community will be interested in. The article is well written and I particularly commend the authors on transparency of methods description, code sharing, etc. - it feels rather exemplary in this regard and I only wish more authors of cardiac simulation studies took such an approach. The training speed of the network is encouraging and the technique is accessible to anyone with a reasonably strong GPU, not needing specialized equipment.

Weaknesses:

Below are several points that I consider to be weaknesses and/or uncertainties of the work:

C I-(a) I am not convinced by the authors’ premise that there is a great need for further acceleration of cellular cardiac simulations - it is easy to simulate tens of thousands of cells per day on a workstation computer, using simulation conditions similar to those of the authors. I do not really see an unsolved task in the field that would require further speedup of single-cell simulations. At the same time, simulations offer multiple advantages, such as the possibility to dissect mechanisms of the model behaviour, and the capability to test its behaviour in a wide array of protocols - whereas a NN is trained for a single purpose/protocol, and does not enable a deep investigation of mechanisms. Therefore, I am not sure the cost/benefit ratio is that strong for single-cell emulation currently.

An area that is definitely in need of acceleration is simulations of whole ventricles or hearts, but it is not clear how much potential for speedup the presented technology would bring there. I can imagine interesting applications of rapid emulation in such a setting, some of which could be hybrid in nature (e.g. using simulation for the region around the wavefront of propagating electrical waves, while emulating the rest of the tissue, which is behaving more regularly/predictable, and is likely to be emulated well), but this is definitely beyond of the scope of this article.

Thank you for this point of view. Simulating a population of few thousand cells is completely feasible on single desktop machines and for fixed, known parameters, emulation may not fill ones need. Yet we still foresee a great untapped potential for rapid evaluations of ionic models, such as for the gradient-based inverse problem, presented in the paper. Such inverse optimization requires several thousand evaluations per cell and thus finding maximum conductances for the presented experimental data set (13 cell pairs control/drug → 26 APs) purely through simulations would require roughly a day of simulation time even in a very conservative estimation (3.5 seconds per simulation, 1000 simulations per optimization). Additionally, the emulator provides local sensitivity information between the AP and maximum conductances in the form of the gradient, which enables a whole new array of efficient optimization algorithms [Beck, 2017]. To further emphasize these points, we added the number of emulations and runtime of each conducted experiment in the specific section and a paragraph in the discussion that addresses this point:

"Cardiomyocyte EP models are already very quick to evaluate in the scale of seconds (see Section 2.3.1), but the achieved runtime of emulations allows to solve time consuming simulation protocols markedly more efficient. One such scenario is the presented inverse maximum conductance estimation problem (see Section 3.1.2 and Section 3.1.3), where for estimating maximum conductances of a single AP, we need to emulate the steady state AP at least several hundred times as part of an optimization procedure. Further applications include the probabilistic use of cardiomyocyte EP models with uncertainty quantification [Chang et al., 2017, Johnstone et al., 2016] where thousands of samples of parameters are potentially necessary to compute a distribution of the steady-state properties of subsequent APs, and the creation of cell populations [Muszkiewicz et al., 2016, Gemmell et al., 2016, Britton et al., 2013]." (Section 4.2)

We believe that rapid emulations are valuable for several use-cases, where thousands of evaluations are necessary. These include the shown inverse problem, but similarly arise in uncertainty quantification, or cardiomyocyte population creation. Similarly, new use-cases may arise as such efficient tools become available. Additionally, we provided the number of evaluations along with the runtimes for each of the conducted experiments, showing how essential these speedups are to realize these experiments in reasonable timeframes. Utilizing these emulations in organ-level electrophysiological models is a possibility, but the potential problems in such scenarios are much more varied and depend on a number of factors, making it hard to pin-point the achievable speed-up using ionic emulations.

C I-(b) The authors run a cell simulation for 1000 beats, training the NN emulator to mimic the last beat. It is reported that the simulation of a single cell takes 293 seconds, while emulation takes only milliseconds, implying a massive speedup. However, I consider the claimed speedup achieved by emulation to be highly context-dependent, and somewhat too flattering to the presented method of emulation. Two specific points below:

First, it appears that a not overly efficient (fixed-step) numerical solver scheme is used for the simulation. On my (comparable, also a Threadripper) CPU, using the same model (”ToR-ORd-dyncl”), but a variable step solver ode15s in Matlab, a simulation of a cell for 1000 beats takes ca. 50 seconds, rather than 293 of the authors. This can be further sped up by parallelization when more cells than available cores are simulated: on 32 cores, this translates into ca. 2 seconds amortized time per cell simulation (I suspect that the NN-based approach cannot be parallelized in a similar way?). By amortization, I mean that if 32 models can be simulated at once, a simulation of X cells will not take X50 seconds, but (X/32)50. (with only minor overhead, as this task scales well across cores).

Second, and this is perhaps more important - the reported speed-up critically depends on the number of beats in the simulation - if I am reading the article correctly, the runtime compares a simulation of 1000 beats versus the emulation of a single beat. If I run a simulation of a single beat across multiple simulated cells (on a 32-core machine), the amortized runtime is around 20 ms per cell, which is only marginally slower than the NN emulation. On the other hand, if the model was simulated for aeons, comparing this to a fixed runtime of the NN, one can get an arbitrarily high speedup.

Therefore, I’d probably emphasize the concrete speedup less in an abstract and I’d provide some background on the speedup calculation such as above, so that the readers understand the context-dependence. That said, I do think that a simulation for anywhere between 250 and 1000 beats is among the most reasonable points of comparison (long enough for reasonable stability, but not too long to beat an already stable horse; pun with stables was actually completely unintended, but here it is...). I.e., the speedup observed is still valuable and valid, albeit in (I believe) a somewhat limited sense.

We agree that the speedup comparison only focused on a very specific case and needs to be more thoroughly discussed and benchmarked. One of the main strengths of the emulator is to cut the time of prepacing to steady state, which is known to be a potential bottleneck for the speed of the single-cell simulations. The time it takes to reach the steady state in the simulator is heavily dependant on the actual maximum conductance configuration and the speed-up is thus heavily reliant on a per-case basis. The differences in architecture of the simulator and emulator further makes direct comparisons very difficult. In the revised version we now go into more detail regarding the runtime calculations and also compare it to an adaptive time stepping simulation (Myokit [Clerx et al., 2016]) in a new subsection:

"The simulation of a single AP (see Section 2.1) sampled at a resolution of 20kHz took 293s on one core of a AMD Ryzen Threadripper 2990WX (clock rate: 3.0GHz) in CARPentry. Adaptive timestep solver of variable order, such as implemented in Myokit [Clerx et al., 2016], can significantly lower the simulation time (30s for our setup) by using small step sizes close to the depolarization (phase 0) and increasing the time step in all other phases. The emulation of a steady state AP sampled at a resolution of 20kHz for t ∈ [−10, 1000]ms took 18.7ms on a AMD Ryzen 7 3800X (clock rate: 3.9GHz) and 1.2ms on a Nvidia A100 (Nvidia Corporation, USA), including synchronization and data copy overhead between CPU and GPU.

"The amount of required beats to reach the steady state of the cell in the simulator has a major impact on the runtime and is not known a-priori. On the other hand, both simulator and emulator runtime linearly depends on the time resolution, but since the output of the emulator is learned, the time resolution can be chosen at arbitrarily without affecting the AP at the sampled times. This makes direct performance comparisons between the two methodologies difficult. To still be able to quantify the speed-up, we ran Myokit using 100 beats to reach steady state, taking 3.2s of simulation time. In this scenario, we witnessed a speed-up of 171 and 2 · 103 of our emulator on CPU and GPU respectively (again including synchronization and data copy overhead between CPU and GPU in the latter case). Note that both methods are similarly expected to have a linear parallelization speedup across multiple cells.

For the inverse problem, we parallelized the problem for multiple cells and keep the problem on the GPU to minimize the overhead, achieving emulations (including backpropagation) that run in 120µs per AP at an average temporal resolution of 2kHz. We consider this the peak performance which will be necessary for the inverse problem in Section 3.1.2." (Section 2.3.1)

Note that the mentioned parallelization across multiple machines/hardware applies equally to the emulator and simulator (linear speed-up), though the utilization for single cells is most likely different (single vs. multi-cell parallelization).

C I-(c) It appears that the accuracy of emulation drops off relatively sharply with increasing real-world applicability/relevance of the tasks it is applied to. That said, the authors are to be commended on declaring this transparently, rather than withholding such analyses. I particularly enjoyed the discussion of the not-always amazing results of the inverse problem on the experimental data. The point on low parameter identifiability is an important one and serves as a warning against overconfidence in our ability to infer cellular parameters from action potentials alone. On the other hand, I’m not that sure the difference between small tissue preps and single cells which authors propose as another source of the discrepancy will be that vast beyond the AP peak potential (probably much of the tissue prep is affected by the pacing electrode?), but that is a subjective view only. The influence of coupling could be checked if the simulated data were generated from 2D tissue samples/fibres, e.g. using the Myokit software.

Given the points above (particularly the uncertain need for further speedup compared to running single-cell simulations), I am not sure that the technology generated will be that broadly adopted in the near future.

However, this does not make the study uninteresting in the slightest - on the contrary, it explores something that many of us are thinking about, and it is likely to stimulate further development in the direction of computationally efficient emulation of relatively complex simulations.

We agree that the parameter identifiability is an important point of discussion. While the provided experimental data gave us great insights already, we still believe that given the differences in the setup, we can not draw conclusions about the source of inaccuracies with absolute certainty. The suggested experiment to test the influence of coupling is of interest for future works and has been integrated into the discussion. Further details are given in the response to the recommendation R III- (t)

Reviewer 2 - Comments

Summary:

This study provided a neural network emulator of the human ventricular cardiomyocyte action potential. The inputs are the corresponding maximum conductances and the output is the action potential (AP). It used the forward and inverse problems to evaluate the model. The forward problem was solved for synthetic data, while the inverse problem was solved for both synthetic and experimental data. The NN emulator tool enables the acceleration of simulations, maintains high accuracy in modeling APs, effectively handles experimental data, and enhances the overall efficiency of pharmacological studies. This, in turn, has the potential to advance drug development and safety assessment in the field of cardiac electrophysiology.

Strengths:

1) Low computational cost: The NN emulator demonstrated a massive speed-up of more than 10,000 times compared to the simulator. This substantial increase in computational speed has the potential to expedite research and drug development processes

2) High accuracy in the forward problem: The NN emulator exhibited high accuracy in solving the forward problem when tested with synthetic data. It accurately predicted normal APs and, to a large extent, abnormal APs with early afterdepolarizations (EADs). High accuracy is a notable advantage over existing emulation methods, as it ensures reliable modeling and prediction of AP behavior

C II-(a) Input space constraints: The emulator relies on maximum conductances as inputs, which explain a significant portion of the AP variability between cardiomyocytes. Expanding the input space to include channel kinetics parameters might be challenging when solving the inverse problem with only AP data available.

Thank you for this comment. We consider this limitation a major drawback, as discussed in Section 4.3. Identifiability is already an issue when only considering the most important maximum conductances. Further extending the problem to include kinetics will most likely only increase the difficulty of the inverse problem. For the forward problem though, it might be of interest to people studying ionic models to further analyze the effects of channel kinetics.

C II-(b) Simplified drug-target interaction: In reality, drug interactions can be time-, voltage-, and channel statedependent, requiring more complex models with multiple parameters compared to the oversimplified model that represents the drug-target interactions by scaling the maximum conductance at control. The complex model could also pose challenges when solving the inverse problem using only AP data.

Thank you pointing out this limitation. We slightly adapted Section 4.3 to further highlight some of these limitations. Note however that the experimental drugs used have been shown to be influenced by this drug interaction in varying degrees [Li et al., 2017] (e.g. dofetilide vs. cisapride). However, the discrepancy in identifiability was mostly channel-based (0%-100%), whereas the variation in identifiability between drugs was much lower (39%-66%).

C II-(c) Limited data variety: The inverse problem was solved using AP data obtained from a single stimulation protocol, potentially limiting the accuracy of parameter estimates. Including AP data from various stimulation protocols and incorporating pacing cycle length as an additional input could improve parameter identifiability and the accuracy of predictions.

The proposed emulator architecture currently only considers the discussed maximum conductances as input and thus can only compensate when using different stimulation protocols. However, the architecture itself does not prohibit including any of these as parameters for future variants of the emulator. We potentially foresee future works extending on the architecture with modified datasets to include other parameters of importance, such as channel kinetics, stimulation protocols and pacing cycle lengths. These will however vary between the actual use-cases one is interested in.

C II-(d) Larger inaccuracies in the inverse problem using experimental data: The reasons for this result are not quite clear. Hypotheses suggest that it may be attributed to the low parameter identifiability or the training data set were collected in small tissue preparation.

The low parameter identifiability on some channels (e.g. GK1) poses a problem, for which we state multiple potential reasons. As of yet, no final conclusion can be drawn, warranting further research in this area.

Reviewer 3 - Comments

Summary:

Grandits and colleagues were trying to develop a new tool to accelerate pharmacological studies by using neural networks to emulate the human ventricular cardiomyocyte action potential (AP). The AP is a complex electrical signal that governs the heartbeat, and it is important to accurately model the effects of drugs on the AP to assess their safety and efficacy. Traditional biophysical simulations of the AP are computationally expensive and time-consuming. The authors hypothesized that neural network emulators could be trained to predict the AP with high accuracy and that these emulators could also be used to quickly and accurately predict the effects of drugs on the AP.

Strengths:

One of the study’s major strengths is that the authors use a large and high-quality dataset to train their neural network emulator. The dataset includes a wide range of APs, including normal and abnormal APs exhibiting EADs. This ensures that the emulator is robust and can be used to predict the AP for a variety of different conditions.

Another major strength of the study is that the authors demonstrate that their neural network emulator can be used to accelerate pharmacological studies. For example, they use the emulator to predict the effects of a set of known arrhythmogenic drugs on the AP. The emulator is able to predict the effects of these drugs, even though it had not been trained on these drugs specifically.

C III-(a) One weakness of the study is that it is important to validate neural network emulators against experimental data to ensure that they are accurate and reliable. The authors do this to some extent, but further validation would be beneficial. In particular for the inverse problem, where the estimation of pharmacological parameters was very challenging and led to particularly large inaccuracies.

Thank you for this recommendation. Further experimental validation of the emulator in the context of the inverse problem would be definitely beneficial. Still, an important observation is that the identifiability varies greatly between channels. While the inverse problem is an essential reason for utilizing the emulator, it is also empirically validated for the pure forward problem and synthetic inverse problem, together with the (limited) experimental validation. The sources of problems arising in estimating the maximum conductances of the experimental tissue preparations are important to discuss in future works, as we now further emphasize in the discussion. See also the response to the recommendations R III-(t).

Reviewer 1 - Recommendations

R I-(a) Could further detail on the software used for the emulation be provided? E.g. based on section 2.2.2, it sounds like a CPU, as well as GPU-based emulation, is possible, which is neat.

Indeed as suspected, the emulator can run on both CPUs and GPUs and features automatic parallelization (per-cell, but also multi-cell), which is enabled by the engineering feats of PyTorch [Paszke et al., 2019]. This is now outlined in a bit more detail in Sec. 2 and 5.

"The trained emulator is provided as a Python package, heavily utilizing PyTorch [Paszke et al., 2019] for the neural network execution, allowing it to be executed on both CPUs and NVidia GPUs." (Section 5)

R I-(b) I believe that a potential use of NN emulation could be also in helping save time on prepacing models to stability - using the NN for ”rough” prepacing (e.g. 1000 beats), and then running a simulation from that point for a smaller amount of time (e.g. 50 beats). One could monitor the stability of states, so if the prepacing was inaccurate, one could quickly tell that these models develop their state vector substantially, and they should be simulated for longer for full accuracy - but if the model was stable within the 50 simulated beats, it could be kept as it is. In this way, the speedup of the NN and accuracy and insightfulness of the simulation could be combined. However, as I mentioned in the public review, I’m not sure there is a great need for further speedup of single-cell simulations. Such a hybrid scheme as described above might be perhaps used to accelerate genetic algorithms used to develop new models, where it’s true that hundreds of thousands to millions of cells are eventually simulated, and a speedup there could be practical. However one would have to have a separate NN trained for each protocol in the fitness function that is to be accelerated, and this would have to be retrained for each explored model architecture. I’m not sure if the extra effort would be worth it - but maybe yes to some people.

Thank you for this valuable suggestion. As pointed out in C I-(a), one goal of this study was to reduce the timeconsuming task of prepacing. Still, in its current form the emulator could not be utilized for prepacing simulators, as only the AP is computed by the emulator. For initializing a simulation at the N-th beat, one would additionally need all computed channel state variables. However, a simple adaptation of the emulator architecture would allow to also output the mentioned state variables.

R I-(c) Re: ”Several emulator architectures were tried on the training and validation data sets and the final choice was hand-picked as a good trade-off between high accuracy and low computational cost” - is it that the emulator architecture was chosen early in the development, and the analyses presented in the paper were all done with one previously selected architecture? Or is it that the analyses were attempted with all considered architectures, and the well-performing one was chosen? In the latter case, this could flatter the performance artificially and a test set evaluation would be worth carrying out.

We apologize for the unclear description of the architectural validation. The validation was in fact carried out with 20% of the training data (data set #1), which is however completely disjoint with the test set (#2, #3, #4, formerly data set #1 and #2) on which the evaluation was presented. To further clarify the four different data sets used in the study, we now dedicated an additional section to describing each set and where it was used (see also our response below R I-(d)), and summarize them in Table 1, which we also added at R II-(a). The cited statement was slightly reworked.

"Several emulator architectures were tried on the training and validation data sets and the final choice was hand-picked as a good trade-off between high accuracy on the validation set (#1) and low computational runtime cost." (Section 2.2.2)

R I-(d) When using synthetic data for the forward and inverse problem, with the various simulated drugs, is it that split of the data into training/validation test set was done by the drug simulated (i.e., putting 80 drugs and the underlying models in the training set, and 20 into test set)? Or were the data all mixed together, and 20% (including drugs in the test set) were used for validation? I’m slightly concerned by the potential of ”soft” data leaks between training/validation sets if the latter holds. Presumably, the real-world use case, especially for the inverse problem, will be to test drugs that were not seen in any form in the training process. I’m also not sure whether it’s okay to reuse cell models (sets of max conductances) between training and validation tests - wouldn’t it be better if these were also entirely distinct? Could you please comment on this?

We completely agree with the main points of apprehension that training, validation and test sets all serve a distinct purpose and should not be arbitrarily mixed. However, this is only a result of the sub-optimal description of our datasets, which we heavily revised in Section 2.2.1 (Data, formerly 2.3.1). We now present the data using four distinct numbers: The initial training/validation data, now called data set #1 (formerly no number), is split 80%/20% into training and validation sets (for architectural choices) respectively. The presented evaluations in Section 2.3 (Evaluation) are purely performed on data set #2 (normal APs, formerly #1), #3 (EADs, formerly #2) and #4 (experimental).

R I-(e) For the forward problem on EADs, I’m not sure if the 72% accuracy is that great (although I do agree that the traces in Fig 12-left also typically show substantial ICaL reactivation, but this definitely should be present, given the IKr and ICaL changes). I would suggest that you also consider the following design for the EAD investigation: include models with less severe upregulation of ICaL and downregulation of IKr, getting a population of models where a part manifests EADs and a part does not. Then you could run the emulator on the input data of this population and be able to quantify true, falsexpositive, negative detections. I think this is closer to a real-world use case where we have drug parameters and a cell population, and we want to quickly assess the arrhythmic risk, with some drugs being likely entirely nonrisky, some entirely risky, and some between (although I still am not convinced it’s that much of an issue to just simulate this in a couple of thousands of cells).

Thank you for pointing out this alternative to address the EAD identification task. Even though the values chosen in Table 2 seem excessively large, we still only witnessed EADs in 171 of the 950 samples. Especially border cases, which are close to exhibiting EADs are hardest to estimate for the NN emulator. As suggested, we now include the study with the full 950 samples (non-EAD & EAD) and classify the emulator AP into one of the labels for each sample. The mentioned 72.5% now represent the sensitivity, whereas our accuracy in such a scenario becomes 90.8% (total ratio of correct classifications):

"The data set #3 was used second and Appendix C shows all emulated APs, both containing the EAD and non-EAD cases. The emulation of all 950 APs took 0.76s on the GPU specified in Section 2.2.3 We show the emulation of all maximum conductances and the classification of the emulation. The comparison with the actual EAD classification (based on the criterion outlined in Appendix A) results in true-positive (EAD both in the simulation and emulation), false-negative (EAD in the simulation, but not in the emulation), false-positive (EAD in the emulation, but not in the simulation) and true-negative (no EAD both in the emulation and simulation). The emulations achieved 72.5% sensitivity (EAD cases correctly classified) and 94.9% specificity (non-EAD cases correctly classified), with an overall accuracy of 90.8% (total samples correctly classified). A substantial amount of wrongly classified APs showcase a notable proximity to the threshold of manifesting EADs. Figure 7 illustrates the distribution of RMSEs in the EAD APs between emulated and ground truth drugged APs. The average RMSE over all EAD APs was 14.5mV with 37.1mV being the maximum. Largest mismatches were located in phase 3 of the AP, in particular in emulated APs that did not fully repolarize." (Section 3.1.1)

R I-(f) Figure 1 - I think a large number of readers will understand the mathematical notation describing inputs/outputs; that said, there may be a substantial number of readers who may find that hard to read (e.g. lab-based researchers, or simulation-based researchers not familiar with machine learning). At the same time, this is a very important part of the paper to explain what is done where, so I wonder whether using words to describe the inputs/outputs would not be more practical and easier to understand (e.g. ”drug-based conductance scaling factor” instead of ”s” ?). It’s just an idea - it needs to be tried to see if it wouldn’t make the figure too cluttered.

We agree that the mathematical notation may be confusing to some readers. As a compromise between using verbose wording and mathematical notation, we introduced a legend in the lower right corner of the figure that shortly describes the notation in order to help with interpreting the figure.

R I-(g) ”APs with a transmembrane potential difference of more than 10% of the amplitude between t = 0 and 1000 ms were excluded” - I’m not sure I understand what exactly you mean here - could you clarify?

With this criterion, we try to discard data that is far away from fully repolarizing within the given time frame, which applies to 116 APs in data set #1 and 50 APs in data set #3. We added a small side note into the text:

"APs with a transmembrane potential difference of more than 10% of the amplitude between t = 0 and 1000ms (indicative of an AP that is far away from full repolarization) were excluded." (Section 2.2.1)

R I-(h) Speculation (for the future) - it looks like a tool like this could be equally well used to predict current traces, as well as action potentials. I wonder, would there be a likely benefit in feeding back the currents-traces predictions on the input of the AP predictor to provide additional information? Then again, this might be already encoded within the network - not sure.

Although not possible with the chosen architecture (see also R I-(b)), it is worth thinking about an implementation in future works and to study differences to the current emulator.

Entirely minor points:

R I-(i) ”principle component analysis” → principal component analysis

Fixed

R I-(j) The paper will be probably typeset by elife anyway, but the figures are often quite far from their sections, with Results figures even overflowing into Discussion. This can be often fixed by using the !htb parameters (\begin{figure}[!htb]), or potentially by using ”\usepackage[section]{placeins}” and then ”\FloatBarrier” at the start and end of each section (or subsection) - this prevents floating objects from passing such barriers.

Thank you for these helpful suggestions. We tried reducing the spacing between the figures and their references in the text, hopefully improving the reader’s experience.

R I-(k) Alternans seems to be defined in Appendix A (as well as repo-/depolarization abnormalities), but is not really investigated. Or are you defining these just for the purpose of explaining what sorts of data were also included in the data?

We defined alternans since this was an exclusion criterion for generating simulation data.

Reviewer 2 - Recommendations

R II-(a) Justification for methods selection: Explain the rationale behind important choices, such as the selection of specific parameters and algorithms.

Thank you for this recommendation, we tried to increase transparency of our choices by introducing a separate data section that summarizes all data sets and their use cases in Section 2.2.1 and also collect many of the explanations there. Additionally we added an overview table (Table 1) of the utilized data.

Author response table 1.

Table 1: Summary of the data used in this study, along with their usage and the number of valid samples. Note that each AP is counted individually, also in cases of control/drug pairs.

R II-(b) Interpretation of the evaluation results: After presenting the evaluation results, consider interpretations or insights into what the results mean for the performance of the emulator. Explain whether the emulator achieved the desired accuracy or compare it with other existing methods. In the revised version, we tried to further expand the discussion on possible applications of our emulator (Section 4.2). See also our response to C I-(a). To the best of our knowledge, there are currently no out-of-the-box methods available for directly comparing all experiments we considered in our work.

Reviewer 3 - Recommendations

R III-(a) In the introduction (Page 3) and then also in the 2.1 paragraph authors speak about the ”limit cycle”: Do you mean steady state conditions? In that case, it is more common to use steady state.

When speaking about the limit cycle, we refer to what is also sometimes called the steady state, depending on the field of research and/or personal preference. We now mention both terms at the first occurence, but stick with the limit cycle terminology which can also be found in other works, see e.g. [Endresen and Skarland, 2000].

R III-(b) On page 3, while comparing NN with GP emulators, I still don’t understand the key reason why NN can solve the discontinuous functions with more precision than GP.

The potential problems in modeling sharp continuities using GPs is further explained in the referenced work [Ghosh et al., 2018] and further references therein:

"Statistical emulators such as Gaussian processes are frequently used to reduce the computational cost of uncertainty quantification, but discontinuities render a standard Gaussian process emulation approach unsuitable as these emulators assume a smooth and continuous response to changes in parameter values [...] Applying GPs to model discontinuous functions is largely an open problem. Although many advances (see the discussion about non-stationarity in [Shahriari et al., 2016] and the references in there) have been made towards solving this problem, a common solution has not yet emerged. In the recent GP literature there are two specific streams of work that have been proposed for modelling non-stationary response surfaces including those with discontinuities. The first approach is based on designing nonstationary processes [Snoek et al., 2014] whereas the other approach attempts to divide the input space into separate regions and build separate GP models for each of the segmented regions. [...]"([Ghosh et al., 2018])

We integrated a short segment of this explanation into Section 1.

R III-(c) Why do authors prefer to use CARPentry and not directly openCARP? The use of CARPentry is purely a practical choice since the simulation pipeline was already set up. As we now point out however in Sec. 2.1 (Simulator), simulations can also be performed using any openly available ionic simulation tool, such as Myokit [Clerx et al., 2016], OpenCOR [Garny and Hunter, 2015] and openCARP [Plank et al., 2021]. We emphasized this in the text.

"Note, that the simulations can also be performed using open-source software such as Myokit [Clerx et al., 2016], OpenCOR [Garny and Hunter, 2015] and openCARP [Plank et al., 2021]." (Section 2.1)

R III-(d) In paragraph 2.1:

(a) In this sentence: ”Various solver and sampling time steps were applied to generate APs and the biomarkers used in this study (see Appendix A)” this reviewer suggests putting the Appendix reference near “biomarkers”. In addition, a figure that shows the test of various solver vs. sampling time steps could be interesting and can be added to the Appendix as well.

(b) Why did the authors set the relative difference below 5% for all biomarkers? Please give a reference to that choice. Instead, why choose 2% for the time step?

1) We adjusted the reference to be closer to “biomarkers”. While we agree that further details on the influence of the sampling step would be of interest to some of the readers, we feel that it is far beyond the scope of this paper.

2) There is no specific reference we can provide for the choice. Our goal was to reach 5% relative difference, which we surpassed by the chosen time steps of 0.01 ms (solver) and 0.05 ms (sampling), leading to only 2% difference. We rephrased the sentence in question to make this clear.

"We considered the time steps with only 2% relative difference for all AP biomarkers (solver: 0.01ms; sampling: 0.05ms) to offer a sufficiently good approximation." (Section 2.1)

R III-(e) In the caption of Figure 1 authors should include the reference for AP experimental data (are they from Orvos et al. 2019 as reported in the Experimental Data section?)

We added the missing reference as requested. As correctly assumed, they are from [Orvos et al., 2019].

R III-(f) Why do authors not use experimental data in the emulator development/training?

For the supervised training of our NN emulator, we need to provide the maximum conductances of our chosen channels for each AP. While it would be beneficial to also include experimental data in the training to diversify the training data, the exact maximum conductances in our the considered retrospective experiments are not known. In the case such data would be available with low measurement uncertainty, it would be possible to include.

R III-(g) What is TP used in the Appendix B? I could not find the acronymous explanation.

We are sorry for the oversight, TP refers to the time-to-peak and is now described in Appendix A.

R III-(h) Are there any reasons for only using ST and no S1? Maybe are the same?

The global sensitivity analysis is further outlined in Appendix B, also showing S1 (first-order effects) and ST (variance of all interactions) together (Figure 11) [Herman and Usher, 2017] and their differences (e.g. in TP) Since S1 only captures first-order effects, it may fail to capture higher-order interactions between the maximum conductances, thus we favored ST.

R III-(i) In Training Section Page 8. It is not clear why it is necessary to resample data. Can you motivate?

The resampling part is motivated by exactly capturing the swift depolarization dynamics, whereas the output from CARPentry is uniformly sampled. This is now further highlighted in the text.

"Then, the data were non-uniformly resampled from the original uniformly simulated APs, to emphasize the depolarization slope with a high accuracy while lowering the number of repolarization samples. For this purpose, we resamled the APs [...]" (Section 2.2.1)

R III-(j) For the training of the neuronal network, the authors used the ADAM algorithm: have you tested any other algorithm?

For training neural networks, ADAM has become the current de-facto standard and is certainly a robust choice for training our emulator. While there may exist slightly faster, or better-suited training algorithms, we witnessed (qualitative) convergence in the training (Equation (2)). We thus strongly believe that the training algorithm is not a limiting factor in our study.

R III-(k) What is the amount of the drugs tested? Is the same dose reported in the description of the second data set or the values are only referring to experimental data? Moreover, it is not clear if in the description of experimental data, the authors are referring to newly acquired data (since they described in detail the protocol) or if they are obtained from Orvos et al. 2019 work.

In all scenarios, we tested 5 different drugs (cisapride, dofetilide, sotalol, terfenadine, verapamil). We revised our previous presentation of the data available, and now try to give a concise overview over the utilized data (Section 2.2.1 and table 1) and drug comparison with the CiPA distributions (Table 5, former 4). Note that in the latter case, the available expected channel scaling factors by the CiPA distributions vary, but are now clearly shown in Table 5.

R III-(l) In Figure 4, I will avoid the use of “control” in the legend since it is commonly associated with basal conditions and not with the drug administration.

The terminology “control” in this context is in line with works from the CiPA initiative, e.g. [Li et al., 2017] and refers to the state of cell conditions before the drug wash-in. We added a minor note the first time we use the term control in the introduction to emphasize that we refer to the state of the cell before administering any drugs

"To compute the drugged AP for given pharmacological parameters is a forward problem, while the corresponding inverse problem is to find pharmacological parameters for given control (before drug administration) and drugged AP." (Section 1)

R III-(m) In Table 1 when you referred to Britton et al. 2017 work, I suggest adding also 10.1371/journal.pcbi.1002061.

We added the suggested article as a reference.

R III-(n) For the minimization problem, only data set #1 has been used. Have you tested data set #2?

In the current scenario, we only tested the inverse problem for data set #2 (former #1). The main purpose for data set #3 (former #2), was to test the possibility to emulate EAD APs. Given the overall lower performance in comparison to data set #2 (former #1), we also expect deteriorated results in comparison to the existing inverse synthetic problem.

R III-(o) In Figure 6 you should have the same x-axis (we could not see any points in the large time scale for many biomarkers). Why dVmMax is not uniformed distributed compared to the others? Can you comment on that?

As suggested, we re-adjusted the x-range to show the center of distributions. Additionally, we denoted in each subplot the number of outliers which lie outside of the shown range. The error distribution on dVmMax exhibits a slightly off-center, left-tailed normal distribution, which we now describe a bit more in the revised text:

"While the mismatches in phase 3 were simply a result of imperfect emulation, the mismatches in phase 0 were a result of the difficulty in matching the depolarization time exactly. [...] Likewise, the difficulty in exactly matching the depolarization time leads to elevated errors and more outliers in the biomarkers influenced by the depolarization phase (TP and dVmMax)," (Section 3.1.1)

R III-(p) Page 14. Can the authors better clarify ”the average RMSE over all APs 13.6mV”: is it the mean for all histograms in Figure 7? (In Figure 5 is more evident the average RMSE).

The average RMSE uses the same definition for Figures 5 and 7: It is the average over all the RMSEs for each pair of traces (simulated/emulated), though the amount of samples is much lower for the EAD data set and not normal distributed.

R III-(q) In Table 4, the information on which drugs are considered should be added. For each channel, we added the names of the drugs for which respective data from the CiPA initiative were available.

R III-(r) Pag. 18, second paragraph, there is a repetition of ”and”.

Fixed

R III-(s) The pair’s combination of scaling factors for simulating synthetic drugs reported in Table 2, can be associated with some effects of real drugs? In this case, I suggest including the information or justifying the choice.

The scaling factors in Table 2 are used to create data set #3 (former #2), and is meant to provide several APs which expose EADs. This is described in more detail in the new data section, Section 2.2.1:

"Data set #3: The motivation for creating data set #3 was to test the emulator on data of abnormal APs showing the repolarization abnormality EAD. This is considered a particularly relevant AP abnormality in pharmacological studies because of their role in the genesis of drug-induced ventricular arrhythmia’s [Weiss et al., 2010]. Drug data were created using ten synthetic drugs with the hERG channel and the Cav1.2 channel as targets. To this end, ten samples with pharmacological parameters for GKr and PCa (Table 2) were generated and the synthetic drugs were applied to the entire synthetic cardiomyocyte population by scaling GKr and PCa with the corresponding pharmacological parameter. Of the 1000 APs simulated, we discarded APs with a transmembrane potential difference of more than 10% of the amplitude between t = 0 and 1000ms (checked for the last AP), indicative of an AP that does not repolarize within 1000ms. This left us with 950 APs, 171 of which exhibit EAD (see Appendix C)." (Section 2.2.1)

R III-(t) A general comment on the work is that the authors claim that their study highlights the potential of NN emulators as a powerful tool for increased efficiency in future quantitative systems pharmacology studies, but they wrote ”Larger inaccuracies were found in the inverse problem solutions on experimental data highlight inaccuracies in estimating the pharmacological parameters”: so, I was wondering how they can claim the robustness of NN use as a tool for more efficient computation in pharmacological studies.

The discussed robustness directly refers to efficiently emulating steady-state/limit cycle APs from a set of maximum conductances (forward problem, Section 3.1.1). We extensively evaluated the algorithm and feel that given the low emulation RMSE of APs (< 1 mV), the statement is warranted. The inverse estimation, enabled through this rapid evaluation, performs well on synthetic data, but shows difficulties for experimental data. Note however that at this point there are multiple potential sources for these problems as highlighted in the Evaluation section (Section 4.1) and Table 5 (former 4) highlights the difference in accuracy of estimating per-channel maximum conductances, revealing a potentially large discrepancy. The emulator also offers future possibilities to incorporate additional informations in the forms of either priors, or more detailed measurements (e.g. calcium transients) and can be potentially improved to a point where also the inverse problem can be satisfactorily solved in experimental preparations, though further analysis will be required.

References [Beck, 2017] Beck, A. (2017). First-order methods in optimization. SIAM.

[Britton et al., 2013] Britton, O. J., Bueno-Orovio, A., Ammel, K. V., Lu, H. R., Towart, R., Gallacher, D. J., and Rodriguez, B. (2013). Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proceedings of the National Academy of Sciences, 110(23).

[Chang et al., 2017] Chang, K. C., Dutta, S., Mirams, G. R., Beattie, K. A., Sheng, J., Tran, P. N., Wu, M., Wu, W. W., Colatsky, T., Strauss, D. G., and Li, Z. (2017). Uncertainty quantification reveals the importance of data variability and experimental design considerations for in silico proarrhythmia risk assessment. Frontiers in Physiology, 8.

[Clerx et al., 2016] Clerx, M., Collins, P., de Lange, E., and Volders, P. G. A. (2016). Myokit: A simple interface to cardiac cellular electrophysiology. Progress in Biophysics and Molecular Biology, 120(1):100–114.

[Endresen and Skarland, 2000] Endresen, L. and Skarland, N. (2000). Limit cycle oscillations in pacemaker cells. IEEE Transactions on Biomedical Engineering, 47(8):1134–1137.

[Garny and Hunter, 2015] Garny, A. and Hunter, P. J. (2015). OpenCOR: a modular and interoperable approach to computational biology. Frontiers in Physiology, 6.

[Gemmell et al., 2016] Gemmell, P., Burrage, K., Rodr´ıguez, B., and Quinn, T. A. (2016). Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia. Progress in Biophysics and Molecular Biology, 121(2):169–184.

[Ghosh et al., 2018] Ghosh, S., Gavaghan, D. J., and Mirams, G. R. (2018). Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models.

[Herman and Usher, 2017] Herman, J. and Usher, W. (2017). SALib: An open-source python library for sensitivity analysis. J. Open Source Softw., 2(9):97.

[Johnstone et al., 2016] Johnstone, R. H., Chang, E. T., Bardenet, R., de Boer, T. P., Gavaghan, D. J., Pathmanathan, P., Clayton, R. H., and Mirams, G. R. (2016). Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? Journal of Molecular and Cellular Cardiology, 96:49–62.

[Li et al., 2017] Li, Z., Dutta, S., Sheng, J., Tran, P. N., Wu, W., Chang, K., Mdluli, T., Strauss, D. G., and Colatsky, T. (2017). Improving the in silico assessment of proarrhythmia risk by combining hERG (human ether`a-go-go-related gene) channel–drug binding kinetics and multichannel pharmacology. Circulation: Arrhythmia and Electrophysiology, 10(2).

[Muszkiewicz et al., 2016] Muszkiewicz, A., Britton, O. J., Gemmell, P., Passini, E., S´anchez, C., Zhou, X., Carusi, A., Quinn, T. A., Burrage, K., Bueno-Orovio, A., and Rodriguez, B. (2016). Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Progress in Biophysics and Molecular Biology, 120(1):115–127.

[Orvos et al., 2019] Orvos, P., Kohajda, Z., Szlov´ak, J., Gazdag, P., Arp´adffy-Lovas, T., T´oth, D., Geramipour, A.,´ T´alosi, L., Jost, N., Varr´o, A., and Vir´ag, L. (2019). Evaluation of possible proarrhythmic potency: Comparison of the effect of dofetilide, cisapride, sotalol, terfenadine, and verapamil on hERG and native iKr currents and on cardiac action potential. Toxicological Sciences, 168(2):365–380.

[Paszke et al., 2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.

[Plank et al., 2021] Plank, G., Loewe, A., Neic, A., Augustin, C., Huang, Y.-L., Gsell, M. A., Karabelas, E., Nothstein, M., Prassl, A. J., S´anchez, J., Seemann, G., and Vigmond, E. J. (2021). The openCARP simulation environment for cardiac electrophysiology. Computer Methods and Programs in Biomedicine, 208:106223.

[Shahriari et al., 2016] Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and de Freitas, N. (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 104(1):148–175. Conference Name: Proceedings of the IEEE.

[Snoek et al., 2014] Snoek, J., Swersky, K., Zemel, R., and Adams, R. (2014). Input Warping for Bayesian Optimization of Non-Stationary Functions. In Proceedings of the 31st International Conference on Machine Learning, pages 1674–1682. PMLR. ISSN: 1938-7228.

[Weiss et al., 2010] Weiss, J. N., Garfinkel, A., Karagueuzian, H. S., Chen, P.-S., and Qu, Z. (2010). Early afterdepolarizations and cardiac arrhythmias. Heart Rhythm, 7(12):1891–1899.

Author Response

We would like to thank the editor and the reviewers for their constructive comments and the chance to revise the manuscript. The suggestions have allowed us to improve our manuscript. We have been able to fulfil all reviewer comments and added new statistical analyses to examine associations for subsets of data. Whilst suggested by a reviewer, we did not perform large-scale experiments to confirm the viability of low sporozoite densities at different time-points post salivary gland colonization. For these assays there are currently no satisfactory in vitro models for sporozoites harvested from single mosquitoes and setting up and validating such experiments could be a PhD project in itself. We do consider this suggestion very relevant but beyond the scope of the current work.

Relevantly, during the time the manuscript was under review at eLife, we have been able to examine the multiplicity of infection in our field experiments. This was, as written in the original manuscript, a key reason to also perform experiments in the field where there is a greater diversity of parasite lines. We have successfully performed AMA-1 amplicon deep sequencing on infected mosquito salivary glands and infected skins. Although this does not change the key messages of the manuscript and is secondary to our main hypothesis, we do consider it a relevant addition since we were able to demonstrate that for some infected mosquitoes from the Burkina Faso study, multiple clones were expelled by mosquitoes during probing on a single piece of artificial skin. We have added a short paragraph to our revised manuscript and updated the acknowledgement section to include the supporting researcher who conducted those experiments.

Reviewer #1 (Public Review):

Summary: There is a long-believed dogma in the malaria field; a mosquito infected with a single oocyst is equally infectious to humans as another mosquito with many oocysts. This belief has been used for goal setting (and modelling) of malaria transmission-blocking interventions. While recent studies using rodent malaria suggest that the dogma may not be true, there was no such study with human P. falciparum parasites. In this study, the numbers of oocysts and sporozoite in the mosquitoes and the number of expelled sporozoites into artificial skin from the infected mosquito was quantified individually. There was a significant correlation between sporozoite burden in the mosquitoes and expelled sporozoites. In addition, this study showed that highly infected mosquitoes expelled sporozoites sooner.

Strengths:

• The study was conducted using two different parasite-mosquito combinations; one was lab-adapted parasites with Anopheles stephensi and the other was parasites, which were circulated in infected patients, with An. coluzzii. Both combinations showed statistically significant correlations between sporozoite burden in mosquitoes and the number of expelled sporozoites.

• Usually, this type of study has been done in group bases (e.g., count oocysts and sporozoites at different time points using different mosquitoes from the same group). However, this study determined the numbers in individual bases after multiple optimization and validation of the approach. This individual approach significantly increases the power of correlation analysis.

Weaknesses:

• In a natural setting, most mosquitoes have less than 5 oocysts. Thus, the conclusion is more convincing if the authors perform additional analysis for the key correlations (Fig 3C and 4D) excluding mosquitoes with very high total sporozoite load (e.g., more than 5-oocyst equivalent load).

In the revised manuscript, we have also performed our analysis including only the subset of mosquitoes with low oocyst burden. In our Burkina Faso experiments, where we could not control oocyst density, 48% (15/31) of skins were from mosquitoes with <5 oocyst sheets. Whilst low oocyst densities were thus not very uncommon, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities.

• As written as the second limitation of the study, this study did not investigate whether all expelled sporozoites were equally infectious. For example, Day 9 expelled sporozoites may be less infectious than Day 11 sporozoites, or expelled sporozoites from high-burden mosquitoes may be less infectious because they experience low nutrient conditions in a mosquito. Ideally, it is nice to test the infectivity by ex vivo assays, such as hepatocyte invasion assay, and gliding assay at least for salivary sporozoites. But are there any preceding studies where the infectivity of sporozoites from different conditions was evaluated? Citing such studies would strengthen the argument.

We appreciate this thought and can see the value of these experiments. We are not aware of any studies that examined sporozoite viability in relation to the day of salivary gland colonization or sporozoite density.

One previous study assessed the NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (https://doi.org/10.1111/cmi.12745), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

• Since correlation analyses are the main points of this paper, it is important to show 95% CI of Spearman rank coefficient (not only p-value). By doing so, readers will understand the strengths/weaknesses of the correlations. The p-value only shows whether the observed correlation is significantly different from no correlation or not. In other words, if there are many data points, the p-value could be very small even if the correlation is weak.

We appreciate this comment and agree that this is indeed insightful. We have added the 95% confidence intervals to all figure legends and main text. We also provide them below.

Fig 3b: 95% CI: 0.74, 0.85

Fig 3c: 95% CI: 0.17, 0.50

Fig 4c: 95% CI: 0.80, 0.95

Fig 4d: 95% CI: 0.52, 0.82

Supp Fig 5a: 95% CI: 0.74, 0.85

Supp Fig 5b: 95% CI: 0.73, 0.93

Supp Fig 6: 95% CI: 0.11, 0.48

Supp Fig 7: 95% CI: -0.12, 0.16

Reviewer #2 (Public Review):

Summary: The malaria parasite Plasmodium develops into oocysts and sporozoites inside Anopheles mosquitoes, in a process called sporogony. Sporozoites invade the insect salivary glands in order to be transmitted during a blood meal. An important question regarding malaria transmission is whether all mosquitoes harbouring Plasmodium parasites are equally infectious. In this paper, the authors investigated the progression of P. falciparum sporozoite development in Anopheles mosquitoes, using a sensitive qPCR method to quantify sporozoites and an artificial skin system to probe for parasite expelling. They assessed the association between oocyst burden, salivary gland infection intensity, and sporozoites expelled.

The data show that higher sporozoite loads are associated with earlier colonization of salivary glands and a higher prevalence of sporozoite-positive salivary glands and that higher salivary gland sporozoite burdens are associated with higher numbers of expelled sporozoites. Intriguingly, there is no clear association between salivary gland burdens and the prevalence of expelling, suggesting that most infections reach a sufficient threshold to allow parasite expelling during a mosquito bite. This important observation suggests that low-density gametocyte carriers, although less likely to infect mosquitoes, could nevertheless contribute to malaria transmission.

Strengths: The paper is well written and the work is well conducted. The authors used two experimental models, one using cultured P. falciparum gametocytes and An. stephensi mosquitoes, and the other one using natural gametocyte infections in a field setup with An. coluzzii mosquitoes. Both studies gave similar results, reinforcing the validity of the observations. Parasite quantification relies on a robust and sensitive qPCR method, and parasite expelling was assessed using an innovative experimental setup based on artificial skin.

Weaknesses: There is no clear association between the prevalence of sporozoite expelling and the parasite burden. However, high total sporozoite burdens are associated with earlier and more efficient colonization of the salivary glands, and higher salivary gland burdens are associated with higher numbers of expelled sporozoites. While these observations suggest that highly infected mosquitoes could transmit/expel parasites earlier, this is not directly addressed in the study. In addition, whether all expelled sporozoites are equally infectious is unknown. The central question, i.e. whether all infected mosquitoes are equally infectious, therefore remains open.

We agree that the manuscript provides important steps forward in our understanding of what makes an infectious mosquito but does not conclusively demonstrate that highly infected mosquitoes are more likely to initiate a secondary infection. We consider this to be beyond the scope of the current work although the current work lays the foundation for these important future studies. For human Plasmodium infections the most satisfactory answer on the infectiousness of low versus high infected mosquitoes comes from controlled human infection models. In response to reviewer comments, we have extended our Discussion section to highlight this importance. To accommodate the (very fair) reviewer comments, we have avoided any phrasings that suggest that our findings demonstrate differences in transmission.

Reviewer #3 (Public Review):

Summary: This study uses a state-of-the-art artificial skin assay to determine the quantity of P. falciparum sporozoites expelled during feeding using mosquito infection (by standardised membrane feeding assay SMFA) using both cultured gametocytes and natural infection. Sporozoite densities in salivary glands and expelled into the skin are quantified using a well-validated molecular assay. These studies show clear positive correlations between mosquito infection levels (as determined by oocyst numbers), sporozoite numbers in salivary glands, and sporozoites expelled during feeding. This indicates potentially significant heterogeneity in infectiousness between mosquitoes with different infection loads and thus challenges the often-made assumption that all infected mosquitoes are equally infectious.

Strengths: Very rigorously designed studies using very well validated, state-of-the-art methods for studying malaria infections in the mosquito and quantifying load of expelled sporozoites. This resulted in very high-quality data that was well-analyzed and presented. Both sources of gametocytes (cultures vs. natural infection) show consistent results further strengthening the quality of the results obtained.

Weaknesses: As is generally the case when using SMFAs, the mosquito infections levels are often relatively high compared to wild-caught mosquitoes (e.g. Bombard et al 2020 IJP: median 3-4 ), and the strength of the observed correlations between oocyst sheet and salivary gland sporozoite load even more so between salivary gland sporozoite load and expelled sporozoite number may be dominated by results from mosquitoes with infection levels rarely observed in wild-caught mosquitoes. This could result in an overestimation of the importance of these well-observed positive relationships under natural transmission conditions. The results obtained from these excellently designed and executed studies very well supported their conclusion - with a slight caveat regarding their application to natural transmission scenarios

For efficiency and financial reasons, we have worked with an approach to enhance mosquito infection rates. If we had worked with gametocytes at physiological concentrations and a small number of donors, we probably have had considerably lower mosquito infection rates. Whilst this would indeed result in lower infection burdens in the sparse infected mosquitoes, addressing the reviewer concern, it would have made the experiments highly inefficient and expensive. The skin mimic was initially provided free of charge when the matrix was close to the expiry date but for the experiments in Burkina Faso we had to purchase the product at market value. Whilst we consider the biological question sufficiently important to justify this investment – and think our findings prove us right – it remained important to avoid using skins for uninfected mosquitoes. Since oocyst prevalence and density are strongly correlated (doi: 10.1016/j.ijpara.2012.09.002; doi: 10.7554/eLife.34463), a low oocyst density in natural infections typically coincides with a high proportion of negative mosquitoes.

Of note, our approach did result in the inclusion of 15 skins from infected mosquitoes with 1-4 oocysts. This number may be modest but we did include observations from this low oocyst range which is, we agree, highly important for better understanding malaria epidemiology.

This work very convincingly highlights the potential for significant heterogeneity in the infectiousness between individual P. falciparum-infected mosquitoes. Such heterogeneity needs to be further investigated and if again confirmed taken into account both when modelling malaria transmission and when evaluating the importance of low-density infections in sustaining malaria transmission.

Reviewer #4 (Public Review):

Summary: The study compares the number of sporozoites expelled by mosquitoes with different Plasmodium infection burden. To my knowledge this is the first report comparing the number of expelled P. falciparum sporozoites and their relation to oocyst burden (intact and ruptured) and residual sporozoites in salivary glands. The study provides important evidence on malaria transmission biology although conclusions cannot be drawn on direct impact on transmission.

Strengths: Although there is some evidence from malaria challenge studies that the burden of sporozoites injected into a host is directly correlated with the likelihood of infection, this has been done using experimental infection models which administer sporozoites intravenously. It is unclear whether the same correlation occurs with natural infections and what the actual threshold for infection may be. Host immunity and other host related factors also play a critical role in transmission and need to be taken into consideration; these have not been mentioned by the authors. This is of particular importance as host immunity is decreasing with reduction in transmission intensity.

Weaknesses: The natural infections reported in the study were not natural as the authors described. Gametocyte enrichment was done to attain high oocyst infection numbers. Studying natural infections would have been better without the enrichment step. The infected mosquitoes have much larger infection burden than what occurs in the wild.

Nevertheless, the findings support the same results as in the experiments conducted in the Netherlands and therefore are of interest. I suggest the authors change the wording. Rather than calling these "natural" infections, they could be called, for example, "experimental infections with wild parasite strains".

We have addressed these concerns and, in the process, also changed our manuscript title. The following sentences have been changed:

“It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and natural infections in Burkina Faso”.

Now reads: “It is currently unknown whether all Plasmodium falciparum infected mosquitoes are equally infectious. We assessed sporogonic development using cultured gametocytes in the Netherlands and experimental infections with naturally circulating parasite strains in Burkina Faso”. 226-228 “Experimental infections with naturally circulating parasite strains show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

Has now replaced the original phrasing: “Natural infected mosquitoes by gametocyte carriers in Burkina Faso show comparable correlation between oocyst density, salivary gland density and sporozoite inoculum”.

I do not believe the study results generate sufficient evidence to conclude that lower infection burden in mosquitoes is likely to result in changes to transmission potential in the field. In study limitations section, the authors say "In addition, our quantification of sporozoite inoculum size is informative for comparisons between groups of high and low-infected mosquitoes but does not provide conclusive evidence on the likelihood of achieving secondary infections. Given striking differences in sporozoite burden between different Plasmodium species - low sporozoite densities appear considerably more common in mosquitoes infected with P. yoelii and P. berghei the association between sporozoite inoculum and the likelihood of achieving secondary infections may be best examined in controlled human infection studies. However, in the abstract conclusion the authors state "Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites and may need to be considered in estimations of transmission potential." Kindly consider ending the sentence at "expelled sporozoites." Future studies on CHMI can be recommended as a conclusion if authors feel fit.

We agree that we need to be very cautious with conclusions on the impact of our findings for the infectious reservoir. We have rephrased parts of our abstract and have updated the Discussion section following the reviewer suggestions. We agree with the reviewer that CHMI studies are recommended and have expanded the Discussion section to make this clearer. The sentence in the abstract now ends as:

"Whilst sporozoite expelling was regularly observed from mosquitoes with low infection burdens, our findings indicate that mosquito infection burden is associated with the number of expelled sporozoites. Future work is required to determine the direct implications of these findings for transmission potential."

Reviewer #1 (Recommendations For The Authors):

• Prevalence data shown in Fig 2A and Table S1 are different. For example, >50K at Day 11, Fig 2A shows ~85% prevalence, but Table S1 says 100%. If the prevalence in Table S1 shows a proportion of observations with positive expelled sporozoites (instead of a proportion of positive mosquitoes shown in Fig 2A), then the prevalence for <1K at Day 11 cannot be 6.7% (either 0 or 20% as there were a total of 5 observations). So in either case, it is not clear why the numbers shown in Fig 2A and Table S1 are different.

Figure 2A and Table S2 are estimated prevalence and odds ratios from an additive logistic regression model (i.e. excluding the interaction between day and sporozoite categories). Table S1 includes this interaction when estimating prevalence and odds ratios and as we can see some categories in the interaction were extremely small resulting in blown up confidence intervals especially in day 11. So Table S1 and Fig 2A are the results from two different models. Whilst our results are thus correct, we can understand the confusion and have added a sentence to explain the model used in the figure/table legends.

Figure. 2 Extrinsic Incubation Period in high versus low infected mosquitoes. A. Total sporozoites (SPZ) per mosquito in body plus salivary glands (x-axis) were binned by infection load <1k; 1k-10k; 10k-50k; >50k and plotted against the proportion of mosquitoes (%) that were sporozoite positive (y-axis) as estimated from an additive logistic regression model with factors day and SPZ categories. Supplementary Table S1. The extrinsic incubation period of P. falciparum in An. stephensi estimated by quantification of sporozoites on day 9, 10, 11 by qPCR. Based on infection intensity mosquitoes were binned into four categories (<1k, 1k-10k, 10k-50k, >50) that was assessed by combining sporozoite densities in the mosquito body and salivary gland. Prevalences and odds ratios were estimated from a logistic regression model with factors day, SPZ category and their interaction.

There are 3 typos in the paper. Please fix them.

Line 464; ...were counted using a using an incident....

Line 473; Supplementary Figure 7 should be Fig S8.

Line 508: ...between days 9 and 10 using a (t=-2.0467)....

We appreciate the rigour in reviewing our text and have corrected all typos.

Reviewer #2 (Recommendations For The Authors):

High infection burdens may result in earlier expelling capacity in mosquitoes, which would reflect more accurately the EIP. The fact that earlier colonization of SG and correlation between SG burden and numbers expelled suggest it could be the case, but it would be interesting to directly measure the prevalence of expelling over time to directly assess the effect of the sporozoite burden (not just at day 15 but before). This could reveal how the parasite burden in mosquitoes is a determinant of transmission.

We appreciate this suggestion and will consider this for future experiments. It adds another variable that is highly relevant but will also complicate comparisons where sporozoite expelling is related to both time since infectious blood meal and salivary gland sporozoite density (that is also dependent on time since infectious bloodmeal). Moreover, we then consider it important to measure this over the entire duration of sporozoite expelling, including late time-points post infectious bloodmeal. This may form part of a follow-up study.

Another question is whether all sporozoites (among expelled parasites) are equally infective, i.e. susceptible to induce secondary infection. If not, this could reconcile the data of this study and previous results in the rodent model where high burdens were associated with an increased probability to transmit.

As also indicated above, we are aware of a single study that assessed NF54 sporozoite infectivity on different days post infection (days 12-13-14-15-16-18) and observed no clear differences in ‘per sporozoite hepatocyte invasion capacity’ over this period (DOI: 10.1111/cmi.12745). We nevertheless agree that it is conceivable that sporozoites require maturation in the salivary glands and might not all be equally infectious. While hepatocyte invasion experiments are conducted with bulk harvesting of all the sporozoites that are present in the salivary glands, it would even be more interesting to assess the invasion capacity of the smaller population of sporozoites that migrate to the proboscis to be expelled. This would, as the reviewer will appreciate, be a major endeavour. To do this well the expelled sporozoites would need to be harvested from the salivary glands/proboscis and used in the best and most natural environment for invasion. The suggested work would thus depend on the availability of primary hepatocytes since conventional cell-lines like HC-04 are likely to underestimate sporozoite invasion. Importantly, there are currently no opportunities to include the barrier of the skin environment in invasion assays whilst this may be highly important in determining the likelihood that sporozoites manage to achieve invasion and give rise to secondary infections. In short, we agree with the reviewer that these experiments are of interest but consider these well beyond the scope of the current work. We have added a section to the Discussion section to highlight these future avenues for research. ‘Of note, our assessments of EIP and of sporozoite expelling did not confirm the viability of sporozoites. Whilst the infectivity of sporozoites at different time-points post infection has been examine previously (ref), these experiments have never been conducted with individual mosquito salivary glands. To add to this complexity, such experiments would ideally retain the skin barrier that may be a relevant determinant for invasion capacity and primary hepatocytes.’

The authors evaluated oocyst rupture at day 18, i.e. 3 days after feeding experiments (performed at day 15). Did they check in control experiments that the prevalence of rupture oocysts does not vary between day 15 and day 18?

We did not do this and consider it very unlikely that there is a noticeable increase in the number of ruptured oocysts between days 15 and 18. We observe that salivary gland invasion plateaus around day 12 and the provision of a second bloodmeal that is known to accelerate oocyst maturation and rupture (doi: 10.1371/journal.ppat.1009131) makes it even less likely that a relevant fraction of oocysts ruptures very late. Perhaps most compellingly, the time of oocyst rupture will depend on nutrient availability and rupture could thus occur later for oocysts from a heavily infected gut compared to oocysts from mosquitoes with a low infection burden. We observe a very strong association between salivary gland sporozoite density (day 15) and oocyst density (assessed at day 18) without any evidence for change in the number of sporozoites per oocyst for different oocyst densities. In our revised manuscript we have also assessed correlations for different ranges of oocyst intensities and see highly consistent correlation coefficients and find no evidence for a change in ‘slope’. If oocyst rupture would regularly happen between days 15 and 18 and this late rupture would be more common in heavily infected mosquitoes, we would expect this to affect the associations presented in figures 3B and 4C This is not the case.

The authors report higher sporozoite numbers per oocyst and a higher proportion of SG invasion as compared to previous studies (30-50% rather than 20%). How do they explain these differences? Is it due to the detection method and/or second blood meal? Or parasite species?

We were also intrigued by these findings in light of existing literature. To address potential discrepancies, it is indeed possible that the 2nd bloodmeal made a difference. In addition, NF54 is known to be a highly efficient parasite in terms of gametocyte formation and transmission. And there are marked differences in these performances between NF54 isolates and definitely between NF54 and its clone 3D7 that is regularly used. We also used a molecular assay to detect and quantify sporozoites but consider it less likely that this is a major factor in terms of explaining SG invasion since sporozoite densities were typically within the range that would be detected by microscopy. We can only hypothesize that the 2nd bloodmeal may have contributed to these findings and acknowledge this in the revised Discussion section.

The median numbers of expelled sporozoites seem to be higher in the natural gametocyte infection experiments as compared to the cultures. Is it due to the mosquito species (An. coluzzii versus An. stephensi?).

The added value of our field experiments, a more relevant mosquito species and more relevant parasite isolates, is also a weakness in terms of understanding possible differences between in vitro experiments and field experiments with naturally circulating parasite strains. We only conclude that our in vitro experiments do not over-estimate sporozoite expelling by using a highly receptive mosquito source and artificially high gametocyte densities. We have clarified this in the revised Discussion.

39% of sporozoite-positive mosquitoes failed to expel, irrespective of infection densities. Could the authors discuss possible explanations for this observation?

In paragraph 304-307 we now write that:” This finding broadly aligns with an earlier study of Medica and Sinnis that reported that 22% of P. yoelii infected mosquitoes failed to expel sporozoites. For highly infected mosquitoes, this inefficient expelling has been related to a decrease of apyrase in the mosquito saliva”.

In Figure 3, it would be interesting to zoom in the 0-1k window, below the apparent threshold for successful expelling.

We have generated correlation estimates for different ranges of oocyst and sporozoite densities and added these in Supplementary Table 5. We agree that this helps the reader to appreciate the contribution of different ranges of parasite burden to the observed associations.

In Fig S8. Did they observe intact oocysts with fixed samples? These could be shown as well in the figure.

We have incorporated this comment. An intact oocyst from fixed samples was now added to Fig S10.

Minor points

-line 119: LOD and LOQ could be defined here.

We agree that this should have been defined. We changed line 119 to explain LOD and LOQ to: …“the limit of detection (LOD) and limit of quantification (LOQ)”….

• line 126: the title does not reflect the content of this paragraph.

We have changed the title: “Immunolabeling allows quantification of ruptured oocysts ”into: A comparative analysis of oocyst densities using mercurochrome staining and anti-CSP immunostaining.

-line 269: infectivity is not appropriate. The data show colonization of SG.

Line 269: infectivity has been changed with colonization of salivary glands.

There seems to be a problem with Fig S6. The graph seems to be the same as Fig 3C. Please check whether the graph and legends are correct.

Supplementary Figure 6 shows the sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites while Fig 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1qPCR without any threshold density. We have explained this in more detail in the revised supplemental figure where we now state

“Of note, this figure differs from Figure 3C in the main text in the following manner. This figure presents sporozoite expelling density in relation to infection burden with a threshold set at > 20 sporozoites to conclude sporozoite positivity while Figure 3C shows the total sporozoite density (residual salivary gland sporozoites + sporozoites expelled, X-axis) in relation to the number of expelled sporozoites (Y-axis) by COX-1 qPCR without any threshold density and thus includes all observations with a qPCR signal”

Reviewer #3 (Recommendations For The Authors):

Congratulations to the authors for the really excellently designed and rigorously conducted studies.

My main concern is in regards to the relatively high oocyst numbers in their experimental mosquitoes (from both sources of gametocytes) compared to what has been reported from wild-caught mosquitoes in previous studies in Burkina Faso.

We have addressed this concern above. For completeness, we include the main points here again. We enriched gametocytes for efficiency reasons, experiments on gametocytes at physiological concentrations would have resulted in a lower oocyst density (and thus more ‘natural’ although a minority of individuals achieves very high oocyst densities in all studies that included a broad range of oocyst densities (e.g. doi: 10.1016/j.exppara.2014.12.010; doi: 10.1016/S1473-3099(18)30044-6). Of note, we did include 15 skins from low oocyst densities (1-4 oocysts). Whilst low oocyst densities were thus not very uncommon in our sample set, we acknowledge that this may have rendered some comparisons underpowered. At the same time, we observe a strong positive trend between oocyst density and sporozoite density and between salivary gland sporozoite density and mosquito inoculum. This makes it very likely that this trend is also present at lower oocyst densities, an association where sporozoite inoculation saturates at high densities is plausible and has been observed before for rodent malaria (DOI: 10.1371/journal.ppat.1008181) whilst we consider it less likely that sporozoite expelling would be more efficient at low (unmeasured) sporozoite densities. In the revised manuscript we have also performed our analysis including only the subset of mosquitoes with low oocyst burden.

The best way to address this would be to do comparable artificial skin-feeding experiments on such wild-caught mosquitoes, but I appreciate that this is very difficult to do.

This would indeed by difficult to do. Mostly because infection status can only be examined post-hoc and it is likely that >95% of mosquitoes are sporozoite negative at the moment experiments are conducted (in many settings this will even be >99%). Importantly, also in wild-caught mosquitoes very high oocyst burdens are observed in a small but relevant subset of mosquitoes (doi: 10.1016/j.ijpara.2020.05.012).

Instead, I would suggest the authors conduct addition analysis of their data using different cut-offs for maximum oocyst numbers (e.g. <5, <10, <20) to determine if these correlations hold across the entire range of observed oocyst sheets and salivary gland sporozoite load.

We have provided these calculations for the proposed range of oocyst numbers. In addition, we also provided them for a range of sporozoite densities. These findings are now provided in

Entire range of observed oocyst sheets and salivary gland sporozoite load. A minor point on the regression lines in Figures 3 & 4: both variables in these plots have inherent variation (measurement & natural), but regression techniques such as reduced major exit regression (MAR) that allow error in both x and y variables may be preferable to a standard lines regression. Also, as it is implausible that mosquitoes with zero sporozoite in salivary glands expel several hundred sporozoites at feeding, the regression should probably also be constrained to pass through the 0,0 point.

Since the main priority of the analyses is the correlation, and not the fit of the regression line – which is only for indication, and also because of the availability of software, we did not change the type of regression. We have however added a disclaimer to the legend, and we have also forced the intercept to 0 – which does indeed better reflect the biological association. Additionally we added 95% confidence intervals to all Spearman’s correlation coefficients in the legends.

Author Response

Responses to public reviews

Reviewer 1

We thank the reviewer for the valuable and constructive comments and are pleased that the re-viewer finds our study timely and our behavioral results clear.

1) The RSA basically asks on the lowest level, whether neural activation patterns (as measured by EEG) are more similar between linked events compared to non-linked events. At least this is the first question that should be asked. However, on page 11 the authors state: "We ex-amined insight-induced effects on neural representations for linked events [...]". Hence, the critical analysis reported in the manuscript fully ignores the non-linked events and their neu-ral activation patterns. However, the non-linked events are a critical control. If the reported effects do not differ between linked and non-linked events, there is no way to claim that the effects are due to experimental manipulation - neither imagination nor observation. Hence, instead of immediately reporting on group differences (sham vs. control) in a two-way in-teraction (pre vs. post X imagination vs. observation), the authors should check (and re-port) first, whether the critical experimental manipulation had any effect on the similarity of neural activation patterns in the first place.

We completely agree that the non-link items are a critical control. Therefore, we had reported not only the results for linked but also for non-linked events on page 15, lines 336-350. We clarified this important point now on page 12 lines 283-286:

“Subsequently, we examined insight-induced effects on neural representations for linked (vs. non-linked) events by comparing the change from pre- to post-insight (post-pre) and the difference between imagination and observation (imagination - observation) between cTBS and sham groups using an independent cluster-based permutation t-test.”

Moreover, to directly compare linked and non-linked events we performed a four-way in-teraction including link vs. non-link. This analysis yielded a significant four-way interaction, showing that the interaction of time (pre vs. post), mode of insight (imagination vs. obser-vation) and cTBS differed for linked vs. non-linked items. We then report the follow-up analyses, separately for linked and non-linked events. Please see pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

2) Overall, the focus on the targeted three-way interaction is poorly motivated. Also, a func-tional interpretation is largely missing.

In order to better explain our motivation for the three-way interaction, we em-phasized in the introduction the importance of disentangling potential differences due to the mode of insight, given the known role of the angular gyrus in imagination on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked event.”

As discussed on page 21 (starting from line 478; see also the intro on page 4), we expected that the angular gyrus would be particularly implicated in imagination-based insight, given its known role in imagination (e.g.: Thakral et al., 2017). Moreover, given the angular gyrus’s strong connectivity with other regions, the results observed may not be driven by this re-gion alone but also by interconnected regions, such as the hippocampus. We clarified these important points at the very end of the discussion on pages 23-24, lines 543-560:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014).”

3) "Interestingly, we observed a different pattern of insight-related representational pattern changes for non-linked events." It is not sufficient to demonstrate that a given effect is pre-sent in one condition (linked events) but not the other (non-linked events). To claim that there are actually different patterns, the authors would need to compare the critical condi-tions directly (Nieuwenhuis et al., 2011).

We completely agree and now compared the two conditions directly. Specifical-ly, we now report the significant four-way interaction, including the factor link vs. non-link, before delving into separate analyses for linked and non-linked events on pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

4) "This analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B)." (p. 11). The authors report results with specificity for certain topographical locations. However, this is in stark contrast to the fact that the authors derived time X time RSA maps.

We did derive time × time similarity maps for each electrode within each partic-ipant, which allowed us to find a cluster consisting of specific electrodes. We apologize for not making this aspect clear enough and have, therefore, modified the respective part of our methods section on page 38, lines 951-952:

“In total, this analysis produced eight Representational Dissimilarity Matrices (RDMs) for each electrode and each participant.”

5) "These theta power values were then combined to create representational feature vectors, which consisted of the power values for four frequencies (4-7 Hz) × 41 time points (0-2 sec-onds) × 64 electrodes. We then calculated Pearson's correlations to compare the power pat-terns across theta frequency between the time points of linked events (A with B), as well as between the time points of non-linked events (A with X) for the pre- and the post-phase separately, separately for stories linked via imagination and via observation. To ensure un-biased results, we took precautions not to correlate the same combination of stories twice, which prevented potential inflation of the data. To facilitate statistical comparisons, we ap-plied a Fisher z-transform to the Pearson's rho values at each time point. This yielded a global measure of similarity on each electrode site. We, thus, obtained time × time similarity maps for the linked events (A and B) and the non-linked events (A and X) in the pre- and post-phases, separately for the insight gained through imagination and observation." (p. 34+35).

If RSA values were calculated at each time point and electrode, the Pearson correlations would have been computed effectively between four samples only, which is by far not enough to derive reliable estimates (Schönbrodt & Perugini, 2013). The problem is aggra-vated by the fact that due to the time and frequency smoothing inherent in the time-frequency decomposition of the EEG data, nearby power values across neighboring theta frequencies are highly similar to start with. (e.g., Schönauer et al., 2017; Sommer et al., 2022).

Alternative approaches would be to run the correlations across time for each electrode (re-sulting in the elimination of the time dimension) or to run the correlations at each time point across electrodes (resulting in the elimination of topographic specificity).

At least, the authors should show raw RSA maps for linked and non-linked events in the pre- and post-phases separately for the insight gained through imagination and observa-tion in each group, to allow for assessing the suitability of the input data (in the supple-ments?) before progressing to reporting the results of three-way interactions.

Although we do see the reviewer’s point, we think that an RSA specific to the theta range yielding electrode specific time × time similarity maps must be run this way, otherwise, as you pointed out, one or the other dimension is compromised. Running an RSA across time for each electrode will lead to computing a similarity measure between the events without information on when these stimuli become more or less similar, thereby ig-noring the temporal dynamics crucial to EEG data and not taking advantage of the high temporal resolution. Conversely, conducting an RSA across electrodes might result in an overall similarity measure per participant, disregarding the spatial distribution and potential variations among electrodes. Although EEG has limited spatial resolution, different elec-trodes can capture differences that may aid in understanding neural processing. However, as suggested by the reviewer, we included the raw RSA maps for linked and non-linked events separately for pre- and post-phases, imagination and observation and link and non-link in the supplement and refer to these data in the results section on pages 12-13, lines 293-295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Reviewer 2

We thank the reviewer for the very helpful and constructive comments and appreciate that the reviewer finds our study relevant to all areas of cognitive research.

1) While the observed memory reconfiguration/changes are attributed to the angular gyrus in this study, it remains unclear whether these effects are solely a result of the AG's role in re-configuration processes or to what extent the hippocampus might also mediate these memory effects (e.g., Tambini et al., 2018; Hermiller et al., 2019).

We agree that, in addition to the critical role of the angular gyrus, there may be an involvement of the hippocampus. We point now explicitly to the modulatory capacities of angular gyrus stimulation on the hippocampus. Please see page 4, lines 81-88:

“One promising candidate that may contribute to insight-driven memory reconfiguration is the angular gyrus. The angular gyrus has extensive structural and functional connections to many other brain regions (Petit et al., 2023), including the hippocampus (Coughlan et al., 2023; Uddin et al., 2010). Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014). Furthermore, the angular gyrus has been implicated in a myriad of cognitive func-tions, including mental arithmetic, visuospatial processing, inhibitory control, and theory-of-mind (Cattaneo et al., 2009; Grabner et al., 2009; Lewis et al., 2019; Schurz et al., 2014).”

We further added a new paragraph to the discussion pointing at the possibility that not solely the angular gyrus but another brain region, such as the hippocampus, may have me-diated the changes observed in our study on pages 23-24, lines 546-562:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

2) Another weakness in this manuscript is the use of different groups of participants for the key TMS intervention, along with underspecified or incomplete hypotheses/predictions.

In our view, the chosen between-subjects design is to be preferred over a crossover design for several reasons. First, our choice aimed to eliminate potential se-quence effects that may have adversely affected performance in the narrative-insight task (NIT). Second, this approach ensured consistency in expectations regarding the story links while also mitigating potential differences induced by fatigue. Additionally, we accounted for the potential advantage of a within-subject design – the stimulation of the same brain – by utilizing neuro-navigated TMS for targeting the stimulation coordinate. Finally, it is im-portant to note that we measured the event representations pre- and post-insight and that also the mode of insight was manipulated within-subject. Thus, our design did include a within-subject component and we are convinced that the chosen paradigm balances the different strengths and weaknesses of within-subject and between-subjects designs in the best possible manner. We specified our rationale for choosing a between-subjects ap-proach in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Moreover, to provide a comprehensive portrayal of the two groups, we incorporated de-scriptions concerning trait and state variables alongside age and motor thresholds and in-cluded t-test comparisons between these variables on page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-404:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

For greater precision in outlining our hypotheses, we specified these at the end of the in-troduction on pages 4-55, lines 107-118:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures.”

3) Furthermore, in some instances, the types of analyses used do not appear to be suitable for addressing the questions posed by the current study, and there is limited explanation pro-vided for the choice of analyses and questionnaires.

We addressed this concern by inserting a new section “control variables” in the methods explaining our rationale for employing the different questionnaires as control var-iables on pages 40-41, lines 1003-1019:

“Control variables In order to ensure that the observed effects were solely attributable to the TMS manipula-tion and not influenced by other factors, we comprehensively evaluated several trait and state variables. To account for potential variations in anxiety levels that could impact our re-sults, we specifically measured state and trait anxiety using STAI-S and STAI-T (Laux et al., 1981), thus minimizing the potential confounding effects of anxiety on our findings (Char-pentier et al., 2021). Additionally, we evaluated participants’ chronic stress levels using the TICS (Schulz & Schlotz, 1999) to exclude any group variations that might explain the effect on memory, cosidering the well-established impact of stress on memory (Sandi & Pinelo-Nava, 2007; Schwabe et al., 2012). Moreover, we assessed participants’ depressive symp-toms employing the BDI (Hautzinger et al., 2006), to guarantee group comparability on this clinical measure. We further assessed fundamental personality dimensions using the BFI-2 (Danner et al., 2016) to exclude any potential group discrepancies that could account for dif-ferences observed. Lastly, we assessed participants’ imaginative capacities using the FFIS (Zabelina & Condon, 2019), to ensure uniformity across groups regarding this central varia-ble, considering the significant role of imagination in relation to the cTBS-targeted angular gyrus (Thakral et al., 2017).”

We further specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-85:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

Reviewer 3

We thank the reviewer for the constructive and very helpful comments. We are pleased that the reviewer considered our experimental design to be strong and our behavioral results to be striking.

1) My major criticism relates to the main claim of the paper regarding causality between the angular gyrus and the authors' behavior of interest. Specifically, I am not convinced by the evidence that the effects of stimulation noted in the paper are attributable specifically to the angular gyrus, and not other regions/networks.

While our results showed specific changes after cTBS over the angular gyrus, demonstrating a causal involvement of the angular gyrus in these effects, we completely agree that this does not rule out an involvement of additional areas. In particular, there is evidence suggesting that cTBS over parietal regions, such as the angular gyrus, could poten-tially influence hippocampal functioning. We address this issue now in a new paragraph that we have added to the discussion, on pages 23-24, lines 546-564:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations.”

Responses to reviewer recommendations

Reviewer 1

1) On page 26, the authors write: "[...] different video events (A, B, and X) were recalled from day one [...]". I may have missed this point, but I had the impression that the task was con-ducted within one day.

Indeed, this study was conducted within a single day. We rephrased the respec-tive statement accordingly. Please see page 7, lines 149-153:

“To test this hypothesis and the causal role of the angular gyrus in insight-related memory reconfigurations, we combined the life-like video-based narrative-insight task (NIT) with representational similarity analysis of EEG data and (double-blind) neuro-navigated TMS over the left angular gyrus in a comprehensive investigation within a single day.”

We further included this information in the methods section on page 27, lines 634-635:

“In total, the experiment took about 4.5 hours per participant and was completed within a single day. ”

Reviewer 2

1) There is a substantial disconnection between the introduction and the methods/results sec-tion. One reason is that there is not sufficient detail regarding the hypotheses/predictions and the specific types of analyses chosen to test these hypotheses/predictions. Additionally, it is not explained what comparisons and outcomes would be informative/expected. This should be made clear. Second and related to the above, the rationale for conducting certain types of analyses (correlation, coherence, see below) sometimes is not specified.

To address this concern, we elaborated on our hypotheses incorporating specif-ic predictions for the free recall, given its higher variability than the other memory measures, and for imagination vs. observation at the end of the introduction on pages 4-5, lines 107-122:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures. Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connec-tivity analysis building upon prior findings concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational ap-proach to relate observations from these two domains.”

Moreover, we made our rationale for the employed analyses more explicit and specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

2) The authors suggest that besides Branzi et al. (2021), this is one of the first studies showing that memory update is linked to the AG. I suggest having a look at work from Tambini, Nee, & D'Esposito, 2018, JoCN, and other papers from Joel Voss' group that target a similar re-gion of AG/Inferior parietal cortex. Many studies, using multiple TMS protocols, have now shown this brain region is causally involved in episodic and associative memory encoding.

As mentioned above, further consideration of this literature is important as it delves into the region's hippocampal connectivity (and other network properties), and how that mediates the memory effects. Indeed because of the nature of the methods employed in this study, we do not know if the memory-related behavioural effects are due to TMS-changes induced at the AG's versus the hippocampal' s level, or both. How do the current findings square with the existing TMS effects from this region? Can the connectivity profile of the target re-gion highlighted by previous studies provide further insight into how the current behaviour-al effect arises? Some comments on this could be added to the discussion.

We completely agree that the other studies showing enhanced associative memory after TMS to parietal regions need to be addressed. Therefore, we updated the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Moreover, we included a section specifically addressing the possibility that the effects ob-served may pertain to having modulated other regions via the targeted region and updated the discussion on pages 23-24, lines 543-562:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) Another comment I have regards the results observed for the observation vs imagination insight conditions. The authors mention that the 'changes in representational similarity for the observation condition should be interpreted with caution, as these seemingly opposite changes appeared to be at least in part driven by group differences already in the pre-phase before participants gained insight.' I wonder what these group differences are and whether the authors have any hypothesis about what factors determined them.

We could only speculate about the basis of the observed pre-insight phase dif-ferences. However, we provide now the raw RSA data as supplemental material to make the pattern of the (raw) RSA findings in the pre- and post-insight phases more transparent. We refer the interested reader to this material on pages 12-13, lines 293 to 295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Furthermore, the age of participants is not reported separately for the two groups (cTBS to AG vs Sham), I think. This should be reported including a t-test showing that the two groups have the same age.

We agree and report now explicitly that groups did not significantly differ in rel-evant control variables including age. Please see page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-412:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

The fact this study is not a within-subject design makes difficult the interpretation of the results and this should be recognised as an important limitation of the study.

As outlined above, a within-subject design would in our view come with several disadvantages, such as significant sequence/carry-over effects. Moreover, the neural rep-resentation change was measured in a pre-post design, enabling us to measure the insight-driven neural reconfiguration at the individual level.

We clarify our rationale for the between-subjects factor TMS in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Furthermore, we included our rationale for choosing a between-subjects approach for the crucial TMS manipulation in the methods section on page 25, lines 601-604:

“We implemented a mixed-design including the within-subject factors link (linked vs. non-linked events), session (pre- vs. post-link), and mode (imagination vs. observation) as well as the between-subjects factor group (cTBS to the angular gyrus vs. sham) to mitigate the risk of carry-over effects and sequence biases of the crucial cTBS manipulation.”

4) The angular gyrus is a heterogeneous region with multiple graded subregions. The one tar-geted in the present study is the ventral AG which has strong connections with the episodic-hippocampal memory system. I was wondering if this might explain why the AG TMS ef-fects on representational changes have been observed for events linked via imagination but not direct observation. Perhaps the stimulation of a more 'visual' AG subregion (see Hum-phreys et al., 2020, Cerebral Cortex) would have resulted in a different (opposite) pattern of results. It would be good to add some comments on this in the discussion.

We appreciate this interesting perspective offered regarding the potential out-comes of our study, particularly in relation to the activation of a more ventral sub region of the angular gyrus. We incorporated this idea into our discussion, alongside considerations regarding the potential effects of a more dorsal angular gyrus stimulation on observation-based linking. However, caution is warranted recognizing the inherent limitations posed by the precision of TMS manipulations, which is further underscored by our electric field simu-lations, utilizing a 10 mm radius. We included this section in the discussion on pages 23-24, lines 546-569:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations. Yet, while acknowledging the functional heterogeneity within the angular gy-rus (Humphreys et al., 2020), pinpointing specific sub regions via TMS remains challenging due to its limited focal precision at the millimeter level (Deng et al., 2013; Thielscher & Kammer, 2004), as reinforced by our electric field simulations utilizing a 10 mm radius. Hence, drawing definitive conclusions regarding distinct angular gyrus sub regions requires future research employing rigorous checks to assess the focality of their stimulation.”

5) Regarding the methods section, I have the following specific queries. It is unclear what is the purpose of the coherence and correlation analyses (pages 35, 36). Could the authors pro-vide further clarification on this? These analyses seem not to be mentioned anywhere in the introduction. This should be clarified briefly in the introduction and then in the methods sec-tion. The same for the questionnaires (anxiety, stress, etc): It is unclear the reason for col-lecting this type of data. This should be clarified in the introduction as well.

We agree, and have updated the introduction as follows on page 5, lines 118-122:

“Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connectivity analysis building upon findings from the RSA anal-yses concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational approach to relate observations from these two domains.”

We additionally provided an explanation for including these questionnaires in the introduc-tion on page 5, lines 126-129:

“To control for any group differences beyond the TMS manipulation, we gathered various control variables through questionnaires, including trait- and state-anxiety, depressive symptoms, chronic stress levels, personality dimensions, and imaginative capacities.”

Moreover, we elaborated on the underlying rationale guiding our chosen analytical ap-proaches. Therefore, we specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Furthermore, we added an explanation on why we opted for the RSA approach in the methods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

6) The preregistration webpage is in German. This is not ideal as it means that the information is available only to German speakers.

This webpage can easily be switched to English by changing the settings in the top right corner:

To address this issue, we included a description of how to set the webpage to English in the methods section on page 25, lines 581-582:

“For translation to English, please adjust the page settings located in the top right corner.”

7) Page 18. 'NIT' and 'MAT' - avoid abbreviations when possible.

We included the full name for the narrative-insight task (NIT) on page 7, line 151, line 153, and line 165, page 8 lines 177-178 and line 187, page 19 on line 427, page 26 on line 615, line 629 and line 632, page 27, line 653, page 30, lines 730-731, page 31, line 754, page 35, line 870, line 873, and page 36 and line 885.

We further included the full name for the multi-arrangements task (MAT) on page 19, lines 428-429.

8) Line 21....we further observed DECREASED...should be replaced with INCREASED, if I am not wrong.

We checked the sentence again and it looks correct to us, since it describes the change for observation-based insight, not imagination-based insight. We clarified that this finding pertains to observation-based linking by modifying the sentence on page 23, lines 525-528, as follows:

“Following cTBS to the angular gyrus, we further observed decreased pattern similarity for non-linked events in the observation-based condition, resembling the pattern change ob-served in the sham group for linked events, which may highlight the role of the angular gy-rus in representational separation during observation-based linking”

Reviewer 3

1) The major claim of the paper is that the angular gyrus is causally involved in insight-driven memory reconfiguration. To the authors' credit, they localized stimulation to the angular gyrus using an anatomical scan, the strength of the estimated electromagnetic field in the angular gyrus correlated with their behavioral results, and there were also brain-behavior correlations involving sensors located in the parietal lobe. However, the minimum evidence needed to claim causality is 1) evidence of a behavioral change (which the authors found) and 2) evidence of target engagement in the angular gyrus. It is also important to show brain-behavior correlations between target engagement and behavior. Although the au-thors stimulated the angular gyrus, that does not mean that rTMS specifically affected this region or that the behavioral results can be attributed to rTMS effects on the angular gyrus. As the authors point out, the angular gyrus has dense connections with other regions such as the hippocampus. In fact, several studies have shown that angular gyrus (or near AG) stimulation affects the hippocampal network (Wang et al., 2014, Science; Freedberg et al. 2019, eNeuro; Thakral et al., 2020, PNAS). EEG also has a poor spatial resolution, so even though the results were attributable to parieto-temporal sensors, this is not sufficient evi-dence to claim that the angular gyrus was modulated. Source localization would be re-quired to reconstruct the signal specifically from the AG. Thus, with the manuscript written as is, the authors can claim that "cTBS to the angular gyrus modulates insight-driven memory reconfiguration," but the current claim is not sufficiently substantiated.

While acknowledging the potential role of the angular gyrus in driving the ob-served changes, we recognize that the available evidence may not be sufficient. Conse-quently, we have introduced several modifications within our manuscript to address this concern.

In the revised Introduction, we now explicitly address the possibility of a stimulation of the hippocampus via the angular gyrus on page 4, lines 84-85:

“Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014).”

Additionally, we included relevant evidence demonstrating previous instances of targeted stimulation of the angular gyrus, which led to alterations in hippocampal connectivity and associative memory. These insights have been included in the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Next, we have integrated crucial modifications essential for establishing a conclusive infer-ence of causality in our study. Moreover, we now explore the potential mediation of the effects observed from angular gyrus stimulation through other brain regions, like the hip-pocampus. In addition, we have highlighted prior work where such stimulation coincided with alterations in associative memory. For the updated discussion section, please see pag-es 23-24, lines 538-562:

“Although our study provided evidence suggesting a causal role of the angular gyrus in in-sight-driven memory reconfigurations – highlighted by behavioral changes after cTBS to the angular gyrus, neural changes in left parietal regions, and relevant brain-behavior associa-tions – it is important to acknowledge the limitations imposed by the spatial resolution of EEG. Consequently, the precise source of the observed signal changes in the parietal re-gions remains uncertain, potentially tempering the definitive nature of these findings. Fur-thermore, the differential impact of cTBS to the angular gyrus on neural reconfigurations between events linked via imagination and those linked via observation may be attributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). An-other intriguing aspect to consider is that the stimulated site was situated in the more ven-tral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

We further replaced terms that imply inhibition of the angular gyrus with a more operation-ally descriptive phrase:

“cTBS to the angular gyrus”

2) The authors frequently claim that cTBS is "inhibitory stimulation" and that inhibition of the angular gyrus caused their effects. There is a common misconception within the cognitive neuroscience literature that stimulation is either "inhibitory" or "excitatory," but there is no such thing as either. The effects of rTMS are dependent on many physiological, state, and trait-specific variables and the location of stimulation. For example, while cTBS does repro-ducibly inhibit behavior supported by the motor cortex (Wilkinson et al., 2010, Cortex; Rosenthal et al., 2009, J Neurosci), cTBS of the posterior parietal cortex reproducibly en-hances hippocampal network functional connectivity and episodic memory (Hermiller et al., 2019, Hippocampus; Hermiller et al., 2020, J Neurosci). The authors reference the Huang et al. (2005) paper as evidence of its inhibitory effects but work in this paper is not sufficient to broadly categorize cTBS as inhibitory. First, Huang et al. stimulated the motor cortex and measured the effects on corticospinal excitability, which is significantly different from what the current authors are measuring. Furthermore, this oft-cited study only included 9 sub-jects. Other studies have found that the effects of theta-burst are significantly more varia-ble when more subjects are used. For example, intermittent theta-burst, which is assumed to be excitatory based on the Huang paper, was found to produce unreliable excitatory ef-fects when more subjects were examined (Lopez-Alonso, 2014, Brain Stimulation). Thus, the a priori assumption that stimulation would be inhibitory is weak and cTBS should not be dis-cussed as "inhibitory."

We agree and included now a statement in the methods section that explicitly states that cTBS effects may be region-specific on page 33, lines 817-819:

“Nonetheless, the effects of cTBS appear to vary based on the targeted region, with cTBS to parietal regions demonstrating the capability to enhance hippocampal connectivity (Hermiller et al., 2019, 2020).”

We further substituted all terminology suggestive of an inhibitory effect with the phrase:

“cTBS to the angular gyrus”.

However, it is important to note, that while other studies (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014) found increased hippocampal connectivity after rTMS to a parie-tal region as well as enhanced associative memory, we observed impaired memory for the linked events. We included this clarification in the discussion on page 24, lines 558-562:

“In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) The hypothesis at the end of the introduction did not strike me as entirely clear. From this hypothesis, it seems that the authors are just comparing the differences in memory and re-configuration during imagination-based insight links. However, the authors also include ob-servation-based links and a non-linking condition, which seem ancillary to the main hy-pothesis. Thus, I am confused about why these extra factors were included and exactly what statistical results would confirm the authors' hypothesis.

We agree, and have clarified our hypotheses on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to reduce the increase in neural similarity for linked events and in-crease of neural dissimilarity for non-linked events.”

4) Many of the distributions throughout the paper do not look normal. Was normality checked? Are non-parametric stats warranted?

We evaluated and reported the normality assumption in our behavioral anal-yses. Despite the non-normal distribution of our data, we chose to utilize linear-mixed models due to their robust performance even in case of deviations from normal distribu-tions. This update in our methods section can be found on page 36, lines 890-896:

“After outlier correction, we identified non-normality in our data using a Shapiro-Wilk test (narrative-insight task: W = 0.92, p < 0.001; multi-arrangements task: W = 0.94, p < 0.001; forced-choice recognition: W = 0.50, p < 0.001; free recall details: W = 0.85, p < 0.001; free recall naming of linking events: W = 0.94, p < 0.001). However, we mitigated this by employ-ing linear-mixed models (LMMs), recognized for their robustness even with non-normally distributed data (Schielzeth et al., 2020).”

We recalculated the correlational analysis between the RSA data and the behavioral recall of linking events by using the Spearman method on page 13, lines 306-308:

“Furthermore, to address a deviation from the normality assumption, the correlational analysis was repeated using the Spearman method, which indicated an even stronger cor-relation (r(59) = 0.32, p = 0.012).”

We further recalculated the correlation between the change in coherence for linked events and the recall of details for events linked via imagination on page 16, lines 376-378:

“Please note that for addressing a deviation from the normality assumption, the correla-tional analysis was repeated using the Spearman method, which yielded a significant corre-lation of similar strength (r(59) = 0.31, p = 0.015).”

Our EEG analyses , including RSA and coherence analyses, utilized a cluster-based permuta-tion test (Fieldtrip; Oostenveld et al., 2011). These tests do not assume a normal distribu-tion by utilizing empirical sampling for statistical inference. This approach ensures robust-ness without constraints imposed by specific distributional assumptions. Subsequent t-tests, stemming from significant clusters identified in the initial non-parametric analyses, were extensions of the robust non-parametric approach and did not require additional normality testing.

5) Can the authors include more detail about the sham coil? Was it subthreshold? Did the EMF cross the skull?

The sham coil, also obtained from MAG & More GmbH, München, Germany, provided a similar sensory experience; however, the company did not specify any field strength (n.a.) as this coil was purposefully designed to prevent the induction of an elec-tromagnetic field (EMF) capable of penetrating the skull, thereby ensuring it had no impact on the brain. We clarified on this point in the methods section on pages 31-32, lines 772-778:

“Two identically looking but different 70 mm figure-of-eight-shaped coils were used de-pending on the TMS condition: The PMD70-pCool coil (MAG & More GmbH, München, Germany) with a 2T maximum field strength was used for cTBS, while the PMD70-pCool-SHAM coil (MAG & More GmbH, München, Germany), with minimal magnetic field strength, was employed for sham, providing a similar sensory experience, with stimulation pulses being scattered over the scalp and not penetrating the skull.”

6) There are differences between exclusion criteria in pre-registration and report. For example, BMI is an exclusion factor in the report, but not in the pre-registration. Can the authors provide a reason for this deviation?

This discrepancy is due to (partial) participant recruitment from previous fMRI studies conducted in our lab that involved a stress induction protocol (as a structural MRI image was needed for the ‘neuronavigated’ TMS). Owing to the distinct cortisol stress reac-tivity observed in individuals with varying body mass indices (BMIs), participants with a BMI below 19 or above 26 kg/m² were excluded from these studies. To maintain consistency within our sample, only participants meeting these criteria were included. We elaborated on this point in the methods section on page 25, lines 586-592:

“Participants were screened using a standardized interview for exclusion criteria that com-prised a history of neurological and psychiatric disease, medication use and substance abuse, cardiovascular, thyroid, or renal disease, evidence of COVID-19 infection or expo-sure, and any contraindications to MRI examination or TMS. Additionally, participants with a body mass index (BMI) below 19 or above 26 kg/m² were excluded. This decision stemmed from recruiting some participants from prior studies that incorporated stress induction pro-tocols, which imposed this specific criterion (Herhaus & Petrowski, 2018; Schmalbach et al., 2020).”

7) Were impedances monitored and minimized during EEG?

Yes, they were monitored. We clarified this point in the methods section on page 34, lines 845-847:

“We maintained impedances within a range of ± 20 μV using the common mode sense (CMS) and driven right leg (DRL) electrodes, serving as active reference and ground, re-spectively”

8) I think there may be a typo related to the Thakral coordinates. I believe Thakral used MNI coordinates -48,-64, 30, whereas the authors stated they used -48,-67,30. Is this a mistake?

Upon reevaluation of our study coordinates, we identified a slight deviation in our stimulation coordinates compared to those reported by Thakral et al. (2017; +3mm on the y-axis). This variance resulted from the required MNI to Talairach (TAL) transformations necessary for utilizing the neuronavigation software Powermag View! (MAG & More GmbH, München, Germany). Notably, this deviation was consistent across all participants in our study. While TMS is more precise than tDCS, its focality is not as fine-grained down to the millimeter level. Despite this, our electric field simulations, adopting a 10mm radius, ef-fectively encompassed the original coordinates specified by Thakral et al. (2017). This radius ensured coverage over the intended target area, mitigating the impact of this minor devia-tion on the overall study outcomes. We updated the methods section accordingly on page 33, lines 800-806:

“Based on the individual T1 MR images, we created 3D reconstructions of the participants' heads, allowing us to precisely locate the left angular gyrus coordinate (MNI: -48, -67, 30), initially derived from previous work (Thakral et al., 2017), for TMS stimulation. Despite a mi-nor deviation in coordinates due to necessary MNI to Talairach transformations for soft-ware compatibility (Powermag View! by MAG & More GmbH, München, Germany), our methodology ensured precise localization of the angular gyrus target area.”

9) How was the tail of the coil positioned during stimulation? Was it individualized so that the lobes of the coil are perpendicular to the nearest gyrus, as is commonly done?

The coil handle always pointed upwards to maintain optimal positioning with the coil holder. We followed the positioning procedure in the neuronavigation software Powermag View!, which did not indicate any positioning of the coil handle but specified the position and angle of the coil itself. To incorporate this aspect, we updated the legend of figure 2 on page 11, lines 260-261:

“Please note that in the study, the coil handle was oriented upwards; however, in this illus-tration, it has been intentionally depicted as pointing downwards for better visibility pur-poses.”

We further updated the method section on page 33, lines 723-824:

“The coil was positioned tangentially on the head and mechanically fixed in a coil holder, with its handle pointing upwards to maintain its position”

Author Response

We are grateful for the insightful suggestions and comments provided by the reviewers. Your constructive feedback has been valuable, and we are thankful for the opportunity to address each point.

We appreciate both reviewers’ recognition of our devotion to rigorous methodology and experimental control in this study, as evidenced by the comments: “remarkable efforts were made to isolate peripheral confounds”, “a clear strength of the study is the multitude of control conditions … that makes results very convincing”, and “thorough design of the study”. Indeed, we hope to have provided more than solid, but compelling evidence for sound-driven motor inhibitory effects of online TUS. We hope that this will be reflected in the assessment. Our conclusions are supported by multiple experiments across multiple institutions using exemplary experimental control including (in)active controls and multiple sound-sham conditions. This contrasts with the sole use of flip-over sham or no-stimulation conditions used in the majority of work to date. Indeed, the current study communicates that substantiated inferences on the efficacy of ultrasonic neuromodulation cannot be made under insufficient experimental control.

In response to the reviewers' comments, we have substantially changed our manuscript. Specifically, we have open-sourced the auditory masking stimuli and specified them in better detail in the text, we have improved the figures to reflect the data more closely, we have clarified the intracranial doseresponse relationship, we have elaborated in the introduction, and we have further discussed the possibility of direct neuromodulation. We hope that you agree these changes have helped to substantially improve the manuscript.

Public reviews

1.1) Despite the main conclusion of the authors stating that there is no dose-response effects of TUS on corticospinal inhibition, both the comparison of Isppa and MEP decrease for Exp 1 and 2, and the linear regression between MEP decrease (relative to baseline) and the estimated Isppa are significant, arguing the opposite, that there is a dose-response function which cannot be fully attributed to difference in sound (since the relationship in inversed, lower intracranial Isppa leads to higher MEP decrease). These results suggest that doseresponse function needs to be further studied in future studies.

We thank the reviewer for bringing up this point. While we are convinced our study provides no evidence for a direct neuromodulatory dose-response relationship, we have realized that the manuscript could benefit from improved clarity on this point.

A dose-response relationship between TUS intensity and motor cortical excitability was assessed by manipulating free-water Isppa (Figure 4C). Here, no significant effect of free-water stimulation intensity was observed for Experiment I or II, thus providing no evidence for a dose-response relationship (Section 3.2). To aid in clarity, ‘N.S.’ has been added to Figure 4C in the revised manuscript.

However, it is likely that the efficacy of TUS would depend on realized intracranial intensity, which we estimated with 3D simulations for on-target stimulation. These simulations resulted in an estimated intracranial intensity for each applied free-water intensity (i.e., 6.35 and 19.06 W/cm2), for each participant. We then tested whether inter-individual differences in intracranial intensity during on-target TUS affected MEP amplitude. We have realized that the original visualization used to display these data and its explanation was unintuitive. Therefore, we have completely revised Supplementary Figure 6. Because of the substantial length of this section, we have not copied it here. Please see the Supplementary material for the implemented improvements.

In brief, we now show MEP amplitudes on the y-axis, rather than expressing values a %change. This plot depicts how individuals with higher intracranial intensities during ontarget TUS exhibit higher MEP amplitudes. However, this same relationship is observed for active control and sound-sham conditions. If there were a direct neuromodulatory doseresponse relationship of TUS, this would be reflected as the difference between on-target and control conditions changing as the estimated intracranial intensity increases. This was not the case. Further, the fact that the difference between on-target stimulation and baseline changes across intracranial intensities is notable, but this occurs to an equal degree in the control conditions. Therefore, these data cannot be interpreted as evidence for a doseresponse relationship.

We hope the changes in Supplementary Figure 6 will make it clear that there is no evidence for direct intracranial dose-response effects.

1.2) Other methods to test or mask the auditory confound are possible (e.g., smoothed ramped US wave) which could substantially solve part of the sound issue in future studies or experiments in deaf animals etc... 

We agree with the reviewer’s statement. We aimed to replicate the findings of online motor cortical inhibition reported in prior work using a 1000 Hz square wave modulation frequency. While ramping can effectively reduce the auditory confound, as noted in the discussion, this is not feasible for the short pulse durations (0.1-0.3 ms) employed in the current study (Johnstone et al., 2021). We have further clarified this point in the methods section of the revised manuscript as follows:

“While ramping the pulses can in principle mitigate the auditory confound (Johnstone et al., 2021; Mohammadjavadi et al., 2019), doing so for such short pulse durations (<= 0.3 ms) is not effective. Therefore, we used a rectangular pulse shape to match prior work.”

Mitigation of the auditory confound by testing deaf subjects is a valid approach, and has now been added to the revised manuscript in the discussion as follows:

“Alternative approaches could circumvent auditory confounds by testing deaf subjects, or perhaps more practically by ramping the ultrasonic pulse to minimize or even eliminate the auditory confound.”

1.3) Dose-response function is an extremely important feature for a brain stimulation technique. It was assessed in Exp II by computing the relationship between the estimated intracranial intensities and the modulation of corticospinal excitability (Fig. 3b, 3c). It is not clear why data from Experiment I could not be integrated in a global intracranial dose-response function to explore wider ranges of intracranial intensities and MEP variability.

We chose not to combine data from Experiment 1 in a global intracranial dose-response function because TUS was applied at different fundamental frequencies and focal depths (Experiment I: 500 kHz, 35 mm; Experiment II: 250 kHz, 28 mm). We have now explicitly communicated this under Supplementary Figure 6:

“It was not appropriate to combine data from Experiments I and II given the different fundamental frequencies and stimulation depths applied… we ran simple linear models for Experiment II, which had a sufficient sample size (n = 27) to assess inter-individual variability.”

1.4) Furthermore, the dose response function as computed with the MEP change relative to baseline shows a significant effect (6.35W/cm2) or a trend (19.06 W/cm2) for a positive linear relationship. This comparison cannot disentangle the auditory confound from the pure neuromodulatory effect but given the direction of the relationship (lower Isppa associated with larger neuromodulatory effect), it is unlikely that it is driven by sound. This relationship is absent for the Active control condition or the Sound Sham condition, more or less matched for peripheral confound. This needs to be further discussed. 

Please refer to point 1.1

1.5) The clear auditory confound arises from TUS pulsing at audible frequencies, which can be highly subject to inter-individual differences. Did the authors individually titrate the auditory mask to account for this intra- and inter-individual variability in auditory perception? 

In Experiments I-III, the auditory mask was identical between participants. In Experiment IV, the auditory mask volume and signal-to-noise ratio were adjusted per participant. In the discussion we recommend individualized mask titration. However, we do note that masking successfully blinded participants in Experiment II, despite using uniform masking stimuli (Supplementary Figure 5).

1.6) How different is the masking quality when using bone-conducting headphones (e.g., Exp. 1) compared to in-ear headphones (e.g., Exp. 2)?

In our experience, bone conducting headphones produce a less clear, fuzzier, sound than in-ear headphones. However, in-ear headphones block the ear canal and likely result in the auditory confound being perceived as louder. We have included this information in the discussion of the revised manuscript:

“Titrating auditory mask quality per participant to account for intra- and inter-individual differences in subjective perception of the auditory confound would be beneficial. Here, the method chosen for mask delivery must be considered. While bone-conducting headphones align with the bone conduction mechanism of the auditory confound, they might not deliver sound as clearly as in-ear headphones or speakers. Nevertheless, the latter two rely on airconducted sound. Notably, in-ear headphones could even amplify the perceived volume of the confound by obstructing the ear canal.”

1.7) I was not able to find any report on the blinding efficacy of Exp. 1. Do the authors have some data on this? 

We do not have blinding data available for Experiment I. Following Experiment I, we decided it would be useful to include such an assessment in Experiment II.

1.8) Was the possibility to use smoothed ramped US wave form ever tested as a control condition in this set of studies, to eventually reduce audibility? For such fast PRF, for fast PRF, the slope would still need to be steep to stimulate the same power (AUC), it might not be as efficient. 

We indeed tested smoothing (ramping) the waveform. There was no perceptible impact on the auditory confound volume. Indeed, prior research has also indicated that ramping over

such short pulse durations is not effective (Johnstone et al., 2021). Taken together, we chose to continue with a square wave modulation as in prior TUS-TMS studies. We have updated the methods section of the manuscript with the following:

“While ramping the pulses can in principle mitigate the auditory confound (Johnstone et al., 2021; Mohammadjavadi et al., 2019), doing so for such short pulse durations (<= 0.3 ms) is not effective. Therefore, we used a rectangular pulse shape to match prior work.”

Importantly, our research shows that auditory co-stimulation can confound effects on motor excitability, and this likely occurred in multiple seminal TUS studies. While some preliminary work has been done on the efficacy of ramping in humans, future work is needed to determine what ramp shapes and lengths are optimal for reducing the auditory confound.

1.9) There are other models or experiments that need to be discussed in order to clearly disassociate the TUS effect from the auditory confound effect, for instance, testing deaf animal models or participants, or experiments with multi-region recordings (to rule out the effects of the dense structural connectivity between the auditory cortex and the motor cortex). 

The suggestion to consider multi-region recording in future experiments is important. Indeed, the effects of the auditory confound are expected to vary between brain regions. In the primary motor cortex, we observe a learned inhibition, which is perhaps supported by dense structural connectivity with the auditory system. In contrast, in perceptual areas such as the occipital cortex, one might expect tuned attentional effects in response to the auditory cue. We suggest that it is likely that the impact of the auditory confound also operates on a more global network level. It is reasonable to propose that, in a cognitive task for example, the confound will affect task performance and related brain activity, ostensibly regardless of the extent of direct structural connectivity between the auditory cortex and the (stimulated) region of interest.

Regarding the testing of deaf subjects, this has been included in the revised discussion as follows:

“Alternative approaches could circumvent auditory confounds by testing deaf subjects, or perhaps more practically by ramping the ultrasonic pulse to minimize or even eliminate the auditory confound.”

1.10) The concept of stochastic resonance is interesting but traditionally refers to a mechanism whereby a particular level of noise actually enhances the response of non-linear systems to weak sensory signals. Whether it applies to the motor system when probed with suprathreshold TMS intensities is unclear. Furthermore, whether higher intensities induce higher levels of noise is not straightforward neither considering the massive amount of work coming from other NIBS studies in particular. Noise effects are indeed a function of noise intensity, but exhibit an inverted U-shape dose-response relationship (Potok et al., 2021, eNeuro). In general SR is rather induced with low stimulation intensities in particular in perceptual domain (see Yamasaki et al., 2022, Neuropsychologia).  In the same order of ideas, did the authors compare inter-trials variability across the different conditions? 

We thank the reviewer for these insightful remarks. Indeed, stochastic resonance is a concept first formalized in the sensory domain. Recently, the same principles have been shown to apply in other domains as well. For example, transcranial electric noise (tRNS) exhibits similar stochastic resonance principles as sensory noise (Van Der Groen & Wenderoth, 2016). Indeed, tRNS has been applied to many cortical targets, including the motor system. In the current manuscript, we raise the question of whether TUS might engage with neuronal activity following principles similar to tRNS. One prediction of this framework would be that TUS might not modulate excitation/inhibition balance overall, but instead exhibit an inverted U-shape dose-dependent relationship with stochastic noise. Please note, we do not use the ‘suprathreshold TMS intensity’ to quantify whether noise could bring a sub-threshold input across the detection threshold, nor whether it could bring a sub-threshold output across the motor threshold. Instead, we use the MEP read-out to estimate the temporally varying excitability itself. We argue that MEP autocorrelation captures the mixture of temporal noise and temporal structure in corticospinal excitability. Building on the non-linear response of neuronal populations, low stochastic noise might strengthen weakly present excitability patterns, while high stochastic noise might override pre-existing excitability. It is therefore not the overall MEP amplitude, but the MEP timeseries that is of interest to us. Here, we observe a non-linear dose-dependent relationship, matching the predicted inverted U-shape. Importantly, we did not intend to assume stochastic resonance principles in the motor domain as a given. We have now clarified in the revised manuscript that we propose a putative framework and regard this as an open question:

“Indeed, human TUS studies have often failed to show a global change in behavioral performance, instead finding TUS effects primarily around the perception threshold where noise might drive stochastic resonance (Butler et al., 2022; Legon et al., 2018). Whether the precise principles of stochastic resonance generalize from the perceptual domain to the current study is an open question, but it is known that neural noise can be introduced by brain stimulation (Van Der Groen & Wenderoth, 2016). It is likely that this noise is statedependent and might not exceed the dynamic range of the intra-subject variability (Silvanto et al., 2007). Therefore, in an exploratory analysis, we exploited the natural structure in corticospinal excitability that exhibits as a strong temporal autocorrelation in MEP amplitude.”

Following the above reasoning, we felt it critical to estimate noise in the timeseries, operationalized as a t-1 autocorrelation, rather than capture inter-trial variability that ignores the timeseries history and requires data aggregation thereby reducing statistical power. Importantly, we would expect the latter index to capture global variability, putatively masking the temporal relationships which we were aiming to test. The reviewer raises an interesting option, inviting us to wonder if inter-trial variability might be sensitive enough, nonetheless. To this end, we compared inter-trial variability as suggested. This was achieved by first calculating the inter-trial variability for each condition, and then running a three-way repeated measures ANOVA on these values with the independent variables matching our autocorrelation analyses, namely, procedure (on-target/active control)intensity (6.35/19.06)masking (no mask/masked). This analysis did not reveal any significant interactions or main effects.

Author response table 1.

1.11) State-dependency/Autocorrelations: These values were extracted from Exp2 which has baseline trials. Can the authors provide autocorrelation values at baseline, with and without auditory mask?  Can the authors comment on the difference between the autocorrelation profiles of the active TUS condition at 6.35W/cm2 or at 19.06W/cm2. They should somehow be similar to my understanding.  Besides, the finding that TUS induces noise only when sound is present and at lower intensities is not well discussed. 

In the revised manuscript, we have now included baseline in the figure (Figure 4D). Regarding baseline with and without a mask, we must clarify that baseline involves only TMS (no mask), and sham involves TMS + masking stimulus (masked).

The dose-dependent relationship of TUS intensity with autocorrelation is critical. One possible observation would have been that TUS at both intensities decreased autocorrelation, with higher intensities evoking a greater reduction. Here, we would have concluded that TUS introduced noise in a linear fashion.

However, we observed that lower-intensity TUS in fact strengthened pre-existing temporal patterns in excitability (higher autocorrelation), while during higher-intensity TUS these patterns were overridden (lower autocorrelation). This non-linear relationship is not unexpected, given the non-linear responses of neurons.

If this non-linear dependency is driven by TUS, one could expect it to be present during conditions both with and without auditory masking. However, the preparatory inhibition effect of TUS likely depends on the salience of the cue, that is, the auditory confound. In trials without auditory masking, the salience of the confound in highly dependent on (transmitted) intensity, with higher intensities being perceived as louder. In contrast, when trials are masked, the difference in cue salience between lower and higher intensity stimulation in minimized. Therefore, we would expect for any nuanced dose-dependent direct TUS effect to be best detectable when the difference in dose-dependent auditory confound perception is minimized via masking. Indeed, the dose-dependent effect of TUS on autocorrelation is most prominent when the auditory confound is masked.

“In sum, these preliminary exploratory analyses could point towards TUS introducing temporally specific neural noise to ongoing neural dynamics in a dose-dependent manner, rather than simply shifting the overall excitation-inhibition balance. One possible explanation for the discrepancy between trials with and without auditory masking is the difference in auditory confound perception, where without masking the confound’s volume differs between intensities, while with masking this difference is minimized. Future studies might consider designing experiments such that temporal dynamics of ultrasonic neuromodulation can be captured more robustly, allowing for quantification of possible state-dependent or nondirectional perturbation effects of stimulation.”

1.12) Statistical considerations. Data from Figure 2 are considered in two-by-two comparisons. Why not reporting the ANOVA results testing the main effect of TUS/Auditory conditions as done for Figure 3. Statistical tables of the LMM should be reported. 

Full-factorial analyses and main effects for TUS/Auditory conditions are discussed from Section 3.2 onwards. These are the same data supporting Figure 2 (now Figure 3). We would like to note that the main purpose of Figure 2 is to demonstrate to the reader that motor inhibition was observed, thus providing evidence that we replicated motor inhibitory effects of prior studies. A secondary purpose is to visually represent the absence of direct and spatially specific neuromodulation. However, the appropriate analyses to demonstrate this are reported in following sections, from Section 3.2 onwards, and we are concerned that mentioning these analyses earlier will negatively impact comprehensibility.

Statistical tables of the LMMs are provided within the open-sourced data and code reported at the end of the paper, embedded within the output which is accessible as a pdf (i.e., analysis/analysis.pdf).

1.13) Startle effects: The authors dissociate two mechanisms through which sound cuing can drive motor inhibition, namely some compensatory expectation-based processes or the evocation of a startle response. I find the dissociation somehow artificial. Indeed, it is known that the amplitude of the acoustic startle response habituates to repetitive stimulation. Therefore, sensitization can well explain the stabilization of the MEP amplitude observed after a few trials. 

Thank you for bringing this to our attention. Indeed, an acoustic startle response would habituate over repetitive stimulation. A startle response would result in MEP amplitude being significantly altered in early trials. As the participant would habituate to the stimulus, the startle response would decrease. MEP amplitude would then return to baseline levels. However, this is not the pattern we observe. An alternative possibility is that participants learn the temporal contingency between the stimulus and TMS. Here, compensatory expectation-based change in MEP amplitude would be observed. In this scenario, there would be no change in MEP amplitude during early trials because the stimulus has not yet become informative of the TMS pulse timing. However, as participants learn how to predict TMS timing by the stimulus, MEP amplitude would decrease. This is also the pattern we observe in our data. We have clarified these alternatives in the revised manuscript as follows:

“Two putative mechanisms through which sound cuing may drive motor inhibition have been proposed, positing either that explicit cueing of TMS timing results in compensatory processes that drive MEP reduction (Capozio et al., 2021; Tran et al., 2021), or suggesting the evocation of a startle response that leads to global inhibition (Fisher et al., 2004; Furubayashi et al., 2000; Ilic et al., 2011; Kohn et al., 2004; Wessel & Aron, 2013). Critically, we can dissociate between these theories by exploring the temporal dynamics of MEP attenuation. One would expect a startle response to habituate over time, where MEP amplitude would be reduced during startling initial trials, followed by a normalization back to baseline throughout the course of the experiment as participants habituate to the starling stimulus. Alternatively, if temporally contingent sound-cueing of TMS drives inhibition, MEP amplitudes should decrease over time as the relative timing of TUS and TMS is being learned, followed by a stabilization at a decreased MEP amplitude once this relationship has been learned.”

1.14) Can the authors further motivate the drastic change in intensities between Exp1 and 2? Is it due to the 250-500 carrier difference? It this coming from the loss power at 500kHz? 

The change in intensities between Experiments I and II was not an intentional experimental manipulation. Following completion of data acquisition, our TUS system received a firmware update that differentially corrected the 250 kHz and 500 kHz stimulation intensities. In this manuscript, we report the actual free-water intensities applied during our experiments.

1.15) Exp 3: Did 4 separate blocks of TUS-TMS and normalized for different TMS intensities used with respect to baseline. But how different was it. Why adjusting and then re adjusting intensities? 

The TMS intensities required to evoke a 1 mV MEP under the four sound-sham conditions significantly differed from the intensities required for baseline. In the revised appendix, we have now included a figure depicting the TMS intensities for these conditions, as well as statistical tests demonstrating each condition required a significantly higher TMS intensity than baseline.

TMS intensities were re-adjusted to avoid floor effects when assessing the efficacy of ontarget TUS. Sound-sham conditions themselves attenuate MEP amplitude. This is also evident from the higher TMS intensities required to evoke a 1 mV MEP under these conditions. If direct neuromodulation by TUS would have further decreased MEP amplitude, the concern was that effects might not be detectible within such a small range of MEP amplitudes.

1.16) In Exp 4, TUS targeted the ventromedial WM tract. Since direct electrical stimulation on white matter pathways within the frontal lobe can modulate motor output probably through dense communication along specific white matter pathways (e.g., Vigano et al., 2022, Brain), how did the authors ensure that this condition is really ineffective? Furthermore, the stimulation might have covered a lot more than just white matter. Acoustic and thermal simulations would be helpful here as well. 

Thank you for pointing out this possibility. Ultrasonic and electrical stimulation have quite distinct mechanisms of action. Therefore, it is challenging to directly compare these two approaches. There is a small amount of evidence that ultrasonic neuromodulation of white matter tracts is possible. However, the efficacy of white matter modulation is likely much lower, given the substantially lesser degree of mechanosensitive ion channel expression in white matter as opposed to gray matter (Sorum et al., 2020, PNAS). Further, recent work has indicated that ultrasonic neuromodulation of myelinated axonal bundles occurs within the thermal domain (Guo et al., 2022, SciRep), which is not possible with the intensities administered in the current study. Nevertheless, based on Experiment IV in isolation, it cannot be definitively excluded that there TUS induced direct neuromodulatory effects in addition to confounding auditory effects. However, Experiment IV does not possess sufficient inferential power on its own and must be interpreted in tandem with Experiments I-III. Taken together with those findings, it is unlikely that a veridical neuromodulation effect is seen here, given the equivalent or lower stimulation intensities, the substantially deeper stimulation site, and the absence of an additional control condition in Experiment IV. This likelihood is further decreased by the fact that inhibitory effects under masking descriptively scale with the audibility of TUS.

Off-target effects such as unintended co-stimulation of gray matter when targeting white matter is always an important factor to consider. Unfortunately, individualized simulations for Experiment IV are not available. However, the same type of transducer and fundamental frequency was used as in Experiment II, for which we do have simulations. Given the size of the focus and the very low in-situ intensities extending beyond the main focal point, it is incredibly unlikely that effective stimulation was administered outside white matter in a meaningful number of participants. Nevertheless, the reviewer is correct that this can only be directly confirmed with simulations, which remain infeasible due to both technical and practical constraints. We have included the following in the revised manuscript:

“The remaining motor inhibition observed during masked trials likely owes to, albeit decreased, persistent audibility of TUS during masking. Indeed, MEP attenuation in the masked conditions descriptively scale with participant reports of audibility. This points towards a role of auditory confound volume in motor inhibition (Supplementary Fig. 8). Nevertheless, one could instead argue that evidence for direct neuromodulation is seen here. This unlikely for a number of reasons. First, white matter contains a lesser degree of mechanosensitive ion channel expression and there is evidence that neuromodulation of these tracts may occur primarily in the thermal domain (Guo et al., 2022; Sorum et al., 2021). Second, Experiment IV lacks sufficient inferential power in the absence of an additional control and must therefore be interpreted in tandem with Experiments I-III. These experiments revealed no evidence for direct neuromodulation using equivalent or higher stimulation intensities and directly targeting grey matter while also using multiple control conditions. Therefore, we propose that persistent motor inhibition during masked trials owes to continued, though reduced, audibility of the confound (Supplementary Fig. 8). However, future work including an additional control (site) is required to definitively disentangle these alternatives.”

1.17) Still for Exp 4. the rational for the 100% MSO or 120% or rMT is not clear, especially with respect to Exp 1 and 2. Equipment is similar as well as raw MEPs amplitudes, therefore the different EMG gain might have artificially increased TMS intensities. Could it have impacted the measured neuromodulatory effects?

Experiment IV was conducted independently at a different institute than Experiments I-II. In contrast to Experiments I-II, a gel pad was used to couple TUS to the participant’s head. The increased TMS-to-cortex distance introduced by the gel pad necessitates higher TMS intensities to compensate for the increased offset. In fact, in 9/12 participants, the intended intensity at 120% rMT exceeded the maximum stimulator output. In those cases, we defaulted to the maximum stimulator output (i.e., 100% MSO). We have clarified in the revised supplementary material as follows:

“We aimed to use 120% rMT (n =3). However, if this intensity surpassed 100% MSO, we opted for 100% MSO instead (n = 9). The mean %MSO was 94.5 ± 10.5%. The TMS intensities required in this experiment were higher than those required in Experiment I-II using the same TMS coil, though still within approximately one standard deviation. This is likely due to the use of a gel pad, which introduces more distance between the TMS coil and the scalp, thus requiring a higher TMS intensity to evoke the same motor activity.”

Regarding the EMG gain, this did not affect TMS intensities and did not impact the measured neuromodulatory effects. The EMG gain at acquisition is always considered during signal digitization and further analyses.

1.18) Exp. 4. It would be interesting to provide the changes in MEP amplitudes for those subjects who rated "inaudible" in the self-rating compared to the others. That's an important part of the interpretation: inaudible conditions lead to inhibition, so there is an effect. The auditory confound is not additive to the TUS effect. 

Previously, we only provided participant’s ratings of audibility, and showed that conditions that were rated as inaudible more often showed less inhibition, descriptively indicating that inaudible stimulation does not lead to inhibition. This interpretation is in line with our conclusion that the TUS auditory confound acts as a cue signaling the upcoming TMS pulse, thus leading to preparatory inhibition.

We have now included an additional plot and discussion in Supplementary Figure 8 (Subjective Report of TUS Audibility). Here, we show the change in MEP amplitude from baseline for the three continuously masked TUS intensities as in the main manuscript, but now split by participant rating of audibility. Descriptively, less audible sounds result in no marked change or a smaller change in MEP amplitude. This supports our conclusion that direct neuromodulation is not being observed here. When participants were unsure whether they could hear TUS, or when they did hear TUS, more inhibition was observed. However, this is still to a lesser degree than unmasked stimulation which was nearly always audible, and likely also more salient. This also supports our conclusion that these results indicate a role of cue salience rather than direct neuromodulation. Regarding masked conditions where participants were uncertain whether they heard TUS, the sound was likely sufficient to act as a cue, albeit potentially subliminally. After all, preparatory inhibition is not a conscious action undertaken by the participant either. We would also like to note that participants reported perceived audibility after each block, not after each trial, so selfreported audibility was not a fine-grained measurement. The data from Experiment IV suggest that the volume of the cue has an impact on motor inhibition. Taken together with the points mentioned in 1.16, it is not possible to conclude there is evidence for direct neuromodulation in Experiment IV.

1.19) I suggest to re-order sub panels of the main figures to fit with the chronologic order of appearance in the text. (e.g Figure 1 with A) Ultrasonic parameters, B) 3D-printed clamp, C) Sound-TMS coupling, D) Experimental condition). 

We have restructured the figures in the manuscript to provide more clarity and to have greater alignment with the eLife format.

2.1) Although auditory confounds during TUS have been demonstrated before, the thorough design of the study will lead to a strong impact in the field.

We thank the reviewer for recognition of the impact of our work. They highlight that auditory confounds during TUS have been demonstrated previously. Indeed, our work builds upon a larger research line on auditory confounds. The current study extends on the confound’s presence by quantifying its impact on motor cortical excitability, but perhaps more importantly by invalidating the most robust and previously replicable findings in humans. Further, this study provides a way forward for the field, highlighting the necessity of (in)active control conditions and tightly matched sham conditions for appropriate inferences in future work. We have amended the abstract to better reflect these points:

“Primarily, this study highlights the substantial shortcomings in accounting for the auditory confound in prior TUS-TMS work where only a flip-over sham control was used. The field must critically reevaluate previous findings given the demonstrated impact of peripheral confounds. Further, rigorous experimental design via (in)active control conditions is required to make substantiated claims in future TUS studies.”

2.2) A few minor [weaknesses] are that (1) the overview of previous related work, and how frequent audible TUS protocols are in the field, could be a bit clearer/more detailed

We have expanded on previous related work in the revised manuscript:

“Indeed, there is longstanding knowledge of the auditory confound accompanying pulsed TUS (Gavrilov & Tsirulnikov, 2012). However, this confound has only recently garnered attention, prompted by a pair of rodent studies demonstrating indirect auditory activation induced by TUS (Guo et al., 2022; Sato et al., 2018). Similar effects have been observed in humans, where exclusively auditory effects were captured with EEG measures (Braun et al., 2020). These findings are particularly impactful given that nearly all TUS studies employ pulsed protocols, from which the pervasive auditory confound emerges (Johnstone et al., 2021).”

2.3) The acoustic control stimulus can be described in more detail

We have elaborated upon the masking stimulus for each experiment in the revised manuscript as follows:

Experiment I: “In addition, we also included a sound-only sham condition that resembled the auditory confound. Specifically, we generated a 1000 Hz square wave tone with 0.3 ms long pulses using MATLAB. We then added white noise at a signal-to-noise ratio of 14:1. This stimulus was administered to the participant via bone-conducting headphones.”

Experiment II: “In this experiment, the same 1000 Hz square wave auditory stimulus was used for sound-only sham and auditory masking conditions. This stimulus was administered to the participant over in-ear headphones.”

Experiment III: “Auditory stimuli were either 500 or 700 ms in duration, the latter beginning 100 ms prior to TUS (Supplementary Fig. 3.3). Both durations were presented at two pitches. Using a signal generator (Agilent 33220A, Keysight Technologies), a 12 kHz sine wave tone was administered over speakers positioned to the left of the participant as in Fomenko and colleagues (2020). Additionally, a 1 kHz square wave tone with 0.5 ms long pulses was administered as in Experiments I, II, IV, and prior research (Braun et al., 2020) over noisecancelling earbuds.”

Experiment IV: “We additionally applied stimulation both with and without a continuous auditory masking stimulus that sounded similar to the auditory confound. The stimulus consisted of a 1 kHz square wave with 0.3 ms long pulses. This stimulus was presented through wired bone-conducting headphones (LBYSK Wired Bone Conduction Headphones). The volume and signal-to-noise ratio of the masking stimulus were increased until the participant could no longer hear TUS, or until the volume became uncomfortable.”

In the revised manuscript we have also open-sourced the audio files used in Experiments I, II, and IV, as well as a recording of the output of the signal generator for Experiment III:

“Auditory stimuli used for sound-sham and/or masking for each experiment are accessible here: https://doi.org/10.5281/zenodo.8374148.”

2.4) The finding that remaining motor inhibition is observed during acoustically masked trials deserves further discussion.

We agree. Please refer to points 1.16 and 1.18.

2.5) In several places, the authors state to have "improved" control conditions, yet remain somewhat vague on the kind of controls previous work has used (apart from one paragraph where a similar control site is described). It would be useful to include more details on this specific difference to previous work.

In the revised manuscript, we have clarified the control condition used in prior studies as follows:

Abstract:

“Primarily, this study highlights the substantial shortcomings in accounting for the auditory confound in prior TUS-TMS work where only a flip-over sham control was used.”

Introduction:

“To this end, we substantially improved upon prior TUS-TMS studies implementing solely flip-over sham by including both (in)active control and multiple sound-sham conditions.”

Methods:

“We introduced controls that improve upon the sole use of flip-over sham conditions used in prior work. First, we applied active control TUS to the right-hemispheric face motor area, allowing for the assessment of spatially specific effects while also better mimicking ontarget peripheral confounds. In addition, we also included a sound-only sham condition that closely resembled the auditory confound.”

2.6) I also wondered how common TUS protocols are that rely on audible frequencies. If they are common, why do the authors think this confound is still relatively unexplored (this is a question out of curiosity). More details on these points might make the paper a bit more accessible to TUS-inexperienced readers. 

Regarding the prevalence of the auditory confound, please refer to point 2.2.

Peripheral confounds associated with brain stimulation can have a strong impact on outcome measures, often even overshadowing the intended primary effects. This is well known from electromagnetic stimulation. For example, the click of a TMS pulse can strongly modulate reaction times (Duecker et al., 2013, PlosOne) with effect sizes far beyond that of direct neuromodulation. Unfortunately, this consideration has not yet fully been embraced by the ultrasonic neuromodulation community. This is despite long known auditory effects of TUS (Gavrilov & Tsirulnikov, 2012, Acoustical Physics). It was not until the auditory confound was shown to impact brain activity by Guo et al., and Sato et al., (2018, Neuron) that the field began to attend to this phenomenon. Mohammadjavadi et al., (2019, BrainStim) then showed that neuromodulation persisted even in deaf mice, and importantly, also demonstrated that ramping ultrasound pulses could reduce the auditory brainstem response (ABR). Braun and colleagues (2020, BrainStim) were the first bring attention to the auditory confound in humans, while also discussing masking stimuli. This was followed by a study from Johnstone and colleagues (2021, BrainStim) who did preliminary work assessing both masking and ramping in humans. Recently, Liang et al., (2023) proposed a new form of masking colourfully titled the ‘auditory Mondrian’. Further research into the peripheral confounds associated with TUS is on the way.

However, we agree that the confound remains relatively unexplored, particularly given the substantial impact it can have, as demonstrated in this paper. What is currently lacking is an assessment of the reproducibility of previous work that did not sufficiently consider the auditory confound. The current study constitutes a strong first step to addressing this issue, and indeed shows that results are not reproducible when using control conditions that are superior to flip-over sham, like (in)active control conditions and tightly matched soundsham conditions. This is particularly important given the fundamental nature of this research line, where TUS-TMS studies have played a central role in informing choices for stimulation protocols in subsequent research.

We would speculate that, with TUS opening new frontiers for neuroscientific research, there comes a rush of enthusiasm wherein laying the groundwork for a solid foundation in the field can sometimes be overlooked. Therefore, we hope that this work sends a strong message to the field regarding how strong of an impact peripheral confounds can have, also in prior work. Indeed, at the current stage of the field, we see no justification not to include proper experimental control moving forward. Only when we can dissociate peripheral effects from direct neuromodulatory effects can our enthusiasm for the potential of TUS be warranted.

2.7) Results, Fig. 2: Why did the authors not directly contrast target TUS and control conditions? 

Please refer to point 1.1.

2.8) The authors observe no dose-response effects of TUS. Does increasing TUS intensity also increase an increase in TUS-produced sounds? If so, should this not also lead to doseresponse effects? 

We thank the reviewer for this insightful question. Yes, increasing TUS intensity results in an increased volume of the auditory confound. Under certain circumstances this could lead to ‘dose-response’ effects. In the manuscript, we propose that the auditory confounds acts as a cue for the upcoming TMS pulse, thus resulting in MEP attenuation once the cue is informative (i.e., when TMS timing can be predicted by the auditory confound). In this scenario, volume can be taken as the salience of the cue. When the auditory confound is sufficiently salient, it should cue the upcoming TMS pulse and thus result in a reduction of MEP amplitude.

If we take Experiment II as an example (Figure 3B), the 19.06 W/cm2 stimulation would be louder than the 6.35 W/cm2 intensity. However, as both intensities are audible, they both cue the upcoming TMS pulse. One could speculate that the very slight (nonsignificant) further decrease for 19.06 W/cm2 stimulation could owe to a more salient cueing.

One might notice that MEP attenuation is less strong in Experiment I, even though higher intensities were applied. Directly contrasting intensities from Experiments I and II was not feasible due to differences in transducers and experimental design. From the perspective of sound cueing of the upcoming TMS pulse, the auditory confound cue was less informative in Experiment I than Experiment II, because TUS stimulus durations of both 100 and 500 ms were administered, rather than solely 500 ms durations. This could explain why descriptively less MEP attenuation was observed in Experiment I, where cueing was less consistent.

Perhaps more convincing evidence of a sound-based ‘dose-response’ effect comes from Experiment IV (Figure 4B). Here, we propose that continuous masking reduced the salience of the auditory confound (cue), and thus, less MEP attenuation was be observed. Indeed, we see less MEP change for masked stimulation. For the lowest administered volume during masked stimulation, there was no change in MEP amplitude from baseline. For higher volumes, however, there was a significant inhibition of MEP amplitude, though it was still less attenuation than unmasked stimulation. These results indicate a ‘doseresponse’ effect of volume. When the volume (intensity) of the auditory confound was low enough, it was inaudible over the continuous mask (also as reported by participants), and thus it did not act as a cue for the upcoming TMS pulse, therefore not resulting in motor inhibition. When the volume (intensity) was higher, less participants reported not being able to hear the stimulation, so the cue was to a given extent more salient, and in line with the cueing hypothesis more inhibition was observed.

In summary, because the volume of the auditory confound scales with the intensity of TUS, there may be dose-response effects of the auditory confound volume. Along the border of (in)audibility of the confound, as in masked trials of Experiment IV, we may observe dose-response effects. However, at clearly audible intensities (e.g., Experiment I & II), the size of such an effect would likely be small, as both volumes are sufficiently audible to act as a cue for the upcoming TMS pulse leading to preparatory inhibition.

2.9) I wonder if the authors could say a bit more on the acoustic control stimulus. Some sound examples would be useful. The authors control for audibility, but does the control sound resemble the one produced by TUS? 

Please refer to point 2.3.

2.10) The authors' claim that the remaining motor inhibition observed during masked trials is due to persistent audibility of TUS relies "only" on participants' descriptions. I think this deserves a bit more discussion. Could this be evidence that there is a TUS effect in addition to the sound effect? 

Please refer to points 1.16 and 1.18.

Author Response

Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “The cation channel mechanisms of subthreshold inward depolarizing currents in the VTA dopaminergic neurons and their roles in the depression-like behavior”. These comments are constructive and very helpful for improving our manuscript. We have studied comments carefully and have made provisional revision which we hope meet with approval. We also respond to the reviewer’s comments point by point as following.

Reviewer #1 (Public Review):

Comment 1:

The pharmacological tools used in this study are highly non-selective. Gd3+, used here to block NALCN is actually more commonly used to block TRP channels. 2-APB inhibits not only TRPC channels, but also TRPM and IP3 receptors while stimulating TRPV channels (Bon and Beech, 2013), while FFA actually stimulates TRPC6 channels while inhibiting other TRPCs (Foster et al., 2009).

We agree with the reviewer that the substances mentioned are not specific. Although we performed shRNA experiments against NALCN and TRPC6, we also used more specific pharmacological modulators for these two channels, L703,606 (the antagonist of NALCN)[1] and larixyl acetate (a potent TRPC6 inhibitor)[2]. The results are shown in figure 3E, F and figure 4C, E.

Comment 2:

-The multimodal approach including shRNA knockdown experiments alleviates much of the concern about the non-specific pharmacological agents. Therefore, the author's claim that NALCN is involved in VTA dopaminergic neuron pacemaking is well-supported.

-However, the claim that TRPC6 is the key TRPC channel in VTA spontaneous firing is somewhat, but not completely supported. As with NALCN above, the pharmacology alone is much too non-specific to support the claim that TRPC6 is the TRP channel responsible for pacemaking. However, unlike the NALCN condition, there is an issue with interpreting the shRNA knockdown experiments. The issue is that TRPC channels often form heteromers with TRPC channels of other types (Goel, Sinkins and Schilling, 2002; Strübing et al., 2003). Therefore, it is possible that knocking down TRPC6 is interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

From our single-cell RNA-seq results, TRPC7 and TRPC4 are found not to be present broadly like TRPC6 in the VTA DA neurons. And in experiments using single cell PCR (sFig. 9A), only a very small proportion of TRPC6-positive DA cells (DAT+) expressed TRPC4 (sFig. 9Bi) or TRPC7 (sFig. 9Bii), in consistent with the results of single-cell RNA-seq (Fig.2). Therefore, it is possible that knocking down TRPC6 maybe not interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

Comment 3:

The claim that TRPC6 channels in the VTA are involved in the depressive-like symptoms of CMUS is supported.

• However, the connection between the mPFC-projecting VTA neurons, TRPC6 channels, and the chronic unpredictable stress model (CMUS) of depression is not well supported. In Figure 2, it appears that the mPFC-projecting VTA neurons have very low TRPC6 expression compared to VTA neurons projecting to other targets. However, in figure 6, the authors focus on the mPFC-projecting neurons in their CMUS model and show that it is these neurons that are no longer sensitive to pharmacological agents non-specifically blocking TRPC channels (2-APB, see above comment). Finally, in figure 7, the authors show that shRNA knockdown of TRPC6 channels (in all VTA dopaminergic neurons) results in depressive-like symptoms in CMUS mice. Due to the low expression of TRPC6 in mPFC-projecting VTA neurons, the author's claims of "broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. Because of the messy pharmacological tools used, it cannot be clamed that TRPC6 in the mPFC-projecting VTA neurons is altered after CMUS. And because the knockdown experiments are not specific to mPFC-projecting VTA neurons, it cannot be claimed that reducing TRPC6 in these specific neurons is causing depressive symptoms.

The reason we focused on the mPFC-projecting VTA DA neurons is that this pathway is indicated in depressive-like behaviors of the CMUS model[3-5]. Although mPFC-projecting VTA DA neurons seem have lower level of TRPC6, we reason they are still functional there. However, we do agree with the reviewer that the statement “broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. We have changed the statements based on the reviewer suggestion. Furthermore, we did selectively knockdown TRPC6 in the mPFC-projecting VTA DA neurons, and then studied the behavior (Fig.8).

Comment 4:

It is important to note that the experiments presented in Figure 1 have all been previously performed in VTA dopaminergic neurons (Khaliq and Bean, 2010) including showing that low calcium increases VTA neuron spontaneous firing frequency and that replacement of sodium with NMDG hyperpolarizes the membrane potential.

We agree with reviewer that similar experiments have been performed previously [6] for the flow of our manuscript and for general readers.

Comment 5:

-The authors explanation for the increase in firing frequency in 0 calcium conditions is that calcium-activated potassium channels would no longer be activated. However, there is a highly relevant finding that low calcium enhances the NALCN conductance through the calcium sensing receptor from Dejian Ren's lab (Lu et al., 2010) which is not cited in this paper. This increase in NALCN conductance with low calcium has been shown in SNc dopaminergic neurons (Philippart and Khaliq, 2018), and is likely a factor contributing to the low-calcium-mediated increase in spontaneous VTA neuron firing.

We agree with the reviewer and thanks for the suggestions. A discussion for this has been added.

Comment 6:

-One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors.

We thank the reviewer for the suggestion. The references and a discussion for this has been added.

Comment 7:

• Out of all seven TRPCs, TRPC5 is the only one reported to have basal/constitutive activity in heterologous expression systems (Schaefer et al., 2000; Jeon et al., 2012). Others TRPCs such as TRPC6 are typically activated by Gq-coupled GPCRs. Why would TRPC6 be spontaneously/constitutively active in VTA DA neurons?

In a complex neuronal environment where VTA DA neurons are located, multiple modulatory factors including the GPCRs could be dynamically active, this could lead to the activation of TRP channels including TRPC6.

Comment 8:

A new paper from the group of Myoung Kyu Park (Hahn et al., 2023) shows in great detail the interactions between NALCN and TRPC3 channels in pacemaking of SNc DA neurons.

The reference mentioned has been added. We thank the reviewer.

Reviewer #2 (Public Review):

Comment 1:

These results do not show that TRPC6 mediates stress effects on depression-like behavior. As stated by the authors in the first sentence of the final paragraph, "downregulation of TRPC6 proteins was correlated with reduced firing activity of the VTA DA neurons, the depression-like behaviors, and that knocking down of TRPC6 in the VTA DA neurons confer the mice with depression behaviors." Therefore, the results show associations between TRPC6 downregulation and stress effects on behavior, occlusion of the effects of one by the other on some outcome measures, and cell manipulation effects that resemble stress effects. There is no experiment that shows reversal of stress effects with cell/circuit-specific TRPC6 manipulations. Please adjust the title, abstract and interpretation accordingly.

We agree with the reviewer’s suggestion. The title was changed to ‘’The cation channel mechanisms of subthreshold inward depolarizing currents in the VTA dopaminergic neurons and their roles in the chronic stress-induced depression-like behavior” and the abstract and interpretation were also adjusted accordingly.

Comment 2:

Statistical tests and results are unclear throughout. For all analyses, please report specific tests used, factors/groups, test statistic and p-value for all data analyses reported. In some cases, the chosen test is not appropriate. For example, in Figure 6E, it is not clear how an experiment with 2 factors (stress and drug) can be analyzed with a 1-way RM ANOVA. The potential impact of inappropriate statistical tests on results makes it difficult to assess the accuracy of data interpretation.

We have redone the statistical analysis as suggested by the reviewer and added specific tests used, factors/groups, test statistic and p-value for all data analyses into the figure legends of the revised manuscript.

Comment 3:

Why were only male mice used? Please justify and discuss in the manuscript. Also, change the title to reflect this.

Although most similar previous studies used male mice or rats[7, 8], we do agree with the reviewer that the female animals should also be tested, in consideration possible role of sex hormones, as such we repeated some key experiments on female mice (sFig.1.6.8. and 13).

Comment 4:

Number of recorded cells is very low in Figure 1. Where in VTA did recordings occur? Given the heterogeneity in this brain region, this n may be insufficient. Additional information (e.g., location within VTA, criteria used to identify neurons) should be included. Report the number of mice (i.e., n = 6 cells from X mice) in all figures.

Yes indeed, the number here is not high. More experiments were performed to increase the N/n number. And the location of recorded cells in VTA and the number of used mice is now shown in all figures; criteria to identify neurons is stated in the Methods-Identification of DA neurons and electrophysiological recordings. At the end of electrophysiological recordings, the recorded VTA neurons were collected for single-cell PCR. VTA DA neurons were identified by single-cell PCR for the presence of TH and DAT.

Comment 5:

Authors refer to VTA DA neurons as those that are DAT+ in line 276, although TH expression is considered the standard of DAergic identity, and studies (e.g., Lammel et al, 2008) have shown that a subset of VTA DA neurons have low levels of DAT expression. Authors should reword/clarify that these are DAT-expressing VTA DA neurons.

The study published by Lammel[9] in 2015 has shown the low dopamine specificity of transgene expression in ventral midbrain of TH-Cre mice; on the other hand, DAT-Cre mice exhibit dopamine-specific Cre expression patterns, although DAT-Cre mice are likely to suffer from their own limitations (for example, low DAT expression in mesocortical DA neurons may make it difficult to target this subpopulation, see Lammel et al., 2008[10]).Hence, in our study, the DAT was used as criteria to identify DAT neurons. Of course, TH and DAT were all tested in single-cell PCR to identify whether the recorded cells were DA neurons.

Comment 6:

Neuronal subtype proportions should be quantified and reported (Fig. 1Aii).

Neuronal subtype proportions are now quantified and reported in Fig. 1Aii.

Comment 7:

In addition to reporting projection specificity of neurons expressing specific channels, it would be ideal to report these data according to spatial location in VTA.

The spatial location of recorded cells in VTA are now shown in all figures.

Comment 8:

The authors state that there are a small number of Glut neurons in VTA, then they state that a "significant proportion" of VTA neurons are glutamatergic.

Thanks, “a significant proportion of neurons” has been changed to “less than half of sequenced DA neurons”.

Comment 9:

It is an overstatement that VTA DA neurons are the key determinant of abnormal behaviors in affective disorders.

Thanks, we have amended the statement to that “Dopaminergic (DA) neurons in the ventral tegmental area (VTA) play an important role in mood, reward and emotion-related behaviors”.

Reviewer #3 (Public Review):

Comment 1:

The authors of this study have examined which cation channels specifically confer to ventral tegmental area dopaminergic neurons their autonomic (spontaneous) firing properties. Having brought evidence for the key role played by NALCN and TRPC6 channels therein, the authors aimed at measuring whether these channels play some role in so-called depression-like (but see below) behaviors triggered by chronic exposure to different stressors. Following evidence for a down-regulation of TRPC6 protein expression in ventral tegmental area dopaminergic cells of stressed animals, the authors provide evidence through viral expression protocols for a causal link between such a down-regulation and so-called depression-like behaviors. The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. However, the interpretation of the results gathered from these behavioral tasks might also be considered one main weakness of the abovementioned approach. Thus, the authors make a confusion (widely observed in numerous publications) with regard to the use of paradigms (forced swim test, tail suspension test) initially aimed (and hence validated) at detecting the antidepressant effects of drugs and which by no means provide clues on "depression" in their subjects. Indeed, in their hands, the authors report that stress elicits changes in these tests which are opposed to those theoretically seen after antidepressant medication. However, these results do not imply that these changes reflect "depression" but rather that the individuals under scrutiny simply show different responses from those seen in nonstressed animals. These limits are even more valid in nonstressed animals injected with TRPC6 shRNAs (how can 5-min tests be compared to a complex and chronic pathological state such as depression?). With regard to anxiety, as investigated with the elevated plus-maze and the open field, the data, as reported, do not allow to check the author's interpretation as anxiety indices are either not correctly provided (e.g. absolute open arm data instead of percents of open arm visits without mention of closed arm behaviors) or subjected to possible biases (lack of distinction between central and peripheral components of the apparatus).

We agree with the reviewer that behavior tests we used here is debatable whether they represent a real depression state, and this is an open question that could be discussed from different respective. Since these testes (forced swimming and tail suspension), as the reviewer noted, were “widely observed in numerous publications”, we used these seemly only options to reflect a “depression-like” state. One could argue that since these testes were initially used for testing antidepressants (“validated”), with decreased immobility time as indications of anti-depressive effects, why not an increased immobility time reflect a “depression-like” state. As for anxiety tests, the data concerning the elevated plus-maze are also changed based on the reviewer’s suggestion.

Recommendations for the authors: please note that you control which, if any, revisions, to undertake

Reviewer #1 (Recommendations For The Authors):

Recommendation 1 for improving the paper:

-The paper needs extensive editing for both overall structural clarity and for the high number of typos and grammatical errors.

We thank the reviewer’s suggestion. The revised manuscript has been edited extensively.

Recommendation 2 for improving the paper:

-Retrobeads are often toxic to cells and build up with increasing time. It is surprising that the authors wait 14-21 days for retrobead expression in their target cells. It is also a problem that the mPFC projecting cells have a longer time with the retrobeads than the other projection-targeting cells because the toxicity could be more extensive with the longer wait time thus confounding the results. The authors should repeat some mPFC experiments at the 14 day time point to confirm that the longer time with the beads is not influencing the differential effects in these cells.

According to the methods published by Stephan Lammel and Jochen Roeper, “For sufficient labeling, survival periods for retrograde tracer transport depended on respective injection areas: DS and NAc lateral shell, 7 days; NAc core, NAc medial shell, and BLA, 14 days; and mPFC, 21 days[10]”, we did the experiments related to mPFC projecting cells at the 21 day time point. Consistent with the mentioned above, the labeled mPFC projecting cells at 14 day time point, is not sufficient, compared with this at 21 day time point, which is shown as followings.

Author response image 1.

Confocal images showing the anatomical distribution of mPFC-projecting DA neurons labelled with retrobeads (red) in the VTA after DAT-immunofluorescence (green) staining at different day time point (A, 14d; B, 21d) after retrobeads injection; Scale bars=10 μm.

Recommendation 3 for improving the paper:

-The experiment with FFA in Figure 4E seems weird. Why is there no baseline before the FFA application? And why is the baseline trending downward immediately? The authors should explain why this example experiment is presented differently from all the others.

We apologize for this part that this example time-course is not typical. Since the FFA is not specific antagonist for TRPC6 and actually stimulates TRPC6 channels, we repeated the experiments with a more specific pharmacological modulator for TRPC6, larixyl acetate (LA), and the results are shown in Figure 4C and 4F.

Recommendation 4 for improving the paper:

-It would be much more useful to see exact p values in the text, as it aids in interpreting the 'insignificance' of specific comparisons. Specifically, in Figure 5F, the 2-APB looks like it is having a small effect, and the already low firing rate (due to the TRPC6 knockdown) makes a big effect less likely. It would be useful to know what the actual p value is here (and everywhere).

OK. We now report all P values in the figure legends of the revised version.

Recommendation 5 for improving the paper:

-In the results, it should be explained that the "RMP" of VTA DA neurons was obtained by treating the cells with TTX.

A sentence indicating the presence of TTX when measuring “RMP” is added in the Results part of the revised version.

Recommendation 6 for improving the paper:

-The spacing of the panels in the figures is somewhat odd. The figures could be more compact.

Thanks, we have re-arranged all figures.

Recommendation 7 for improving the paper:

The paper is difficult to read because of significant grammatical errors. Here are some examples by line number, but this list is not at all exhaustive.

We thank the reviewer for pointing out grammatical errors and we corrected them.

Reviewer #2 (Recommendations For The Authors):

Recommendation 1 for improving the paper:

Fix typos: e.g., change HCH to HCN, change EMP to EPM, "these finding", "compact par" should read "pars compacta", "substantial" in line 475 should read "substantia", Incomplete sentences on line 73 and line 107, etc. Also, what is meant by "autonomic" firing activity? What is meant by "expression files"? Change "depression behaviors" to depression-like behaviors. "The HCN" as written in line 69 is a bit misleading, as HCN channels in the heart and brain are different members of a family of channels, although as written in the text, it seems that they are identical. In Figure 2, rearrange order of brain regions (e.g., from "BLA-VTA" to "VTA-BLA"), because as written, it seems that the focus is on projections into the VTA from each brain region, rather than VTA neurons that project to each respective region.

We thank the reviewer for pointing out these errors and we corrected them. Autonomic firing activity has been changed to spontaneous firing activity. Expression files has been changed to expression levels. All the “depression behavior” have been changed to depression-like behaviors. In the Figure 2, all “xx-VTA” have been changed to “VTA-xx”.

Reviewer #3 (Recommendations For The Authors):

Recommendation 1 for improving the paper:

Methodology: as opposed to sFig. 8 where the order through which mice were repeatedly tested is precise, such a key information is lacking in Fig. 6 as well as in the Methods section (for example, when such traumatic stress as forced swimming is performed with regard to the other tests?). Relevant to this point is the possible bias triggered by such chronological testing as exposure to the forced swim test likely affects the behaviors recorded in the other tests. Furthermore, the way this test is conducted is appealing as it is mentioned that the water depth was set to 10 cms which is quite low given that immobility scores might be affected by the ability of mice to stand on their tails.

With regard to the elevated plus-maze, data are erroneously provided. Absolute values regarding open arm behaviors should be provided as percentages of the number of visits (or time spent therein) over the total (open + closed) number of arm visits. Indeed, closed arm visits should also be provided. This variable, also considered an index of locomotor activity, would allow the reader to exclude any effect of locomotion on the exploration in the open field.

As they stand, data in the open field seem to indicate parallel changes at the center(center time) and the periphery (total distance), hence suggesting locomotor effects rather than anxiogenic effects. Data related to the center and the periphery should be clearly distinguished. Lastly, the number of weeks allowed for the mice to recover from surgeries aimed at delivering viruses are not mentioned. This is important as it could have affected the amplitude of the sensitivity to the stressors.

We thank the reviewer for the suggestion. The lack information in Figure 6 and the Methods is now supplied. We apologize for the wrong number of “10 cm” in the forced swimming test, this has been corrected. The data concerning the elevated plus-maze are also changed based on the reviewer’s suggestion. For a possible role of locomotor effect, we tested the mice on the rota-rod test. From the result, there is no difference in locomotor activity between control and depressed-like mice (sFig.10G, sFig.12I and sFig.13G). We modified the experimental procedure timeline in Figure 6 and in the method- AAV for gene knockdown or overexpression and viral construct and injection, we added “Mice were singly housed with enough food and water to recover for 4-5 weeks after injection of virus, before behavior tests and electrophysiological recordings.” to report the number of weeks allowed for the mice to recover from surgeries aimed at delivering virus.

Recommendation 2 for improving the paper:

Results/conclusions: as yet mentioned, the authors make a confusion in the interpretation of their tail suspension tests and forced swimming tests. I acknowledge that such a confusion is frequent but it is important to note that the tests used by the authors were INITIALLY aimed at detecting the antidepressant effects of drugs under investigation. However, it is not because a test reveals such antidepressant properties that they also provide indices of depression. The authors will surely agree that it is unlikely that a 5-min test provides a model of a chronic pathology accounted for by a complex intrication between genetics and environmental factors. I would propose the authors to read for example Molendijk and De Kloet (Eur J Neurosci 2022). I think that the authors should just neutrally mention their results without any interpretation related to depression. On the other hand, what could have been interesting is to test whether the so-called "depressive-like" responses recorded in the study were sensitive to chronic antidepressant treatments. This would have allowed the authors to further suggest some relevance (if any) with depression-like pathologies.

As we discussed above, we again agree with the reviewer’s concern. However, if as stated by the reviewer that “However, it is not because a test reveals such antidepressant properties that they also provide indices of depression”, then the experiments suggested by the reviewer “….. to test whether the so-called "depressive-like" responses recorded in the study were sensitive to chronic antidepressant treatments”

Recommendation 3 for improving the paper:

A close examination of the responses to CMUS or chronic restraint suggests that indeed two populations of animals were detected, possibly sensitive and resilient to these stressors. Did the authors try to examine this possibility?

Based on the results of behavior test in CMUS and CRS, animals might be divided into two populations of animals highly-sensitive and moderately-sensitive ones.

Recommendation 4 for improving the paper:

There are some text changes that need to be performed:

Page 2 line 46: ref 4 uses a social stress model which brings no clearcut evidence for it being a "depression" model. Indeed, this model can also be suggested to be a model of chronic anxiety (Kalueff et al., Science 2006; Chaouloff, Cell tissue Res 2013), hence indicating that VTA dopaminergic neurons might also be involved in anxiety.

page 11, line 329: the references supporting the hypothesis that VTA DA neurons are linked to depression cannot be found in the reference list (10-15 do not correspond to the appropriate references).

page 11, line 3341: reference 47 does not fit with the authors' assertion as it did not include any behavior.

Fig. S8: body weight data are likely provided as changes rather than absolute values (e.g. 8 g)

We agreed with the reviewer’s comments. The line 46“……such as depression states” has been changed to “such as depression- or anxiety-related states”. And we corrected the references in line 329 and 341. Finally, the body weight has been changed to the change in body weight.

References:

1. Um, K.B., et al., TRPC3 and NALCN channels drive pacemaking in substantia nigra dopaminergic neurons. Elife, 2021. 10.

2. Urban, N., et al., Identification and Validation of Larixyl Acetate as a Potent TRPC6 Inhibitor. Mol Pharmacol, 2016. 89(1): p. 197-213.

3. Zhong, P., et al., HCN2 channels in the ventral tegmental area regulate behavioral responses to chronic stress. Elife, 2018. 7.

4. Liu, D., et al., Brain-derived neurotrophic factor-mediated projection-specific regulation of depressive-like and nociceptive behaviors in the mesolimbic reward circuitry. Pain, 2018. 159(1): p. 175.

5. Walsh, J.J. and M.H. Han, The Heterogeneity of Ventral Tegmental Area Neurons: Projection Functions in a Mood-Related Context. Neuroscience, 2014. 282: p. 101-108.

6. Khaliq, Z.M. and B.P. Bean, Pacemaking in dopaminergic ventral tegmental area neurons: depolarizing drive from background and voltage-dependent sodium conductances. J Neurosci, 2010. 30(21): p. 7401-13.

7. Li, L., et al., Selective targeting of M-type potassium K(v) 7.4 channels demonstrates their key role in the regulation of dopaminergic neuronal excitability and depression-like behaviour. Br J Pharmacol, 2017. 174(23): p. 4277-4294.

8. Friedman, A.K., et al., Enhancing depression mechanisms in midbrain dopamine neurons achieves homeostatic resilience. Science, 2014. 344(6181): p. 313-9.

9. Lammel, S., et al., Diversity of transgenic mouse models for selective targeting of midbrain dopamine neurons. Neuron, 2015. 85(2): p. 429-38.

10. Lammel, S., et al., Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron, 2008. 57(5): p. 760-73.

Author Response

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

Reviewer #1:

This work describes a new and powerful approach to a central question in ecology: what are the relative contributions of resource utilisation vs interactions between individuals in the shaping of an ecosystem? This approach relies on a very original quantitative experimental set-up whose power lies in its simplicity, allowing an exceptional level of control over ecological parameters and of measurement accuracy.

In this experimental system, the shared resource corresponds to 10^12 copies of a fixed single-stranded target DNA molecule to which 10^15 random single-stranded DNA molecules (the individuals populating the ecosystem) can bind. The binding process is cycled, with a 1000x-PCR amplification step between successive binding steps. The composition of the population is monitored via high-throughput DNA sequencing. Sequence data analysis describes the change in population diversity over cycles. The results are interpreted using estimated binding interactions of individuals with the target resource, as well as estimated binding interactions between individuals and also self-interactions (that can all be directly predicted as they correspond to DNA-DNA interactions). A simple model provides a framework to account for ecosystem dynamics over cycles. Finally, the trajectory of some individuals with high frequency in late cycles is traced back to the earliest cycles at which they are detected by sequencing. Their propensities to bind the resource, to form hairpins, or to form homodimers suggest how different interaction modes shape the composition of the population over cycles.

The authors report a shift from selection for binding to the resource to interactions between individuals and self-interactions over the course of cycles as the main drivers of their ecosystem. The outcome of the experiment is far from trivial as the individual resource binding energy initially determines the relative enrichment of individuals, and then seems to saturate. The richness of the population dynamics observed with this simple system is thus comparable to that found in some natural ecosystems. The findings obtained with this new approach will likely guide the exploration of natural ecosystems in which parameters and observables are much less accessible.

My review focuses mainly on the experimental aspects of this work given my own expertise. The introduction exposes very convincingly the scientific context of this work, justifying the need for such an approach to address questions pertaining to ecology. The manuscript describes very clearly and rigorously the experimental setup. The main strengths of this work are (i) the outstanding originality of the experimental approach and (ii) its simplicity. With this setup, central questions in ecology can be addressed in a quantitative manner, including the possibility of running trajectories in parallel to generalize the findings, as reported here. Technical aspects have been carefully implemented, from the design of random individuals bearing flanking regions for PCR amplification, binding selection and (low error) amplification protocols, and sequencing read-out whose depth is sufficient to capture the relevant dynamics.<br /> :<br /> We thank the reviewer for summarizing our work and the main findings in a very clear and effective manner.

One missing aspect in the data analysis is the quantification of the effect of PCR amplification steps in shaping the ecosystem (to be modeled if significant). In addition, as it stands the current work does not fully harness the power of the approach. For instance, with this setup, one can tune the relative contributions of binding selection vs amplification for instance (to disentangle forces that shape the ecosystem). One can also run cycles with new DNA individuals, designed with arbitrarily chosen resource binding vs self-binding, that are predicted to dominate depending on chosen ecological parameters. I have three main recommendations to the authors:

1) PCR amplification steps (and not only binding selection steps) should be taken into account when interpreting the outcome of experiments.

2) More generally, a systematic analysis of the possible modes of propagation of a DNA molecule from one cycle to the next, including those considered as experimental noise, would help with interpreting the results.

3) Testing experimentally the predictions from the analysis and the modelling of results would strengthen the case for this approach.

Despite its conceptual simplicity, our approach has indeed a few experimental handles that enable exploring a relevant variety of conditions much beyond those described in this paper, of which we are very aware. These involve selection vs. amplification or set the stage to explore competition, parasitism or cooperation among specific species, as the reviewer points out, but also introduce mutations and explore the kinetics of evolution in static or dynamic environments. Ongoing experiments are considering some of these conditions. We modified the text to mention more explicitly these possibilities, which are now mentioned in p11 lines 376-378 and lines 416-417. The three points raised by the reviewer helped us to further improve and clarify strengths and limitations of our work, as detailed below.

Regarding the first point, here are my suggestions :

• Run one cycle of just amplification vs 'binding + amplification', or simply increase the number of PCR cycles (and subsample the product) to check whether it impacts the population composition, in particular for sequences with predictions derived from the current analysis.

The point raised by the reviewer is indeed very relevant and not discussed in our manuscript. Prompted by the reviewer’s comment, we performed two new experiments to distinguish resource-binding selection from PCR amplification effects.

First, we performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

This indicates that the drift due to non-specific binding + PCR amplification is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.

This results are now cited in p14 lines 468-470, and described in Appendix 1, Experimental controls.

Second, we tested the effect of PCR amplification on the selection process. We exploited the fact that we have aliquots for each generation of our evolution experiment, which we sampled and saved after PCR and before sequencing. We thus chose a specific generation - specifically generation 9 from Oligo 1 experiments - and performed another PCR round we proceeded directly to sequencing with no beadsselection step. We then compared the ensemble of oligos obtained in this way, which we named Oligo 1 “cycle 9 replica”, with both the original Oligo1 cycle 9, and with Oligo1 cycle 10.

We sampled 20 times 4 x 10^5 sequences from the cycle 9 dataset, from cycle 9 replica and from cycle 10 with a bootstrap approach. To compare the three systems we extracted the fraction of the population of each covered by the 10 most abundant individuals. The results are shown in Figure 2 - Figure Supplement 4. In the figure caption further details on the analysis can be found. The similarity between cycle 9 and cycle 9 replica and the marked difference between cycle 9 replica and cycle 10

indicates that the relevant part of the selection is indeed performed by the resourcebinding mechanism, while drifts induced by PCR play a secondary role.

As a further check, we compared the specific sequences across the 20 samples in cycle 9 and cycle 9 replica datasets and found that the 10 most abundant sequences are almost always the same. In particular, the first 8/9 are always the same, possibly shuffled.

These new pieces of evidence are now cited in p14 lines 483-484 and described in Appendix 1, Experimental controls.

• Sequencing read-out includes the same PCR protocol as the one used for amplification steps, so read-out potentially has an effect on the composition of the ecosystem. Again, varying the number of PCR cycles is a direct way to test this.

The PCR amplification involved in the read-out might have a minor effect on the sequencing outcome but not on the composition of the ecosystem. In fact, the sample that undergoes sequencing is taken from the pool at each cycle, and not inserted back into it. Thus, it does not participate in the following selection steps. This is specified in the text at p3 line 104

• Could self-interactions (hairpins of homodimers) benefit individuals during amplification steps? The role of self-interactions during binding selection steps could also be tested directly over one cycle (again varying the relative weight of the binding vs amplification to disentangle both).

Our choice of conditions for PCR amplification were thought to minimize effects of this type. PCR amplification is carried out at 68 C, a temperature at which, given the level of self and mutual complementarity in the sequences analyzed in the text, hairpins or homodimers should be melted and thus have no effect. This is specified in the text at p. 14 lines 479-480 However, if an effect is present, it gives a disadvantage (rather than an advantage) to self-interacting individuals. For the amplification step we used Q5® Hot Start HighFidelity DNA Polymerase, which does not possess strand displacement activity. Therefore, in theory, if during amplification the polymerase encounters a double strand portion, it stops and synthesizes only a truncated product, which will be then lost during the purification step. In other words, sequences with secondary and/or tertiary structures are less likely to be amplified during the polymerization step. As a consequence, a DNAi that is characterized by this kind of structures, will be negatively selected even in the case of optimal binding to the resource, and will be underrepresented in the pool.

About the second point:

• Regarding the effect of sampling (sequencing read-out), PCR amplification errors: explicitly check the consistency of observations with the expected outcome, in the methods section (right now these aspects are only briefly mentioned in the main text), which would highlight again the level of control and accuracy of the system.

Hoping to have well interpreted the request, we performed a technical replicate sequencing Oligo 1 cycle 9 again and analyzed the sequences that have at least 100 reads (corresponding to 27.42% of the total reads). We find that among the 800 DNA species that have at least 100 reads, 93.6% are found in both replicates. All the nonoverlapping sequences have very low abundance, close to 100.

Moreover, we compare the population size of each DNA species between the two replicas, after having equalized the database sizes. The results are now cited in p14 lines 509-510, In Appendix 1, Experimental Controls and shown in Figure 2-figure supplement 3, where we plot the ratio of the number of reads in the two replicates for each sequence as a function of the number of reads in one. We found an average of 0.965 with a standard deviation of 0.119. High fluctuations are found in the most rare species, as expected.

We think this evaluation indeed strengthens the solidity of our results.

• I have a small concern about target resource accessibility: is there any spacer between the ssDNA and the bead? The methods section does not mention any, and I would expect such a proximity between the target DNA and the bead to yield steric repulsion that impedes interactions with random DNA individuals.

Yes, there is a 12-carbon spacer between the bead and the resource, which was inserted exactly to make the resource more accessible. This information is now available in Table 1 of Supplementary Information detailing the sequences used in the experiment. However, as now described in the text (p8 lines 284-286), we observe that the interaction with the resource is always shifted to the 3', the terminal furthest from the bead, indicating some residual issue of accessibility to the resource sections closest to the bead.

• Regardless of the existence of a spacer, binding of random DNA molecules to beads instead of the target DNA constitutes a potential source of noise (described for now as '1-x' in the IBEE model), which can be probed by swapping targets, selecting without target etc.

This issue is addressed by the test with bare beads described above, in which we found little effects, corresponding to small 1−𝑥 value.

• Is there any recombination potentially occurring during amplification steps? This could be tested with a set of known molecules amplified over 24 amplification steps in a row (no binding step).

It is possible for recombination to occur during the amplification steps. In Appendix 2, the section "By-Product Formation from PCR Amplification", discusses PCR byproducts as aberrant forms of amplification, such as recombination events. We adopted several strategies to limit by-product formation, such as: i) use of “blockers” characterized by a phosphate group at 3’ end (thus inhibiting their usage during the amplification and allowing a better control of the reaction conditions over the PCR cycles), ii) a high annealing temperature (to limit the possibility of a spurious primer annealing to the random region), iii) fewer PCR cycles, iv) a high primer concentration, v) a very short elongation step (all these strategies have been implemented to avoid a possible mispriming event between different DNAi, and the formation of concatemers). However, the formation of by-products is a problem inherent to the technique: in fact, it is a known issue for classical SELEX technology (Tolle et al. 2014), mainly due to the random region within the DNAi. Q5® Hot Start High-Fidelity DNA Polymerase (New England Biolabs, Ipswich, MA, USA) has an error rate of <0.44 x 10-6/base.

In classic SELEX technology, the average number of selection cycles is 10. This limitation is partly due to the increase in PCR by-products. As we can see from Figure 2 Supplementary Figure 1, the percentage of PCR by-products is less than 20% at cycle 12, and then increases dramatically in the following cycles. We are performing a series of experiments with known and limited sequences to verify and better understand the phenomenon for future applications of the SEDES platform. On this issue we decided not to modify the manuscript since we think it is already well discussed in Appendix 1.

And the third point:

• Perform one cycle (or a few cycles) with random DNA individuals, the most frequent individuals at the end of the current experiment, newly designed individuals with higher binding affinity to the target than currently dominating individuals, newly designed individuals with higher propensity to form hairpins or to form homodimers. Such experimental testing of predictions from the data analysis/modeling, typical of a physics approach, would illustrate the level of understanding one can reach with a simple yet powerful experimental setup.

We perfectly agree that the approach we propose and the set of results we obtained call for further investigations that could strengthen analysis and modeling. The final aim we envisage is the understanding, within this simplified approach, of key evolutionary factors such as fitness. Indeed, becoming able to write an explicit fitness function would be a significant new contribution to the understanding of evolutionary processes, even within the limited settings of the ADSE approach, as discussed in the conclusions of the manuscript.

However, undergoing such an analysis is a long and expensive job, which we have started and will be completed in a not immediate future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population and discuss in a future contribution the behavior of smaller designed sets of competing, collaborating or parasitic individuals.

Looking ahead, additional stages of investigations will also include mutations - to investigate the kinetics of speciation, and, in an even further stage, the interplay between evolution kinetics and dynamical mutation of resources.

I have a few smaller points:

• It would be very useful to provide the expected dynamic range of binding free energies (in terms of DeltaG and omega): what is the maximum binding free energy for the perfect complement?

The NUPACK-computed binding free energy of a 20 basis-long oligomer complementary to the resource (𝜔=20) is -24.36 Kcal/mol for Oligo1 and -23.08 Kcal/mol for oligo 2. This is the best answer we can offer to the reviewer’s request, since the maximum binding free energy of DNAi individuals (much longer than the target strand) would include contributions from the unpaired bases. Indeed, the values give above are approached by the left tail of the distribution of Fig. 3a, which however includes DNAi self-energies.<br /> The perfect complement binding free energy is now cited in the text as a reference for the dynamical range of DeltaG (p4 lines 151-152).

• How is the number of captured DNA molecules quantified? Is 10^12 measured, estimated, or hypothesized?

The number of sequences was calculated from data obtained from 260 nm absorbance quantification. We have now added this information in the Methods, Selection Phase” section.

Reviewer #2:

Summary:

In this manuscript, the authors introduced ADSE, a SELEX-based protocol to explore the mechanism of emergency of species. They used DNA hybridization (to the bait pool, "resources") as the driving force for selection and quantitatively investigated the factors that may contribute to the survival during generation evolution (progress of SELEX cycle), revealing that besides individual-resource binding, the inter- and intra-individual interactions were also important features along with mutualism and parasitism.

Strengths:

The design of using pure biochemical affinity assay to study eco-evolution is interesting, providing an important viewpoint to partly explain the molecular mechanism of evolution.

Weaknesses:

Though the evidence of the study is somewhat convincing, some aspects still need to be improved, mostly technical issues.

Major:

1) There are a few technical issues that the authors should clarify in the manuscript to make the analysis more transparent:

1.1) To my understanding, it is difficult to guarantee the even distribution of different species (individuals) in the initial individual pool. Even though the authors have shown in Fig. 2a that the top 10 sequences take up ~ 0% in the pool, it remains unclear how abundant these top and bottom representative sequences are, given the huge number of the pool (10E15). Can the author show the absolute number of these sequences in different quantiles? Please show both Oligo sets.<br /> : First, we thank the reviewer for both positive and critical comments that have guided us in reformulating or clarifying some messages of our work.

As for this specific point: 10E15 is a small number compared to 4^50 = 10E30, the number of possible sequences of length 30. Thus, we don’t expect more than one individual per sequence in the initial pool. However, sequencing requires a preparation amplification, which may lead to detecting a few sequences with more than one individual.

Specifically, in the initial pool of Oligo 1, the most abundant individual (of sequence GAACTAAAGGGGCGGTGTCCACTTGCCTGTAGTGGTTATCAGTCCGGTTG)has 3 copies. The 0.7% of the sequences has 2 copies, while the vast majority of strings (99.3% on a sample of about 1.5 x 10E6 sequenced DNAi) is present in one copy only. A similar situation holds for Oligo 2, with 4 DNAi present in 3 copies and the 0.8% of the sequences (in a pool of 2 x 10E6 DNAi) in 2 copies.

It is worth noticing that none of the 10 most abundant species in the last cycle is present in the sample. Indeed, the fraction of the pool which is sequenced is removed from the population that undergoes evolution (as now specified in p2, line 104). We specified in the text (p2, lines 69-70, p3 lines 94-96) the fact that in the initial pool no sequence is expected to be present in more than one individual.

1.2) The author claimed that they used two different oligo sets (Oligo1 and Oligo2) in this study. It is unclear which data was used in the presentation. How reproducible are they? Similar to this concern, how reproducible if the same oligo set was used to repeat the experiment?

The oligo used in the main text was declared in Methods, Replica section. It is now declared also in the main text (p3 lines 106-108 and in the captions of Figure 2, Figure 3 and Figure 4). Reproducibility is addressed in: Figure 2-figure supplement 5; Figure 2-figure supplement 6; Appendix 2: Results of the experimental replica.

It should also be noted that two starting pools of random 50mers are necessarily disjoint sets for the same reason discussed in the previous answer: the probability of common sequences in two 10E15 selections from a 10E30 is negligibly small. Thus, it is expected that each time a new evolution experiment is started, different dominant sequences are found. However, the statistical properties of the DNAi pool during the evolution process of Oligo1 and Oligo2 are similar as discussed in Appendix 2 of the paper.

1.3) PCR and illumina sequencing itself introduced selection bias. How would the analysis eliminate them? The authors only discussed the errors created during PCR cycles (page 3, lines 115-122). However the PCR itself would prefer to amplify some sequences over the others (e.g. with high GC content). Similarly, the illumina sequencing would be difficult to sequence the low complexity sequences. How would this be circumvented?

Yes, both PCR and Illumina sequencing have some known biases in the amplification process (e.g. sequencing of homopolymers or amplification of GC-rich sequences) that are intrinsic to the used techniques. Regarding PCR, we implemented a thermal protocol optimized for our chosen experimental setup, characterized by very short denaturation, annealing and amplification steps performed at high temperatures. Regarding Illumina sequencing, we can’t rule out a bias against specific sequences (e.g, homopolymers), which however should not be captured during the selection step, due to the design of the resource. Also, the libraries subjected to sequencing are characterized by a low complexity: according to the experimental design, the first and last 25 nucleotides are the same for all DNAi, the only differences being in the central 50 nt-long sequence. It is known that a low complexity library might encounter problems during sequencing due to the design of Illumina instruments: nucleotide diversity, especially in the first sequencing cycles, is critical for cluster filtering, optimal run performance and high-quality data generation. To overcome this limitation, the obtained libraries were run together with more complex and diverse library preparations: the ADSE sequences were about 1-2% of the total reads per run, corresponding to only a few million reads.

This discussion is now in Appendix 1, Intrinsic limitations of the molecular approach.

1.4) Some DNA sequences would bind to the beads instead of the resource sequence coated on them. Should the author run the experiment using bead alone as a control?<br /> : We performed a negative control experiment in which we performed the “selection step” with bear beads, i.e. beads without with no DNA grafted on them. We then compared the results with the corresponding results of the original experiments on Oligo 1 and 2.

After 6 cycles, the most abundant sequence in the negative dataset has a relative occurrence of 0.05%, whereas the dominant strand in Oligo 1 and Oligo 2 has an abundance of 8% and 16%, respectively, i.e. 40-80 times larger.

This indicates that the drift due to non-specific binding (+ PCR amplification) is at least two orders of magnitude smaller than the selection induced by the affinity with the resource.<br /> This part is now discussed in Appendix 1, Experimental controls.

2) It would be interesting to study the impact of environmental factors, for example, changing pH, salt concentration, and detergent. Would these factors accelerate/decelerate the evolution?

We agree that the approach we propose and the set of results we obtained call for further investigations. However, performing these additional experiments, which would require a minimum of 6 generations each, is a long and expensive job, which we have started and will not be completed in the near future. For this reason, given the already significant body of results we are presenting here, we prefer to keep this paper confined to the study of the evolution of a random DNAi population in the selected conditions and leave the exploration of new conditions, potentially opening new evolutionary scenarios, to a future contribution. In fact, our aim was to show that through our platform we can indeed observe fundamental elements of evolution in a non-biological system, which, in the set of chosen parameters, we do.

3) The concentration of individual oligo is apparently one of the most important factors in determining the interactions. In later cycles, some oligos become dominant, namely with extremely higher concentrations compared to their concentration in earlier cycles. This would definitely affect its interaction with resources, or self-interaction, or interaction with other oligos in the pool. However, the authors failed to discuss this factor, which may explain the exponential enrichment in later cycles.

We agree with the reviewer that this is an important point, but we disagree that we have not discussed it. We introduce the topic at the end of the “Null Model and Eco-evolutionary Algorithm”, where we comment on the change of the gamma parameter by saying that there must be a shift in the evolution process, first dominated by the interactions with the resources, and in later stages by some other factors (lines 227230) that we then discuss in “Self and mutual DNAi interactions are evolutionary drivers”. In this latter chapter and in the following, we indeed discussed the effects of mutual and self interactions between DNAi.

Indeed, a key point in our paper is the change in the gamma parameter necessary to match the IBEE model to experiments, as it is now more openly stated (p5 lines 217218 where we also mention figure 2-supplement 8 which clearly shows the necessity of a variable gamma). The two regimes enlightened by the gamma value must reflect a change in the competition for the resources and interactions among species. In the first generations, where the diversity of species is large (there are few strings for each species) and binding to the resources generally very week (small <omega>), the affinity with the resource is the main driving force (fast growth of <omega>), while mutual interactions remain too random to favor any species in particular. In the later cycles instead, when <omega> becomes large enough to provide a significant stability to the resource-binding of the majority of species, the dominating species compete more intensively on the basis of their structure and capacity of self-defense, parasitism and mutualism, a condition in which evolution affects more modifications in sequences than in <omega>.

Certainly, our understanding of this shift is based on statistical behavior and it is inferential, based on the study of specific DNAi described in the last part of the manuscript. For a better molecular model, more experiments with selected DNAi competing, cooperating or being parasitic would be necessary, with the final aim of defining a predictive fitness function. Alas, this requires months of further investigation. :

4) The author observed the different behaviors of medium 𝜔 in early and late cycles, referring to Fig 2h. Using the IBEE model, they found out it is the change of gamma. However, the authors did not further discuss the molecular mechanism. It could be very interesting to understand the evolutionary change of these individuals.

This comment might be related to the previous one. It is true that our discussion and understanding of the whole process is statistical, and misses a molecular model to predict the value of gamma.

However, the specific behavior that the reviewer asks about (those in Fig. 2h) is not related to the change in gamma. Even if gamma remains as in the first part of the evolution (gamma = 3), the species with overlap between 6 and 10 would first grow in number and later decrease. Indeed, during the first cycles they have an advantage with respect to the majority of species with lower maximum overlap, a condition that favors their amplification. However, in the second stage of the evolution dominant species with a larger affinity emerge and outcompete the individuals of this class. We added a sentence in the text to clarify this point (p7 lines 227-229).

5) In Figure 2f, some high w become quite missing. Should the authors give some interpretation? It is not observed in cycle 12 though (panel e).

Such an effect is just due to under-sampling. In a pool of 10^n oligomers, any sequence with a given 𝜔 with P(omega) < 10E-n will have a vanishing probability to appear in that sample.<br /> At cycle 12 the overall number of sequenced strands is larger than at cycle 24, due to the growing presence of PCR by-products. Thus, the right tail of the cyan distribution at the last cycle is sampled with less accuracy than at cycle 12. We have added a sentence in the revised manuscript (p5 lines 177-178) to clarify this point.

6) It would be interesting to further explore if another type of selection resource is used, for example protein that binds to particular sequences, i.e. transcription factors. Previous studies have used a large amount of sequence-specific transcription factors to run SELELX. Since the data have existed there, why not explore?

This is an interesting suggestion: can we use data from “ordinary” SELEX favoring specific sequences to explore sequence evolution? Two limitations make us a bit skeptical on this path: first, the consensus sequences of DNA-binding proteins are rather short and typically target dsDNA rather than ssDNA; second, the free energy of interaction is known only for the consensus sequence but not for sequences with all possible mutations with respect to the consensus sequence, making very hard to develop any molecular understanding of the process.

Minor:

1) There is no figure legend or in-text citation of Figure 2b.

2) Please correct "⁃C" with "{degree sign}C" in lines 470, 471, 472, 477 et al.

3) Typos and grammar issues should be corrected. Examples are shown below (but not limited to these only):

• mixed use of past and present tense.

• Line 152, "basis" should be "bases".

• Line 277, "a impediment" should be "an impediment"

• Line 278, "a major deadly threats" should be "major deadly threats"<br /> :<br /> We are sorry for the mistakes, and we have corrected them. Many thanks to the reviewer!

Author Response

Reviewer #1 (Public Review):

The goal of the current study was to evaluate the effect of neuronal activity on blood-brain barrier permeability in the healthy brain, and to determine whether changes in BBB dynamics play a role in cortical plasticity. The authors used a variety of well-validated approaches to first demonstrate that limb stimulation increases BBB permeability. Using in vivo-electrophysiology and pharmacological approaches, the authors demonstrate that albumin is sufficient to induce cortical potentiation and that BBB transporters are necessary for stimulus-induced potentiation. The authors include a transcriptional analysis and differential expression of genes associated with plasticity, TGF-beta signaling, and extracellular matrix were observed following stimulation. Overall, the results obtained in rodents are compelling and support the authors' conclusions that neuronal activity modulates the BBB in the healthy brain and that mechanisms downstream of BBB permeability changes play a role in stimulus-evoked plasticity. These findings were further supported with fMRI and BBB permeability measurements performed in healthy human subjects performing a simple sensorimotor task. While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain. Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies. The authors could have also better integrated the RNAseq findings into mechanistic experiments, including testing whether the upregulation of OAT3 plays a role in cortical plasticity observed following stimulation. Overall, this study provides novel insights into how neurovascular coupling, BBB permeability, and plasticity interact in the healthy brain.

While there are many strengths in this study, there is literature to suggest that there are sex differences in BBB dysfunction in pathophysiological conditions. The authors only used males in this study and do not discuss whether they would also expect to sex differences in stimulation-evoked BBB changes in the healthy brain.

We agree with the reviewer regarding the importance of examining sex differences on stimulation-evoked BBB changes. To address this issue we have: (1) clarified in the methods section that the human study involved both males and females; (2) added a section to the discussion highlighting the male bias as a key limitation of our animal experiments; and (3) stated that future work should examine whether stimulation-evoked BBB changes differ between makes and females.

Another minor limitation is the authors did not address the potential impact of anesthesia which can impact neurovascular coupling in rodent studies.

We are grateful for this comment and agree with the reviewer that the potential effects of anesthesia should be discussed. We have added the following discussion paragraph:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018).”

Reviewer #2 (Public Review):

Summary:

This study builds upon previous work that demonstrated that brain injury results in leakage of albumin across the bloodbrain barrier, resulting in activation of TGF-beta in astrocytes. Consequently, this leads to decreased glutamate uptake, reduced buffering of extracellular potassium, and hyperexcitability. This study asks whether such a process can play a physiological role in cortical plasticity. They first show that stimulation of a forelimb for 30 minutes in a rat results in leakage of the blood-brain barrier and extravasation of albumin on the contralateral but not ipsilateral cortex. The authors propose that the leakage is dependent upon neuronal excitability and is associated with an enhancement of excitatory transmission. Inhibiting the transport of albumin or the activation of TGF-beta prevents the enhancement of excitatory transmission. In addition, gene expression associated with TGF-beta activation, synaptic plasticity, and extracellular matrix are enhanced on the "stimulated" hemisphere. That this may translate to humans is demonstrated by a breakdown in the blood-brain barrier following activation of brain areas through a motor task.

Strengths:

This study is novel and the results are potentially important as they demonstrate an unexpected breakdown of the blood-brain barrier with physiological activity and this may serve a physiological purpose, affecting synaptic plasticity.

The strengths of the study are:

1) The use of an in vivo model with multiple methods to investigate the blood-brain barrier response to a forelimb stimulation.

2) The determination of a potential functional role for the observed leakage of the blood-brain barrier from both a genetic and electrophysiological viewpoint.

3) The demonstration that inhibiting different points in the putative pathway from activation of the cortex to transport of albumin and activation of the TGF-beta pathway, the effect on synaptic enhancement could be prevented.

4) Preliminary experiments demonstrating a similar observation of activity-dependent breakdown of the blood-brain barrier in humans.

Weaknesses:

There are both conceptual and experimental weaknesses.

1) The stimulation is in an animal anesthetized with ketamine, which can affect critical receptors (ie NMDA receptors) in synaptic plasticity.

We agree that the potential effects of anesthesia should be considered. The Discussion was revised to address this point: “A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can affect various receptors within the NVU, potentially altering neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketamine-xylazine anesthesia, which unlike other anesthetics, was shown to generate robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018)”

2) The stimulation protocol is prolonged and it would be helpful to know if briefer stimulations have the same effect or if longer stimulations have a greater effect ie does the leakage give a "readout" of the stimulation intensity/length.

Thank you for this important comment. We are also very curious about the potential relationship between stimulation magnitude/duration and subsequent leakage and have added the following statement to the discussion:

“Future studies should also explore the effects of stimulation magnitude/duration on BBB modulation, as well as the stimulation threshold between physiological and pathological increase in BBB permeability.”

Our current findings indicate that a one-minute stimulation does not affect vascular permeability or SEP and we aim to test additional stimulation paradigms in future studies.

3) For some of the experiments (see below), the numbers of animals are low and the statistical tests used may not be the most appropriate, making the results less clear cut.

We appreciate this comment and have revised the statistical analysis of Figure 1J,K. We now use a nested t-test to test for differences between rats (as opposed to sections). The differences remain significant (EB, p=0.0296; Alexa, p=0.0229). The text was modified accordingly.

4) The experimental paradigms are not entirely clear, especially the length of time of drug application and the authors seem to try to detect enhancement of a blocked SEP.

Thank you for pointing this out. Figures 2&3 were revised for clarification and a ‘Drug Application’ subsection was added to the methods section.

5) It is not clear how long the enhancement lasts. There is a remark that it lasts longer than 5 hours but there is no presentation of data to support this.

Thank you for this comment. As the length of experiments differed between animals, the exact length could not be specifically stated. To clarify this point, we revised the text to indicate that LTP was recorded until the end of each experiment (between 1.5-5 hours, depending on the condition the animal was in). We also added a panel to figure 2 (Figure 2d) with exemplary data showing potentiation 60, 90, and 120 min post stimulation.

6) The spatial and temporal specificity of this effect is unclear (other than hemispheric in rats) and even less clear in humans.

Our animal experiments (using both in vivo imaging and histological analysis) showed no evidence of BBB modulation outside the cortical somatosensory area corresponding to the limbs. We looked at the entirety of the coronal section of the brain and found enhancement solely in the somatosensory area corresponding to limb. The right side of panels h and i in Figure 1 show an x20 magnification of the section, focusing on the enhanced area. The whole section was not shown, as no fluorescence was found outside the magnified area. Moreover, our quantification showed that the enhancement was specific to the contralateral and not ipsilateral somatosensory cortex (Figure 1 j-k).

We agree that temporal specificity needs to be further explored, and we have now stated that in the discussion: “Future studies are needed to explore the BBB modulating effects of additional stimulation protocols – with varying durations, frequencies, and magnitudes. Such studies may also elucidate the temporal and ultrastructural characteristics that may differentiate between physiological and pathological BBB modulation.”

We also agree that larger studies are needed to better understand the specificity of the observed effect in humans, and to account for potential inter-human variability in vascular integrity and brain function due to different schedules, diets, exercise habits, etc.

8) The experimenters rightly use separate controls for most of the experiments but this is not always the case, also raising the possibility that the application of drugs was not done randomly or interleaved, but possibly performed in blocks of animals, which can also affect results.

Thank you for pointing out this lack of clarity. We have now highlighted that drug application was done randomly.

9) Methyl-beta-cyclodextrin clears cholesterol so the effect on albumin transport is not specific, it could be mediating its effect through some other pathway.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10uM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is observed at 5mM mβCD and not at 2.5mM/5mM (Koudinov & Koudinova, 2001). This point was added to the discussion.

10) Since the breakdown of the blood-brain barrier can be inhibited by a TGF-beta inhibitor, then this implies that TGFbeta is necessary for the breakdown of the blood-brain barrier. This does not sit well with the hypothesis that TGF-beta activation depends upon blood-brain barrier leakage.

Thank you for pointing out this lack of clarity. We have added a discussion paragraph that clarifies our hypothesis: “As mentioned above, albumin is a known activator of TGF-β signaling, and TGF-β has a well-established role in neuroplasticity. Interestingly, emerging evidence suggests that TGF-β also increases cross-BBB transcytosis (Betterton et al., 2022; Kaplan et al., 2020; McMillin et al., 2015; Schumacher et al., 2023). Hence, we propose the following two-part hypothesis for the TGF-β/BBB-mediated synaptic potentiation observed in our experiments: (1) prolonged stimulation triggers TGF-β signaling and increased caveolae-mediated transcytosis of albumin; and (2) extravasated albumin induces further TGF-β signaling, leading to synaptogenesis and additional cross-BBB transport – in a self-reinforcing positive feedback loop. Future research is needed to examine the validity of this hypothesis.

Reviewer #3 (Public Review):

Summary:

This study used prolonged stimulation of a limb to examine possible plasticity in somatosensory evoked potentials induced by the stimulation. They also studied the extent that the blood-brain barrier (BBB) was opened by prolonged stimulation and whether that played a role in the plasticity. They found that there was potentiation of the amplitude and area under the curve of the evoked potential after prolonged stimulation and this was long-lasting (>5 hrs). They also implicated extravasation of serum albumin, caveolae-mediated transcytosis, and TGFb signalling, as well as neuronal activity and upregulation of PSD95. Transcriptomics was done and implicated plasticity-related genes in the changes after prolonged stimulation, but not proteins associated with the BBB or inflammation. Next, they address the application to humans using a squeeze ball task. They imaged the brain and suggested that the hand activity led to an increased permeability of the vessels, suggesting modulation of the BBB.

Strengths:

The strengths of the paper are the novelty of the idea that stimulation of the limb can induce cortical plasticity in a normal condition, and it involves the opening of the BBB with albumin entry. In addition, there are many datasets and both rat and human data.

Weaknesses:

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

The conclusions are not compelling however because of a lack of explanation of methods and quantification.

Thank you for this comment. In the revised paper, we expanded the Methods section to better describe the procedures and approaches we used for data analysis.

It also is not clear whether the prolonged stimulation in the rat was normal conditions.

We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1) In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2) Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3) The levels of SEP potentiation we observed are similar to those reported in:

a) Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b) Humans following a 15 min task (McGregor et al., 2016).

This important point is now presented in the discussion.

Reviewer #1 (Recommendations For The Authors):

The discussion would benefit from additional discussion of the potential impacts of sex and anesthesia in their findings.

We agree with the reviewer and have added the following paragraph to the discussion:

“A key limitation of our animal experiments is the fact they were performed under anesthesia, due to the complex nature of the experimental setup (i.e., simultaneous cortical imaging and electrophysiological recordings). Anesthetic agents can potentially alter neuronal activity, SEPs, CBF, and vascular responses (Aksenov et al., 2015; Lindauer et al., 1993; Masamoto & Kanno, 2012). To minimize these effects, we used ketaminexylazine anesthesia, which unlike other anesthetics, was shown to maintain robust BOLD and SEP responses to neuronal activation (Franceschini et al., 2010; Shim et al., 2018). Another limitation of our animal study is the potentially non-specific effect of mβCD – an agent that disrupts caveola transport but may also lead to cholesterol depletion (Keller & Simons, 1998). To mitigate this issue, we used a very low mβCD concentration (10uM), orders of magnitude below the concentration reported to deplete cholesterol (Koudinov et al). Lastly, our animal study is limited by the inclusion of solely male rats. While our findings in humans did not point to sex-related differences in stimulation-evoked BBB modulation, larger animals and human studies are needed to examine this question.”

The figure text is quite small.

Thank you for pointing this out, we revised all figures and increased font size for clarity.

Including pharmacological concentrations within the figure legends would improve the readability of the manuscript.

Thank you for this suggestion, the figure legends were modified accordingly.

In methods for immunoassays the 5 groups could be more clear by stating that there are 3 timepoints for stimulation experiments. There is a typo in this section where the 24-hour post is stated twice in the same sentence.

Thank you for pointing this out, the text was modified accordingly.

Reviewer #2 (Recommendations For The Authors):

1) In Figure 1, J and K seem to indicate that in these experiments the statisitics were done per slice and not per animal. This is not a reasonable approach, a repeat measure ANOVA or averaging for each animal are more appropriate statistical approaches.

We thank the reviewer for pointing this out. The statistical analysis for Figure 1j,k was modified. We now use a nested ttest to test for differences between rats and not sections. The differences are still significant (EB, p=0.0296; Alexa, p=0.0229). The manuscript was modified accordingly.

2) In Figure 2, the protocol does not seem to give much idea about time course. There was a stimulation test for 1 minute before and then 1 minute after the 30-minute stimulation train. How was potentiation assessed for the next 5 hours and where are the data?

Potentiation was assessed by repeating 1min test stim every 30 min for the duration of the experiment, we added a panel to show late potentiation, see response above.

3) In Figure 2, there is a notable lack of controls eg the effect of sham stimulation and application of saline. These are important as the drift of response magnitude can be a problem in long experiments.

We did test for the potential presence of response drift, by examining whether SEPs of non-stimulated animals change over time (at baseline, 30 or 60 minutes of recording; n=6). No statistical differences were found. Our analysis focused on using each animal as its own control (i.e., comparing baseline SEP to SEP post albumin perfusion), because SEP studies highlight the importance of comparing each animal to its own baseline, due to the large inter-animal variability (All et al., 2010; Mégevand et al., 2009; Zandieh et al., 2003).

4) Figure 3 a is not clear – were the drugs applied throughout?

Thank you for pointing this out. We have revised Figure 3 a to show that the drugs were applied for 50 min before the stimulation.

5) In Figure 3 panel d is repeated in panel j. This needs correcting

Thank you. This mistake was fixed.

6) In LTP-type experiments usually the antagonist is applied during the stimulation and then washed out. This avoids the problem in this figure in which CNQX effectively blocks transmission and so it is not possible to detect any enhancement if it were there. Eg in panel e, CNQX block transmission, and then the assessment is performed when the AMPA receptors are blocked after 30 minutes of stimulation. If receptors are blocked no enhancement will be detectable. Moreover, surely the question is the ratio of the effect of 30-minute stimulation on the SEP in the presence of CNQX and so the statistics should be done on the fold change in the SEP following 30-minute stimulation in the presence of CNQX.

Thank you. The protocol might have been misrepresented in the original figure. We modified Fig 3a to clarify that the antagonists were indeed washed out upon stimulation start to make sure the receptors are not blocked during the test stimulation following the 30 min stimulation. In addition, we tested for the difference in fold change between 30 min stim, and 30 min stimulation following antagonists wash-in (Fig 3f and Fig S2a).

7) Interesting in Figure f, stimulation, albumin, and AP5 all seem to have the same enhancement of the SEP. Is the lack of effect of 30-minute stimulation in the presence of AP5, a ceiling effect ie AP5 has enhanced the SEP, and no further enhancement from stimulation is possible.

This is a very interesting point that will require further research.

8) SJN seems to block neurotransmission. What is the mechanism? The same analysis as for CNQX should be performed ie what is the fold change not compared to baseline but in the presence of SJN.

Our quantification showed that SJN did not significantly reduce the SEP max amplitude, and we therefore did not include this graph in the figure.

9) Please acknowledge that the effect of mbetaCD is non-specific. There is a large literature on the effects of cholesterol depletion on LTP.

We agree that the effect of mβCD may not be specific. To mitigate this issue, we used a very low mβCD concentration (10µM). Notably, this is markedly lower than the concentrations reported by Koudinov et al, showing that cholesterol depletion is only observed at a concentration of 5mM (Koudinov & Koudinova, 2001). This point is now discussed under the discussion paragraph describing the study’s limitations.

10) k&l seem to have used the same control in which case they should not be analysed separately (they are all part of the same experiment).

We agree with the reviewer and have revised the figure accordingly.

11) The difference in gene expression in Figure 4 would be more convincing if it could be prevented by for example a TGFbeta inhibitor.

We agree and acknowledge the impact such experiments could provide. We plan to incorporate these experiments into our future studies.

12) Figure 5 seems to indicate bilateral and widespread BBB modulation arguing that this may be a non-specific effect. Panel g should look at other neocortical regions eg occipital cortex.

We agree and thank the reviewer for this comment. We revised the figure to include other cortical areas, such as the frontal and occipital cortices (Figure 5g)

Minor comments

1) Paired data eg in Fig 2D are better represented by pairing the dots usually with a line.

2) Please correct the %fold baseline in axes in graphs which show % change for baseline.

3) Figure 4 is not correctly referred to in the text.

We agree with all the points raised by the reviewer and revised the figures and text accordingly.

Reviewer #3 (Recommendations For The Authors):

The conclusions are not compelling however because of a lack of explanation of methods and quantification. It also is not clear whether the prolonged stimulation in the rat was normal conditions. To their credit, the authors recorded the neuronal activity during stimulation, but it seemed excessive excitation. Since seizures open the BBB this result calls into question one of the conclusions. that the results reflect a normal brain. The authors could either conduct studies with stimulation that is more physiological or discuss the caveats of using a supraphysiological stimulus to infer healthy brain function.

Major concerns:

Methods need more explanation. Rationales need more justification. Examples are provided below.

Throughout many sections of the paper, sample sizes and stats are often missing. For stats, please provide p-values and other information (tcrit, U statistic, F, etc.)

Thank you, we added the relevant information where it was missing throughout the manuscript.

For transcriptomics, they might have found changes in BBB-related genes if they assayed vessels but they assayed the cortex.

We agree with the reviewer that this would be a very interesting future direction. The present study could not include this kind of analysis due to lack of access to vasculature isolation methods or single-cell RNA seq.

What were the inclusion/exclusion criteria for the subjects?

Thank you for pointing out this lack of clarity. The methods section (under ‘Magnetic Resonance Imaging’ – ‘Participants’) was expanded to include the following:

“Male and female healthy individuals, aged 18-35, with no known neurological or psychiatric disorders were recruited to undergo MRI scanning while performing a motor task (n=6; 3 males and 3 females). MRI scans of 10 sex- and age- matched individuals (with no known neurological or psychiatric disorders) who did not perform the task were used as control data (n=10; 5 males and 5 females.

Were they age and sex-matched?

They were, indeed, age and sex-matched. This was now clarified in the relevant Methods section.

Were there other factors that could have influenced the results?

Certainly. Human subjects are difficult to control for due to different schedules, diets, exercise habits, and other factors that may impact vascular integrity and brain function. Larger multimodal studies are needed to better understand the observed phenomenon.

Fig. 1. Images are very dim. Text here and in other figures is often too small to see. Some parts of the figures are not explained.

Our apologies. Figures and legends were revised accordingly.

Fig 2a, f. I don't see much difference here- do the authors think there was?

We agree that the difference may not be visually obvious. The quantification of trace parameters (amplitude and area under curve) does, however, reveal a significant SEP difference in response to both stimulation (panels X and y) and albumin (panels z and q).

Fig 3 d and j seem the same.

We thank the reviewer for noticing. This was a copy mistake that was now rectified.

Lesser concerns and examples of text that need explana9on:

Introduction

Insulin-like growth factor is transported. From where to where?

The text was edited to clarify that this was cross-BBB influx of insulin-like growth factor-I.

RMT that underlies the transport of plasma proteins was induced by physiological or non-physiological stimulation.

This was shown without stimulation, in normal physiology of young and aged healthy mice. The text was edited to clarify this point.

What was the circadian modulation that was shown to implicate BBB in brain function?

The text was edited for clarity.

Results

When the word stimulation is used please be specific if whiskers are moved by an experimenter, an electrode is used to apply current, etc.

We have now moved the ‘Stimulation protocol’ section closer to beginning of the Methods and emphasized that we administered electrical stimulation to the forepaw or hindlimb using subdermal needle electrodes.

Please explain how the authors are convinced they localized the vascular response.

The vascular response was localized via: (1) visual detection of arterioles that dilated in response to stimulation (due to functional hyperemia / neurovascular coupling) [figure 1 d]; and (2) quantitative mapping of increased hemoglobin concentration (Bouchard et al., 2009) [Figure 1 b]. This is now mentioned in the methods (under ‘In vivo imaging’) and results (under the ‘Stimulation increases BBB permeability’).

"30 min of limb stimulation" means what exactly? 6 Hz 2mA for 30 min?

Thank you. The text was revised for clarity (Methods under ‘Stimulation protocol’):

“The left forelimb or hind limb of the rat was stimulated using Isolated Scmulator device (AD Instruments) attached with two subdermal needle electrodes (0.1 ms square pulses, 2-3 mA) at 6 Hz frequency. Test stimulation consisted of 360 pulses (60 s) and delivered before (as baseline) and after long-duration stimulation (30 min, referred throughout the text as ‘stimulation’). In control and albumin rats, only short-duration stimulations were performed. Under sham stimulation, electrodes were placed without delivering current.”

Histology that was performed to confirm extravasation needs clarification because if tissue was removed from the brain, and fixed in order to do histology, what is outside the vessels would seem likely to wash away.

Thank you for pointing out the need to clarify this point. The Histology description in the Methods section was revised in the following manner:

“Albumin extravasacon was confirmed histologically in separate cohorts of rats that were anesthetized and stimulated without craniotomy surgery. Assessment of albumin extravasacon was performed using a well-established approach that involves peripheral injection of either labeled-albumin (bovine serum albumin conjugated to Alexa Flour 488, Alexa488-Alb) or albumin-labeling dye (Evans blue, EB – a dye that binds to endogenous albumin and forms a fluorescent complex), followed by histological analysis of brain tissue (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Obermeier et al., 2013; Veksler et al., 2020). Since extravasated albumin is taken up by astrocytes (Ivens et al., 2007; Obermeier et al., 2013), it can be visualized in the brain neuropil after brain removal and fixation (Ahishali & Kaya, 2020; Ivens et al., 2007; Lapilover et al., 2012; Veksler et al., 2020). Five rats were injected with Alexa488-Alb (1.7 mg/ml) and five with EB (2%, 20 mg/ml, n=5). The injections were administered via the tail vein. Following injection, rats were transcardially perfused with…”

It is not clear why there was extravasacon contralateral but not ipsilateral if there are cortical-cortical connections.

Interpersonally, we also did not observe ipsilateral SEP in response to limb stimulation, with evidence of SEP and BBB permeability only in the contralateral sensorimotor region. This finding is consistent with electrophysiological and fMRI studies showing that peripheral stimulation results in predominantly contralateral potentials (Allison et al., 2000; Goff et al., 1962).

After injection of Evans blue or Alexa-Alb, how was it shown that there was extravasacon?

Extravasalon in cortical sections was visualized using a fluorescent microscope (Figure 1 h-i). Since extravasated albumin is taken up by astrocytes, fluorescent imaging can be used for visualizing and quantifying labeled albumin (Ahishali & Kaya, 2020; Ivens et al., 2007; Knowland et al., 2014). Here is the relevant methods excerpt:

“Coronal sections (40-μm thick) were obtained using a freezing microtome (Leica Biosystems) and imaged for dye extravasacon using a fluorescence microscope (Axioskop 2; Zeiss) equipped with a CCD digital camera (AxioCam MRc 5; Zeiss).”

How is a sham control not stimulated - what is the sham procedure?

In the sham stimulation protocol electrodes were placed, but current was not delivered. A section titled ‘Stimulation protocol’ was added to the methods to clarify this point.

What was the method for photothrombosis-induced ischemia?

The procedure for photothrombosis-induced ischemia is described under the Methods section ‘Immunoassays’ – ‘Enzyme-linked immunosorbent assay (ELISA) for albumin extravasalon’:

“Rats were anesthetilzed and underwent … photothrombosis stroke (PT) as previously described (Lippmann et al., 2017; Schoknecht et al., 2014). Briefly, Rose Bengal was administered intravenously (20 mg/kg) and a halogen light beam was directed for 15 min onto the intact exposed skull over the right somatosensory cortex.”

Fig 1d. All parts of d are not explained.

Thank you for pointing this out. In the revised manuscript, the panels of this figure were slightly reordered, and we made sure all panels are explained in the legend.

e. Is the LFP a seizure? How physiological is this- it does not seem very physiological.

Thank you for your comment. We believe that this activity is not a seizure because it lacks the typical slow activity that corresponds to the “depolarizalon shir” observed during seizures (Ivens et al., 2007; Milikovsky et al., 2019; Zelig et al., 2022).

f. Permeability index needs explanation. How was the area chosen for each rat? Randomly? Was it the same across rats?

We have now revised the Methods section to provide a clearer description of the permeability index calculation and the choice of the imaging area:

“Across all experiments, acquired images were the same size (512 × 512 pixel, ~1x1 mm), centered above the responding arteriole. Images were analyzed offline using MATLAB as described (Vazana et al., 2016). Briefly, image registration and segmentation were performed to produce a binary image, separating blood vessels from extravascular regions. For each extravascular pixel, a time curve of signal intensity over time was constructed. To determine whether an extravascular pixel had tracer accumulation over time (due to BBB permeability), the pixel’s intensity curve was divided by that of the responding artery (i.e., the arterial input function, AIF, representing tracer input). This ratio was termed the BBB permeability index (PI), and extravascular pixels with PI > 1 were identified as pixels with tracer accumulation due to BBB permeability.”

g. For Evans blue and Alexa-Alb was the sample size rats or sections?

Thank you for this question. We revised the statistical analysis for Figure 1j,k to appropriately asses the differences between rats. We used a nested t-test to test for differences between rats (and not sections). The differences remained significant (EB, p=0.0296; Alexa, p=0.0229) and the text was modified accordingly.

h, i, j need more contrast and/or brightness to appreciate the images. Arrows would help. The text is too small to read.

Thank you. This issue was addressed in the revised paper.

To induce potentiation, 6 Hz 2 mA stimuli were used for 30 min. Please justify this as physiological.

Thank you for the comment. We believe that the used stimulation protocol is within the physiological range (and relevant to plasticity, learning and memory) for the following reasons:

1. In our continuous electrophysiological recordings, we did not observe any form of epileptiform or otherwise pathological activity.

2. Memory/training/skill acquisition experiments in humans often involve similar training duration or longer (Bengtsson et al., 2005), e.g., a 30 min thumb training session performed by (Classen et al., 1998).

3. The levels of SEP potentiation we observed are similar to those reported in:

a. Rats following a 10-minute whisker stimulation (one hour post stimulation, (Mégevand et al., 2009)).

b. Humans following a 15 min task (McGregor et al., 2016).

We have revised the Discussion of the paper to clarify this important point.

The test stimulus to evoke somatosensory evoked potentials was 1 min. Was this 6 Hz 2 mA for 1 min? Please justify.

Yes. We chose these parameters as these ranges were shown to induce the largest changes in blood flow (with laserdoppler flowmetry) and summated SEP (Ngai et al., 1999), corresponding with our findings. We also show that these stimulation parameters do not induce changes in BBB permeability nor synaptic potentiation, therefore served as test control.

How long after the 30 min was the test stimulus triggered- immediately? 30 sec afterwards?

The test stimulus was applied 5 min afterwards to allow for BBB imaging protocol (now explained in the Methods section).

How were amplitude and AUC measured? Baseline to peak? For AUC is it the sum of the upward and downward deflections comprising the LFP?

Yes, and yes. This is now clarified in the ‘Analysis of electrophysiological recordings’ section in the Methods.

How was the same site in the somatosensory cortex recorded for each animal?<br /> Potentiation was said to last >5 hrs. How often was it measured? Was potentiation the same for the amplitude and the AUC?

The location of the cranial window over the somatosensory cortex was the same in all rats. The location of the specific responding arteriole may change between animals, but the recording electrode was places around the responding arteriole in the same approaching angle and depth for all animals.

As the length of experiments differed between animals, the exact length could not be specifically stated. We therefore revised the text to clarify that LTP was recorded until the end of each experiment (depending on the animal condition, between 1.5-5 hours) and added a panel to figure 2 (Figure 2f) with exemplary data showing potentiation 120 min (2hr) post stimulation.

Why was 25% of the serum level of albumin selected- does the brain ever get exposed to that much? Was albumin dissolved in aCSF or was aCSF chosen as a control for another reason?

Yes, albumin was dissolved in aCSF and the solution was allowed to diffuse through the brain. The relatively high concentration of albumin was chosen to account for factors that lower its effective tissue concentration:

1. The low diffusion rate of albumin (Tao & Nicholson, 1996).

2. The likelihood of albumin to encounter a degradation site or a cross-BBB efflux transporter (Tao & Nicholson, 1996; Zhang & Pardridge, 2001).

Figure 2.

a. Please show baseline, the stimulus, and aftier the stimulus.

Please point out when there was stimulacon.

What is the inset at the top?

The inset on top is the example trace of the stimulus waveform, the legend of the figure was modified for clarity.

b. Please show when the stimulus artifact occurred. The end of the 1-minute test stimulus period is fine. Why are the SEPs different morphologies? It suggests the different locations in the cortex were recorded.

What is shown is the averaged SEP response over 1min test stimulus, each SEP is time locked to each stimulus. Regarding SEP waveform, it does indeed show different morphology between animals, as sometimes different arterioles respond to the stimulation, and we localize the recording to the responding vessel in each rat. However, in each rat the recording is only from one location. Once the electrode was positioned near the responding arteriole it was not moved.

d, e. What are the stats?

h, i. Add stats. Are all comparisons Wilcoxon? Please provide p values.

The comparisons were performed with the Wilcoxon test. We now state that and provide the exact p values.

j. What was selected from the baseline and what was selected during Albumin and how long of a record was selected?

What program was used to create the spectrogram?

What is meant by changes at frequencies above 200 Hz, the frequencies of HFOs?

The Method section (under ‘Electrophysiology – Data acquisition and analyses’) has been revised for clarification. Spectrogram was created with MATLAB and graphed with Prism. For analysis, we selected a 10 min recorded segment before starting albumin perfusion, and 10 min after terminating albumin perfusion.

When the cortial window was exposed to drugs, what were concentrations used that were selective for their receptor? How long was the exposure?

Was the vehicle tested?

We have revised the Methods section (under ‘Animal preparation and surgical procedures - Drug application’) to clarify the duration and concentration used and justification. All blockers were exposed for 50 min. The vehicle was an artificial cerebrospinal fluid solution (aCSF).

For PSD-95, what was the area of the cortex that was tested?

Were animals acutely euthanized and the brain dissected, frozen, etc?

We have revised the Methods section (under ‘Immunoassays’) for clarity.

What is mbetaCD?

The full term was added to the results section. It is also mentioned in the Methods.

Is SJN specific at the concentration that was chosen? Did it inhibit the SEP?

In the concentration used in our experiments, SJN is a selective TGF-β type I receptor ALK5 inhibitor (see (Gellibert et al., 2004)).

Fig. 3b. It looks like CNQX increased the width of the vessels quite a bit. Please explain.

For AP5, very large vessels were imaged, making it hard to compare to the other data.

The vascular dilation in response to the stimulation under CNQX was similar to that seen under “normal” conditions (i.e. aCSF). As for AP5, in some experiments the responding arteriole was in close proximity to a large venule that cannot be avoidable while imaging. For quantification we always measured arterioles within the same diameter range.

e. Sometimes CNQX did not block the response after 30 min stimulation. Why?

CNQX is washed out before the 30 min stimulation starts, so it is not expected to block the response to stimulation. However, in some cases the response to stimulation was lower in amplitude, likely due to residual CNQX that did not wash out completely.

Regarding DEGs, on the top of p 10 what are the percentages of?

In this analysis we tested in each hemisphere how many genes expressed differentially between 1 and 24 hours post stimulation (either up- or down- regulated). The results were presented as the percentages of differentially expressed genes in each hemisphere (13.2% contralateral, and 7.3% ipsilateral). The text was rephrased for clarity.

Please add a ref for the use of the JSD metric methods and support for its use as the appropriate method. Other methods need explanation/references.

References were added to the text to clarify. The Jensen-Shannon Divergence metric is commonly used to calculate the statistical pairwise distance among two distributions (Sudmant et al., 2015). From comparing a few different distance metric calculations including JSD, our results were similar irrespective of the distance metric applied. Therefore, we demonstrate the variability between paired samples of stimulated and non-stimulated cortex of each animal at two time points following stimulation (24 h vs. 1 h) using JSD.

What synaptic plasticity genes were selected for assay and what were not?

What does "largely unaffected" mean? Some of the genes may change a small amount but have big functional effects.

The selected genes of interest were taken from a large list compiled from previous publications (see (Cacheaux et al., 2009; Kim et al., 2017)) and are well documented in gene ontology databases and tools (e.g., Metascape, (Zhou et al., 2019)).

We agree that the term ‘largely unaffected’ is suboptimal, and we rephrased this section of the results to indicate that “No significant differences were found in BBB or inflammation related genes between the hemispheres”. We also agree that a small number of genes can have big functional effects. Future studies are needed to better understand the genes underlying the observed BBB modulation.

Please note that Slc and ABCs are not only involved in the BBB.

Thank you. We modified the text to no longer specify that these are BBB-specific transporters.

Please explain the choice of the stress ball squeeze task, and DCE.

DCE is a well-established method for BBB imaging in living humans, and it is cited throughout the manuscript. The ball squeeze task was chosen as it is presumed to involve primarily sensory motor areas, without high-level processing (Halder et al., 2005). This is now stated in the discussion.

What is Gd-DOTA?

Gd-DOTA is a gadolinium-based contrast agent (gadoterate meglumine, AKA Dotarem). Text was revised for clarity. Please see the Methods section under ‘Magnetic Resonance Imaging’ - ‘Data Acquisition’.

What does a higher percentage of activated regions mean- how was activacon defined and how were regions counted?

Higher percentage of activated regions refers to regions in which voxels showed significant BOLD changes due to the motor task preformed. The statistical approaches and analyses are detailed in the Methods section under ‘Magnetic Resonance Imaging - Preprocessing of functional data, and fMRI Localizer Motor Task’.

Figure. 4

Was stimulation 1 min or 30 min.?

30 min, Text has been revised for clarity.

What is the Wald test and how were p values adjusted-please add to the Stats section.

The Methods section under ‘Statistical analysis’ was revised to clarify this point.

Is there a reason why p values are sometimes circles and otherwise triangles?

The legend was revised to explain that ”Circles represent genes with no significant differences between 1 and 24 h poststimulation. Upward and downward triangles indicate significantly up- and down- regulated genes, respectively.”

How can a p-value be zero? Please explain abbreviations.

The p-value is very low (~10-10) and therefore appears to be zero due to the scale of the y-axis.

Fig. 5b.

There are unexplained abbreviations.

The x on the ball and hand is not clear relative to the black ball and hand.

Thank you for noticing. We revised the figure for clarity.

c. What was the method used to make an activator map and what is meant by localizer task?

The explanation of the “fMRI Localizer Motor Task” section in the methods was revised for added clarity.

f. What is the measurement "% area" that indicates " BBB modulation"?

Is it in f, the BBB permeable vessels (%)? f. Please explain: "Heatmap of BBB modulated voxels percentage in motor/sensory-related areas of task vs. controls."

The %area measurement indicates the percentage of voxels within a specific brain region that have a leaky BBB. See Methods.

Is Task - the control?

Yes.

Supplemental Fig. 2.

Why is AUC measured, not amplitude?

The amplitude, and now also the AUC are shown in Figure 3.

b. There is no comparison to baseline. The arrowhead points to the start of stimulation but there is no arrowhead marking the end.

In the revised paper we added a grey shade over the stimulation period to better visualize the difference to baseline. In this panel we wanted to show that NMDA receptor antagonist did not block the SEP, while AMPA receptor antagonist did.

c. In the blot there are two bands for PSD95- which is the one that is PSD95? There is no increase in PSD95 uncl 24 hrs but in the graph in d there is. In the blot, there is a strong expression of PSD95 ipsilateral compared to contralateral in the sham-why?

What is the percent change fold?

The PSD-95 is the top and larger band. The lower band was disregarded in the analysis. The example we show may not fully reflect the group statistics presented in panel d. Upon quantification of 8 animals, PSD-95 is significantly higher 30 min and 24 hours post stimulation in the contralateral hemisphere. No significant changes were found in sham animals. The % change fold refers to the AUC change compared to baseline. This panel was now incorporated in Figure 3 (panel h), and the title was corrected to “|AUC|, % change from baseline”.

Supplemental Fig. 4.

a. If ipsilateral and contralateral showed many changes why do the authors think the effects were only contralateral?

Our gene analysis was designed to complement our in vivo and histological findings, by assessing the magnitude of change in differentially expressed genes (DEGs). This analysis showed that: (1) the hemisphere contralateral to the stimulus has significantly more DEGs than the ipsilateral hemisphere; and (2) the DEGs were related to synaptic plasticity and TGF-b signaling. These findings strengthen the hypothesis raised by our in vivo and histological experiments.

Supplemental Fig. 5 includes many processes not in the results. Examples include dorsal cuneate and VPL, dynamin, Kir, mGluR, etc. The top right has numbers that are not mentioned. If the drawings are from other papers they should be cited.

The drawings of Figure 5 are original and were not published before. This hypothesis figure points to mechanisms that may drive the phenomena described in the paper. The legend of the figure was revised to include references to mechanisms that were not tested in this study.

Papers referenced in this letter:

Ahishali, B., & Kaya, M. (2020). Evaluation of Blood-Brain Barrier Integrity Using Vascular Permeability Markers: Evans Blue, Sodium Fluorescein, Albumin-Alexa Fluor Conjugates, and Horseradish Peroxidase. Methods in Molecular Biology, 2367, 87–103. https://doi.org/10.1007/7651_2020_316

Aksenov, D. P., Li, L., Miller, M. J., Iordanescu, G., & Wyrwicz, A. M. (2015). Effects of anesthesia on BOLD signal and neuronal activity in the somatosensory cortex. Journal of Cerebral Blood Flow and Metabolism, 35(11), 1819–1826. https://doi.org/10.1038/jcbfm.2015.130

All, A. H., Agrawal, G., Walczak, P., Maybhate, A., Bulte, J. W. M., & Kerr, D. A. (2010). Evoked potential and behavioral outcomes for experimental autoimmune encephalomyelitis in Lewis rats. Neurological Sciences, 31(5), 595–601. https://doi.org/10.1007/s10072-010-0329-y

Allison, J. D., Meador, K. J., Loring, D. W., Figueroa, R. E., & Wright, J. C. (2000). Functional MRI cerebral activation and deactivation during finger movement. Neurology, 54(1), 135–142. https://doi.org/10.1212/wnl.54.1.135

Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & Ullén, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature Neuroscience, 8(9), 1148–1150. https://doi.org/10.1038/nn1516

Betterton, R. D., Abdullahi, W., Williams, E. I., Lochhead, J. J., Brzica, H., Stanton, J., Reddell, E., Ogbonnaya, C., Davis, T. P., & Ronaldson, P. T. (2022). Regula/on of Blood-Brain Barrier Transporters by Transforming Growth Factor-β/Activin Receptor-Like Kinase 1 Signaling: Relevance to the Brain Disposition of 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase Inhibitors (i.e., Sta/ns). Drug Metabolism and Disposition, 50(7), 942–956. https://doi.org/10.1124/dmd.121.000781

Bouchard, M. B., Chen, B. R., Burgess, S. A., & Hillman, E. M. C. (2009). Ultra-fast multispectral optical imaging of cortical oxygenation, blood flow, and intracellular calcium dynamics. Optics Express, 17(18), 15670. https://doi.org/10.1364/oe.17.015670

Cacheaux, L. P., Ivens, S., David, Y., Lakhter, A. J., Bar-Klein, G., Shapira, M., Heinemann, U., Friedman, A., & Kaufer, D. (2009). Transcriptome profiling reveals TGF-β signaling involvement in epileptogenesis. Journal of Neuroscience, 29(28), 8927–8935. https://doi.org/10.1523/JNEUROSCI.0430-09.2009

Classen, J., Liepert, J., Wise, S. P., Hallett, M., & Cohen, L. G. (1998). Rapid plasticity of human cortical movement representation induced by practice. Journal of Neurophysiology, 79(2), 1117–1123. https://doi.org/10.1152/JN.1998.79.2.1117/ASSET/IMAGES/LARGE/JNP.JA47F4.JPEG

Franceschini, M. A., Radhakrishnan, H., Thakur, K., Wu, W., Ruvinskaya, S., Carp, S., & Boas, D. A. (2010). The effect of different anesthetics on neurovascular coupling. NeuroImage, 51(4), 1367–1377. https://doi.org/10.1016/j.neuroimage.2010.03.060

Gellibert, F., Woolven, J., Fouchet, M. H., Mathews, N., Goodland, H., Lovegrove, V., Laroze, A., Nguyen, V. L., Sautet, S., Wang, R., Janson, C., Smith, W., Krysa, G., Boullay, V., De Gouville, A. C., Huet, S., & Hartley, D. (2004). Identification of 1,5-naphthyridine derivatives as a novel series of potent and selective TGF-β type I receptor inhibitors. Journal of Medicinal Chemistry, 47(18), 4494–4506. https://doi.org/10.1021/jm0400247

Goff, W. R., Rosner, B. S., & Allison, T. (1962). Distribution of cerebral somatosensory evoked responses in normal man. Electroencephalography and Clinical Neurophysiology, 14(5), 697–713. https://doi.org/10.1016/0013-4694(62)90084-6

Halder, P., Sterr, A., Brem, S., Bucher, K., Kollias, S., & Brandeis, D. (2005). Electrophysiological evidence for cortical plasticity with movement repetition. European Journal of Neuroscience, 21(8), 2271–2277. https://doi.org/10.1111/J.1460-9568.2005.04045.X

Ivens, S., Kaufer, D., Flores, L. P., Bechmann, I., Zumsteg, D., Tomkins, O., Seiffert, E., Heinemann, U., & Friedman, A. (2007). TGF-β receptor-mediated albumin uptake into astrocytes is involved in neocortical epileptogenesis. Brain, 130(2), 535–547. https://doi.org/10.1093/brain/awl317

Kaplan, L., Chow, B. W., & Gu, C. (2020). Neuronal regulation of the blood–brain barrier and neurovascular coupling. In Nature Reviews Neuroscience (Vol. 21, Issue 8, pp. 416–432). Nature Research. https://doi.org/10.1038/s41583-020-0322-2

Keller, P., & Simons, K. (1998). Cholesterol is required for surface transport of influenza virus hemagglutinin. Journal of Cell Biology, 140(6), 1357–1367. https://doi.org/10.1083/jcb.140.6.1357

Kim, S. Y., Senatorov, V. V., Morrissey, C. S., Lippmann, K., Vazquez, O., Milikovsky, D. Z., Gu, F., Parada, I., Prince, D. A., Becker, A. J., Heinemann, U., Friedman, A., & Kaufer, D. (2017). TGFβ signaling is associated with changes in inflammatory gene expression and perineuronal net degradation around inhibitory neurons following various neurological insults. Scientific Reports, 7(1), 7711. https://doi.org/10.1038/s41598-017-07394-3

Knowland, D., Arac, A., Sekiguchi, K. J., Hsu, M., Lutz, S. E., Perrino, J., Steinberg, G. K., Barres, B. A., Nimmerjahn, A., & Agalliu, D. (2014). Stepwise Recruitment of Transcellular and Paracellular Pathways Underlies Blood-Brain Barrier Breakdown in Stroke. Neuron, 82(3), 603–617. https://doi.org/10.1016/j.neuron.2014.03.003

Koudinov, A. R., & Koudinova, N. V. (2001). Essen/al role for cholesterol in synaptic plasticity and neuronal degeneration. The FASEB Journal, 15(10), 1858–1860. https://doi.org/10.1096/r.00-0815re

Lapilover, E. G., Lippmann, K., Salar, S., Maslarova, A., Dreier, J. P., Heinemann, U., & Friedman, A. (2012). Periinfarct blood-brain barrier dysfunction facilitates induction of spreading depolarization associated with epileptiform discharges. Neurobiology of Disease, 48(3), 495–506. htttts://doi.org/10.1016/j.nbd.2012.06.024

Lindauer, U., Villringer, A., & Dirnagl, U. (1993). Characterization of CBF response to somatosensory stimulation: Model and influence of anesthetics. American Journal of Physiology - Heart and Circulatory Physiology, 264(4 33-4), 223–1228. https://doi.org/10.1152/ajpheart.1993.264.4.h1223

Lippmann, K., Kamintsky, L., Kim, S. Y., Lublinsky, S., Prager, O., Nichtweiss, J. F., Salar, S., Kaufer, D., Heinemann, U., & Friedman, A. (2017). Epileptiform activity and spreading depolarization in the bloodbrain barrier-disrupted peri-infarct hippocampus are associated with impaired GABAergic inhibition and synaptic plasticity. Journal of Cerebral Blood Flow and Metabolism, 37(5), 1803–1819. https://doi.org/10.1177/0271678X16652631

Masamoto, K., & Kanno, I. (2012). Anesthesia and the quantitative evaluation of neurovascular coupling. In Journal of Cerebral Blood Flow and Metabolism (Vol. 32, Issue 7, pp. 1233–1247). SAGE PublicationsSage UK: London, England. https://doi.org/10.1038/jcbfm.2012.50

McGregor, H. R., Cashaback, J. G. A., & Gribble, P. L. (2016). Functional Plasticity in Somatosensory Cortex Supports Motor Learning by Observing. Current Biology, 26(7), 921–927. https://doi.org/10.1016/j.cub.2016.01.064

McMillin, M. A., Frampton, G. A., Seiwell, A. P., Patel, N. S., Jacobs, A. N., & DeMorrow, S. (2015). TGFβ1 exacerbates blood-brain barrier permeability in a mouse model of hepatic encephalopathy via upregulation of MMP9 and downregulation of claudin-5. Laboratory Investigation, 95(8), 903–913. https://doi.org/10.1038/labinvest.2015.70

Mégevand, P., Troncoso, E., Quairiaux, C., Muller, D., Michel, C. M., & Kiss, J. Z. (2009). Long-term plasticity in mouse sensorimotor circuits after rhythmic whisker stimulation. Journal of Neuroscience, 29(16), 5326– 5335. https://doi.org/10.1523/JNEUROSCI.5965-08.2009

Milikovsky, D. Z., Ofer, J., Senatorov, V. V., Friedman, A. R., Prager, O., Sheintuch, L., Elazari, N., Veksler, R., Zelig, D., Weissberg, I., Bar-Klein, G., Swissa, E., Hanael, E., Ben-Arie, G., Schefenbauer, O., Kamintsky, L., Saar-Ashkenazy, R., Shelef, I., Shamir, M. H., … Friedman, A. (2019). Paroxysmal slow cortical activity in Alzheimer’s disease and epilepsy is associated with blood-brain barrier dysfunction. Science Translational Medicine, 11(521), eaaw8954–eaaw8954. https://doi.org/10.1126/scitranslmed.aaw8954

Ngai, A. C., Jolley, M. A., D’Ambrosio, R., Meno, J. R., & Winn, H. R. (1999). Frequency-dependent changes in cerebral blood flow and evoked potentials during somatosensory stimulation in the rat. Brain Research, 837(1–2), 221–228. https://doi.org/10.1016/S0006-8993(99)01649-2

Obermeier, B., Daneman, R., & Ransohoff, R. M. (2013). Development, maintenance and disruption of the blood-brain barrier. In Nature Medicine (Vol. 19, Issue 12, pp. 1584–1596). Nature Publishing Group. https://doi.org/10.1038/nm.3407

Schoknecht, K., Prager, O., Vazana, U., Kamintsky, L., Harhausen, D., Zille, M., Figge, L., Chassidim, Y., Schellenberger, E., Kovács, R., Heinemann, U., & Friedman, A. (2014). Monitoring stroke progression: In vivo imaging of cortical perfusion, blood-brain barrier permeability and cellular damage in the rat photothrombosis model. Journal of Cerebral Blood Flow and Metabolism, 34(11), 1791–1801. https://doi.org/10.1038/jcbfm.2014.147

Schumacher, L., Slimani, R., Zizmare, L., Ehlers, J., Kleine Borgmann, F., Fitzgerald, J. C., Fallier-Becker, P., Beckmann, A., Grißmer, A., Meier, C., El-Ayoubi, A., Devraj, K., Mittelbronn, M., Trautwein, C., & Naumann, U. (2023). TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes. Biomedicines, 11(1), 1–19. https://doi.org/10.3390/biomedicines11010214

Shim, H. J., Jung, W. B., Schlegel, F., Lee, J., Kim, S., Lee, J., & Kim, S. G. (2018). Mouse fMRI under ketamine and xylazine anesthesia: Robust contralateral somatosensory cortex ac/va/on in response to forepaw stimulation. NeuroImage, 177, 30–44. https://doi.org/10.1016/J.NEUROIMAGE.2018.04.062

Sudmant, P. H., Alexis, M. S., & Burge, C. B. (2015). Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome Biology, 16(1), 287. https://doi.org/10.1186/s13059-015-0853-4

Tao, L., & Nicholson, C. (1996). Diffusion of albumins in rat cortical slices and relevance to volume transmission. Neuroscience, 75(3), 839–847. https://doi.org/10.1016/0306-4522(96)00303-X

Vazana, U., Veksler, R., Pell, G. S., Prager, O., Fassler, M., Chassidim, Y., Roth, Y., Shahar, H., Zangen, A., Raccah, R., Onesti, E., Ceccanti, M., Colonnese, C., Santoro, A., Salvati, M., D’Elia, A., Nucciarelli, V., Inghilleri, M., & Friedman, A. (2016). Glutamate-mediated blood–brain barrier opening: Implications for neuroprotection and drug delivery. Journal of Neuroscience, 36(29), 7727–7739. https://doi.org/10.1523/JNEUROSCI.0587-16.2016

Veksler, R., Vazana, U., Serlin, Y., Prager, O., Ofer, J., Shemen, N., Fisher, A. M., Minaeva, O., Hua, N., SaarAshkenazy, R., Benou, I., Riklin-Raviv, T., Parker, E., Mumby, G., Kamintsky, L., Beyea, S., Bowen, C. V., Shelef, I., O’Keeffe, E., … Friedman, A. (2020). Slow blood-to-brain transport underlies enduring barrier dysfunction in American football players. Brain, 143(6), 1826–1842. https://doi.org/10.1093/brain/awaa140

Zandieh, S., Hopf, R., Redl, H., & Schlag, M. G. (2003). The effect of ketamine/xylazine anesthesia on sensory and motor evoked potentials in the rat. Spinal Cord, 41(1), 16–22. https://doi.org/10.1038/sj.sc.3101400

Zelig, D., Goldberg, I., Shor, O., Ben Dor, S., Yaniv-Rosenfeld, A., Milikovsky, D. Z., Ofer, J., Imtiaz, H., Friedman, A., & Benninger, F. (2022). Paroxysmal slow wave events predict epilepsy following a first seizure. Epilepsia, 63(1), 190–198. https://doi.org/10.1111/epi.17110

Zhang, Y., & Pardridge, W. M. (2001). Mediated efflux of IgG molecules from brain to blood across the blood– brain barrier. Journal of Neuroimmunology, 114(1–2), 168–172. https://doi.org/10.1016/S01655728(01)00242-9

Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10(1), 1–10. https://doi.org/10.1038/s41467-019-09234-6

Author Response

Reviewer #1 (Public Review)

Midbrain dopamine neurons have attracted attention as a part of the brain's reward system. A different line of research, on the other hand, has shown that these neurons are also involved in higher cognitive functions such as short-term memory. However, these neurons are thought not to encode short-term memory itself because they just exhibit a phasic response in short-term memory tasks, which cannot seem to maintain information during the memory period. To understand the role of dopamine neurons in short-term memory, the present study investigated the electrophysiological property of these neurons in rodents performing a T-maze version of a short-term memory task, in which a visual cue indicated which arm (left or right) of the T-maze was associated with a reward. The animal needed to maintain this information while they were located between the cue presentation position and the selection position of the T-maze. The authors found that the activity of some dopamine neurons changed depending on the information while the animals were located in the memory position. This dopamine neuron modulation was unable to explain the motivation or motor component of the task. The authors concluded that this modulation reflected the information stored as short-term memory.

I was simply surprised by their finding because these dopamine neurons are similar to neurons in the prefrontal cortex that store memory information with sustained activity. Dopamine neurons are an evolutionally conserved structure, which is seen even in insects, whereas the prefrontal cortex is developed mainly in the primate. I feel that their findings are novel and would attract much attention from readers in the field. But the authors need to conduct additional analyses to consolidate their conclusion.

We thank reviewer #1 for the positive assessment and for the valuable and constructive comments on our manuscript.

Reviewer #1 (Recommendations to The Authors)

(1) The authors found the dopamine neuron modulation that reflected the memory information during the delay period. Here the dopamine neuron activity was aligned by the position, not by time, in which the animals needed to maintain the information. Usually, the activity was aligned by time, and many studies found that dopamine neurons exhibited a short duration burst in response to rewards and behaviorally relevant stimuli including visual cues presented in short-term memory tasks. For comparison, I (and probably other readers) want to see the time-aligned dopamine neuron modulation that reflected the memory information. Did the modulation still exist? Did it have a long duration? The authors just showed the time-aligned "population" activity that exhibited no memory-dependent modulation.

We agree that the point raised by the reviewer is important. To address this question, we added a new paragraph to the Methods section titled “Methodological considerations” (in line 793 of the revised manuscript), where we explain the caveats of using time alignment in the T-maze task study. We also created a new sup figure 5 to clarify our argument. As the figure shows, we did not observe major differences in the firing rates when they were arranged by position or time. More importantly, we did not detect brief bursts of activity in response to the visual cue which could reflect an RPE signaling scheme. Our interpretation is that in the T-maze task, DA neurons encode “miniature” RPE signals between successive states in the T-maze, which are hard to detect, especially when neurons receive a continuous sensory input during trials.

(2) Several studies have reported that dopamine neurons at different locations encode distinct signals even within the VTA or SNr. Were the locations of dopamine neurons maintaining the memory information different from those of other dopamine neurons?

We thank the reviewer’s comment. Indeed, there is evidence from recent studies demonstrating that DA neurons form functional and anatomical clusters in the VTA and SN. Following the reviewer’s advice, we report the anatomical structure of memory and non-memory-specific neurons in the revised manuscript. You can read these results in the paragraph “Anatomical organization of trajectory-specific neurons.” in the “Results” section (in line 383 of the revised manuscript) and in the new sup figure 11. We only observed a clear functional-anatomical segregation in GABA neurons, but not in DA neurons. But we should note that the absence of segregation in the DA neurons could be accounted for by the fact that we recorded mostly from the lateral VTA, therefore we do not have any numbers from the medial VTA.

(3a) Did the dopamine neurons maintaining the memory information respond to reward?

We believe that we have already provided the data that can partially answer this question by correlating the firing rate difference between the reward and memory delay sections. This result was described in the “Neuronal activities in delay and reward are unrelated.” paragraph and in Figure 6. Moreover, motivated by the reviewer’s question, we also performed additional analysis, which is included in the revised manuscript. Briefly, we clustered significant responses between the memory delay and reward sections (Category 1: Left-signif, R-signif or No-signif / Category 2: Memory delay or Reward). We discovered that only a very small number of neurons showed the same significant trajectory preference in the memory delay and reward sections (i.e., significant preference for left trials in the memory delay and significant preference for the left reward). In fact, more significant neurons showed a preference for opposite trajectories (i.e. significant preference for left trials in memory delay and a significant preference for right rewards). A description of the new results is included in the “Neuronal activities in delay and reward are unrelated.” paragraph (in line 349 of the revised manuscript) and in the new supplementary Figure 11.

(3b) Did they encode reward prediction error? The relationship between the present data and the conventional theory may be valuable.

We understand that the readers of this study will come up with the question of how memory-specific activities are related to RPE signaling. However, the T-maze task we used in this research was designed for studying working memory and was not adequate to extract information about the RPE signaling of DA neurons.

RPE signaling is mainly studied in Pavlovian conditioning. These are low-dimensional tasks with usually four (4) states (state1: ITI, state2: trial start, state3: stimulus presentation, state4: reward delivery). Evidence of RPE signaling is extracted from the firing activity of states 3 and 4 (which is theorized to be related to the difference in the values for states 3 and 4).

However, in the T-maze task, the number of states is hard to define and practically countless. In these conditions, it has been suggested that numerous small RPEs are signaled while the mice navigate the maze; Thus, they are very difficult to detect. To our knowledge, only Kim et al 2020, Cell, vol183, pg1600, managed to detect the RPE signaling activity of DA neurons while mice were teleported in a virtual corridor.

Another confounding factor in extracting RPE signals in the T-maze task is that the environment is high-dimensional and DA neurons are multitasking. Therefore, it is likely that RPE signaling could be masked by other parallel encoding schemes.

We have added these descriptions in the “Methodological considerations” (in line 793 of the revised manuscript).

(4) Did the dopamine neurons maintaining the memory information (left or right) prefer a contralateral direction like neurons in the motor cortex?

We thank the reviewer for this comment. Indeed, the majority of the memory-specific DA neurons showed a preference for the contralateral direction. We report this result in the legend of the new sup fig 10 (in line 1668 of the revised manuscript).

(5) As shown in Table S2, the proportion of GABA neurons maintaining the memory information (left or right during delay) was much larger than that of dopamine neurons. It seems to be strange because the main output neurons in the VTA are dopaminergic. What is the role of these GABA neurons?

We thank the reviewer for pointing this out. The present study shows that in both populations a sizeable portion of neurons show memory-specific encoding activities. However, the percentage of memory-encoding GABA neurons is more than twice as large as in the DA neurons. Moreover, we show that GABA neurons are functionally and anatomically segregated.

From this evidence, one could raise the hypothesis that the GABA neurons have a primary role and that the activity of DA neurons is a collateral phenomenon, triggered in a sequence of events within the VTA network. To characterize the (1) role and (2) importance of GABA neurons in memory-guided behavior, one should first identify the afferent and efferent projections of these cells in great detail. Unfortunately, we do not provide anatomical evidence.

So far, with the electrophysiological data we have collected (unit and field recordings), we can address an alternative hypothesis. It has been reported earlier (but we have also observed) that the VTA circuit engages in behaviorally related network oscillations which range from 0.4Hz up to 100Hz. Converging evidence from different brain regions, in vitro preparations but also in vivo recordings agree that local networks of inhibitory neurons are crucial for the generation, maintenance, and spectral control of network oscillations. Ongoing analysis, which we hope will lead to a publication, is looking for the behavioral correlates of network oscillations on the T-maze task, as well as the correlation of single-unit firing activity to the field oscillations. We expect to detect a higher field-unit coherence in GABA neurons, which could explain their stronger engagement in memory-specific encoding activity.

The potential role of GABA neurons in network oscillations is discussed in the revised manuscript in a newly added paragraph in line 564.

Reviewer #2 (Public Review)

The authors phototag DA and GABA neurons in the VTA in mice performing a t-maze task, and report choice-specific responses in the delay period of a memory-guided task, more so than in a variant task w/o a memory component. Overall, I found the results convincing. While showing responses that are choice selective in DA neurons is not entirely novel (e.g. Morris et al NN 2006, Parker et al NN 2016), the fact that this feature is stronger when there is a memory requirement is an interesting and novel observation.

I found the plots in 3B misleading because it looks like the main result is the sequential firing of DA neurons during the Tmaze. However, many of the neurons aren't significant by their permutation test. Often people either only plot the neurons that are significant, or plot with cross-validation (ie sort by half of the trials, and plot the other half).

Relatedly, the cross-task comparisons of sequences (Fig, 4,5) are hampered by the fact that they sort in one task, then plot in the other, which will make the sequences look less robust even if they were equally strong. What happens if they swap which task's sequences they use to order the neurons? I do realize they also show statistical comparisons of modulated units across tasks, which is helpful.

We thank reviewer #2 for the valuable and constructive comments on our manuscript. If, as the reviewer commented, the rate differences between left and right trajectories were only the result we want to claim, there may be a way to show only those whose left and right are significant. However, the sequential activity is also one of the points we wanted to display. We did not emphasize this result because it has already been shown by Engelhard et al. 2019. However, after reading the reviewer's comments, we decided to add a few lines in the "Results" (in lines 205 - 215 of the revised manuscript) and "Discussion" (in line 453 of the revised manuscript) describing the sequential activity of the VTA circuit. In those lines, we explained that DA activity is position-specific (resulting in sequential activity) and that a fraction of them also have left-right specificity.

Overall, the introduction was scholarly and did a good job covering a vast literature. But the explanation of t-maze data towards the end of the introduction was confusing. In Line 87, I would not say "in the same task" but "in a similar task" because there are many differences between the tasks in question.

We thank the reviewer for pointing out this mistake. In the revised manuscript, we replaced “in the same task” with “in a similar task” (in line 85 of the revised manuscript).

And not clear what is meant by "by averaging neuronal population activities, none of these computational schemes would have been revealed. " There was trial averaging, at least in Harvey et al. I thought the main result of that paper related to coding schemes was that neural activity was sequential, not persistent. I think it would help the paper to say that clearly.

We admit that this sentence leaves room for misunderstanding. We were mainly referring to DA studies using microdialysis or fiber photometry techniques. We decided to delete this sentence in the revised manuscript.

Also, I'm not aware it was shown that choice selectivity diminishes when the memory demand of the task is removed - please clarify if that is true in both referenced papers.

The reviewer’s remark is correct. None of these reports show explicitly that memory-specific activities are diminished without the memory component. Therefore, we deleted this sentence in the revised manuscript.

If so, an interpretation of this present data could be found in Lee et al biorxiv 2022, which presents a computational model that implies that the heterogeneity in the VTA DA system is a reflection of the heterogeneity found in upstream regions (the state representation), based on the idea that different subsets of DA neurons calculate prediction errors with respect to different subsets of the state representation.

We thank the reviewer for sharing this interpretation. We agree that this theory would support our results. In the revised manuscript we briefly discuss the Lee et al. report (in line 460 of the revised manuscript).

I am surprised only 28% of DA neurons responded to the reward - the reward is not completely certain in this task. This seems lower than other papers in mice (even Pavlovian conditioning, when the reward is entirely certain). It would be helpful if the authors comment on how this number compares to other papers.

In Pavlovian conditioning, neuronal responses to rewards are compared to a relatively quiet period of firing activity (usually the inter-trial interval epoch). As the reviewer pointed out, in the present study, the number of DA neurons responding to reward is smaller compared to the earlier studies. We hypothesize that this is due to our comparison method. We compared the post-reward response to an epoch when the animal was running along the side arms and the majority of neurons were highly active, instead of comparing it to a quiescent baseline epoch.

Reviewer #2 (Recommendations to The Authors)

Can you clarify what disparity you are referring to here? "Disparities between this 438 and our study in the proportions of modulated neurons could be attributed to the 439 different recording techniques applied as well as the maze regions of interest; for 440 example, Engelhard et al. analyzed neuronal firing activities in the visual-cue period 441 (Engelhard et al., 2019), whereas we focused on memory delay.". Is it the fact that Engelhard et al did not report choice-selective activity? They did report cue-side-selective activity, with some neurons responsive to cues on one side, and other neurons responsive to cues on the other side. Because there are more cues on the left when the mouse turns left, these neurons do indeed have choice-selective responses.

We thank the reviewer for this comment. We agree that we need to clarify further our argument. As the reviewer pointed out, Engelhard et al identified choice-specific DA neurons. However, they reported the encoding properties of DA neurons only in the visual-cue period and the reward period. Remarkably, although the task has a memory delay, they did not report the neuronal firing activities for this delay period. Instead, in the present study we dedicated most of our analysis to characterizing the firing properties of VTA neurons in the delay period.

Also, in response to your comment, we edited the paragraph where we describe the disparities between our study and Engelhard et al (in line 466 in the revised manuscript).

I don't think this sentence of intro is needed since it doesn't really contain new info: "Therefore, we looked for hints 116 of memory-related encoding activities in single DA and GABA neurons by 117 characterizing their firing preference for opposite behavioral choices.".

We agree with the reviewer. Therefore, we deleted this sentence in the revised manuscript.

I didn't understand this line of discussion: "Our evidence does not question the validity of this computational model, since we do not provide evidence of how the selective preference for one response over the other translates into the release site.".

The gating theory is based on experimental evidence of neuronal firing activities of DA neurons but also takes into consideration (to a lesser degree) the pre- and post-synaptic processes at the DA release sites (inverted U-shape of D1R activity). We thought that the reader may come to the conclusion that we question the validity of the gating theory. But this is not our intention, especially when we do not provide important evidence such as (1) the projection sites of DA and GABA neurons and (2) the sequence of events that take place at the synaptic triads following the DA and GABA release.

After reading your comment we came to the conclusion that this sentence should be omitted because it is not within the scope of this study to question the validity of the gating theory. Instead, we dedicated a few lines of text to explaining which components of the gating theory (“update”, “maintenance & manipulation” and “motor preparation”) could be attributed to the trajectory-specific activities in the memory delay of the T-maze task. (section “Activities of midbrain DA neurons in short-term memory” in line 417 of the revised manuscript).

In 1B, please illustrate when the light pulses are on & off?

Following the reviewer’s instruction, we added colored bars on top of the raster plots in Figure 1B, indicating the light induction conditions.

In legend for 6C, please clarify it's a correlation between the difference in R and L choice activity across the epochs (if my understanding is correct).

The reviewer’s understanding is correct. We took this advice into consideration to further clarify the methods of analysis that led to the plot in Figure 6C (in line 1246 in the revised manuscript).

Author Response

We thank you for the time you took to review our work and for your feedback!

The major changes to the manuscript are:

1. We have extended the range of locomotion velocity over which we compare its dependence with cholinergic activity in Figures 2E and S2H.

2. We have quantified the contributions of cholinergic stimulation on multiplicative and additive gains on visual responses (Figure S7).

3. We have provided single cell examples for the change in latency to visual response (Figure S12).

4. We have added an analysis to compare layer 2/3 and layer 5 locomotion onset responses as a function of visuomotor condition (Figure S8).

A detailed point-by-point response to all reviewer concerns is provided below.  

Reviewer #1 (Public Review):

The paper submitted by Yogesh and Keller explores the role of cholinergic input from the basal forebrain (BF) in the mouse primary visual cortex (V1). The study aims to understand the signals conveyed by BF cholinergic axons in the visual cortex, their impact on neurons in different cortical layers, and their computational significance in cortical visual processing. The authors employed two-photon calcium imaging to directly monitor cholinergic input from BF axons expressing GCaMP6 in mice running through a virtual corridor, revealing a strong correlation between BF axonal activity and locomotion. This persistent activation during locomotion suggests that BF input provides a binary locomotion state signal. To elucidate the impact of cholinergic input on cortical activity, the authors conducted optogenetic and chemogenetic manipulations, with a specific focus on L2/3 and L5 neurons. They found that cholinergic input modulates the responses of L5 neurons to visual stimuli and visuomotor mismatch, while not significantly affecting L2/3 neurons. Moreover, the study demonstrates that BF cholinergic input leads to decorrelation in the activity patterns of L2/3 and L5 neurons.

This topic has garnered significant attention in the field, drawing the interest of many researchers actively investigating the role of BF cholinergic input in cortical activity and sensory processing. The experiments and analyses were thoughtfully designed and conducted with rigorous standards, leading to convincing results which align well with findings in previous studies. In other words, some of the main findings, such as the correlation between cholinergic input and locomotor activity and the effects of cholinergic input on V1 cortical activity, have been previously demonstrated by other labs (Goard and Dan, 2009; Pinto et al., 2013; Reimer et al., 2016). However, the study by Yogesh and Keller stands out by combining cutting-edge calcium imaging and optogenetics to provide compelling evidence of layerspecific differences in the impact of cholinergic input on neuronal responses to bottom-up (visual stimuli) and top-down inputs (visuomotor mismatch).

We thank the reviewer for their feedback.

Reviewer #2 (Public Review):

The manuscript investigates the function of basal forebrain cholinergic axons in mouse primary visual cortex (V1) during locomotion using two-photon calcium imaging in head-fixed mice. Cholinergic modulation has previously been proposed to mediate the effects of locomotion on V1 responses. The manuscript concludes that the activity of basal forebrain cholinergic axons in visual cortex provides a signal which is more correlated with binary locomotion state than locomotion velocity of the animal. Cholinergic axons did not seem to respond to grating stimuli or visuomotor prediction error. Optogenetic stimulation of these axons increased the amplitude of responses to visual stimuli and decreased the response latency of layer 5 excitatory neurons, but not layer 2/3 neurons. Moreover, optogenetic or chemogenetic stimulation of cholinergic inputs reduced pairwise correlation of neuronal responses. These results provide insight into the role of cholinergic modulation to visual cortex and demonstrate that it affects different layers of visual cortex in a distinct manner. The experiments are well executed and the data appear to be of high quality. However, further analyses are required to fully support several of the study's conclusions.

We thank the reviewer for their feedback.

1) In experiments analysing the activity of V1 neurons, GCaMP6f was expressed using a ubiquitous Ef1a promoter, which is active in all neuronal cell types as well as potentially non-neuronal cells. The manuscript specifically refers to responses of excitatory neurons but it is unclear how excitatory neuron somata were identified and distinguished from that of inhibitory neurons or other cell types.

This might be a misunderstanding. The Ef1α promoter has been reported to drive highly specific expression in neurons (Tsuchiya et al., 2002) with 99.7% of labeled cells in layer 2/3 of rat cortex being NeuN+ (a neuronal marker), with only 0.3% of labeled cells being GFAP+ (a glial marker) (Yaguchi et al., 2013). This bias was even stronger in layer 5 with 100% of labeled cells being NeuN+ and none GFAP+ (Yaguchi et al., 2013). The Ef1α promoter in an AAV vector, as we use it here, also biases expression to excitatory neurons. In layer 2/3 of mouse visual cortex, we have found that 96.8% ± 0.7% of labeled neurons are excitatory three weeks after viral injection (Attinger et al., 2017). Similar results have also been found in rats (Yaguchi et al., 2013), where on expressing GFP under Ef1a promoter delivered using Lenti virus, 95.2% of labeled neurons in layer 2/3 were excitatory and 94.1% in layer 5 were excitatory. These numbers are comparable to the ones obtained with promoters commonly used to target expression to excitatory neurons. To do this, typically two variants of promoters based on the transcription start region of CaMKIIα gene have been used. The first, the CaMKIIα-0.4 promoter, results in 95% excitatory specificity (Scheyltjens et al., 2015). The second, the CaMKIIα-1.3 promoter, results in only 82% excitatory specificity (Scheyltjens et al., 2015), and is thus not far from chance. We have clarified this in the manuscript. Nevertheless, we have removed the qualifier “excitatory” when talking about neurons in most instances, throughout the manuscript.

2) The manuscript concludes that cholinergic axons convey a binary locomotion signal and are not tuned to running speed. The average running velocity of mice in this study is very slow - slower than 15 cm/s in the example trace in Figure 1D and speeds <6 cm/s were quantified in Figure 2E. However, mice can run at much faster speeds both under head-fixed and freely moving conditions (see e.g. Jordan and Keller, 2020, where example running speeds are ~35 cm/s). Given that the data in the present manuscript cover such a narrow range of running speeds, it is not possible to determine whether cholinergic axons are tuned to running speed or convey a binary locomotion signal.

Our previous analysis window of 0-6.25 cm/s covered approximately 80% of all data. We have increased the analysis window to 0-35 cm/s that now covers more than 99% of the data (see below). Also, note that very high running speeds are probably overrepresented in the Jordan and Keller 2020 paper as mice had to be trained to run reliably before all experiments given the relatively short holding times of the intracellular recordings. The running speeds in our current dataset are comparable to other datasets we have acquired in similar experiments.

Figure 2E has now been updated to reflect the larger range of data. Please note, as the number of mice that contribute to the data now differs as a function of velocity (some mice run faster than others), we have now switched to a variant of the plot based on hierarchical bootstrap sampling (see Methods). This does not overtly change the appearance of the plot. See Author response image 1 for a comparison of the original plot, the extended range without bootstrap sampling, and the extended range with bootstrap sampling currently used in the paper.

Author response image 1.

Average activity of cholinergic axons as a function of locomotion velocity. (A) As in the previous version of the manuscript. (B) As in A, but with the extended velocity range. (C) As in B, but using hierarchical bootstrap sampling to estimate median (red dots) and 95% confidence interval (shading) for each velocity bin.

3) The analyses in Figure 4 only consider the average response to all grating orientations and directions. Without further analysing responses to individual grating directions it is unclear how stimulation of cholinergic inputs affects visual responses. Previous work (e.g. Datarlat and Stryker, 2017) has shown that locomotion can have both additive and multiplicative effects and it would be valuable to determine the type of modulation provided by cholinergic stimulation.

We thank the reviewer for this suggestion. To address this, we quantified how cholinergic stimulation influenced the orientation tuning of V1 neurons. The stimuli we used were full field sinusoidal drifting gratings of 4 different orientations (2 directions each). For each neuron, we identified the preferred orientation and plotted responses relative to this preferred orientation as a function of whether the mouse was running, or we were stimulating cholinergic axons. Consistent with previous work, we found a mixture of a multiplicative and an additive components during running. With cholinergic axon stimulation, the multiplicative effect was stronger than the additive effect. This is now quantified in Figure S7.

4) The difference between the effects of locomotion and optogenetic stimulation of cholinergic axons in Figure 5 may be confounded by differences in the visual stimulus. These experiments are carried out under open-loop conditions, where mice may adapt their locomotion based on the speed of the visual stimulus. Consequently, locomotion onsets are likely to occur during periods of higher visual flow. Since optogenetic stimulation is presented randomly, it is likely to occur during periods of lower visual flow speed. Consequently, the difference between the effect of locomotion and optogenetic stimulation may be explained by differences in visual flow speed and it is important to exclude this possibility.

We find that in general locomotion is unaffected by visual flow in open loop conditions in this type of experiment (in this particular dataset, there was a small negative correlation between locomotion and visual flow in the open loop condition, Author response image 2).

Author response image 2.

Correlation between visual flow and locomotion in open loop conditions. Average correlation of locomotion velocity and visual flow speed in open loop for all mice in Figure 5. Each dot is an imaging site. In the open loop, the correlation between locomotion and visual flow speed is close to zero, but significantly negative in this dataset.

However, to directly address the concern that our results are influenced by visual flow, we can restrict our analysis only to locomotion onsets that occurred in absence of visual flow (Author response image 3A and R3B). These responses are not substantially different from those when including all data (Figures 5A and 5B). Thus, the difference between the effect of locomotion and optogenetic stimulation cannot be explained by differences in visual flow speed.

Author response image 3.

Open loop locomotion onset responses without visual flow. (A) Average calcium response of layer 2/3 neurons in visual cortex to locomotion onset in open loop in the absence of visual flow. Shading indicates SEM. (B) As in A, but for layer 5 neurons.

5) It is unclear why chemogenetic manipulations of cholinergic inputs had no effect on pairwise correlations of L2/3 neuronal responses while optogenetic stimulation did.

This is correct – we do not know why that is the case and can only speculate. There are at least two possible explanations for this difference:

1) Local vs. systemic. The optogenetic manipulation is relatively local, while the chemogenetic manipulation is systemic. It is not clear how cholinergic release in other brain regions influences the correlation structure in visual cortex. It is conceivable that a cortex-wide change in cholinergic release results in a categorically different state with a specific correlation structure in layer 2/3 neurons different from the one induced by the more local optogenetic manipulation.

2) Layer-specificity of activation. Cholinergic projections to visual cortex arrive both in superficial and deep layers. We activate the axons in visual cortex optogenetically by illuminating the cortical surface. Thus, in our optogenetic experiments, we are primarily activating the axons arriving superficially, while in the chemogenetic experiment, we are likely influencing superficial and deep axons similarly. Thus, we might expect a bias in the optogenetic activation to influencing superficial layers more strongly than the chemogenetic activation does.

6) The effects of locomotion and optogenetic stimulation on the latency of L5 responses in Figure 7 are very large - ~100 ms. Indeed, typical latencies in mouse V1 measured using electrophysiology are themselves shorter than 100 ms (see e.g. Durand et al., 2016). Visual response latencies in stationary conditions or without optogenetic stimulation appear surprisingly long - much longer than reported in previous studies even under anaesthesia. Such large and surprising results require careful analysis to ensure they are not confounded by artefacts. However, as in Figure 4, this analysis is based only on average responses across all gratings and no individual examples are shown.

This is correct and we speculate this is the consequence of a combination of different reasons.

1) Calcium imaging is inherently slower than electrophysiological recordings. While measuring spiking responses using electrophysiology, response latencies of on the order of 100 ms have indeed been reported, as the reviewer points out. Using calcium imaging these latencies are typically 4 times longer (Kuznetsova et al., 2021). This is likely a combination of a) calcium signals that are slower than electrical changes, b) delays in the calcium sensor itself, and c) temporal sampling used for imaging that is about 3 orders of magnitude slower than what typically used for electrophysiology.

2) Different neurons included in analysis. The calcium imaging likely has very different biases than electrophysiological recordings. Historically, the fraction of visually responsive neurons in visual cortex based on extracellular electrophysiological recordings has been systematically overestimated (Olshausen and Field, 2005). One key contributor to this is the fact that recordings are biased to visually responsive neurons. The criteria for inclusion of “responsive neurons” strongly influences the “average” response latency. In addition, calcium imaging has biases that relate to the vertical position of the somata in cortex. Both layer 2/3 and layer 5 recordings are likely biased to superficial layer 2/3 and superficial layer 5 neurons. Conversely, electrical recordings are likely biased to layer 4 and layer 5 neurons. Thus, comparisons at this level of resolution between data obtained with these two methods are difficult to make.

We have added example neurons as Figure S12, as suggested.  

Reviewer #1 (Recommendations For The Authors):

While the study showcases valuable insights, I have a couple of concerns regarding the novelty of their research and the interpretation of results. By addressing these concerns, the authors can clarify the positioning of their research and strengthen the significance of their findings.

(Major comments)

1) Page 1, Line 21: The authors claim, "Our results suggest that acetylcholine augments the responsiveness of layer 5 neurons to inputs from outside of the local network, enabling faster switching between internal representations during locomotion." However, it is not clear which specific data or results support the claim of "switching between internal representations." Overall, their study primarily presents responses averaged across all neurons imaged, lacking a detailed exploration of individual neuron response patterns. Population analysis, such as PCA and decoding, can be used to assess the encoding of each stimulus by V1 neurons - "internal representation."<br /> To strengthen their claim regarding "switching between internal representations," the authors could consider an experiment measuring the speed at which the population activity pattern A transitions to the population activity pattern B when the visual stimulus switches from A to B. Such experiments would significantly enhance the impact of their study, providing a clearer understanding of how BF cholinergic input influences the dynamic representation of stimuli during locomotion.

We thank the reviewer for bringing this up. That acetylcholine enables a faster switching between internal representations in layer 5 is a speculation. We have attempted to make this clearer in the discussion. Our speculation is based on the finding that the population response in layer 5 to sensory input is faster under high levels of acetylcholine (Figures 4D and 7B). In line with the reviewer’s intuition, the neuronal response to a change in visual stimulus, in our experiment from a uniform grey visual stimulus to a sinusoidal grating stimulus, is indeed faster. Based on evidence in favor of layer 5 encoding internal representation (Heindorf and Keller, 2023; Keller and Mrsic-Flogel, 2018; Suzuki and Larkum, 2020), we interpret the decrease in latency of the population response as a faster change in internal representation. We are not sure a decoding analysis would add much to this, given that a trivial decoder simply based on mean population response would already find a faster transition. We have expanded on our explanation of these points in the manuscript.

2) Page 4, Line 103: "..., a direct measurement of the activity of cholinergic projection from basal forebrain to the visual cortex during locomotion has not been made." This statement is incorrect. An earlier study by Reimer et al. indeed imaged cholinergic axons in the visual cortex of mice running on a wheel. They found that "After walking onset, ... ACh activation, and a large pupil diameter, were sustained throughout the walking period in both cortical areas V1 and A1." Their findings are very similar to the results presented by Yogesh and Keller - that is, BF cholinergic axons exhibited locomotion statedependent activity. The authors should clarify the positioning of this study relative to previous studies.

Reimer, J., McGinley, M., Liu, Y. et al. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat Commun 7, 13289 (2016). https://doi.org/10.1038/ncomms13289

We have clarified this as suggested. However, we disagree slightly with the reviewer here. The key question is whether the cholinergic axons imaged originate in basal forebrain. While Reimer et al. 2016 did set out to do this, we believe a number of methodological considerations prevent this conclusion:

1) In their analysis, Reimer et al. 2016 combine data from mice with cholinergic axons labeled with either viral injection to basal forebrain or germline cross of ChAT-cre mice with reporter line. Unfortunately, it is unclear what the exact number of mice labeled with either strategy was. Based on the information in the paper, we can conclude that of the 6 mice used for experiments between 2 and 5 were germline cross. The problem with germline labeling of ChAT positive neurons is that when using a cross, VIP-ChAT+ neurons in cortex are also labeled. Based on the fact that Reimer et al. 2016 find an anticipatory increase in activity on locomotion onset, that is also seen by Larsen et al. 2018 (they use a germline cross strategy), an effect we do not see in our data, we speculate that a significant part of the signals reported in the Reimer et al. 2016 paper are from local VIP-ChAT+ neurons.

2) In their analysis, Reimer et al. 2016 also combine all imaging data obtained from both primary auditory cortex and primary visual cortex. Given the heterogeneity in the basal forebrain cholinergic neuronal population and their projection selectivity, to better understand these signals, it’s important to acquire the signals from cholinergic axons selectively in specific cortical regions, which we do in visual cortex. Based on the information provided in their paper, we were unfortunately not able to discern the injection location for their viral labeling strategy. Given the topographic selectivity in projection from basal forebrain, this could give hints as to the relative contribution of cholinergic projections to A1 vs V1 in their data. The injection coordinates given in the methods of the Reimer paper, of 4 mm lateral and 0.5 mm posterior to bregma to target basal forebrain, are likely wrong (they fall outside the head of the mouse).

Given the heterogeneity in the basal forebrain cholinergic neuronal population and their projection selectivity, to better understand these signals, it’s important to acquire the signals from cholinergic axons both selectively in a cortical region, as we do in visual cortex, and purely originating from basal forebrain. Collins et al. 2023 inject more laterally and thus characterize cholinergic input to S1 and A1, while Lohani et al. 2022 use GRAB sensors which complement our findings. Please note, we don’t think there is any substantial disagreement in the results of previous studies and ours, with very few exceptions, like the anticipatory increase in cholinergic activity that precedes locomotion onset in the Reimer et al. 2016 data, but not in ours. This is a rather critical point in the context of the literature of motor-related neuronal activity in mouse V1. Based on early work on the topic, it is frequently assumed that motor-related activity in V1 is driven by a cholinergic input. This is very likely incorrect given our results, hence we feel it is important to highlight this methodological caveat of earlier work.

3) Fig. 4H: The authors found that L5 neurons exhibit positive responses at the onset of locomotion in a closed-loop configuration. Moreover, these responses are further enhanced by photostimulation of BF axons.

In a previous study from the same authors' group (Heindorf and Keller, 2023), they reported 'negative' responses in L5a IT neurons during closed-loop locomotion. This raises a question about the potential influence of different L5 neuron types on the observed results between the two studies. Do the author think that the involvement of the other neuronal type in L5, the PT neurons, might explain the positive responses seen in the present study? Discussing this point in the paper would provide valuable insights into the underlying mechanisms.

Yes, we do think the positive response observed on locomotion onset in closed loop is due to non-Tlx3+ neurons. Given that Tlx3-cre only labels a subset of inter-telencephalic (IT) neurons (Gerfen et al., 2013; Heindorf and Keller, 2023), it’s not clear whether the positive response is explained by the pyramidal tract (PT) neurons, or the non-Tlx3+ IT neurons. Dissecting the response profiles of different subsets of layer 5 neurons is an active area of research in the lab and we hope to be able to answer these points more comprehensively in future publications. We have expanded on this in the discussion as suggested.

Furthermore, it would be valuable to investigate whether the effects of photostimulation of BF axons vary depending on neuronal responsiveness. This could help elucidate how neurons with positive responses, potentially putative PT neurons, differ from neurons with negative responses, putative IT neurons, in their response to BF axon photostimulation during locomotion.

We have attempted an analysis of the form suggested. In short, we found no relationship between a neuron’s response to optogenetic stimulation of ChAT axons and its response to locomotion onset, or its mean activity. Based on their response to locomotion onset in closed loop, we split layer 5 neurons into three groups, 30% most strongly decreasing (putative Tlx3+), 30% most strongly increasing, and the rest. We did not see a response to optogenetic stimulation of basal forebrain cholinergic axons in any of the three groups (Author response image 4A). We also found no obvious relationship between the mean activity of neurons and their response to optogenetic stimulation (Author response image 4B).

Author response image 4.

Neither putative layer 5 cell types nor neuronal responsiveness correlates with the response to optogenetic stimulation of cholinergic axons. (A) Average calcium response of layer 5 neurons split into putative Tlx3 (closed loop locomotion onset suppressed) and non-Tlx3 like (closed loop locomotion onset activated) to optogenetic stimulation of cholinergic axons. (B) Average calcium response of layer 5 neurons to optogenetic stimulation of cholinergic axons as a function of their mean response throughout the experimental session. Left: Each dot is a neuron. Right: Average correlation in the response of layer 5 to optogenetic stimulation and mean activity over all neurons per imaging site. Each dot is an imaging site.

(Minor comments)

1) It is unclear which BF subregion(s) were targeted in this study.

Thanks for pointing this out. We targeted the entire basal forebrain (medial septum, vertical and horizontal limbs of the diagonal band, and nucleus basalis) with our viral injections. All our axonal imaging data comes from visual cortex and given the sensory modality-selectivity of cholinergic projections to cortex, the labeled axons originate from medial septum and the diagonal bands (Kim et al., 2016). We have now added the labels for basal forebrain subregions targeted next to the injection coordinates in the manuscript.

2) Page 43, Line 818: The journal name of the cited paper Collins et al. is missing.

Fixed.

3) In the optogenetic experiments, how long is the inter-trial interval? Simulation of BF is known to have long-lasting effects on cortical activity and plasticity. It is, therefore, important to have a sufficient interval between trials.

The median inter-trial interval for different stimulation events are as follows:

• Optogenetic stimulation only : 15 s

• Optogenetic stimulation + grating : 12 s

• Optogenetic stimulation + mismatch: 35 s

• Optogenetic stimulation + locomotion onset: 45 s

We have added this information to the methods in the manuscript.

Assuming locomotion is the primary driver of acetylcholine release (as we argue in Figures 1 and 2), the frequency of stimulation roughly corresponds to the frequency of acetylcholine release experienced endogenously. It is of course possible that being awake and mobile puts the entire system in a longlasting acetylcholine driven state different from what would be observed during long-term quite wakefulness or during sleep. But the main focus of the optogenetic stimulation experiments we performed was to investigate the consequences of the rapid acetylcholine release driven by locomotion.

4) Page 11, Line 313: "..., we cannot exclude the possibility of a systemic contribution to the effects we observe through shared projections between different cortical and subcortical target." This possibility can be tested by examining the effect of optogenetic stimulation of cholinergic axons on locomotor activity, as they did for the chemogenetic experiments (Fig. S7). If the optogenetic manipulation changes locomotor activity, it is likely that this manipulation has some impact on subcortical activity and systemic contribution to the changes in cortical responses observed.

Based on the reviewer suggestion we tested this and found no change in the locomotor activity of the mice on optogenetic stimulation of cholinergic axons locally in visual cortex (we have added this as Figure S5 to the manuscript). Please note however, we can of course not exclude a systemic contribution based on this.

5) Fig. 4 and 5: In a closed-loop configuration, L2/3 neurons exhibit a transient increase in response at the onset of locomotion, while in an open-loop configuration, their response is more prolonged. On the other hand, L5 neurons show a sustained response in both configurations. Do the authors have any speculation on this difference?

This is correct. Locomotion onset responses in layer 2/3 are strongly modulated by whether the locomotion onset occurs in closed loop or open loop configurations (Widmer et al., 2022). This difference is absent in our layer 5 data here. We suspect this is a function of a differential within-layer cell type bias in the different recordings. In the layer 2/3 recordings we are likely biased strongly towards superficial L2/3 neurons that tend to be negative prediction error neurons (top-down excited and bottom-up inhibited), see e.g. (O’Toole et al., 2023). A reduction of locomotion onset responses in closed loop is what one would expect for negative prediction error neurons. While layer 5 neurons exhibit mismatch responses, they do not exhibit opposing top-down and bottom-up input that would result in such a suppression (Jordan and Keller, 2020).

We can illustrate this by splitting all layer 2/3 neurons based on their response to gratings and to visuomotor mismatch into a positive prediction error (PE) type (top 30% positive grating response), a negative prediction error type (top 30% positive visuomotor mismatch response), and the rest (remaining neurons and neurons responsive to both grating and visuomotor mismatch). Plotting the response of these neurons to locomotion onset in closed loop and open loop, we find that negative PE neurons have a transient response to locomotion onset in closed loop while positive PE neurons have a sustained increase in response in closed loop. In open loop the response of the two populations is indistinguishable. Splitting the layer 5 neurons using the same criteria, we don’t find a striking difference between closed and open loop between the two groups of neurons. We have added this as Figure S8.

Reviewer #2 (Recommendations For The Authors):

Major concerns:

1) As a ubiquitous promoter was used to drive GCaMP expression, please explain how excitatory neurons were identified.

2) As the data cover a very small range of running speeds, it is important to confirm that the binary locomotion signal model still applies when mice run at higher speeds - either by selecting recordings where mice have a wider range of running speeds or conducting additional experiments. In addition, please show the running speed tuning of individual axons.

3) Please provide a more detailed analysis of the effects of locomotion and cholinergic modulation on visual responses. How does cholinergic modulation affect orientation and direction tuning? Are the effects multiplicative or additive? How does this compare to the effects of locomotion on single neurons?

4) To ensure that the analyses in Figure 5 are not confounded by differences in the visual stimulus, please include average visual flow speed traces for each condition.

5) Please clarify why chemogenetic manipulations of cholinergic inputs had no effect on pairwise correlations in L2/3.

6) The latency effect is quite an extraordinary claim and requires careful analysis. Please provide examples of single neurons illustrating the latency effect - including responses across individual grating orientations/directions. One possible confound is that grating presentation could itself trigger locomotion or other movements. In the stationary / noOpto conditions, the grating response might not be apparent in the average trace until the animal begins to move. Thus the large latency in the stationary / noOpto conditions may reflect movement-related rather than visual responses.

Please see our responses to these points in the public review part above.

There are some minor points where text and figures could be improved:

1) When discussing the decorrelation of neuronal responses by cholinergic axon activation, it is important to make it clear that Figure 6D quantifies the responses of layer 5 apical dendrites rather than neurons.

We have added this information to the results section.

2) In Figure S7, please clarify why velocity is in arbitrary units.

This was an oversight and has been fixed.

3) Please clarify how locomotion and stational trials are selected in Figure 4.

We thank the reviewers for pointing this out. Trials were classified as occurring during locomotion or while mice were stationary as follows. We used a time-window of -0.5 s to +1 s around stimulus onset. If mice exhibited uninterrupted locomotion above a threshold of 0.25 cm/s in this time-window, we considered the stimulus as occurring during locomotion, otherwise it was defined as occurring while the mice were stationary. Note, the same criteria to define locomotion state was used to isolate visuomotor mismatch events, and also during control optogenetic stimulation experiments. We have added this information to the methods.

4) When testing whether cholinergic activation is sufficient to explain locomotion-induced decorrelation in Figure 6G-H, please show pre-CNO and post-CNO delta-correlation, not just their difference.

We can do that, but the results are harder to parse this way. We have added this as Figure S11 to the manuscript. The problem with parsing the figure is that the pre-CNO levels are different in different groups. This is likely a function of mouse-to-mouse variability and makes it harder to identify what the CNO induced changes are. Using the pre-post difference removes the batch influence. Hence, we have left this as the main analysis in Figure 6G and 6H.

Author Response

Reviewer #1 (Public Review):

Summary:

In this paper, the effects of two sensory stimuli (visual and somatosensory) on fMRI responsiveness during absence seizures were investigated in GEARS rats with concurrent EEG recordings. SPM analysis of fMRI showed a significant reduction in whole-brain responsiveness during the ictal period compared to the interictal period under both stimuli, and this phenomenon was replicated in a structurally constrained whole-brain computational model of rat brains.

The conclusion of this paper is that whole-brain responsiveness to both sensory stimuli is inhibited and spatially impeded during seizures.

I also suggest the manuscript should be written in a way that is more accessible to readers who are less familiar with animal experiments. In addition, the implementation and interpretation of brain simulations need to be more careful and clear.

Several sections of the manuscript were clarified and simplified to be more accessible. Also, implementation and interpretations of brain simulations were modified to be more precise.

Strengths:

1) ZTE imaging sequence was selected over traditional EPI sequence as the optimal way to perform fMRI experiments during absence seizures.

2) A detailed classification of stimulation periods is achieved based on the relative position in time of the stimulation period with respect to the brain state.

3) A whole-brain model embedded with a realistic rat connectome is simulated on the TVB platform to replicate fMRI observations.

We thank the reviewer for indicating the strengths of our manuscript.

Weaknesses:

1) The analysis in this paper does not directly answer the scientific question posed by the authors, which is to explore the mechanisms of the reduced brain responsiveness to external stimuli during absence seizures (in terms of altered information processing), but merely characterizes the spatial involvement of such reduced responsiveness. The same holds for the use of mean-field modeling, which merely reproduces experimental results without explaining them mechanistically as what the authors have claimed at the head of the paper.

We agree with the reviewer that the manuscript does not answer specifically about the mechanisms of reduced brain responsiveness. The main scientific question addressed in the manuscript was to compare whole-brain responsiveness of stimulus between ictal and interictal states. The sentence that can lead to misinterpretations in the manuscript abstract: “The mechanism underlying the reduced responsiveness to external stimulus remains unknown.” was therefore modified to the following “The whole-brain spatial and temporal characteristics of reduced responsiveness to external stimulus remains unknown”.

2) The implementations of brain simulations need to be more specific.

Contribution:

The contribution of this paper is performing fMRI experiments under a rare condition that could provide fresh knowledge in the imaging field regarding the brain's responsiveness to environmental stimuli during absence seizures.

Reviewer #2 (Public Review):

Summary:

This study examined the possible effect of spike-wave discharges (SWDs) on the response to visual or somatosensory stimulation using fMRI and EEG. This is a significant topic because SWDs often are called seizures and because there is non-responsiveness at this time, it would be logical that responses to sensory stimulation are reduced. On the other hand, in rodents with SWDs, sensory stimulation (a noise, for example) often terminates the SWD/seizure.

In humans, these periods of SWDs are due to thalamocortical oscillations. A certain percentage of the normal population can have SWDs in response to photic stimulation at specific frequencies. Other individuals develop SWDs without stimulation. They disrupt consciousness. Individuals have an absent look, or "absence", which is called absence epilepsy.

The authors use a rat model to study the responses to stimulation of the visual or somatosensory systems during and in between SWDs. They report that the response to stimulation is reduced during the SWDs. While some data show this nicely, the authors also report on lines 396-8 "When comparing statistical responses between both states, significant changes (p<0.05, cluster-) were noticed in somatosensory auditory frontal..., with these regions being less activated in interictal state (see also Figure 4). That statement is at odds with their conclusion.

We thank the reviewer for noting this discrepancy. The statement should have been written vice versa and it has been corrected as: “When comparing statistical responses between both states, significant changes (p<0.05, cluster-level corrected) were noticed in the somatosensory, auditory and frontal cortices: these regions were less activated in ictal than in interictal state (see also Figure 4).”

They also conclude that stimulation slows the pathways activated by the stimulus. I do not see any data proving this. It would require repeated assessments of the pathways in time.

We agree with the reviewer that there are no data showing slowing of the pathways in response to stimulus. However, we are a bit confused about this comment, as to what part in conclusion section it refers to. We did not intentionally claim that stimulation slows the activated pathways in the manuscript.

The authors also study the hemodynamic response function (HRF) and it is not clear what conclusions can be made from the data.

Hemodynamic response functions were studied for two reasons:

• To account for possible change in HRF during the detection of activated regions. Indeed, a physiological change in HRF can mask the detection of an activation when the software uses a standard HRF to convolve the design matrix (David et al. 2008).

• To characterize the shape and polarity of fMRI activations in brain regions that we noticed to be differently activated between ictal and interictal states and evaluate whether alteration in activation was associated to alteration in hemodynamic.

The observed HRF decreases (rather than increases) in the cortex when stimulation was applied during SWD, was discussed in section 4.4., where we speculated that neuronal suppression caused by SWD can prevent responsiveness. In this case, the decreased HRF could either be a consequence or a cause of the observed neuronal suppression. The assumption that the HRF reduction is causal would be supported by a possible vascular steal effect from other activation regions. However, in the conclusion section we did not state this and therefore the following sentence was added to conclusions: “Moreover, the detected decreases in the cortical HRF when sensory stimulation was applied during spike-and-wave discharges, could play a role in decreased sensory perception. Further studies are required to evaluate whether this HRF change is a cause or a consequence of the reduced neuronal response”.

Finally, the authors use a model to analyze the data. This model is novel and while that is a strength, its validation is unclear. The conclusion is that the modeling supports the conclusions of the study, which is useful.

Details about the model were added.

Strengths:

Use of fMRI and EEG to study SWDs in rats.

Weaknesses:

Several aspects of the Methods and Results are unclear.

Reviewer #3 (Public Review):

Summary:

This is an interesting paper investigating fMRI changes during sensory (visual, tactile) stimulation and absence seizures in the GAERS model. The results are potentially important for the field and do suggest that sensory stimulation may not activate brain regions normally during absence seizures. However the findings are limited by substantial methodological issues that do not enable fMRI signals related to absence seizures to be fully disentangled from fMRI signals related to the sensory stimuli.

Strengths:

Investigating fMRI brain responses to sensory stimuli during absence seizures in an animal model is a novel approach with the potential to yield important insights.

The use of an awake, habituated model is a valid and potentially powerful approach.

Weaknesses:

The major difficulty with interpreting the results of this study is that the duration of the visual and auditory stimuli was 6 seconds, which is very close to the mean seizure duration per Table 1. Therefore the HRF model looking at fMRI responses to visual or auditory stimuli occurring during seizures was simultaneously weighting both seizure activity and the sensory (visual or auditory) stimuli over the same time intervals on average. The resulting maps and time courses claiming to show fMRI changes from visual or auditory stimulation during seizures will therefore in reality contain some mix of both sensory stimulation-related signals and seizure-related signals. The main claim that the sensory stimuli do not elicit the same activations during seizures as they do in the interictal period may still be true. However the attempts to localize these differences in space or time will be contaminated by the seizure-related signals.

The claims that differences were observed for example between visual cortex and superior colliculus signals with visual stim during seizures vs. interictal are unconvincing due to the above.

We understand this concern expressed by the reviewer and agree that seizure-related signals must be considered in the analysis when studying stimulation responses. Therefore, in modelling the responses in the SPM framework, we considered both stimulation and seizure-only states as regressors of interest and used seizure-only responses as nuisance regressors to account for error variance. Thereby, the effects caused by the stimulation should be, in theory, separated as much as possible from the effects caused by the seizure itself. Additionally, the cases where stimulations occurred fully inside a seizure (included in Figure 3, “...stimulation during ictal state) actually had a longer average seizure duration of 45 ± 60 s, therefore being much longer than 6s which an average duration taken from all seizures.

However, we acknowledge that there is a potential that some leftover effects from a seizure are still present, and we have noted this caution in the “Physiologic and methodologic considerations” section: “We note a caution that presented maps and time courses showing fMRI changes from visual or whisker stimulation during seizures may contain mixture of both sensory stimulation-related signals and seizure-related signals. To minimize this contamination, we considered in SPM both stimulation and seizure-only states as regressors of interest and used seizure-only responses as nuisance regressors to account for error variance. Thereby, the effects caused by the seizure itself should be separated as much as possible from the effects caused by stimulation.”

The maps shown in Figure 3 do not show clear changes in the areas claimed to be involved.

We clarified the overall appearance of Figure 3, by enlarging the selected cross sections for better anatomical differentiation and added anterior and posterior directions on all images.

Reviewer #1 (Recommendations For The Authors):

1) The implementations of brain simulations need to be more specific: How is the stimulation applied in the mean-field model in terms of its mathematical expression? The state variable of the model is the rate of neuronal firing, but how is it subsequently converted into fMRI responses? How are the statistical plots calculated? How much does this result depend on the model parameter?

Further details and explanations about the model have now been added to the manuscript. The stimulation of a specific region is simulated as an increase in the excitatory input to the specific node. In particular we use a square function for representing the stimulus (see for example panel A in Figure 6–figure supplement 1). As the referee mentions, the model describes the dynamics of the neuronal firing rates. This provides direct information about neuronal activity and responsiveness for which all the statistical analyses of the simulations shown in the paper were performed using the firing rates. For these analyses, no conversion to fMRI was needed. To build the statistical maps, an ANOVA (analysis of variance) test was used. The ANOVA test is originally designed to assess the significance of the change in the mean between two samples, and is calculated via an F-test as the ratio of the variance between and within samples. In our case it allowed us to assess the impact of the stimulation on the ongoing neuronal activity by performing a comparison of the timeseries of the firing rate with and without stimulation (this was performed independently for each state). For the results presented in this paper, the ANOVA analysis was performed using the “f_oneway” function of the scipy.stats. module in python. Regarding the dependence on the model parameter, the main results obtained in our paper are related with the responsiveness of the system under two quantitatively different types of ongoing dynamics: an asynchronous irregular activity (interictal period) and an oscillatory SWD type of dynamics (ictal period). In particular, we show how for the SWD dynamics the activity evoked by the stimulus is overshadowed by the ongoing activity which imposes a strong limitation in the response of the system and the propagation of the stimulus. In this sense, the main results of the simulations are very general, and no significant dependence on specific cellular or network parameters was observed within a physiologically relevant range or should be expected. Nevertheless, we point out that, as mentioned in the text, the key parameter that triggers the transition between the two types of dynamics is the strength of the adaptation current (in particular the strength of the spike-triggered adaptation parameter ‘b’ described in the Supplementary information), which in addition has the capacity of controlling the frequency of the oscillations. In the paper, this parameter was set such that the SWD frequency falls within the range observed in the GAERS (between 7-12Hz). We believe that further analysis around the region of transition between states, in particular from a dynamical point of view, could be of relevance for future work.

2) In the abstract, what exactly does "typical information flow in functional pathways" mean and which part of the results does this refer to?

We note that this sentence was overly complicated. By “typical information flow”, we were referring to sensory responsiveness during interictal state. Therefore, we made the following modifications to the abstract: “These results suggest that sensory processing observed during an interictal state can be hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness.”

3) Figure 4 - Figure Supplement 1 performed an analysis of comparing states between 'when stimulation ended a seizure' and 'stimulation during an ictal period'. The authors should explain more clearly in the manuscript what is the reason and significance of considering the state of 'when stimulation ended a seizure'. And how is a seizure considered to be terminated by stimulation rather than ending spontaneously?

We have now added explanations to the manuscript section 2.5.3 as why this state was also of interest: “The case when stimulation ended a seizure is particularly interesting for studying the spatial and temporal aspects explaining shift from ictal, i.e. non-responsiveness state, to non-ictal, i.e. responsiveness state.” We agree that there is a possibility that seizures ended spontaneously at the same time as stimulus was applied but argue that seizures most probably end due to stimulation, based on results published previously (https://doi.org/10.1016/j.brs.2012.05.009).

4) In Section 3.1, some detailed descriptions of methods should be moved to Section 2, e.g. how the spatial and temporal SNR is obtained and the description of bad quality data. Also, I suggest the significance of selecting the optimal MRI sequence be stated earlier in the paper, as Section 3.1 cannot be expected from reading the abstract and introduction.

We moved some technical explanations of SNRs from section 3.1. to section 2.4.1. Significance of the selection of the MRI sequence is also now stated earlier in the introduction section: “For this purpose, the functionality of ZTE sequence was first piloted, and selected over traditional EPI sequence for its lower acoustic noise and reduced magnetic susceptibility artefacts. The selected MRI sequence thus appeared optimal for awake EEG-fMRI measurements.”

Some minor issues:

1) How is ROI defined in this paper? What type of atlas is used?

Anatomical ROIs were drawn based on Paxinos and Watson rat brain atlas 7th edition. Region was selected if there were statistically significant activations detected inside that region, based on activation maps. We clarified the definition of ROI as the following: “Anatomical ROIs, based on Paxinos atlas (Paxinos and Watson rat brain atlas 7th edition), were drawn on the brain areas where statistical differences were seen in activation maps.”

2) Section 4.3.2, "In addition, some responses were seen in the somatosensory cortex during the seizure state, which may be due to the fact that the linear model used did not completely remove the effect of the seizure itself" What is the reason for the authors to make such comments?

This claim was made because we saw similar trend of responses (deactivation) in F-contrast maps in the somatosensory cortex, when comparing “stimulation during ictal state” maps to "seizure map", leading us to assume that the effect of seizure was still apparent in the maps (even though “seizure only” states were used as nuisance regressors). However, as this claim is highly speculative, we have decided to delete this sentence in the manuscript.

3) Abbreviations such as SPM, HRF, CBF, etc. are not defined in the manuscript.

Definitions for these abbreviations were added.

4) Supplementary information-AdEx mean-field model, 've and vi', e and i should be subscripted.

Subscripts were added.

Reviewer #2 (Recommendations For The Authors):

Below are more detailed questions and concerns. Many questions are about the Methods, which seem to be written by a specialist. However, there are also questions about the experimental approach and conclusions.

One of the strengths of the study is the use of fMRI and EEG. However, to allow rats to be still in the magnet, isoflurane was used, and then as soon as rats recovered they were imaged. However isoflurane has effects on the brain long after the rats have appeared to wake up. Moreover, to train rats to be still, repetitive isoflurane sessions had to be used. Repetitive isoflurane should have a control of some kind, or be discussed as a limitation.

The repetitive use of isoflurane is indeed an important limiting factor that was not yet discussed in the manuscript. We have added the following sentences to the “Physiologic and methodologic considerations” section:

“As the used awake habituation and imaging protocol didn’t allow us to avoid the usage of isoflurane during the preparation steps, we cannot rule out the possible effect of using repetitive anesthesia on brain function. However, duration (~15 min) and concentration of anesthesia (~1.5%) during these steps were still moderate, whereas extended durations (1-3 h) of either single or repetitive isoflurane exposures have been used in previous studies where long-term effects on brain function have been observed (Long II et al., 2016; Stenroos et al., 2021). Moreover, there was a 5-15 min waiting period between the cessation of anesthesia and initiation of fMRI scan, to avoid the potential short-term effects of isoflurane that has been found to be most prominent during the 5 min after isoflurane cessation (Dvořáková et al., 2022).

An assumption of the study is that interictal periods are normal. However, they may not be. A control is necessary. One also wants to know how often GAERS have spontaneous spike-wave discharges (SWDs), what the authors call seizures. The reason is that the more common the SWDs, the less likely interictal periods are normal. It seems from the Methods that rats were selected if they had frequent seizures so many could be captured in a recording session. Those without frequent seizures were discarded.

A good control would be a normal rat that has spontaneous SWDs, since almost all rat strains have them, especially with age and in males (PMID: 7700522). However, whether they are frequent enough might be a problem. Alternatively, animals could be studied with rare seizures to assess the normal baseline, and compared to interictal states in GAERS.

We appreciate this concern raised by the Reviewer. Even though it would be interesting to study different strains and SWD frequency dependence, the aim of this study was to compare interictal vs ictal states in this specific animal model. We also understand that interictal periods could not necessarily model “normal” state and therefore went through the manuscript again to remove any claims referring to this.

About the mechanisms of SWDs, the authors should update their language which seems imprecise and lacks current citations (starting on line 71):

"Although the origin of absence seizures is not fully understood, current studies on rat models of absence seizures suggest that they arise from atypical excitatory-inhibitory patterns in the barrel field of the somatosensory cortex (Meeren et al. 2002; Polack et al. 2007) and lead to synchronous cortico-thalamic activity (Holmes, Brown, and Tucker 2004)."

Some of the best explanations for SWDs that I know of are from the papers of John Huguenard. His reviews are excellent. They discuss the mechanisms of thalamocortical oscillations.

We have reformatted the sentences discussing the mechanism of SWDs and included the explanations provided by manuscripts from Huguenard and McCafferty et al.: “Although the origin of absence seizures is not fully understood, current studies on rat models of absence seizures suggest that they arise from excitatory drive in the barrel field of the somatosensory cortex (Meeren et al. 2002; Polack et al. 2007, 2009, David et al., 2008) and then propagate to other structures (David et al., 2008) including thalamus, knowing to play an essential role during the ictal state (Huguenard, 2019). Notably, the thalamic subnetwork is believed to play a role in coordinating and spacing SWDs via feedforward inhibition together with burst firing patterns. These lead to the rhythms of neuronal silence and activation periods that are detected in SWD waves and spikes (McCafferty et al., 2018; Huguenard, 2019).”

The following also is not precise:

"Although seizures are initially triggered by hyperactive somatosensory cortical neurons, the majority of neuronal populations are deactivated rather than activated during the seizure, resulting in an overall decrease in neuronal activity during SWD (McCafferty et al. 2023)." What neuronal populations? Cortex? Which neurons in the cortex? Those projecting to the thalamus? What about thalamocortical relay cells? Thalamic gabaergic neurons?

Lines 85-8: "In addition, a previous fMRI study on GAERS, which measured changes in cerebral blood volume, found both deactivated and activated brain areas during seizures (David et al. 2008). Which areas and conditions led to reduced activity? Increased activity? How was it surmised?

"concurrent stimuli and therefore could contribute to the alterations in behavioral responsiveness" - This idea has been raised before by others (Logthetis, Barth). Please discuss these as the background for this study.

The particular section was modified to the following:

“Previous results on GAERS have indicated that, during an absence seizure, hyperactive electrophysiological activity in the somatosensory cortex can contribute to bilateral and regular SWD firing patterns in most parts of the cortex. These patterns propagate to different cortical areas (retrosplenial, visual, motor and secondary sensory), basal ganglia, cerebellum, substantia nigra and thalamus (David et al. 2008; Polack et al. 2007). Although SWDs are initially triggered by hyperactive somatosensory cortical neurons, neuronal firing rates, especially in majority of frontoparietal cortical and thalamocortical relay neurons, are decreased rather than increased during SWD, resulting in an overall decrease in activity in these neuronal populations (McCafferty et al. 2023). Previous fMRI studies have demonstrated blood volume or BOLD signal decreases in several cortical regions including parietal and occipital cortex, but also, quite surprisingly, increases in subcortical regions such as thalamus, medulla and pons (David et al., 2008; McCafferty et al., 2023). In line with these findings, graph-based analyses have shown an increased segregation of cortical networks from the rest of the brain (Wachsmuth et al. 2021). Altogether, alterations in these focal networks in the animal models of epilepsy impairs cognitive capabilities needed to process specific concurrent stimuli during SWD and therefore could contribute to the lack of behavioral responsiveness (Chipaux et al. 2013; Luo et al. 2011; Meeren et al. 2002; Studer et al. 2019), although partial voluntary control in certain stimulation schemes can be still present (Taylor et al., 2017).”

Please discuss the mean-field model more. What are its assumptions? What is its validation? Do other models also provide the same result?

We have now extended the discussion and explanation of the mean-field model, both in the main text and in the Supplementary information. The mean-field model is a statistical tool to estimate the mean activity of large neuronal populations, and as such its main assumptions are centered around the size of the population analyzed and the characteristic times of the neuronal dynamics under study. It has been shown that the formalism is valid for characteristic times of neuronal dynamics with a lower bond in the order of few milliseconds and with population size of in the order thousands of neurons (see El Boustani and Destexhe, Neural computation 2009; and Di Volo et al, Neural computation 2019), with both conditions satisfied in the simulations made for this work. Regarding the validation, the model has been extensively validated and used for simulating different brain states (Di Volo et al. 2009; Goldman et al. 2023), signal propagation in cortical circuits (Zerlaut et al, 2018) and to perform whole-brain simulations (Goldman et al, 2023). The standard validation of the mean-field implies its comparison with the activity obtained from the corresponding spiking neural network. For completeness we show in Author response image 1 an example of the SWD type of dynamics obtained from a spiking neural network together with the one obtained from the mean-field. This figure has been added now to the Supplementary information of the paper. Regarding the extension of the results to other models, we think that the generality of our results is an interesting point from our work. The main results obtained from our simulation are related with the responsiveness of the system during two different type of ongoing activity: in the interictal state there is a significant variation on the ongoing activity evoked by the stimulation that is propagated to other regions, while in the SWD state the evoked activity is overshadowed by the ongoing activity which imposes a strong limit to the responsiveness of the system and the propagation of the signal. In this sense, the results of the simulations are very general and should be extensible to other models. Of course, the advantage of using a model like ours is the capability of reproducing the different states, its applicability to large scale simulations, and the fact that it is built from biologically relevant single-cell models (AdEx).

Author response image 1.

Comparison of the SWD dynamics in the mean-field model and the underlying spiking-neural network of AdEx neurons. A) Raster plot (top) and mean firing rate (bottom) from an SWD type of dynamics obtained from the spiking- network simulations. The network is made of 8000 excitatory neurons and 2000 inhibitory neurons. Neurons in the network are randomly connected with probability p=0.05 for inhibitory-inhibitory and excitatory-inhibitory connections, and p=0.06 for excitatory-excitatory connections. Cellular parameters correspond to the ones used in the mean-field, with spike-triggered adaptation for excitatory neurons set to b=200pA. We show the results for excitatory (green) and inhibitory (red) neurons. B) Mean-firing rate obtained from a single mean-field model. We see that, although the amplitude of oscillations is larger in the spiking-network, the mean-field can correctly capture the general dynamics and frequency of the oscillations.

Line 11: "rats were equally divided by gender." Given n=11, does that mean 5 males and 6 females or the opposite?

Out of 11 animals, 6 were males, and 5 females. This is now mentioned in the manuscript.

What was the type of food?

Type of food was added to the manuscript (Extrudat, vitamin-fortified, irradiated > 25 kGy)

What were the electrodes?

This was provided in the manuscript. Carbon fiber filament was produced by World Precision Instruments. The tips of this filament were spread to brush-like shape to increase the contact surface above the skull.

"low noise zero echo time (ZTE) MRI sequence"- please explain for the non-specialist or provide references.

Reference added.

Lines 148-150: "The length of habituation period was selected based on pilot experiments and was sufficient for rats to be in low-stress state and produce absence seizures inside the magnet." How do the authors know the rats were in a low-stress state?

This claim was based on two factors. At the end of the habituation protocol, the motion of animals was considerably decreased according to previous study using similar restraint/habituation protocol (DOI: 10.3389/fnins.2018.00548). In this study the decreased motion is also correlated with decreased blood corticosterone levels which reduced to baseline levels (indicating low-stress state) after 4 days of habituation. Another factor is when epileptic rodents are continuously recorded for 24h, most SWDs occur during a state of passive wakefulness or drowsiness (Lannes et al. 1988, Coenen et al. 1991) . Either way, as we don’t have a way to provide direct evidence of low-stress state, we modified the sentence to the following:

“The length of habituation period was selected based on pilot experiments to provide low-motion data therefore giving rats a better chance to be in a low-stress state and thus produce absence seizures inside the magnet.”

Lines 150-2: "Respiration rate and motion were monitored during habituation sessions using a pressure pillow and video camera to estimate stress level." What were the criteria for a high stress level?

Criteria for high (or low) stress levels were based mostly on motion levels according to previous study (DOI: 10.1016/s0149-7634(05)80005-3). Still, as we didn’t measure direct measures of stress, we modified the sentence to the following:

“Pressure pillow and video camera were used to estimate physiological state, via breathing rate, and motion level, respectively.”

Lines 152-3: "During the last habituation session, EEG was measured to confirm that the rats produced a sufficient amount of absence seizures (10 or more per session)." If 10 min, the rats would basically be seizing the entire session, leading to doubt about what the interictal state was.

The length of the last habituation session was 60min and the fMRI scan 45min. Given that rats produced ~40-50 seizures during fMRI scan, on average they produced ~1 seizures/min, and one seizure lasting on average of 5-6s, giving ~45s periods for interictal states. 10 or more seizures were used as a threshold to give statistically meaningful findings based on pilot experiments.

Line 153: "Total of 2-5 fMRI experiments were conducted per rat within a 1-3-week period." What was the schedule for each animal? A table would be useful. If it varied, how do the authors know this was justified?

Please see Figure 1–figure supplement 2 for examples of habituation timelines for individual rats:

We found an error when stating 2-5 fMRI experiments, but it should be 3-5 fMRI experiments. This was corrected. We had an aim to acquire 12-14 sessions per stimulation condition and once a sufficient number of sessions were acquired, part of the animals was not used further. Two of the animals that were found to have good quality EEG and produced sufficient amounts of SWDs were kept, and briefly retrained for later second stimulation condition experiments. This was done to replace animals that needed to be excluded in the second stimulation condition due to bad quality EEG or lost implant. Extended use of some animals could theoretically bring slight variation to results but could actually be an advantage as animals were already well trained providing low-motion data.

"Before and after each habituation session, rats were given a treat of sugar water and/or chocolate cereals as positive reinforcement. " How much and what was the concentration of sugar water; chocolate cereal?

Rats were given 3 chocolate cereals and/or 1% sugar water. This was added to the manuscript now.

Line 188: "We relied on pilot calibration of the heated water to maintain the body temperature" Please explain.

Sentence was clarified:

“We relied on pilot calibration of the temperature of heated water circulating inside animal bed to maintain the normal body temperature of ~37 °C"

Line 190: "After manual tuning and matching of the transmit-receive coil, shimming and anatomical imaging" Please explain for the non-specialist.

Sentence was simplified:

“After routine preparation steps in the MRI console were done"

Lines 199-201: "Anatomical imaging was conducted with a T1-FLASH sequence (TR: 530 ms, TE: 4 ms, flip angle 196 18{degree sign}, bandwidth 39,682 kHz, matrix size 128 x 128, 51 slices, field-of-view 32 x 32 mm², resolution 0.25 x 0.25 x 0.5 mm3). fMRI was performed with a 3D ZTE sequence (TR: 0.971 ms, TE: 0 ms, flip angle 4{degree sign}, pulse length 1 µs, bandwidth 150 kHz, oversampling 4, matrix size 60 x 60 x 60, field-of-view 30 x 30 x 60 mm3 , resolution of 0.5 x 0.5 x 1 mm3 , polar under sampling factor 5.64 nr. of projections 2060 resulting to a volume acquisition time of about 2 s). A total of 1350 volumes (45 min) were acquired." Please explain for the non-specialist.

These technical parameters are provided for the sake of repeatability. Section was however clarified as the following and citation was added:

Anatomical imaging was conducted with a T1-FLASH sequence (repetition time: 530 ms, echo time: 4 ms, flip angle 18°, bandwidth 39,682 kHz, matrix size 128 x 128, 51 slices, field-of-view 32 x 32 mm², spatial resolution 0.25 x 0.25 x 0.5 mm3). fMRI was performed with a 3D ZTE sequence (repetition time: 0.971 ms, TE: 0 ms, flip angle 4°, pulse length 1 µs, bandwidth 150 kHz, oversampling 4, matrix size 60 x 60 x 60, field-of-view 30 x 30 x 60 mm3, spatial resolution of 0.5 x 0.5 x 1 mm3, polar under sampling factor 5.64, number of projections 2060 resulting to a volume acquisition time of about 2 s (look Wiesinger & Ho, 2022 for parameter explanations)). A total of 1350 volumes (45 min) were acquired.

"Visual (n=14 sessions, 5 rats) and somatosensory whisker (n=14 sessions, 4 rats)" - Please explain how multiple sessions were averaged for a single rat. Please justify the use of different numbers of sessions per rat.

All the sessions belonging to the same stimulus scheme (multiple sessions per rat) were put at the once as sessions in SPM analysis together with all the stimulus conditions belonging to these sessions. Justifications for using a different number of sessions per rat, were given above.

Lines 205-206: "For the visual stimulation, light pulses (3 Hz, 6 s total length, pulse length 166 ms) were produced by a blue led, and light was guided through two optical fibers to the front of the rat's eyes. What wavelength of blue? Why blue? Is the stimulation strong? Weak?

Wavelength was 470 nm and brightness 7065 mcd with a current of 20mA. Blue was selected as it is in the frequency range that rat can differentiate and this color has been used in previous literature ( https://doi.org/10.1016/j.neuroimage.2020.117542, https://doi.org/10.1016/j.jneumeth.2021.109287)

Line 212: "Stimulation parameters were based on previous rat stimulation fMRI studies to produce robust responses" What is a robust response? One where a lot of visual cortical voxels are activated?

Sentence was corrected as the following:

“Stimulation parameters were based on previous rat stimulation fMRI studies and chosen to activate voxels widely in visual and somatosensory pathways, correspondingly.”

Line 245: "Seizures were confirmed as SWDs if they had a typical regular pattern, had at least double the amplitude compared to baseline signal..." What was the "typical" pattern? What baseline signal was it compared to? Was the baseline measured as an amplitude? Peak to trough?

Sentence was corrected to the following:

“Seizures were confirmed as SWDs if they had a typical regular spike and wave pattern with 7-12 Hz frequency range and had at least double the amplitude compared to baseline signal. All other signals were classified as baseline i.e. signal absent of a distinctive 7-12 Hz frequency power but spread within frequencies from 1 to 90 Hz.”

"using rigid, affine, and SYN registrations" Please explain for the non-specialist.

Corrected as the following:

“using rigid, affine (linear) and SYN (non-linear) registrations”

Line 274-5: "However, there were also intermediate cases where the seizure started or ended during the stimulation block (Figure 1 - Figure Supplement 1). These intermediate cases were modeled as confounds" Why confounds? They could be very interesting because the stimulation may not be affected if timed at the end of the seizure. What was the definition of start and end? Defining the onset and end of seizures is tricky.

We agree that these cases are also highly interesting. Indeed, all the intermediate cases were also analyzed separately but not included in the manuscript (other than the case when stimulation immediately ended a seizure) as no statistical findings were found when comparing these cases to the baseline. E.g. for the case when stimulation was applied towards the end of seizure, it provided weakened responses but still stronger compared to case when stimulation was applied fully during a seizure (indicating some responsiveness after the cessation of seizure). As these intermediate cases led to results with higher variance, we considered them as confounds in the general linear model (i.e. reducing unwanted variance from the results of interests).

Definition of onset and end of seizure can be difficult in some cases. When looking at the signal itself, especially towards the end of seizure the amplitude of SWDs can get weaker and thus the shift from seizure to baseline signal can be more problematic to differentiate. However, when looking at the power spectrum the boundaries were more easily detectable. Thus, in the definitions of onsets and ends of seizure we relied on both the signal and power spectrum (stated in the manuscript).

"in the SPM analysis" Please explain for the non-specialist.

Definition of SPM together with a link to software site was added.

Line 276: "of fMRI data (see 2.5.3.) and thus explained variance that was not accounted for by the main effects of interest. " Please clarify.

Clarified as:

“Intermediate cases, where the seizure started or ended during the stimulation block (Figure 1–figure supplement 1), were considered as confounds of no-interest in the SPM analysis of fMRI data and the explained variance caused by the confounds were reduced from the main effects of interests”

Line 277: "Additionally, a contrast..." What is meant?

This chapter in 2.5.3. was modified as a whole to be more clear.

Line 278-9: "...was given to two cases: i) when stimulation ended a seizure (0-2 s between stimulation start and seizure end)..." Again, how is the seizure onset and end defined?

Look comment above.

Lines 281-2: "Stimulations that did not fully coincide with a seizure were considered as nuisance regressors in the second level analysis." What is meant by nuisance regressor?

Reference to SPM 12 manual was given for technical terms referring to analysis software.

Lines 283-8: "Motion periods were also included as multiple regressors (not convolved with a basis function) to be used as nuisance regressors. Stimulations that coincided with a motion above 0.3% of the voxel size were not considered stimulation inputs. Stimulation and seizure inputs were convolved with "3 gamma distribution basis functions" (i.e. 3rd 285 order gamma) in SPM (option: basis functions, gamma functions, order: 3), to account for temporal and dispersion variations in the hemodynamic response. The choice of 3rd order gamma was based on the expectation that time-to peak and shape of HRFs of seizure could vary across voxels (David et al. 2008)." Please explain the technical terms.

Reference for SPM 12 manual was given for technical terms referring to analysis software, and HRF was defined.

"BAMS rat connectome" - Please explain the technical terms.

Modified as:

“…connection matrix of the rat nervous system (BAMS rat connectome, Bota, Dong, and Swanson 2012).”

Results

After removing problematic animals and sessions, was there sufficient power? There probably wasn't enough to determine sex differences.

After removing problematic sessions, we found statistically significant results (multiple comparison corrected) results in both activation maps, and hemodynamic responses. To determine sex differences, there were not enough animals for statistical findings (p>0.05).

Figure 2 - I don't understand "tSNR" here. What is the point here?

B vs C. Are these different brain areas or the same but SNR was adjusted?

D. Where is FD explained? I think explaining what the parts of the figure show would be helpful.

tSNR, the temporal signal-to-noise ratio, demonstrates the behavior of noise through time. Readers who are planning to mimic the used awake fMRI protocol together with the single loop coil, might be interested on data quality aspect, and ability for the coil to capture signal from noise, as it is one of the most important factors in fMRI designs where small signal changes have to be distinguished from the background noise.

B and C illustrate the same brain area, but B was acquired with high resolution anatomical scanning (T1 FLASH), and C was acquired with low resolution ZTE scanning. We clarified the figure legend to the following:

“…spatial signal-to-noise ratios of an illustrative high resolution anatomical T1-FLASH (B), and low resolution ZTE image (C)

FD was explained in section 2.5.1. Some parts of the explanation were clarified: “Framewise displacement (FD) (Figure 2E) was calculated as follows. First, the differential of successive motion parameters (x, y, z translation, roll, pitch, yaw rotation) was calculated. Then absolute value was taken from each parameter and rotational parameters were divided by 5 mm (as estimate of the rat brain radius) to convert degrees to millimeters (Power et al. 2012). Lastly, all the parameters were summed together.”

Table 1 has no statistical comparisons.

Table 1 is purely an illustration of stimulation and seizure occurrence. There is no specific interest to compare stimulation types (in what state of seizure it occurred) as it does not provide any meaningful inferences to the study.

Statistical activation maps - it is not clear how this was done.

Creation of statistical maps are explained in section 2.5.3.

Line 384-5: "In addition, some responses were observed in the somatosensory cortex during a seizure state, probably due to incomplete nuisance removal of the effect of the seizure itself by the linear model used." I don't see why the authors would not suggest that the result is logical given that stimuli should activate the somatosensory cortex.

Sentence was modified as the following:

“In addition, responses were observed in the somatosensory cortex during a seizure state”

Fig 3 "F-contrast maps." Please explain.

Creation of statistical maps are explained in section 2.5.3.

HRF- please define. The ROI selection is unclear - it "was based on statistical differences seen in activation maps." But how were ROIs drawn? Also, why were HRFs examined at the end of seizures?

HRF was defined, and definitions of HRF and ROI were moved from results section 3.3. to method section 2.5.3.

Definition of ROI was clarified:

“Anatomical ROIs, based on Paxinos atlas (Paxinos and Watson rat brain atlas 7th edition), were drawn on the brain areas where statistical differences were seen in activation maps.”

HRFs were estimated additionally at the end of seizure as it was specifically interesting to study brain state shifts from ictal to interictal. This shift was also providing us statistically significant findings in means that brain responses differed from ictal stimulation.

Line 421: "Interestingly, the response amplitude was higher when the stimulation ended a seizure compared to when it did not" Why is this interesting?

Word “interestingly” was changed to “additionally” to avoid any inferences in the results section.

Line 427: "Notably, HRFs amplitudes were both negatively and positively signed during the ictal 427 state, depending on the brain region." Why is this notable?

Word “notably” was removed to avoid any inferences in the results section.

Please explain the legends of Figures 4 and 6 more clearly.

Figure 4, and figure 4 – figure supplement 1, legends were clarified:

“HRFs was calculated in selected ROI, belonging to visual or somatosensory area, by multiplying gamma basis functions (Figure 1–figure supplement 1, B) with their corresponding average beta values over a ROI and taking a sum of these values.”

Using the comments above as a guide, please revise the Discussion to be more precise and more clear about what was shown and what can be concluded in light of limitations. Please ensure the literature is cited where appropriate.

Some parts of the discussion and conclusion sections were modified.

Reviewer #3 (Recommendations For The Authors):

Minor comments:

Formatting: fMRI maps in Figures 3 and 5 should be more clearly labeled, indicating anterior and posterior directions on all images, and the cross sections should be enlarged to enable anatomical areas to be more clearly differentiated.

Anterior and posterior directions were added, and cross sections were enlarged.

The Methods section 2.41 and other places in the text, and Figure 2 - Figure Supplement 1 say that there was less artifact on the EEG with ZTA than with GE-EPI. However the EEG shown in Figure 2 - Figure Supplement 1 Part C shows much more artifact in the left (ZTE) trace than the right (GE-EPI) trace. This apparent contradiction should be resolved.

The figure was actually demonstrating the relative change to the signal when MRI sequences were on, and by this standard, the ZTE produced both less amplitude and frequency changes than EPI. In the example figure, the baseline fluctuations in the EEG trace in the left were higher in amplitude than in the right, and this could potentially lead to misconception of ZTE producing more noise. Figure legend was clarified to highlight relative change:

“ZTE also caused relatively less artificial noise on EEG signal, keeping both amplitude of the signal and frequencies relatively more intact, which improved live detection of absence seizures.”

Figure 2 - Supplement 1, part B horizontal axis should provide units.

Units were added.

Figure 2 - Supplement 1, legend last sentence says arrows mark the beginning of each "sequence." Is this a typo and should this instead say "each seizure"?

Should state “each fMRI sequence” which was corrected.

Line 307, Methods "to reveal brain areas where ictal stimulation provided higher amplitude response than interictal" - should this be reversed, ie weren't the authors analyzing a contrast to determine where interictal signals were higher than ictal signals?

This should be reversed, and was corrected, thank you for noting this.

Figure 6 - Figure Supplement 1, the scales are very different for many of the plots so they are hard to compare. Especially in the ictal periods (D, E, F) it is hard to see if any changes are happening during ictal stimulation similar to interictal stimulation due to very different scales. The activity related to SWD is so large that it overshadows the rest and perhaps should be subtracted out.

We point out that Figure 6 - Figure Supplement 1 reproduces with a higher level of detail the results shown of Figure 6 from the main text, where all signals are plotted in the same scale. The difference between scales used in this figure is intended, and its purpose is to show and highlight the large differences observed on the ongoing activity and the evoked response between the two states (ictal and interictal). In interictal periods the ongoing activity is characterized by fluctuations around a baseline level whose variance is highly affected by the application of the stimulus. On the contrary, ictal periods are characterized by large oscillations, with periods of high and synchronized activity followed by periods of nearly no activity, where the effect of the stimulus on the dynamics is overshadowed by the ongoing dynamics (both from local and from afferent nodes) as the referee mentions, and which imposes a strong limit to the responsiveness of the system and the propagation of the signal.

Reviewer #2 (Public Review):

The authors demonstrate convincingly the potential of single mesodermal cells, removed from zebrafish embryos, to show cell-autonomous oscillatory signaling dynamics and differentiation. Their main conclusion is that a cell-autonomous timer operates in these cells and that additional external signals are integrated to tune cellular dynamics. Combined, this is underlying the precision required for proper embryonic segmentation, in vivo. I think this work stands out for its very thorough, quantitative, single-cell real-time imaging approach, both in vitro and also in vivo. A very significant progress and investment in method development, at the level of the imaging setup and also image analysis, was required to achieve this highly demanding task. This work provides new insight into the biology underlying embryo axis segmentation.<br /> The work is very well presented and accessible. I think most of the conclusions are well supported. Here a my comments and suggestions:

1) The authors state that "We compare their cell-autonomous oscillatory and arrest dynamics to those we observe in the embryo at cellular resolution, finding remarkable agreement."

I think this statement needs to be better placed in context. In absolute terms, the period of oscillations and the timing of differentiation are actually very different in vitro, compared to in vitro. While oscillations have a period of ~30 minutes in vivo, oscillations take twice as long in vitro. Likewise, while the last oscillation is seen after 143 minutes in vivo, the timing of differentiation is very significantly prolonged, i.e.more than doubled, to 373min in vitro (Supplementary Figure 1-9). I understand what the authors mean with 'remarkable agreement', but this statement is at the risk of being misleading. I think the in vitro to in vivo differences (in absolute time scales) needs to be stated more explicitly. In fact, the drastic change in absolute timescales, while preserving the relative ones,i.e. the number of oscillations a cell is showing before onset of differentiation remains relatively invariant, is a remarkable finding that I think merits more consideration (see below).

2) One timer vs. many timers<br /> The authors show that the oscillation clock slowing down and the timing of differentiation, i.e. the time it takes to activate the gene mesp, are in principle dissociable processes. In physiological conditions, these are however linked. We are hence dealing with several processes, each controlled in time (and hereby space). Rather than suggesting the presence of 'a timer', I think the presence of multiple timing mechanisms would reflect the phenomenology better. I would hence suggest separating the questions more consistently, for instance into the following three:<br /> a. what underlies the slowing down of oscillations?<br /> b. what controls the timing of onset of differentiation?<br /> c. and finally, how are these processes linked?

Currently, these are discussed somewhat interchangeably, for instance here: "Other models posit that the slowing of Her oscillations arise due to an increase of time-delays in the negative feedback loop of the core clock circuit (Yabe, Uriu, and Takada 2023; Ay et al. 2014), suggesting that factors influencing the duration of pre-mRNA splicing, translation, or nuclear transport may be relevant. Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock."(page 14). In the first part, the slowing down of oscillations is discussed and then the authors conclude on 'the timer', which however is the one timing differentiation, not the slowing down. I think this could be somewhat misleading.

3) From this and previous studies, we learn/know that without clock oscillations, the onset of differentiation still occurs. For instance in clock mutant embryos (mouse, zebrafish), mesp onset is still occurring, albeit slightly delayed and not in a periodic but smooth progression. This timing of differentiation can occur without a clock and it is this timer the authors refer to "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14). This 'timer' is related to what has been previously termed 'the wavefront' in the classic Clock and Wavefront model from 1976, i.e. a "timing gradient' and smooth progression of cellular change. The experimental evidence showing it is cell-autonomous by the time it has been laid down,, using single cell measurements, is an important finding, and I would suggest to connect it more clearly to the concept of a wavefront, as per model from 1976.

4) Regarding question a., clearly, the timer for the slowing down of oscillations is operating in single cells, an important finding of this study. It is remarkable to note in this context that while the overall, absolute timescale of slowing down is entirely changed by going from in vivo to in vitro, the relative slowing down of oscillations, per cycle, is very much comparable, both in vivo and in vivo. To me, while this study does not address the nature of this timer directly, the findings imply that the cell-autonomous timer that controls slowing down is, in fact, linked to the oscillations themselves. We have previously discussed such a timer, i.e. a 'self-referential oscillator' mechanism (in mouse embryos, see Lauschke et al., 2013) and it seems the new exciting findings shown here in zebrafish provide important additional evidence in this direction. I would suggest commenting on this potential conceptual link, especially for those readers interested to see general patterns.

5) Regarding question c., i.e. how the two timing mechanisms are functionally linked, I think concluding that "Whatever the identity, our results indicate the timer ought to exert control over differentiation independent of the clock." (page 14), might be a bit of an oversimplification. It is correct that the timer of differentiation is operating without a clock, however, physiologically, the link to the clock (and hence the dependence of the timescale of clock slowing down), is also evident. As the author states, without clock input, the precision of when and where differentiation occurs is impacted. I would hence emphasize the need to answer question c., more clearly, not to give the impression that the timing of differentiation does not integrate the clock, which above statement could be interpreted to say.

6) A very interesting finding presented here is that in some rare examples, the arrest of oscillations and onset of differentiation (i.e. mesp) can become dissociated. Again, this shows we deal here with interacting, but independent modules. Just as a comment, there is an interesting medaka mutant, called doppelkorn (Elmasri et al. 2004), which shows a reminiscent phenotype "the Medaka dpk mutant shows an expansion of the her7 expression domain, with apparently normal mesp expression levels in the anterior PSM.". The authors might want to refer to this potential in vivo analogue to their single cell phenotype.

7) One strength of the presented in vitro system is that it enables precise control and experimental perturbations. A very informative set of experiments would be to test the dependence of the cell-autonomous timing mechanisms (plural) seen in isolated cells on ongoing signalling cues, for instance via Fgf and Wnt signaling. The inhibition of these pathways with well-characterised inhibitors, in single cells, would provide important additional insight into the nature of the timing mechanisms, their dependence on signaling and potentially even into how these timers are functionally interdependent.

Reviewer #1 (Public Review):

Summary:<br /> This study examines a hypothesized link between autism symptomatology and efference copy mechanisms. This is an important question for several reasons. Efference copy is both a critical brain mechanism that is key to rapid sensorimotor behaviors, and one that has important implications for autism given recent empirical and theoretical work implicating atypical prediction mechanisms and atypical reliance on priors in ASD.

The authors test this relationship in two different experiments, both of which show larger errors/biases in spatial updating for those with heightened autistic traits (as measured by AQ in neurotypical (NT) individuals).

Strengths:<br /> The empirical results are convincing - effects are strong, sample sizes are sufficient, and the authors also rule out alternative explanations (ruling out differences in motor behavior or perceptual processing per se).

Weaknesses:<br /> My main concern is that the paper should be more transparent about both (1) that this study does not include individuals with autism, and (2) acknowledging the limitations of the AQ.

On the first point, and I don't think this is intentional, there are several instances where the line between heightened autistic traits in the NT population and ASD is blurred or absent. For example, in the second sentence of the abstract, the authors state "Here, we examine the idea that sensory overload in ASD may be linked to issues with efference copy mechanisms". I would say this is not correct because the authors did not test individuals with ASD. I don't see a problem with using ASD to motivate and discuss this work, but it should be clear in key places that this was done using AQ in NT individuals.

For the second issue, the AQ measure itself has some problems. For example, reference 38 in the paper (a key paper on AQ) also shows that those with high AQ skew more male than modern estimates of ASD, suggesting that the AQ may not fully capture the full spectrum of ASD symptomatology. Of course, this does not mean that the AQ is not a useful measure (the present data clearly show that it captures something important about spatial updating during eye movements), but it should not be confused with ASD, and its limitations need to be acknowledged. My recommendation would be to do this in the title as well - e.g. note impaired visuomotor updating in individuals with "heightened autistic traits".

Suggestions for improvement:<br /> - Figure 5 is really interesting. I think it should be highlighted a bit more, perhaps even with a model that uses the results of both tasks to predict AQ scores.<br /> - Some discussion of the memory demands of the tasks will be helpful. The authors argue that memory is not a factor, but some support for this is needed.<br /> - With 3 sessions for each experiment, the authors also have data to look at learning. Did people with high AQ get better over time, or did the observed errors/biases persist throughout the experiment?

Reviewer #2 (Public Review):

Summary:

We often have prior expectations about how the sensory world will change, but it remains an open question as to how these expectations are integrated into perceptual decisions. In particular, scientists have debated whether prior knowledge principally changes the decisions we make about the perceptual world, or directly alters our perceptual encoding of incoming sensory evidence.

The authors aimed to shed light on this conundrum by using a novel psychophysical task while measuring EEG signals that have previously been linked to either the sensory encoding or response selection phase of perceptual choice. The results convincingly demonstrate that both features of perceptual decision making are modulated by prior expectations - but that these biases in neural process emerge over different time courses (i.e., decisional signals are shaped early in learning, but biases in sensory processing are slower to emerge).

Another interesting observation unearthed in the study - though not strictly linked to this perceptual/decisional puzzle - is that neural signatures of focused attention are exaggerated on trials where participants are given neutral (i.e. uninformative) cues. This is consistent with the idea that observers are more attentive to incoming sensory evidence when they cannot rely on their expectations.

In general, I think the study makes a strong contribution to the literature, and does an excellent job of separating 'perceiving' from 'responding'. More perhaps could have been done though to separate 'perceiving' and 'responding' from 'deciding' (see below).

Strengths:

The work is executed expertly and focuses cleverly on two features of the EEG signals that can be closely connected to specific loci of the perceptual decision making process - the SSVEP which connects closely to sensory (visual) encoding, and Mu-Beta lateralisation which connects closely to movement preparation. This is a very appropriate design choice given the authors' research question.

Another advantage of the design is the use of an unusually long training regime (i.e., for humans) - which makes it possible to probe the emergence of different expectation biases in the brain over different timecourses, and in a way that may be more comparable to work with nonhuman animals (who are routinely trained for much longer than humans).

Weaknesses:

In my view, the principal shortcoming of this study is that the experimental task confounds expectations about stimulus identity with expectations about to-be-performed responses. That is, cues in the task don't just tell participants what they will (probably) see, but what they (probably) should do.

In many respects, this feature of the paradigm might seem inevitable, as if specific stimuli are not connected to specific responses, it is not possible to observe motor preparation of this kind (e.g., de Lange, Rahnev, Donner & Lau, 2013 - JoN).

However, the theoretical models that the authors focus on (e.g., drift diffusion models) are models of decision (i.e., commitment to a proposition about the world) as much as they are models of choice (i.e., commitment to action). Expectation researchers interested in these models are often interested in asking whether predictions influence perceptual processing, perceptual decision and/or response selection stages (e.g., Feuerriegel, Blom & Hoogendorn, 2021 - Cortex), and other researchers have shown that parameters like drift bias and start point bias can be shifted in paradigms where observers cannot possibly prepare a response (e.g., Thomas, Yon, de Lange & Press, 2020 - Psych Sci).

The present paradigm used by Walsh et al makes it possible to disentangle sensory processing from later decisional processes, but it blurs together the processes of deciding about the stimulus and choosing/initiating the response. This ultimately limits the insights we can draw from this study - as it remains unclear whether rapid changes in motor preparation we see reflect rapid acquisition of new decision criterion or simple cue-action learning. I think this would be important for comprehensively testing the models the authors target - and a good avenue for future work.

In revising the manuscript after an initial round of revisions, the authors have done a good job of acknowledging these complexities - and I don't think that any of these outstanding scientific puzzles detract from the value of the paper as a whole.

This is all a hypothetical situation that cannot be proven. However, I do believe the same could be said regarding the statement that every person who has previously been just will act with the same integrity when placed in a similar situation. It IS all hypothetical. But really thinking about it, I think we all have the capacity to act unjust in situations and justify ourselves. Just think if your loved one's life was at risk and you HAD to be unjust to save that person, would you do it? Many people may say no, or it depends on what I would have to do. But I think in reality, we do not want to admit that we would indeed do anything even if it means being unjust. When we are put under pressure, we show our true colors. We just fear what others may think of us. Or we fear what we may think of ourselves. Either way, we tend to justify ourselves and not view things as they are.

We have a tendency to want to be above others in society and make our own rules and excuses.

Earlier I mentioned conscience as something that can drive a person to justice. So, I'm also curious what Plato would think about altruism and how natural it is to humans?

Reviewer 3 Megan Hagenauer - Original Submission

Review of "A Multi-omics Data Analysis Workflow Packaged as a FAIR Digital Object" by Niehues et al. for GigaScience08-31-2023I want to begin by apologizing for the tardiness of this review - my whole family caught Covid during the review period, and it has taken several weeks for us to be functional again.OverviewAs a genomics data analyst, I found this manuscript to be a fascinating, inspiring, and, quite honestly, intimidating, view into the process of making analysis code and workflow truly meet FAIR standards. I have added recommendations below for elements to add to the manuscript that would help myself and other analysts use your case study to plan out our own workflows and code release. These recommendations fall quite solidly into the "Minor Revision" category and may require some editorial oversight as this article type is new to me. Please note that I only had access to the main text of the manuscript while writing this review.Specific Comments1) As a case study, it would be useful to have more explicit discussion of the expertise and effort involved in the FAIR code release and the anticipated cost/benefit ratio:As a data analyst, I have a deep, vested interest in reproducible science and improved workflow/code reusability, but also a limited bandwidth. For me, your overview of the process of producing a FAIR code release was both inspiring and daunting, and left me with many questions about the feasibility of following in your footsteps. The value of your case study would be greatly enhanced by discussing cost/benefit in more detail:a. What sort of expertise or training was required to complete each step in the FAIR release? E.g.,i. Was your use of tools like Github, Jupyter notebook, WorkflowHub, and DockerHub something that could be completed by a scientist with introductory training in these tools, or did it require higher level use?ii. Was there any particular training required for the production of high quality user documentation or metadata? (e.g., navigating ontologies?)b. With this expertise/training in place, how much time and effort do you estimate that it took to complete each step of adapting your analysis workflow and code release to meet FAIR standards?i. Do you think this time and effort would differ if an analyst planned to meet FAIR standards for analysis code prior to initiating the analysis versus deciding post-hoc to make the release of previously created code fit FAIR standards?c. The introduction provides an excellent overview of the potential benefits of releasing FAIR analysis code/workflows. How did these benefits end up playing out within your specific case study?i. e.g., I thought this sentence in your discussion was a particularly important note about the benefits of FAIR analysis code in your study: "Developing workflows with partners across multiple institutions can pose a challenge and we experienced that a secure shared computing environment was key to the success of this project."ii. Has the FAIR analysis workflow also been useful for collaboration or training in your lab?iii. How many of the analysis modules (or other aspects of the pipeline) do you plan on reusing? In general, what do you think is the size for the audience for reuse of the FAIR code? (e.g., how many people do you think will have been saved significant amounts of work by you putting in this effort?)iv. … Or is the primary benefit mostly just improving the transparency/reproducibility of your science?d. If there is any way to easily overview these aspects of your case study (effort/time, expertise, immediate benefits) in a table or figure, that would be ideal. This is definitely the content that I would be skimming your paper to find.2) As a reusable code workflow, it would be useful to provide additional information about the data input and experimental design, so that readers can determine how easily the workflow could be adapted to their own datasets. This information could be added to the text or to Fig 1. E.g.,i. The dimensionality of the input (sample size, number of independent variables & potential co-variates, number of dependent variables in each dataset, etc)ii. Data types for the independent variables, co-variates, and dependent variables (e.g., categorical, numeric, etc)iii. Any collinearity between independent variables (e.g., nesting, confounding).3) As documentation of the analysis, it would be useful to provide additional information about how the analysis workflow may influence the interpretation of the results.a. It would be especially useful to know which aspects of the analysis were preplanned or following a standard procedure/protocol, and which aspects of the analysis were customized after reviewing the data or results. This information can help the reader assess the risk of overfitting or HARKing.b. It would also be useful to call out explicitly how certain analysis decisions change the interpretation of the results. In particular, the decision to use dimension reduction techniques within the analysis of both the independent and dependent variables, and then focus only on the top dimensions explaining the largest sources of variation within the datasets, is especially important to justify and describe its impact on the interpretation of the results. Is there reason to believe that externalizing behavior should be related to the largest sources of variation within buccal DNA methylation or urinary metabolites? Within genetic analyses, the assumption tends to be the opposite - that genetic variation related to behavior (such as externalizing) is likely to be present in a small percent of the genome, and that the top sources of variation within the genetics dataset are uninteresting (related to population) and therefore traditionally filtered out of the data prior to analysis. Within transcriptomics, if a tissue is involved in generating the behavior, some of the top dimensions explaining the largest sources of variation in the dataset may be related to that behavior, but the absolute largest sources of variation are almost always technical artifacts (e.g., processing batches, dissection batches) or impactful sources of biological noise (e.g., age, sex, cell type heterogeneity in the tissue). Is there reason to believe that cheek cells would have their main sources of epigenetic variation strongly related to externalizing behavior? (maybe as a canary in a coal mine for other whole organism events like developmental stress exposure?). Is there reason to believe that the primary variation in urinary metabolites would be related to externalizing behavior? (perhaps as a stand-in for other largescale organismal states that might be related to the behavior - hormonal states? metabolic states? inflammation?). Since the goal of this paper is to provide a case study for creating a FAIR data analysis workflow, it is less important that you have strong answers for these questions, and more important that you are transparent about how the answers to these questions change the interpretation of your results. Adding a few sentences to the discussion is probably sufficient to serve this purpose. Thank you for your hard work helping advance our field towards greater transparency and reproducibility. I look forward to seeing your paper published so that I can share it with the other analysts in our lab.

Author Response

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

Reviewer #2 (Public Review)

Weaknesses

1) The usage of young growing mice (8-10 weeks) versus adult mice (>4 months) in the murine mechanical overload experiments. The usage of adult mice would be preferable for these experiments given that maturational growth may somehow affect the outcomes.

The basis for this critique is not clear as it has been shown that the longitudinal growth of bones is complete by ⁓8 weeks of age (e.g., PMID: 28326349, and 31997656). These studies, along with others, also indicate that 8 weeks is a post-pubescent age in mice. For these reasons, 8 weeks of age was viewed as being representative of the human equivalent of when people start to perform resistance exercise with the goal of increasing muscle mass. Also, it’s important to consider that the mice were 10-12 weeks of age when the muscles were collected which would be equivalent to a human in their lower 20’s. In our human study, the mean age of the subjects was 23. Given the above points, it’s hard for us to appreciate why the use of mice that started at 8-10 weeks of age is viewed as a weakness. With that being said, we recognize that there may be age-related changes in mechanisms of mechanical load-induced growth, but it was not our intent to address this topic.

1b) No consideration for biological sex.

We appreciate this point and we agree that sex is an important variable to consider. In this study, we explored an unchartered topic and therefore we wanted to minimize as many known variables as possible. We did that, in part, by focusing specifically on male subjects. In the future, it will certainly be important to explore whether sex (and age) impact the structural adaptations that drive the mechanical load-induced growth of muscle fibers.

2) Information on whether myofibrillogenesis is dependent on hypertrophy induced by loading, or just hypertrophy in general. To provide information on this, the authors could use, for instance, inducible Myostatin KO mice (a model where hypertrophy and force production are not always in lockstep) to see whether hypertrophy independent from load induces the same result as muscle loading regarding myofibrillogenesis.

This is a great suggestion, but it goes beyond the intended scope of our study. Nevertheless, with the publication of our FIM-ID methodology, the answer to this and related questions can now be obtained in a time- and cost-effective manner.

3) Limited information on Type 1 fiber hypertrophy. A "dual overload" model is used for the mouse where the soleus is also overloaded, but presumably, the soleus was too damaged to analyze. Exploring hypertrophy of murine Type 1 fibers using a different model (weight pulling, weighted wheel running, or forced treadmill running) would be a welcome addition.

The point is well taken and further studies that are aimed at determining whether there are differences in how Type I vs. Type II fibers grow would be an excellent subject for future studies.

Reviewer #3 (Public Review)

1) Supplemental Figure 1 is not very clear.

Supplemental Figure 1 is now presented as Supplemental Figure 2. We carefully reexamined this figure and, in our opinion, the key points have been appropriately conveyed. We would be more than happy to revise the figure, but we would need guidance with respect to which aspect(s) of the figure were not clear to the reviewer.

Reviewer #1 (Recommendations For The Authors)

Introduction.

1) I do not think the first paragraph is really necessary. Cell growth is a fundamental property of cell biology that requires no further justification.

We believe that it is essential to remind all readers about the importance of skeletal muscle research. For some, the detrimental impact of skeletal muscle loss on one’s quality of life and the greater burden on the healthcare system may not be known.

2) I prefer "fundamental" over "foundationally".

All mentions of the word “foundational” and “foundationally” have been changed to “fundamental” and “fundamentally.”

3) As usual for the Hornberger lab, the authors do an excellent job of providing the (historical) context of the research question.

Thank you for this positive comment.

4) I prefer “Goldspink” as “Dr. Goldspink” feels too personal especially when you are critical of his studies.

All instances of “Dr.” have been removed when referring to the works of others. This includes Dr. Goldspink and Dr. Tokuyasu.

5) Fourth paragraph, after reference #17. I felt like this discussion was not necessary and did not really add any value to the introduction.

We believe that this discussion should remain since it highlights the widely accepted notion that mechanical loading leads to an increase in the number of myofibrils per fiber, yet there is no compelling data to support this notion. This discussion highlights the need for documented evidence for the increase in myofibril number in response to mechanical loading and, as such, it serves as a major part of the premise for the experiments that were conducted in our manuscript.

6) The authors do a nice job of laying out the challenge of rigorously testing the Goldspink model of myofiber hypertrophy.

Thank you!

Results

1). For the EM images, can the authors provide a representative image of myofibril tracing? From the EM image provided, it is difficult to evaluate how accurate the tracing is.

-Representative images and an explanation of myofibril calculation have been provided in Supplemental Figure 5.

2) In the mouse, how does the mean myofibril CSA compare between EM and FIM-ID?

Author response image 1.

The above figures compare the myofibril CSA and fiber CSA measurements that were obtained with EM and FIM-ID for all analyzed fibers, as well as the same fibers separated according to the fiber type (i.e., Ox vs. Gly). The above figure shows that the FIM-ID measurements of myofibril CSA were slightly, yet significantly, lower than the measurements obtained with EM. However, we believe that it would be misleading to present the data in this manner. Specifically, as shown in Fig. 4C, a positive linear relationship exists between myofibril CSA and fiber CSA. Thus, a direct comparison of myofibril CSA measurements obtained from EM and FIM-ID would only be meaningful if the mean CSA of the fibers that were analyzed were the same. As shown on the panel on the right, the mean CSA of the fibers analyzed with FIM-ID was slightly, yet significantly, lower than the mean CSA of the fibers analyzed with EM. As such, we believe that the most appropriate way to compare the measurements of the two methods is to express the values for the myofibril CSA relative to the fiber CSA and this is how we presented the data in the main figure (i.e., Fig. 4E).

3) Looking at Fig. 3D, how is intermyofibrillar space calculated when a significant proportion of the ROI is odd-shaped myofibrils that are not outlined? It is not clear how the intermyofibrillar space between the odd-shaped myofibrils is included in the total intermyofibrillar space calculation for the fiber.

The area occupied by the intermyofibrillar components is calculated by using our custom “Intermyofibrillar Area” pipeline within CellProfiler. Briefly, the program creates a binary image of the SERCA signal. The area occupied by the white pixels in the binary image is then used to calculate the area that is occupied by the intermyofibrillar components. To help readers, an example of this process is now provided in supplemental figure 4.

4) What is the average percentage of each ROI that was not counted by CP (because a myofibril did not fit the shape criteria)? The concern is that the method of collection is biasing the data. In looking at EM images of myofibrils (from other studies), it is apparent that myofibrils are not always oval; in fact, it appears that often myofibrils have a more rectangular shape. These odd-shaped myofibrils are excluded from the analysis yet they might provide important information; maybe these odd-shaped myofibrils always hypertrophy such that their inclusion might change the overall conclusion of the study. I completely understand the challenges of trying to quantify odd-shaped myofibrils. I think it is important the authors discuss this important limitation of the study.

First, we would like to clarify that myofibrils of a generally rectangular shape were not excluded. The intent of the filtering steps was to exclude objects that exhibited odd shapes because of an incomplete closure of the signal from SERCA. To illustrate this point we have annotated the images from Figure 3B-D with a red arrow which points to a rectangular object and blue arrows which point to objects that most likely consisted of two or more individual myofibrils that were falsely identified as a single object.

Author response image 2.

We appreciate the reviewer's concern that differences in the exclusion rates between groups could have biased the outcomes. Indeed, this was something that we were keeping a careful eye on during our analyses, and we hope that the reviewer will take comfort in knowing that objects were excluded at a very similar rate in both the mouse and human samples (44% vs. 46% for SHAM vs. MOV in mice, and 47% vs. 47% for PRE vs. POST in humans). We realize that this important data should have been included in our original submission and it is now contained with the results section of the revised version of our manuscript. Hopefully the explanation above, along with the inclusion of this data, will alleviate the reviewers concerns that differences between the groups may have been biased by the filtering steps.

Discussion.

1) I think the authors provided a balanced interpretation of the data by acknowledging the limitation of having only one time-point. i.e., not being able to assess the myofibril splitting mechanism.

Thank you!

2) I think a discussion on the important limitation of only quantifying oval-shaped myofibrils should be included in the discussion.

Please refer to our response to comment #4 of the results section.

Reviewer #2 (Recommendations For The Authors)

Overall, this is a thoughtful, clear, and impactful manuscript that provides valuable tools and information for the skeletal muscle field. My specific comments are as follows:

1) In the introduction, I really appreciate the historical aspect provided on myofbrillogenesis. As written, however, I was expecting the authors to tackle the myofibril "splitting" question in greater detail with their experiments given the amount of real estate given to that topic, but this was not the case. Consider toning this down a bit as I think it sets a false expectation.

We acknowledge that the study does not directly address the question about myofibril splitting. However, we believe that it is important to highlight the background of this untested theory since it serves as a major part of the premise for the experiments that were performed.

2) In the introduction, is it worth worth citing this study? https://rupress.org/jcb/articlepdf/111/5/1885/1464125/1885.pdf.

This is a very interesting study but, despite the title, we do not believe that it is accurate to say that this study investigated myofibrillogenesis. Instead (as illustrated by the author in Fig. 9) the study focused on the in-series addition of new sarcomeres at the ends of the pre-existing myofibrils (i.e., it studied in-series sarcomerogenesis). In our opinion, the study does not provide any direct evidence of myofibrillogenesis, and we are not aware of any studies that have shown that the chronic stretch model employed by the authors induces myofibrillogenesis. However, numerous studies have shown that chronic stretch leads to the in-series addition of new sarcomeres.

3) Is there evidence for myofbrillogenesis during cardiac hypertrophy that could be referenced here?

This is a great question, and one would think that it would have been widely investigated. However, direct evidence for myofibrillogenesis during load-induced cardiac hypertrophy is just as sparse as the evidence for myofibrillogenesis during load-induced skeletal muscle hypertrophy.

4) In the introduction, perhaps mention that prolonged fixation is another disadvantage of EM tissue preparation. This typically prevents the usage of antibodies afterwards, whereas the authors have been able to overcome this using their method, which is a great strength.

Thank you for the suggestion. This point has been added the 5th paragraph of the introduction.

5) In the introduction, are there not EM-compatible computer programs that could sidestep the manual tracing and increase throughput? Why could software such as this not be used? https://www.nature.com/articles/s41592-019-0396-9

While we agree that automated pipelines have been developed for EM, such methods require a high degree of contrast between the measured objects. With EM, the high degree of contrast required for automated quantification is rarely observed between the myofibrils and the intermyofibrillar components (especially in glycolytic fibers). Moreover, one of the primary goals of our study was to develop a time and cost-effective method for identifying and quantifying myofibrils. As such, we developed a method that would not require the use of EM. We only incorporated EM imaging and analysis to validate the FIM-ID method. Therefore, utilizing an EM-compatible program to sidestep the manual tracing would have sped up the validation step, but it would not have accomplished one of the primary goals of our study.

6) In the results, specifically for the human specimens, were "hybrid" fibers detected and, if so, how did the pattern of SERCA look? Also, did the authors happen to notice centrallynucleated muscle fibers in the murine plantaris after overload? If so, how did the myofibrils look? Could be interesting.

For the analysis of the human fibers, two distinct immunolabeling methods were performed. One set of sections was stained for SERCA1 and dystrophin, while the other set was stained for SERCA2 and dystrophin. In other words, we did not perform dual immunolabeling for SERCA1 and SERCA2 on the same sections. Therefore, during the analysis of the human fibers, we did not detect the presence of hybrid fibers. Furthermore, while we did not perform nuclear staining on these sections, it should be noted that nuclei do not contain SERCA, and to the best of our recollection, we did not detect any SERCAnull objects within the center of the fibers. Moreover, our previous work has shown that the model of MOV used in this study does not lead to signs of degeneration/regeneration (You, Jae-Sung et al. (2019). doi:10.1096/fj.201801653RR). Therefore, it can be safely assumed that very few (if any) of the fibers analyzed in this study were centrally nucleated.

7) In the Results, fixed for how long? This is important since, at least in my experience, with 24+ hours of fixation, antibody reactivity is significantly reduced unless an antigen retrieval step is performed (even then, not always successful). Also, presumably these tissues were drop-fixed? These details are in the Methods but some additional detail here could be warranted for the benefit of the discerning and interested reader.

For both the mouse and human, the samples were immersion-fixed (presumably the equivalent of “drop-fixed”) in 4% paraformaldehyde in 0.1M phosphate buffer solution for a total of 24 hours (as described in the Methods section). We agree that prolonged aldehyde fixation can affect antibody reactivity; however, the antibodies used for FIM-ID did not require an antigen retrieval step.

8) In the results regarding NADH/FAD autofluorescence imaging, a complimentary approach in muscle was recently described and could be cited here: https://journals.physiology.org/doi/full/10.1152/japplphysiol.00662.2022

We appreciate the reviewer’s recommendation to add this citation for the support of our method for fiber type classification and have added it to the manuscript in the second paragraph under the “Further refinement and validation of the automated measurements with FIM-ID” subsection of the Results as citation number 57.

9) In the results, "Moreover, no significant differences in the mean number of myofibrils per fiber CSA were found when the results from the FIM-ID and EM-based measurements were directly compared, and this point was true when the data from all analyzed fibers was considered..." Nit-picky, but should it be "were considered" since data is plural?

Thanks, this error was corrected.

10) In the discussion, are the authors developing a "methodology" or a "method"? I think it may be the latter.

We agree that “method” is the correct term to use. Instances of the word “methodology” have been replaced with “method.”

11) In the discussion, since the same fibers were not being tracked over time, I'm not sure that saying "radial growth" is strictly correct. It is intuitive that the fibers were growing during loading, of course, but it may be safer to say "larger fibers versus control or the Pre sample" or something of the like. For example, "all the fiber types that were larger after loading versus controls" as opposed to "showed significant radial growth"

While we agree that the fiber size was not tracked over time, the experiments were designed to test for a main effect of mechanical loading. Therefore, we are attributing the morphological adaptations to the mechanical loading variable (i.e., mechanical loadinduced growth). The use of terms like “the induction of radial growth” or “the induction of hypertrophy” are commonly used in studies with the methods employed in this study. Respectfully, we believe that it would be more confusing for the readers if we used the suggested terms like "all the fiber types that were larger after loading versus controls". For instance, if I were the reader I would think to myself… but there fiber types that were larger than others before loading (e.g., Ox vs. Gly), so what are the authors really trying to talk about?

12) I would suggest making a cartoon summary figure to complement and summarize the Methods/Results/Discussion

Thank you for this suggestion. We created a cartoon that summarizes the overall workflow for FIM-ID and this cartoon is now presented in Supplemental Figure 1.

To me, Hobbes is saying that In simple terms, people are generally equally smart. The idea that some are wiser might be because individuals often think they are smarter than others. Hobbes argues that, in reality, people are more equal in their mental abilities than they realize.

This is such an interesting point. We like to assume that we better than, in any aspect, our peers, but when it comes down to it, we are all equal. We all carry that belief that we are better then someone else, believing we are unequal to said person. They may carry that same idea about you, they think they outrank you in some aspect, making them believe they are better then you. Therefore, we are all the same. The same arrogant people.

I think we need to understand the nuances of recommendation algorithms, which is critical in addressing their influence on individual experiences. As these systems are designed to enhance user interaction, they can inadvertently perpetuate biases and present content that may not always align with the best interests or intentions of the users.

In this section, Adam responds to Eve as to why the stars and heavens shine. He explains to her that the sun must shine over all the earth, for those who will inhabit it in the future. They sleep at night, so that they may work harder in the day. He also talks about various "celestial voices"(4. 682) that he has heard at night, praising the glory of God. This heavenly chorus will protect them, as they “divide the night”(4. 688) to keep watch over Adam and Eve, while continuing to exalt their Creator. While reading this section, it seemed to me that the difference between night and day was emphasised heavily. This is an important distinction to make, as God is attributed as the giver of light, and Satan as a bringer of darkness.

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

Learn more at Review Commons

1. General Statements

We thank the reviewers for their excellent work that greatly improved our work. We are very content that reviewer #1 considered our work to be “novel, interesting and important for understanding the mitochondrial biology of PD”. This reviewer also valued our work as “a significant advancement” and suggested further study of the relationship of CISD1 (dimerization) to general mitophagy/autophagy. We addressed this in the already transferred revision (version 1, v1).

Also reviewer #2 considered our work to be “an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate”. This reviewer had a very specific suggestion on how to improve our manuscript which makes a lot of sense and is feasible. As the suggested experiments include fly breeding and behavioral analysis, these experiments will be included in the second revision to be uploaded as soon as possible (version 2, v2).

Finally, reviewer #3 gathered that parts of our results “are confirmatory to recently published work” but also appreciated that our results established that iron-depleted apo-Cisd is an important determinant of toxicity which has not been shown before. I would like to comment here, that in contrast to the paper mentioned by this reviewer, our contribution includes data from dopaminergic neurons obtained from human patients suffering from familial Parkinson’s disease that demonstrate the same increase in apo-Cisd levels as the flies. This reviewer mainly suggested that the manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data which we did in the revised version of our manuscript. We also added data on the expression levels of Cisd and apo-Cisd in transgenic flies as also suggested.

2. Description of the planned revisions

Reviewer #1 (Evidence, reproducibility and clarity):

Summary: The manuscript focuses on mitochondrial CISD1 and its relationship to two Parkinson's disease (PD) proteins PINK1 and Parkin. Interestingly, CISD1 is a mitochondrial iron sulfur binding protein and an target of Parkin-mediated ubiquitinylation. Disruption of iron metabolism and accumulation of iron in the brain has long since been reported in PD but the involvement of iron sulfur binding is little studied both in vivo and in human stem cell models of PD. This work addresses the relationship between CISD1 and two mitochondrial models of PD (PINK1 and Parkin) making use of in vivo models (Drosophila), PINK1 patient models (iPSC derived neurons) and Mouse fibroblasts. The authors report a complex relationship between CISD1, PINK1 and Parkin, where iron-depleted CISD1 may illicit a toxic gain of function downstream of PINK1 and Parkin.

Major comments:

The conclusions are overall modest and supported by the data. One question remains unaddressed. Is mitochondrial CISD1 a downstream target that specifically mediates PINK1 and Parkin loss of function phenotypes or are the phenotypes being mediated because CISD1 is downstream of mitophagy in general?

It would be interesting to know what happens to CISD1 (dimerization?) upon initiation of mitophagy in wild type cells? Would dissipation of mitochondrial membrane potential be sufficient to induce changes to CISD1 in wild type cells or PINK1 deficient cells? Since iron chelation is a potent inducer of mitophagy (Loss of iron triggers PINK1/Parkin-independent mitophagy. George F G Allen, Rachel Toth, John James, Ian G Ganley. EMBO Reports (2013)14:1127-1135) it would be useful to show one experiment addressing the role of CISD1 dimerization under mitochondrial depolarizing and non-depolarizing conditions in cells.

Based on the overall assumption of the reviewer that our work is “novel, interesting and important for understanding the mitochondrial biology of PD” and “a significant advancement” we understand the word “modest” here as meaning “not exaggerated”. To address this question, we studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

Alternatively, the authors should discuss the topic of mitophagy (including PINK1-parkin independent mitophagy), the limitation of the present study not being able to rule out a general mitophagy effect and previous work on the role of iron depletion on mitophagy induction in the manuscript.

The data and the methods are presented in such a way that they can be reproduced.

The experiments are adequately replicated and statistical analysis is adequate.

Minor comments:

Show p values even when not significant (ns) since even some of the significant findings are borderline < p0.05.

Here, I decided to leave it as it is, because the figures became very cluttered and less easy to understand. Borderline findings are however indicated and mentioned in the text.

Because the situation for CISD1 is complicated (overexpression, different models etc.) it would be helpful if in the abstract the authors could summarize the role. E.g. as in the discussion that iron-depleted CISD1 could represent a toxic function.

The abstract has been completely rewritten and now mentions the potential toxic function of iron-depleted CISD1.

If there is sufficient iron (accumulation in PD) why would CISD1 be deactivated? Perhaps that could be postulated or discussed in a simplified way?

We actually think that apo-CISD1 without its iron/sulfur cluster is incapable of transferring its Fe/S cluster to IRP1 and IRP2. This then results in increased levels of apo-IRP1/2 and subsequent changes that lead to iron overload. Such a sequence of events would place CISD1 upstream of the changes in iron homeostasis observed in PD and models of PD. This is now discussed in more detail.

In the methods section both reducing and non-reducing gel/Western blotting is mentioned but the manuscript only describes data from blots under reducing conditions. Are there blots under non-reducing conditions that could be shown to see how CISD1 and dimerized CISD1 resolve?

We now show these blots as supplemental data in new supplemental Figure 2.

In the results section, PINK1 mutant flies, it is said that the alterations to CISD1 (dimerization) are analogous to the PINK1 mutation patient neurons. The effect is seen in old but not young flies. Since iPSC-derived neurons are relatively young in the dish, would one not expect that young flies and iPSC-derived neurons have similar CISD1 phenotypes? Could the authors modify the text to reflect that? or discuss the finding in further context.

We only studied one time point in PINK1 mutation patient neurons and controls. It would indeed be interesting whether neuronal aging (as far as this can be studied in the dish) would result in increased CISD1 dimerization. This is now discussed.

Reviewer #1 (Significance):

The strengths of this work are in the novelty of the topic and the use of several well established in vivo and cell models including patient-derived neurons. The findings discussed in the text are honest and avoid over-interpretation. The findings are novel, interesting and important for understanding the mitochondrial biology of PD.

We thank the reviewer for their kind words.

Limitations include the lack of strong phenotypes in the CISD1 models and the lack of robust, sustained and consistent increase in CISD1 dimers in the patient and fly models (just significant because of variability). The relationship of CISD1 (dimerization) to general mitophagy/autophagy is not shown here.

We do not completely agree with the assumption that all CISD1 models lack a strong phenotype. At least the CISD1-deficient fibroblasts exhibit a strong phenotype consisting of fragmented mitochondria and increased oxidative stress. The lack of a strong phenotype in Cisd-deficient flies could actually hint to a potential compensatory mechanism that could also protect the Pink1 mutant x Cisd-deficient double-knockout flies. It is correct that the increase in CISD1/Cisd dimers in the PD models are not overwhelming but – as also mentioned by the reviewer – this could be increased in “older” cultures. This is now discussed in more detail. As suggested by the reviewer, we have now added experiments that study the relationship between CISD1 dimerization and conventional mitophagy as described above.

There is a significant advancement. So far researchers were able to describe the importance of iron metabolism in PD (For example refer to work from the group of Georg Auburger such as PMID 33023155 and discussion of therapeutic intervention such as reviewed by Ma et al. PMID: 33799121) but few papers describe involvement of iron sulfur cluster proteins specifically (such as Aconitase) in relation to PINK1 and parkin (these are cited). The fact that CISD1 is a protein of the mitochondrial outer membrane makes it particularly interesting and further studies looking more closely at the interaction of CISD1 with mitochondrial proteins associated with PD will be of interest.

We thank the reviewer for pointing out these excellent publications. Key et al present an enormous wealth of data on protein dysregulation of wildtype and Pink1-/- fibroblast cell lines upon perturbation of the iron homeostasis (Key et al, 2020). Both cell lines exhibit a downregulation of CISD1 levels upon iron deprivation with the agent 2,2′ -Bipyridine possibly as a compensatory mechanism to limit the toxic gain of function of iron-depleted CISD1. The other paper, Ma et al. is a recent review on changes in iron homeostasis in PD and PD models (Ma et al, 2021). Both papers are now cited in the manuscript.

This paper describes CISD1 as a new and relevant player in PINK1 and Parkin biology. Further work could lead to exploration of whether CISD1 could be a therapeutic target, considering its role in maintaining mitochondrial redox and mitochondrial health. This is of particular interest to mitochondrial biologists and pre-clinical research in PD.

This preprint was reviewed by three scientists whose research focus in the mitochondrial biology underlying Parkinson's disease. The group has a special interest in the functions of the mitochondrial outer membrane. We work with several cell models of Parkinson's disease and work with patient donated samples. We do not have expertise in Drosophila models of PD nor the quantification of iron described in the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary: In the paper entitled 'Mitochondrial CISD1 is a downstream target that mediates PINK1 and Parkin loss-of-function phenotypes', Bitar and co-workers investigate the interaction between CISD1 and the PINK1/Parkin pathway. Mutations in PINK1 and PARKIN cause early onset Parkinson's disease and CISD1 is a homodimeric mitochondrial iron-sulphur binding protein. They observed an increase in CISD1 dimer formation in dopaminergic neurons derived from Parkinson's disease patients carrying a PINK1 mutation. Immuno-blots of cells expressing CISD1 mutants that affects the iron sulphur cluster binding and as well as cells treated with iron chelators, showed that the tendency of CISD1 to form dimers is dependent on its binding to iron-sulphur clusters. Moreover, the Iron-depleted apo-CISD1 does not rescue mitochondrial phenotypes observed in CISD1 KO mouse cells. Finally, In vivo studies showed that overexpression of Cisd and mutant apo-Cisd in Drosophila shortened fly life span and, using a different overexpression model, apo-Cisd caused a delay in eclosion. Similar as patient derived neurons, they observed an increase in Cisd dimer levels in Pink1 mutant flies. Additionally, the authors showed that double mutants of Cisd and Pink1 alleviated all Pink1 mutant phenotypes, while double mutants of Prkn and Cisd rescued most Prkn mutant phenotypes.

Major comments:

1) The authors observed an increase in the levels of Cisd dimers in Pink1 mutant flies and removing Cisd in Pink1 mutant background rescues all the mutant phenotypes observed in Pink1 mutant flies, suggesting that the Cisd dimers are part and partial of the Pink1 mutant phenotype. The authors also generated a UAS_C111S_Cisd fly which can overexpress apo-Cisd. Overexpression of the C111S_Cisd construct with Tub-Gal4 showed a developmental delay. Since apo-Cisd forms more dimeric Cisd, my question is: does the strong overexpression (e.g. with Tub-Gal4) of the C111S_Cisd in wild type flies shows any of the Pink1 mutant phenotypes? If not, the authors should mention this and elaborate on it.

We thank the reviewer for their comments. In fact, we only observed very few flies ecclosing after overexpression of wildtype Cisd or C111S Cisd using the strong tubP-Gal4 driver during development. We considered these very few flies to be escapees (also indicated by the rather low induction of Cisd mRNA suggesting compensatory downregulation) and only used them to conduct the analysis shown in Figure 4c-e. This is now mentioned in more detail in the manuscript.

2) Figure 6g: Shows the TEM pictures of the indirect flight muscles of Pink1 mutant flies and Pink1, Cisd double mutants. To me, the Picture of Pink1 mutant mitochondria is not very convincing. We expect swollen (enlarged) mitochondria with disrupted mitochondrial matrix. However, this is not clear in the picture. Moreover, in my opinion, Figure6 g, is missing an EM Picture of the Cisd mutant indirect flight muscles.

We now show exemplary pictures from Pink1 mutant and DKO in a higher magnification which better demonstrate the rounded Pink1 mutant mitochondria and the disrupted cristae structure. EM pictures of all four genotypes in different magnifications are now shown in new supplemental Figure 6.

3) OPTIONAL: The authors suggest that most probably apo-Cisd, assumes a toxic function in Pink1 mutant flies and serves as a critical mediator of Pink1-linked phenotypes. If this statement is correct, we can hypothesize that increasing apo-Cisd in Pink1 mutant background should worsen the pink1 mutant defects.

Therefore, I suggest overexpressing Cisd1 wild type (and/or C111S Cisd) in pink1 mutant flies, as pink1 is on the X chromosome, and mild overexpression of Cisd1 with da is not lethal, these experiments could be done in 3-4 fly crosses and hence within 1.5 - 2 months.

We have set up this experiment and will report in the second revision (v2) of our manuscript.

Since Pink1 mutant flies contain higher levels of endogenous Cisd dimers, we can expect that overexpression of wild type Cisd will result in an even stronger increase of dimers. If these dimers indeed contribute to Pink1 mutant phenotypes we can expect that overexpression of Cisd will result in a worsening of the Pink1 mutant phenotypes.

We have set up this experiment and will report in the second revision of our manuscript.

Minor Comments:

-) In the Introduction (Background) there are some parts without references:

E.g., there is not a single reference in the following part between

'However, in unfit mitochondria with a reduced mitochondrial membrane potential ...&... compromised mitochondria safeguards overall mitochondrial health and function.'

We thank the reviewer for pointing out this flaw. We have now added a suitable reference to the introduction.

-) In the introduction there is some confusion about the nomenclature used in the article: e.g. following comments are made in the text: Cisd2 (in this publication referred to as Dosmit) or fly Cisd2 (in this publication named MitoNEET).

However, the names Dosmit and MitoNEET do not appear in the manuscript (except in references)

The literature and nomenclature for CISD1 are indeed confusing. We have now revised the introduction.

-) Figure 1: I am not sure why some gels are shown in this figure. The two last lanes of figure 1c are redundant and Figure 1c' which is also not mentioned in the text, is also a repetition of figure 1c.

The blots in 1c and 1c’ represent all data points (different patients and different individual differentiations) shown in the quantification in 1d. This is now explained better in the revised manuscript.

-) The authors mention in material and methods that T2A sites are used at the C-terminus of CISD1 to avoid tagging of CISD1. However, this is not entirely true as T2A will leave some amino acids (around 20) after the self-cleaving and therefore CISD1 will be tagged.

This is indeed true and we have now changed the wording in the revised manuscript.

-) In figure 5 P1 is used to abbreviate Pink1 mutants, however P1, to me, refers to pink1 wild type. It would be clearer to abbreviate Pink1 mutants as P1B9 in the graphs as B9 is the name of the mutant pink1 allele.

We thank the reviewer for pointing out this flaw. We have now altered Fig. 5 to be clearer.

-) In figure 7: Parkin is abbreviated both as Prkn and as Park

We thank the reviewer for pointing out this flaw, we indeed mixed up both names because it is complicated. The gene symbol is Prkn, the fly line is called Park25. We have now clarified this in the text and Fig. 7.

-) I suggest changing the title. Recently an article (Ham et al, 2023 PMID: 37626046) was published showing similar genetic interactions between Pink1/Prkn and Cisd. However, the article of Ham et al, 2023 was focused on Pink1/Prkn regulation of ER calcium release, while this article is more related to iron homeostasis. I suggest that the title shows this distinction.

This is indeed a very good suggestion. We have now altered the title to “Iron/sulfur cluster loss of mitochondrial CISD1 mediates PINK1 loss-of-function phenotypes”.

Reviewer #2 (Significance):

In general, this is an exciting and well-executed piece of research focusing on the defects in iron homeostasis observed in Parkinson's disease which a wide audience will appreciate. Very recently, a similar genetic interaction between Cisd and Pink1/Prkn in flies was published (Ham et al, 2023 PMID: 37626046) however, from a different angle. While, Ham et al focused on the role of Pink1/Prkn and Cisd in IP3R related ER calcium release, this manuscript approaches the Pink1/Prkn - Cisd interaction from an iron homeostasis point of view. Since, iron dysregulation contributes to the pathogenesis of Parkinson's disease, the observations in this manuscript are relevant for the disease. Hence, the work is sufficiently novel and deserves publication. However, additional experiments are suggested to strengthen the authors' conclusions.

We thank the reviewer for their kind words. As mentioned above, these additional experiments are on their way and will be included in version 2 of our revised manuscript (v2).

I work on Drosophila models of Parkinson's disease

Referees cross-commenting

I agree with the reviewer number 1 that it would be interesting to investigate CISD1 dimerisation status during mitophagy.

As mentioned above, we now studied CISD1 dimerization in response to more classical activators of mitophagy namely FCCP and antimycin/oligomycin which had no significant effect on dimerization suggesting that this phenotype is more pronounced under iron depletion. These data are shown in the new Fig. 2c.

Reviewer #3 (Evidence, reproducibility and clarity):

Here the authors provide evidence that Cisd is downstream of Parkin/Pink1 and suggest that the levels of apo-Cisd correlate with neurotoxicity. The data presented generally supports the conclusions of the authors and will be useful to those in the field. The manuscript would be improved by a more balanced discussion of the strengths and weaknesses of the study and more circumspection in interpretation of data.

We thank the reviewer for their comments aimed to improve our manuscript. We have now discussed the strengths and weaknesses of our study in more detail.

Introduction. While iron has been implicated in Parkinson's disease, it is an overstatement to say that disruption in iron metabolism contributes significantly to the pathogenesis of the disease.

There is certainly a plethora of data implicating perturbed iron homeostasis in PD as also pointed out by reviewer #1. We have tried to tone down our wording in the text and added a recent review on the topic (Ma et al, 2021) as also suggested by reviewer #1.

Introduction. The discussion of the various names for Cisd2 is important, but confusing as written. Specifically, the use of "this" makes the wording unclear.

We thank the reviewer for pointing out this flaw. We have altered the wording in the introduction.

Methods. It would be preferable to use heterozygous driver lines or a more similar genetic control rather than w-1118.

The exact controls were indeed not well explained in the Methods section, this has been corrected in the revised version. In brief, homozygous driver and UAS lines were indeed used in Fig. 4, this will be addressed in the second revision of our manuscript together with the experiments reviewer #2 suggested. The data shown in Fig. 5, 6, and 7 all used w1118 as control because all other fly strains are on the same genetic background.

Page 10. It appears that the PINK1 lines have been described previously. The authors should clarify this point and ensure that the new data presented in the current manuscript (presumably the mRNA levels, Fig. 1a) is indicated, as well as data that is confirmatory of prior findings (Fig. 1b).

Yes, these PINK1 lines have been described previously as pointed out in the manuscript. The original paper did not quantify the PINK1 mRNA levels shown in Fig. 1a. The blots shown in Fig. 1b are from new differentiations and have also not been shown before but confirm findings published in Jarazo et al. (Jarazo et al, 2022). This has been clarified in the revised version of our manuscript.

Fig. 3 legend. There is a typographical error, "ne-way ANOVA."

We thank the reviewer for pointing out this flaw. This has been corrected in the revised version.

Page 15. The nature of the Pink1-B9 mutant should be specified.

We now added a supplemental Figure 1 that depicts the specific mutation in these flies.

Fig. 4. Levels of mutant and wild type Cisd should be compared in transgenic flies.

We now added a quantification of mutant and wildtype Cisd levels to the new Figure 4d.

Fig. 5b,d. The striking change seems to be the decrease in dimers in young Pink1 mutant animals, not the small increase in dimers in the older Pink1 mutants.

It is always difficult to find a “typical” picture that reflects all changes observed in quantitative data. This Figure actually shows a decrease of total Cisd levels in young flies in Fig. 5c but no difference of the dimer/monomer ratio in Fig. 5d.

Fig. 5f. Caution should be used in interpreting the results. Deferiprone has toxicity to wildtype flies (trend) and may simply be making sick Pink1 mutants sicker.

There is certainly a tendency for wildtype flies to thrive less in food containing deferiprone. To make this more obvious, we have now added the exact p value (0.0764, which we don’t consider borderline but a tendency) to this figure and mention this fact in the text.

Fig. 5e. The data are hard to interpret. The number of animals is very small for a viability study and the strains are apparently in different genetic backgrounds, though this is not clearly specified. The experiment in Supplementary Fig. 1 appears better controlled and supports the Pink1 data; however, a similar concern pertains to Fig. 7. The authors may thus wish to be more circumspect in their interpretation, especially of the Parkin data.

In Fig 5e we quantified total iron levels and the Fe3+/Fe2+ ratio using capillary electrophoresis-inductively coupled plasma mass spectrometry (CE-ICP-MS). Although indeed not so many flies were used in this quantification, the results are highly significant. If the reviewer was referring to Fig. 5f, we agree that this experiment was not well (to be honest, even wrongly explained) which we corrected in the revised version of this manuscript. We thank the reviewer for pointing out this flaw.

Reviewer #3 (Significance):

The major significance of the study is in putting downstream of Parkin/Pink1 (largely confirmatory to recently published work) and suggesting that the levels of apo-Cisd are an important determinant of toxicity. The work will be of interest to those in the field.

3. Description of the revisions that have already been incorporated in the transferred manuscript

The changes already carried out and included in the transferred manuscript (v1) are indicated above in bold orange. All changes pending on ongoing experiments to be included in the second revision of the manuscript are indicated above in bold magenta.

4. Description of analyses that authors prefer not to carry out

All changes suggested by the reviewers were addressed (v1) or will be addressed (v2).

References

Jarazo J, Barmpa K, Modamio J, Saraiva C, Sabaté-Soler S, Rosety I, Griesbeck A, Skwirblies F, Zaffaroni G, Smits LM, et al (2022) Parkinson’s Disease Phenotypes in Patient Neuronal Cultures and Brain Organoids Improved by 2-Hydroxypropyl-β-Cyclodextrin Treatment. Mov Disord 37: 80–94

Key J, Sen NE, Arsović A, Krämer S, Hülse R, Khan NN, Meierhofer D, Gispert S, Koepf G & Auburger G (2020) Systematic Surveys of Iron Homeostasis Mechanisms Reveal Ferritin Superfamily and Nucleotide Surveillance Regulation to be Modified by PINK1 Absence. Cells 9

Ma L, Gholam Azad M, Dharmasivam M, Richardson V, Quinn RJ, Feng Y, Pountney DL, Tonissen KF, Mellick GD, Yanatori I, et al (2021) Parkinson’s disease: Alterations in iron and redox biology as a key to unlock therapeutic strategies. Redox Biol 41: 101896